An Investigation of the Attentional Mechanisms Underpinning
Human Perceptual Learning
Shu Lai (Tony) Wang
This thesis has been submitted in fulfilment of the requirements for the degree of
Doctor of Philosophy
School of Psychology
The University of New South Wales
September 2012
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Originality Statement
‘I hereby declare that this submission is my own work and to the best of my knowledge it contains no materials previously published or written by another person, or substantial proportions of material which have been accepted for the award of any other degree or diploma at UNSW or any other educational institution, except where due acknowledgement is made in the thesis. Any contribution made to the research by others, with whom I have worked at UNSW or elsewhere, is explicitly acknowledged in the thesis. I also declare that the intellectual content of this thesis is the product of my own work, except to the extent that assistance from others in the project's design and conception or in style, presentation and linguistic expression is acknowledged.’
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Acknowledgements
First and foremost, I want to thank Professor Chris Mitchell for his encouragement and dedication to my research and to this thesis. My development as a research scientist owes much to his support, understanding, and endless patience. Thank you also to
Professor Geoff Hall for his helpful comments on some of the empirical work. I am also grateful to Professor Peter Lovibond for his support and encouragement in the final stages of my PhD.
Thank you to my friends and colleagues in the School of Psychology, in particular, to Adrian Camilleri, Daniel de Zilva, Mel Onden-Lim, Oren Griffiths, Shruti Ventaktesh,
Zayra Millan, Xerox Tang, and Joyce Siette for their support and help throughout my PhD.
Finally, I want to thank my Mother for her unconditional love and support. She has made many sacrifices to afford me the opportunities to pursue my dreams. I am eternally grateful for this.
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Abstract
Perceptual learning is a process whereby organisms learn to differentiate two very similar stimuli. For example, exposure to two very similar visual checkerboard patterns allows participants to accurately tell them apart. A general explanation for this phenomenon suggests that exposure has increased the participants’ attention to the distinguishing features of these stimuli. However, the specific mechanism responsible for this process of salience modulation is unclear. Three theories from the associative learning literature have been proposed to explain the role of attention in perceptual learning. The current thesis examined whether these mechanisms can account for the role of attention in human perceptual learning.
Experiments 1-3 showed that an attentional process is involved in human perceptual learning. The using of eyetracking showed that exposure to two similar checkerboard patterns (e.g. AX and BX) increased the amount of time participants spent looking at the distinguishing features (A and B). In addition, Experiments 1-3 replicated three different perceptual learning effects that are commonly observed with animal subjects. This indicates that animal models of perceptual learning are applicable to human studies.
Experiments 4-6 showed that exposure to AX and BX increased the salience of A and B such that they become more salient than novel unique features. This shows that increased attention to A and B is not simply the result of these elements losing less salience than X during exposure. Experiments 7-10 showed that exposure to X alone prior to intermixed AX and BX trials in preexposure further enhance discrimination of these patterns, compared to another pair of intermixed patterns, CY and DY. Finally, Experiment
11 showed that participants discriminated the preexposed AX and BX by attending to the spatial locations of the distinguishing features A and B. vi
In the final chapter, the theoretical implications of the findings are discussed in relation to the different models of perceptual learning. In brief, the findings suggest that participants utilise one attentional process to detect the unique features in preexposure. A separate process is then utilised to guide attention to these features for discrimination. The limitations of these experiments are also discussed.
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Publication
Wang, T., & Mitchell, C. J. (2011). Attention and relative novelty in human perceptual
learning. Journal of Experimental Psychology: Animal Behavior Processes, 37(4),
436-445. doi:10.1037/a0023104
Wang, T., Lavis, Y., Hall, G., & Mitchell, C. J. (in press). Location and salience in human
perceptual learning. Journal of Experimental Psychology: Animal Behaviour
Processes.
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Table of Contents
Declaration of Originality ...... ii
Copyright Statement ...... iii
Authenticity Statement ...... iii
Acknowledgements ...... iv
Abstract ...... v
Publications ...... vii
Table of Contents ...... viii
CHAPTER ONE ...... 13
The Perceptual Learning Effect ...... 16
The Relative Novelty Explanation ...... 18
An Associative Analysis of the Intermixed-blocked Effect ...... 20
Perceptual Learning via Modulation of Salience ...... 22
Reverse Habituation via Associative Activation...... 25
Evidence for Salience Modulation via Associative Activation...... 26
Unitization ...... 27
Human Perceptual Learning ...... 29
Salience Modulation via Short-term Habituation ...... 33
Evidence for Short-term Habituation ...... 37
Attention and Eye Movements ...... 39
Eye Movements and Scene Perception ...... 39
Eye Movements and Visual Orienting ...... 41 ix
The Current Project ...... 42
CHAPTER TWO ...... 46
Experiment 1 ...... 49
Method ...... 49
Results ...... 52
Discussion ...... 58
Experiment 2 ...... 59
Method ...... 60
Results ...... 62
Discussion ...... 65
Experiment 3 ...... 66
Method ...... 67
Results ...... 69
Discussion ...... 72
Chapter Summary and Discussion ...... 73
CHAPTER THREE ...... 76
Experiment 4 ...... 78
Method ...... 78
Results ...... 80
Discussion ...... 82 x
Experiment 5 ...... 84
Method ...... 84
Results ...... 87
Discussion ...... 88
Re-analysis of Eyegaze Data from Experiments 4 and 5 ...... 89
Experiment 6 ...... 95
Method ...... 96
Results ...... 98
Discussion ...... 101
Chapter Summary and Discussion ...... 101
CHAPTER FOUR ...... 104
Experiment 7 ...... 106
Method ...... 107
Results and Discussion ...... 109
Summary ...... 114
Experiment 8 ...... 115
Method ...... 115
Results and Discussion ...... 117
Summary ...... 121
Chapter Summary and Discussion ...... 121
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CHAPTER FIVE ...... 126
Experiment 9 ...... 127
Method ...... 127
Results ...... 130
Discussion ...... 132
Experiment 10 ...... 132
Method ...... 135
Results ...... 137
Discussion ...... 139
Cross-experiment Analysis of Experiments 9 and 10 ...... 140
Experiment 11 ...... 142
Method ...... 144
Results ...... 146
Discussion ...... 150
Chapter Summary and Discussion ...... 151
CHAPTER SIX ...... 155
Summary of Experimental Findings ...... 155
Attention and Perceptual Learning...... 155
Salience Modulation via Unitization ...... 156
Comparing the Reverse Habituation and Short-term Habituation Models ...... 157
Encoding of Spatial Locations ...... 160
Summary ...... 161 xii
The Role of Salience in Stimulus Detection...... 162
Attentional Capture and Relative Novelty ...... 162
Role of Memory in Deliberate Search ...... 164
Attention in Visual Search ...... 168
Attention for Discrimination ...... 169
Contextual Cueing ...... 170
Contextual Cueing and Eyegaze ...... 173
Role of Physical Identity in Perceptual Learning ...... 175
Perceptual Learning as Mere Exposure ...... 176
Future Research ...... 179
Concluding Comments ...... 181
REFERENCES ...... 182
Chapter 1 13
CHAPTER ONE
The ease with which two similar stimuli are discriminated increases as a result of experience with those stimuli. This phenomenon is referred to as perceptual learning
(Gibson, 1969). For example, a sommelier can perceive subtle flavours that differentiate wines from different regions. The perceptual sensitivity required to achieve this can be acquired through experience and training. For example, a novice wine taster might find it difficult to distinguish between the two wine varieties, cabernet-merlot and cabernet-shiraz.
The presence of the common cabernet flavour makes differentiation of the two wines difficult. Experience with the wines however, will render the two wines more discriminable, such that the distinguishing features of each wine (merlot and shiraz) will become easier to detect.
William James (1890) suggested that perceptual learning of this nature could be acquired through explicit feedback. People learn to discriminate between similar stimuli by associating these stimuli with different labels, such as the names of the wines “cabernet- merlot” and “cabernet-shiraz”. Subsequent presentations with the cabernet-merlot will associatively activate its label, thus rendering it more different from cabernet-shiraz (which will activate a different label). Early studies in perceptual learning have found support for this notion. For example, Lawrence (1949) showed that the ability of rats to learn a discrimination involving black and white cues was improved if the rats had learned previously that those cues were associated with different outcomes. This effect, called
“acquired distinctiveness”, has been demonstrated in both animals (Honey & Hall, 1989) and humans (Hall, Mitchell, Graham, & Lavis, 2003). Chapter 1 14
Perceptual learning can also occur in the absence of explicit feedback (Gibson,
1969). In a study by Gibson and Walk (1956), infant rats were exposed to two metal shapes, a circle and a triangle. These shapes were hung on the wall of the rearing cages of these rats. The experimental rats were allowed to explore these shapes for 90 days. No form of reinforcement was associated with these shapes during this exposure phase. A second group of rats received no exposure to the shapes during this period. All rats completed a discrimination task following the 90 day period. In the discrimination task, one shape was associated with a food reward, and the other was associated with no reward. The experimental rats demonstrated better discrimination performance than the control rats.
That is, mere exposure to the shapes during the animal’s infancy enhanced subsequent discrimination of the shapes.
Perceptual learning, as the name implies, is investigated by both researchers of perception (psychophysicists) and learning. The psychophysical approach typically examines how experience affects perception of simple stimuli. For example, Shiu and
Pashler (1992) asked participants to judge whether the orientation of two lines, presented in succession on a computer screen, were the same or different. This task was relatively difficult because each line was short (1.5 cm) and there was only a minor difference in orientation (7.0° to 9.8°). Furthermore, participants received no feedback, correct or incorrect, after they made their responses on each training trial. Nevertheless, participants improved performance accuracy with more training trials. Shiu and Pashler argued that enhanced discrimination in the absence of explicit feedback is evidence for the existence of an “unsupervised learning mechanism”, by which exposure increases the sensitivity of the neurons specific for each orientation. This psychophysical approach is primarily concerned with “early” sensory systems and the neuronal changes that result from training (Dosher & Chapter 1 15
Lu, 1999; Fine & Jacobs, 2002; Watanabe, Náñez, & Sasaki, 2001). Thus, the stimuli under examination differ in terms of a single dimension for which a small population of neurons are responsible for their detection.
The perceptual learning research that has been conducted from a learning perspective has utilised different sorts of stimuli from those investigated by psychophysicists, and has also provided a quite different kind of explanation for the changes observed. In this tradition, the goal has been to identify the psychological processes responsible for learning to discriminate complex stimuli that vary along a number of different dimensions. For example, participants, through experience, can learn to discriminate complex visual checkerboard patterns (Wills & McLaren, 1998; Wills, Suret,
& McLaren, 2004). Because of the complex nature of the stimuli, and the fact that the stimuli do not differ in terms of a single “basic feature”, such as angle or the presence of a particular colour, any perceptual learning observed cannot be described in terms of an increase in sensitivity of a single population of neurons (Goldstone, 1998; Hall, 2008).
Rather, the aim is to provide a psychological explanation of the changes, using as a starting point the idea that perceptual learning involves a change in the mental representations of the stimuli in question. The overall aim of this thesis is to investigate the psychological processes that are responsible for human perceptual learning through this learning approach.
Many of the early psychological studies of perceptual learning were conducted with nonhuman animal subjects (see Hall, 1991 for review). As a consequence, a number of mechanisms of perceptual learning have been proposed that relate to theories of associative learning. Recently, a number of studies have shown that there are similarities between perceptual learning effects in humans and animals (e.g., Lavis & Mitchell, 2006). Thus, the Chapter 1 16
mechanisms used to describe animal perceptual learning may also be applicable to human perceptual learning. One of the aims of the current experiments is to test this hypothesis.
Chapter 1 will first review the animal perceptual learning literature and the models of perceptual learning that are derived from these studies. The chapter will then examine the applicability of these animal experiments to human perceptual learning.
The Perceptual Learning Effect
Many animal perceptual learning studies, since the Gibson and Walk (1956) study, have shown that perceptual learning is not a developmental phenomenon and that the effect can be demonstrated with adult rats. One common procedure in these studies is the flavour conditioning procedure (e.g., Mackintosh, Kaye, & Bennett, 1991). In this procedure, one group of experimental rats receives passive exposure to two similar flavours, sucrose-lemon and saline-lemon. These flavours taste alike because they share the lemon flavour, but the compounds can be distinguished by the sucrose and saline flavours. A group of control rats receives only water in the exposure phase. Following the exposure phase, all rats experience a conditioning phase in which injections of lithium chloride (LiCl) are administered after consuming the sucrose-lemon flavour. This establishes an aversion to sucrose-lemon because LiCl induces nausea in rats. In the final phase, all rats are tested for their consumption of the second flavour, saline-lemon. Avoidance of saline-lemon indicates that the animal has generalised the conditioned aversion from sucrose-lemon to the similar saline-lemon. Thus, if the animals readily consume saline-lemon, this suggests that they are able to discriminate saline-lemon from sucrose-lemon. Mackintosh and colleagues reported that the preexposed rats consumed more saline-lemon than did the control rats; preexposure appeared to enhance discrimination of the two compound flavours. Chapter 1 17
A variant of this flavour conditioning procedure has been used to demonstrate perceptual learning effects in humans (see Dwyer, Hodder, & Honey, 2004). The same basic procedure has also been extended to other types of stimuli, such as visual stimuli. For example, Mundy, Honey, and Dwyer (2007) exposed participants to a pair of similar faces.
These faces were created by morphing two pictures of real faces and then selecting two similar artificial faces from various points along the resulting continuum. During the preexposure phase, participants saw these faces one at a time on a computer screen. On test, participants were required to indicate, using the keyboard, whether the person in each photo was left-handed or right-handed. One face in each preexposure pair was designated left- handed, and the other face was right-handed. Learning this discrimination is contingent upon the participants’ ability to detect the distinguishing features of each face. Mundy and colleagues reported that participants learned this discrimination faster for a pair of preexposed (familiar) faces than for a pair of novel faces.
In accounting for these findings, researchers have adopted an elemental approach to stimulus representation (Atkinson & Estes, 1961; Estes, 1959). Thus, two similar flavours
(e.g. sucrose-lemon and saline-lemon) or two similar faces can be described as AX and BX.
The presence of the common element (X) makes the two flavours difficult to distinguish.
Each stimulus however, is distinguished by the presence of a unique element A and B
(sucrose and saline). The strength of the X element relative to A and B determines the degree of similarity between the two stimuli. For example, discriminating the two similar flavours, sucrose-lemon and saline-lemon, would be difficult if the concentration of the lemon flavour was high. Increasing the physical intensity of the unique flavours is one way to enhance discrimination between sucrose-lemon and saline-lemon. Perceptual learning shows that discrimination of AX and BX can increase without increasing the physical Chapter 1 18
intensity of A or B. Instead, exposure appears to increase the perceived intensity of the unique elements.
The elemental approach to stimulus representation allows one to assume that exposure can independently affect the common and unique elements (Harris, 2006).
Exposure to AX and BX may increase the perceivability of A and B whilst independently reducing the influence of X on perception. The notion of relative novelty provides the simplest account of this process.
The Relative Novelty Explanation
One common explanation for perceptual learning is the notion of relative novelty.
McLaren and Mackintosh (2000; McLaren, Kaye & Mackintosh, 1989) have argued that the perceptual learning effect is consistent with the latent inhibition phenomenon. Latent inhibition is an associative learning effect in which non-reinforced exposure to a conditioned stimulus (CS) retards the ability of the CS to form subsequent associations with an unconditioned stimulus (US; Lubow & Moore, 1959). The most common explanation for latent inhibition is that exposure reduces the salience of the CS (Mackintosh, 1975;
Pearce & Hall, 1980), and low salience impairs the rate at which a CS forms associations with other stimuli (Rescorla & Wagner, 1972). Latent inhibition explains perceptual learning in the following way. The common element X suffers greater latent inhibition than either A or B during exposure to AX and BX because it is presented twice as often. During the conditioning phase (AX+ trials), therefore, X will form only a weak association with the US. Element A is more salient than X, and its presence will overshadow X during the
AX-US pairings. The strength of the response that generalises from AX to BX is governed by the strength of the X-US association. Consequently, the weak X-US association following AX and BX exposure will reduce generalisation between AX and BX. Chapter 1 19
A prediction of the relative novelty explanation is that exposure to only the common element can also produce a perceptual learning effect. Bennett, Wills, Wells, and
Mackintosh (1994) gave one group of rats alternating exposure to two different flavours, sucrose-lemon and saline-lemon (AX and BX). A second group of rats experienced the lemon flavour (X), and a third group of rats experienced only water in the preexposure phase. All rats were then conditioned to avoid AX by an injection of LiCl. In the second X- alone group, X will lose salience during preexposure, but A and B will not lose any salience because they are not preexposed. A perceptual learning effect is expected to occur in both the intermixed and X alone groups because, in both cases, the more novel A element should overshadow the more familiar X element during the AX-US pairings in the conditioning phase. Bennett and colleagues observed that generalisation of the conditioned response from AX to BX was weaker in both the intermixed and the X alone groups than in the control group. Reduced generalisation from AX to BX following X alone exposure supports the notion of relative novelty.
The relative novelty explanation also predicts that the effect of preexposure should be strongest in the X alone group. Overshadowing of X by A during the AX-US conditioning pairings should be greatest in the X alone group. Element A is novel in this group and it is more salient than it is following AX and BX preexposure in the intermixed group. Consequently, the X-US association should be weaker in the X alone than in the intermixed group. However, Bennett and colleagues (1994) reported no difference in aversion to BX between the two preexposure groups. In fact, generalisation from AX to BX was slightly stronger in the X alone group. This suggests that relative novelty may be an incomplete explanation of perceptual learning (Mackintosh et al., 1991). Chapter 1 20
Symonds and Hall (1995; see also Honey, Bateson, & Horn, 1994) provided further evidence that some mechanism, over and above relative novelty, is responsible for perceptual learning. They showed that the schedule on which the stimuli are presented is important for discrimination. In their study, one group of rats experienced an intermixed exposure schedule in which one flavour, sucrose-lemon, was presented in alternation with a second flavour, saline-lemon (AX, BX, AX, BX…). A second group of rats experienced a blocked exposure schedule in which a series of presentations of the first flavour was followed by a series of presentations of the second flavour (AX, AX…, BX, BX…).
Symonds and Hall observed that following the establishment of an aversion to AX, generalisation of the conditioned response from AX to the BX flavour was greater in the blocked than in the intermixed group. Thus, discriminability of AX and BX was greater following intermixed than blocked presentations to these stimuli.
This intermixed-blocked effect contradicts the relative novelty explanation because the relative novelty of the unique and common elements is equivalent in the two groups.
Consequently, the intermixed-blocked effect implies that alternating exposure to AX and
BX engages additional mechanisms to relative novelty.
An Associative Analysis of the Intermixed-Blocked Effect
McLaren and Mackintosh (2000) acknowledged that relative novelty alone could not explain the finding that intermixed exposure to AX and BX produces better perceptual learning than blocked exposure. They made use of an additional associative mechanism to explain this effect. They argued that presentations of AX and BX allow the formation of within-compound associations between the common and unique elements (X-A and X-B).
The common element X will activate a representation of B on AX trials and A on BX trials through these excitatory associations. As a consequence, AX and BX will become more Chapter 1 21
confusable and any conditioned response to AX will generalise to BX. Thus, in the flavour conditioning procedure, X and A will form an association with the aversive US in the conditioning phase. In the test of generalisation to BX, X will elicit some conditioned aversion to BX. In addition, X will activate a representation of A, and the activation of A will also elicit a conditioned response.
To compensate for this source of generalisation, McLaren and Mackintosh (2000) argued that intermixed presentations of AX and BX would also allow the formation of inhibitory links between A and B. These links can be expected to form because the presence of A on AX trials predicts the absence of B, and the presence of B on BX trials predicts the absence of A. Inhibition between A and B can be expected to counteract the effect of the X-A and X-B excitatory associations. Specifically, when BX is presented in the generalisation test, B will inhibit the activation of A (via the X-A link) and so reduce this source of conditioned aversion.
Inhibition between A and B is unlikely to develop during blocked presentations of
AX and BX. The presence of A does not predict the absence of B on AX trials because B has not yet been presented. On BX trials, B may come to inhibit A, but the continued absence of A means that the X-A link will be extinguished. This prevents further development of inhibition between A and B. In support of this idea, standard conditioned inhibition has been shown to occur more readily in an intermixed than a blocked design
(Yin, Barnet, & Miller, 1996). The absence of associative inhibition in the blocked group will mean that the expression of the A-US association is not prevented on BX test trials by the presence of B. This results in a greater degree of generalisation of the conditioned response from AX to BX. In sum, McLaren and Mackintosh (2000) predicts that inhibition between the unique elements should form following intermixed but not blocked exposure, Chapter 1 22
and this will reduce conditioned generalisation from AX to BX in the intermixed group.
Dwyer and colleagues (Dwyer, Bennett, & Mackintosh, 2001; Dwyer & Mackintosh,
2002), using the retardation and summation tests of conditioned inhibition (Rescorla, 1969) showed that inhibition between the unique elements developed following intermixed but not blocked exposure to two flavours (sucrose-lemon and saline-lemon).
The presence of associative inhibition however, does not mean it is the mechanism responsible for perceptual learning. For example, Artigas, Sansa, and Prados (2006) showed that rats could discriminate AX from BX prior to the development of associative inhibition between the unique elements. In their experiment, rats showed reduced generalisation of the conditioned response from AX to BX following only a few presentation trials to each stimuli. Inhibition between the unique elements however, developed only after prolonged exposures to AX and BX. An alternative approach to the intermixed-blocked effect suggests that intermixed and blocked exposure modulates the salience of the unique elements in different ways. In particular, the unique elements may be more salient following intermixed than blocked exposure (Gibson, 1969; Hall, 2003).
Greater attention to the intermixed unique elements would allow these elements to be more easily detected than the blocked elements.
Perceptual Learning via Modulation of Salience
According to Gibson (1969), stimulus exposure affects the internal representation of that stimulus and the ease with which the stimulus is perceived. Thus, at first, subjects may not be able to detect or perceive A and B during initial AX and BX presentations.
Prolonged exposure to AX and BX will allow the subject to extract the unique elements A and B, which will improve discrimination. Gibson termed the process by which the distinguishing features are extracted from the global representations of AX and BX, Chapter 1 23
“differentiation”. The notion of differentiation implies that exposure to AX and BX will allow subjects to perceive separate features of A, B, and X. Gibson argued that differentiation is more likely to occur when subjects are given an opportunity to compare the stimuli. There are more opportunities to compare AX and BX on the intermixed than blocked schedule, so Gibson would predict superior discrimination following intermixed than blocked exposure as observed.
According to Gibson (1969) comparison of AX and BX highlights the relative differences between the stimuli and this directs attention to the unique elements. As such, the unique elements are thought to be more “perceptually effective” and attention will be paid to them. The common elements will come to be ignored because they are irrelevant for discrimination, and so attention to these elements is reduced. Blair and Hall (2003) found evidence for this prediction. In their study, rats were presented with two flavours (AX and
BX) on an intermixed schedule. A third flavour (CX) was presented in a separate block of trials. The order in which the subjects received exposure to these schedules was counterbalanced. This presentation schedule will be referred to as AX/BX_CX. Following preexposure, an aversion to X was established by an injection of LiCl. According to Gibson
(1969), the intermixed element B should be more salient than the blocked element C following preexposure. This leads to the prediction that consumption of BX will be greater than CX on test. This is because the more salient (intermixed) B element will interfere more effectively with perception of the aversive X than will the less salient (blocked) C element.
Blair and Hall (2003) confirmed this prediction; rats consumed more BX than CX on test.
Blair and Hall (2003) argued that the associative inhibition mechanism (McLaren &
Mackintosh, 2000) could not account for their result. The absence of A in the conditioning phase means that A will not become associated with the US. Activation of A by X on the Chapter 1 24
BX and CX test trials should not increase the strength of the conditioned response to BX and CX, and any inhibition of A (by B and C) will not, therefore, modulate the size of this response. In short, conditioning with X alone (rather than AX) precludes the operation of the associative inhibition mechanism. Blair and Hall also showed that, when a separate flavour, Y, was conditioned following preexposure, consumption of BY was greater than
CY. These findings are consistent with the idea that the unique features are more perceptually effective following intermixed than blocked preexposure, and so intermixed elements attract more attention away from any other stimuli that are present.
The perceived salience of a stimulus should also affect the strength of the unconditioned response (UR) elicited by that stimulus (Hall, 1991). For example, rats show strong freezing responses to a novel footshock because the shock is salient and aversive.
Prolonged exposure to the footshock will reduce its salience and thus reduce the strength of the freezing response. The strength of the UR is, thus, proportional to the salience of the stimulus. Thus, the intermixed, but not the blocked, unique elements should be expected to elicit a stronger UR. Blair, Wilkinson, and Hall (2004) confirmed that the perceived salience of the unique elements of two similar stimuli, AX and BX, is indeed greater following intermixed than blocked exposure. The unpleasant flavour quinine was presented as a unique element in the intermixed condition for one group of rats (it served as element
B in the AX/BX_CX schedule) and in the blocked condition for a second group of rats
(serving as C in the AX/BX_CX schedule). On test, consumption of quinine was lower (it was perceived as less pleasant) when it was presented on an intermixed (B) than blocked
(C) schedule. This is consistent with the idea that the unpleasant quinine was perceived to be stronger (more salient) following intermixed than blocked exposure. Blair and colleagues also showed that consumption of the pleasant flavour sucrose was greater Chapter 1 25
following intermixed than blocked exposure. This suggests that when exposed on an intermixed schedule, the sucrose solution was perceived to be sweeter than when it was exposed on the blocked schedule. Thus, both experiments show that the intermixed B elicited a stronger UR than the blocked element C.
The experiments by Blair and colleagues (Blair & Hall, 2003; Blair et al., 2004) support Gibson’s (1969) notion of stimulus differentiation. In particular, salience was observed to be greater for the unique elements presented in an intermixed than in a blocked exposure schedule. McLaren and Mackintosh’s (2000) associative inhibition mechanism cannot account for these findings since associative inhibition does not affect the salience of the unique elements. Gibson however, never specified a psychological mechanism to account for the differentiation process. Hall (2003) proposed a mechanism that is consistent with Gibson’s notion of stimulus differentiation, reverse habituation.
Reverse Habituation via Associative Activation
Hall’s (2003) model begins with the assumption that a stimulus loses salience through simple exposure via the process of habituation. Thus, one component of Hall’s approach is the idea that the unique features are less familiar, and so more salient, than the common features (relative novelty; McLaren & Mackintosh, 2000). Like McLaren &
Mackintosh, Hall suggested that another important factor is that intermixed (but not blocked) exposure to AX and BX allows the formation and maintenance of within compound associations between the common and unique elements (X-A and X-B links).
These excitatory associations allow the unique features of intermixed stimuli to be associatively activated by the common element (X) on the later trials of preexposure (A will be associatively activated on BX trials and B will be associatively activated on AX trials). What distinguishes Hall’s (2003) approach from that of McLaren and Mackintosh is Chapter 1 26
the consequence of this associative activation. Hall suggested that associative activation will maintain or increase the salience of A and B. Consequently, because associative activation of A and B occurs on the intermixed, but not the blocked, schedule, the intermixed unique elements will be more salient than the blocked unique elements.
Evidence for Salience Modulation via Associative Activation
Hall, Prados, and Sansa (2005) showed evidence for an increase in stimulus salience following associative activation of that stimulus. In their study, rats received pairings of a light with a footshock. The rats were trained to suppress lever press for food in the presence of the CS (conditioned suppression). One group of rats was placed on a continuous reinforcement schedule (CRF) in which the footshock followed the light on each trial. A second group of rats was placed on a partial reinforcement schedule (PRF); on some trials, the light was followed by the shock, but on other trials, the light was followed by no shock.
Both groups received the same number of light-shock pairing trials. The salience of the shock should decrease during training because the animals will habituate to the shock when it is presented (on the light-shock trials). According to Hall (2003) however, activation of the representation of the shock on the light-no shock trials through the light-shock association in the PRF group should restore salience to the shock. Thus, salience of the shock is predicted to be greater in the PRF group than in the CRF group.
In the second phase of the experiment, Hall and colleagues (2005) exposed all the rats to another conditioning phase in which a second CS, tone, was paired with the footshock. In a final test phase, the strength of the conditioned suppression was assessed in the presence of the tone alone. Hall and colleagues observed that rats showed a stronger conditioned suppression in the PRF than in the CRF group. This shows that a stronger tone- shock association was formed in the PRF than in the CRF group, and it implies that the Chapter 1 27
shock was more salient in the PRF group. Hall and colleagues also showed that placing the footshock as a CS instead of a US produced a similar result. The subjects again experienced the light-shock associations in either the PRF or CRF group. In the subsequent phase, the footshock signalled a food reward, such that the rats learned to collect food pellets in the
10-second interval following the presentation of the shock. On test, rats approached the magazine more readily in the PRF than in the CRF group. Again, this finding implies that the footshock was more salient in the PRF than in the CRF group, and so a stronger food- footshock association was formed in the PRF group. According to Hall (2003), partial reinforcement of the light-footshock pairings provides a better opportunity for associative activation of the footshock. The salience of the footshock is therefore greater in the PRF than in the CRF group, and this leads the shock to serve as a more effective US or CS.
The reverse habituation model does not specify a mechanism by which associative activation might increase stimulus salience. The evidence presented by Hall and colleagues
(Hall et al., 2005; see also Hall, Blair, & Artigas, 2006; Hall & Rodriguez, 2009) only describes situations in which associative activation should occur, and that stimulus salience is greater under these conditions. However, McLaren and Mackintosh (2000) did describe a mechanism, unitization, which may explain why associative activation modulates salience.
Unitization
The notion of unitization assumes that any stimulus can be divided into a set of individual units. The number of units that constitute a stimulus depends on the complexity of the stimulus. Each presentation of the stimulus will allow an animal to process only a proportion of these units, and which units are sampled will be different on each presentation. Unitization occurs when repeated presentations of a stimulus allow the formation of associations between individual units. The units join together to form a Chapter 1 28
coherent representation of the stimulus – a unitized representation. One consequence is that activation of a single unit may retrieve, by association, a representation of the entire stimulus through these links (Goldstone, 1998; 2000).
McLaren and Mackintosh (2000) also argued that stimulus salience is, in part, determined by the extent to which the stimulus is unitized. A novel stimulus is salient because its units have not yet formed associations with one another. This model states that the salience of individual units will decrease once it has formed associations with other units. This allows attention to be directed to the as yet unprocessed units. In the context of perceptual learning, unitization will reduce the salience of the common and unique features during exposure to AX and BX. In addition, associations will form between the common and unique elements (X-A and X-B). The McLaren and Mackintosh model allows the unitization process to be reversed through associative activation during intermixed exposure to AX and BX. For example, on AX trials, the units representing B are associatively activated through the X-B link. According to the model, the associations that bind together the units representing B will be extinguished when these units are associatively activated in the absence of the stimulus itself. Since unitization reduces stimulus salience, the process of reverse unitization should restore the salience of B.
Similarly, associative activation of A on BX trials should restore salience to A, and this process effectively renders A and B more novel. As mentioned previously, associative activation of A and B is less likely to occur during blocked exposure, and so there is limited opportunity for salience to return to A and B. Consequently, the unique elements become more salient following intermixed than blocked exposure.
There is certainly much in common between Hall’s (2003) reverse habituation theory and McLaren and Mackintosh’s (2000) reverse unitization. However, there are also Chapter 1 29
important differences. In particular, McLaren and Mackintosh asserts that a stimulus is most salient when it is novel. At best, reverse unitization will completely sever the links between the representational units. Salience for the preexposed elements will then be equivalent to novel elements. However, Hall invites the possibility that salience can increase beyond that of a novel stimulus.
Recently, a number of studies (Lavis & Mitchell, 2006; Dwyer et al., 2004; Mundy et al., 2007; Mundy, Honey, & Dwyer, 2009) have examined whether animal models of perceptual learning (Hall, 2003; McLaren & Mackintosh, 2000) are also applicable to human perceptual learning. These studies have attempted to replicate the effects of perceptual learning (Bennett et al., 1994; Mackintosh et al., 1991; Symonds & Hall, 1995) demonstrated with animals with human perceptual learning procedures. If there is consistency in the results observed in the two approaches, then a common mechanism might be responsible for perceptual learning in both humans and nonhuman animals (Hall,
2009).
Human Perceptual Learning
Humans are predominately visual creatures, and a large proportion of the human brain is dedicated to visual processing. For this reason and for convenience, human perceptual learning often involves discrimination of visual stimuli. For example, Mundy and colleagues (2007) found that exposure to two similar faces improved discrimination of this pair compared to a novel pair of faces. In a subsequent experiment, Mundy and colleagues also demonstrated the intermixed-blocked effect (Symonds & Hall, 1995; Blair
& Hall, 2003) with these face stimuli. Participants initially received intermixed (AX and
BX) and blocked (CY and DY) exposure to two pairs of similar faces. In the subsequent discrimination task, Mundy and colleagues observed that discrimination performance was Chapter 1 30
better in the intermixed than in the blocked condition. Performance in both conditions however, was better than in a novel control condition (i.e., EZ and FZ).
Figure 1.1. Example of a stimulus set similar to those used by Lavis and Mitchell (2006).
Both the salience modulation (Hall, 2003) and inhibition (McLaren & Mackintosh,
2000) mechanisms used to explain the intermixed-blocked effect in animals provide potential explanations for the intermixed-blocked effect in humans. Lavis and Mitchell
(2006) investigated associative inhibition in the intermixed-blocked effect in humans. In their experiment, participants received exposure to six multi-coloured complex visual checkerboard patterns (similar to those shown in Figure 1.1). These checkerboards were visually similar because the majority of the constituents of each checkerboard were held in common (thus representing the X element). A small cluster of coloured squares (A-D unique elements) was superimposed on the common background, and these were unique to each checkerboard. Participants received two pairs of these patterns on separate intermixed schedules (AX/BX & CX/DX) and one pair on a blocked exposure schedule (EX_FX). Chapter 1 31
Following preexposure, Lavis and Mitchell measured participants’ discrimination performance with the preexposed patterns using a same-different task. Participants were asked to judge whether two patterns, presented in succession, were the same or different
(e.g., AX versus BX is a different trial, and AX versus AX is a same trial). The accuracy of this response is thought to reflect the ease with which the participants can perceive the unique elements.
In the same-different task, Lavis and Mitchell (2006) presented test trials in which the two test patterns were consistent with their arrangement in preexposure (i.e., AX & BX,
CX & DX, and EX & FX test trials). There were also test trials in which the two patterns were selected from two different intermixed pairs (i.e., AX and CX). According to
McLaren and Mackintosh (2000), associative inhibition should only develop between the unique elements within each preexposure pair. Discrimination of AX and BX, and of CX and DX should be better than that of EX and FX. The absence of inhibition between A and
C however, means that discrimination of AX and CX should not be better than of EX and
FX. Alternatively, if intermixed exposure increases the salience of the unique elements, then elements A-D will attract more attention than E and F regardless of the test pairing in the same-different task. Consistent with this latter prediction, discrimination of AX-DX, across both trial types, was better than of EX and FX. Similar to the animal studies, salience modulation, and not associative inhibition, might best account for the intermixed- blocked effect with human participants (Artigas, Sansa, & Prados, 2006; Mitchell, Kadib,
Lavis, & Hall, 2008).
There are examples however, in which the findings from animal and human studies of perceptual learning are not consistent. For example, Mundy and colleagues (2007) showed that presenting two similar faces simultaneously (side by side on a computer Chapter 1 32
screen) produced better discrimination of these faces than presenting the two faces successively. This is consistent with Gibson’s (1969) notion of stimulus differentiation since simultaneous exposure provides a better opportunity for stimulus comparison than successive exposure. The opposite finding however, is observed with animal subjects. In a flavour aversion procedure, Bennett and Mackintosh (1999; see also Honey & Bateson,
1996) mimicked the simultaneous exposure schedule by rapidly presenting flavours AX and BX (e.g., saline-lemon and sucrose-lemon) every twenty seconds. In the successive exposure group, rats received the two flavours every two minutes. Bennett and Mackintosh observed that generalisation from AX to BX was weaker in the successively than in the simultaneous (rapid exposure) group.
Bennett and Mackintosh (1999) suggested that the shorter interval between the A and B presentations might facilitate the formation of an excitatory link between A and B in the simultaneous group. The unique elements may have been more salient in the simultaneous group, but an A-B link would have increased conditioned generalisation from
AX to BX. Recently, Rodriguez, Blair, and Hall (2008) presented evidence that dispelled this possibility. In their study, one group of rats received simultaneous exposure to flavours
AX and X (e.g. saline-vanilla and saline) in a procedure similar to that described by
Bennett and Mackintosh. An excitatory A-B link would not form in this design since the second unique flavour (B) is absent. Another two groups received successive exposure to flavours AX and X on either intermixed or blocked schedules. Rodriguez and colleagues suggested that latent inhibition to X may be greater following successive than simultaneous exposure to AX and X (see also Bennett & Mackintosh, 1999, Experiment 2), and this may lead to greater aversion being established to AX following simultaneous exposure. To overcome this, conditioned aversion was established to a novel flavour Y following Chapter 1 33
preexposure in all groups. In the generalisation test, rats consumed more AY in both the simultaneous and intermixed groups than in the blocked group. However, there was no difference between the simultaneous and intermixed groups. Consequently, simultaneous and successive exposures to AX and X are equally effective in increasing the salience of A.
Simultaneous exposure to similar stimuli seems to produce better perceptual learning than successive exposure with human but not with animal subjects. This divergence implies that separate mechanisms may be responsible for human and nonhuman perceptual learning (Mitchell, 2009). For example, the aforementioned mechanisms of salience modulation (Hall, 2003; McLaren & Mackintosh, 2000) operate within an associative learning framework. However, Gibson (1969) believed that perceptual learning is a phenomenon independent of associative learning. The perceptual learning effects observed with human subjects might result from this non-associative process. Recently, a non-associative model of human perceptual learning has been proposed based on the notion of short-term habituation (Honey & Bateson, 1996; Mundy et al., 2007; Mitchell, Nash, &
Hall, 2008).
Salience Modulation via Short-term Habituation
Honey and Bateson (1996) proposed another mechanism to account for salience modulation in the intermixed-blocked procedure. Their basic assumption is that a stimulus suffers short-term habituation following repeated exposures. The stimulus loses salience and thus receives little attention. The interval between each stimulus presentation is also important in determining the degree of short-term habituation observed. Short-term habituation to a stimulus will increase with shorter intervals between stimulus presentations. For example, intermixed exposure to AX and BX results in greater short- term habituation to X than to either A or B since X is presented on each trial, but A and B Chapter 1 34
are presented on every other trial. In blocked exposure to AX and BX, A and B will suffer the same fate as X since they are repeatedly presented on successive AX and BX trials.
Consequently, A and B should experience greater habituation during blocked than intermixed exposure. This leads to subjects affording greater attention to A and B in the intermixed schedule.
One problem for this account is that it fails to specify how increased attention to the unique elements during preexposure might lead to better discrimination in the test phase. In fact, one might equally predict the opposite outcome; greater attention to A and B during preexposure may lead to greater unitization and lower salience of these elements when they are presented later on test (after any effects of short-term habituation have worn off).
Honey and Bateson (1996) suggested that increased attention to the unique elements in preexposure leads the unique elements to become more effectively processed. The representation of A is more likely to be activated than the representations of X on AX presentation trials. Nevertheless, this notion does not explain how superior short-term attention to the unique elements in preexposure leads to enhanced long-term discrimination of the preexposed stimuli (Bennett & Mackintosh, 1999). Perhaps, an additional process is required to link these two events.
Recently, Mitchell, Nash, and Hall (2008; see also Mundy et al., 2007) suggested that short-term habituation might allow subjects to engage in a process that produces long- term changes to the representation of the stimuli. In particular, attention to the unique elements during preexposure allows subjects to process and encode internal representations of these elements. The strength of these representations depends on the attentional resources afforded to them during preexposure. Mitchell, Nash and colleagues suggested that once the unique elements have been encoded, subjects engage in a top-down attentional Chapter 1 35
process to search for these elements on test. The ability of subjects to detect unique stimulus elements, when they search for them in a discrimination test, depends on their memory for those elements (Duncan & Humphreys, 1989; Shiffrin & Schneider, 1977).
Thus, there are two components to Mitchell, Nash and colleagues’ suggestion: an attentional process for detecting and encoding of the unique elements in memory, and a separate top-down attentional process to search for those unique features once they have been encoded.
Wagner’s (1981) SOP model best describes the process in which short-term habituation to the unique and common elements might develop in intermixed preexposure.
In this model, Wagner assumes that any stimulus is represented by a series of units (as do
McLaren & Mackintosh, 2000). These units can occupy one of three states of activation.
Presentation of a novel stimulus will activate the units representing that stimulus into the
A1 state. The stimulus will receive full attention for processing within this state. Wagner also specified a second state of attention, A2, in which the stimulus will receive little attention for processing. Stimulus units can enter this state via two ways. Firstly, a stimulus can be associatively activated into the A2 state. Secondly, when a stimulus is presented and then removed, its units will decay from the A1 into the A2 state. Finally, units representing a stimulus will decay from the A2 state into an inactivate state (the “I” state). Wagner stated that units cannot move from the A2 state into the A1 state, and only units from the inactive state can move into the A1 state. This rule prevents recently experienced stimuli from receiving full attention. For example, a presented stimulus will be activated into the
A1 state, and then into the A2 state after it has been removed from presentation. The stimulus cannot then enter the A1 state again until its representational units have entered Chapter 1 36
the I state. Continued presentations of the stimulus mean its representational units will remain in the A2 state where it will receive little attention.
Wagner’s (1981) model accounts for the intermixed-blocked effect in the following way. When AX and BX are intermixed, both A and X will be activated into the A1 state on the first AX trial, where they will receive full attention. At the end of the AX trial, A and X will decay into the A2 state. On the subsequent BX trial, B will be activated into the A1 state. Recent exposure to X means that it cannot be activated into the A1, and it must remain in the A2 state during the BX trial. The units representing X cannot move from the
A2 into the A1 state. Consequently, B will receive more attention than X. Prior to the next
AX trial, sufficient time has elapsed for A to decay from A2 into the I state. This does not imply that all the units representing A will decay into the I state concurrently. The rate of decay depends on factors such as trial length, salience, and stimulus complexity. Critically, there is more time for A than for X to decay into I, and so A is more likely to be activated into A1 on the next AX trial than is X. Thus, more units representing A will be activated into A1 than those representing X, most of which will remain in the A2 state. The next AX trial will give time for units representing B to decay into the I state.
Intermixed exposure to AX and BX therefore allows A and B to be activated into the A1 state whilst X remains, to a greater extent, in the A2 state throughout preexposure.
Consequently, X will receive very little attention during preexposure. Blocked preexposure is quite different. Both the common and unique elements will suffer the same fate in blocked exposure to AX and BX. The interval between each successive presentation of the
A and X is equal on repeated AX trials, and so there should be no attentional bias to either
A or X on these trials. The unique elements should therefore receive more attention during intermixed than blocked exposure. Chapter 1 37
As mentioned, greater attentional resources to the unique elements in the intermixed schedule leads to better encoding of these elements than in the blocked schedule. The elements that are best encoded should also be the easiest to detect. Consequently, the intermixed, and not blocked, unique elements will receive more attention in test.
Evidence for Short-term Habituation
The finding that simultaneous exposure to AX and BX produces better discrimination than successive exposures (Mundy et al., 2007) can be explained in terms of short-term habituation. For example, participants in the successive schedule receive one presentation of X on each AX or BX trial. In the simultaneous schedule however, participants receive two presentations of X on each trial. The interval between each X presentation is shorter in the simultaneous than in the successive schedule, and this should lead to greater habituation of X in the former schedule. On this basis, attention to the unique elements can be expected to be greater in the simultaneous than in the successive schedule.
Wagner (1981) predicted that short-term habituation to a stimulus might be attenuated if a distracter stimulus was presented between successive trials of the to-be- habituated stimulus. The presence of the distracter might increase the chance that the representational units of the target stimulus will decay into the I from the A2 state because the units representing the distracter might occupy the A2 state. The implication for perceptual learning is that presenting a distracter between presentations of AX and BX will disrupt short-term habituation to X. The demand for attention from X will increase since some units of X will be activated into the A1 state. This will then lead to a decrease in the ability for A and B to command attention. Consequently, the distracter will reduce the Chapter 1 38
effectiveness of AX and BX presentations since X will not remain in the A2 state on these trials.
Recently, Dwyer, Mundy, and Honey (2011) found evidence consistent with this prediction. In this experiment, participants were required to discriminate similar faces in three different exposure conditions. In the distracter condition, a distracter face was presented during the inter-stimulus interval (ISI) of each exposure pair (e.g., AX – distracter - BX). The to-be-discriminated stimuli were pictures of female faces, and the distracter stimuli were pictures of male faces. In the masking condition, the distracter was presented before or after each preexposure pair (e.g., distracter – CY – DY distracter). No distracters were presented in the third, non-distracter, control condition. Discrimination performance in the two conditions that featured a distracter stimulus was poorer than performance in the control condition. Consistent with Wagner’s (1981) prediction however, the impairment was most severe when the distracter was placed between AX and BX trials.
That is, the distracter disrupted the opportunity for participants to compare these faces.
In sum, the findings by Dwyer and colleagues (Dwyer et al., 2011; Mundy et al.,
2007) suggest that stimulus comparison is critical in human perceptual learning. Consistent with the short-term habituation explanation, disrupting the comparison process will greatly reduce the effectiveness of preexposure. The finding by Dwyer and colleagues (2011) however, does not necessarily rule out the mechanisms proposed by McLaren and
Mackintosh (2000), and Hall (2003). For example, all three mechanisms can account for why intermixed exposure increases attention to the unique elements. What is missing from the available findings, however, is direct evidence to show that perceptual learning involves an attentional process. The common assumption is that performance in the same-different task (or similar discrimination task) indicates the degree to which the unique elements can Chapter 1 39
capture attention (Lavis & Mitchell, 2006). However, perception of the unique elements can also be affected by factors other than salience, such as associative inhibition (Dwyer et al., 2004; Mundy, Dwyer, & Honey, 2006). An independent measure is therefore required to verify that attention to the stimulus elements changes through exposure in a perceptual learning experiment. One possibility is to use eye movements as a measure for variations in attention during preexposure and test (Le Pelley, 2010; Rehder & Hoffman, 2005).
Attention and Eye Movements
In general, eye movement is thought to be closely related to attention (Deubel &
Schnieder, 1996; Kowler, Anderson, Dosher, & Blaser, 1995). The eye acquires the highest quality visual information from the fovea (two degrees from the centre of vision), but visual information from outside the fovea is poor (Anstis, 1974). One must therefore, direct gaze to an object in order to attend and process that object in detail. It is only in tasks with very simple stimuli that attention and eye movements may be independent of each other. In a study by Posner (1980), participants were asked to judge whether a simple stimulus, such as a cross or a dot, was present or absent in the periphery of vision. Participants could accurately perform this task even though their eyes were not fixated on to the target. This decoupling of attention and eye movements however, is less likely to be observed for cognitive tasks with more complex stimuli, such as reading and scene perception (Rayner,
2009). Similarly, attention and eye movements are expected to co-vary when participants attempt to discriminate between complex visual stimuli.
Eye Movements and Scene Perception
The relationships between attention, eye movements, and salience have long interested researchers in the scene perception literature. Eye movements in scene perception are not random, with fixations clustering on specific features (or regions) within the scene Chapter 1 40
(Loftus & Mackworth, 1978). Consequently, researchers have focused on how features in a scene control eye movements (see Henderson & Hollingworth, 1999 for review). Yarbus
(1967) first showed that participants would spend more time looking at task-relevant features in the scene. In this study, participants were presented with a scene, such as a picture of a large family sitting in a large room. They would spend more time looking at the faces of the people in the picture if they were asked to describe ages of these people.
Conversely, participants spent more time looking at the background when they were asked to describe the room. Presumably, a top-down attentional process is controlling eye movements in this task. In this study, the goal of the task has changed with each set of instructions, and so participants placed different attentional weights on features that were relevant to their instructions. These attentional weights guide where participants look at the scene.
There is also evidence to show that the low-level saliency of features in a scene affect eye movements. For example, Itti and Koch (2000) proposed a computational algorithm that calculated the visual salience of different areas of a scene based on variations in orientation, intensity, and colour. Parkhurst, Law, and Niebur (2002) examined whether there is a correlation between visual saliency (calculated by this model) and eye movements. In this study, participant viewed various scenes (fractals, natural landscapes, buildings and city scenes, and home interiors) in a free viewing task. They found that participants were more likely to allocate their first fixation to areas with high than with low visual saliency. This type of eye movement is interpreted as a bottom-up or stimulus-driven process (Parkhurst et al., 2002). Eye movements are therefore sensitive to both top-down and bottom-up attentional processes. The type of task often mediates which process controls eye movements (Underwood & Foulsham, 2006). For example, low-level visual Chapter 1 41
saliency has a greater influence on eye movements in a free viewing task, than when participants are asked to find a specific object or feature in the scene.
Eye Movements and Visual Orienting
In the current thesis, the predictions of attention are derived from animal perceptual learning models (McLaren & Mackintosh, 2000; Hall, 2003; Honey & Bateson, 1996). It is important to establish that attention, measured by eye movements, corresponds to a similar process of attention in animals. In animal learning, the orienting response is thought to reflect attention to a particular stimulus (Groves & Thompson, 1970; Hall, 1991; Sokolov,
1963). For example, a rat will orient towards a novel light stimulus, but the strength of this orienting response will decline through repeated exposure to the light stimulus (e.g,
Holland, 1977). Thus, the strength of the orienting response is thought to reflect the amount of attention the animal pays to the stimulus. A salient stimulus will elicit a stronger orienting response than a less salient stimulus. In humans, there is some evidence from tests of the Pearce and Hall (1980) model of learning to suggest that eye movements in humans are analogous to the orienting response in animals.
Pearce and Hall (1980) proposed a model that describes how animals might select and attend to certain stimuli in the environment for learning. The premise of their model is that attention is needed to learn new relationships about events in the environment.
Attention will decrease once the stimulus becomes a reliable predictor of an outcome.
Attention will not, however, decrease if the stimulus is an unreliable (or partial) predictor of a US. Thus, the Pearce-Hall model predicts that the orienting response in animals will be greater for a partially reinforced CS than for a continuously reinforced CS. Kaye and
Pearce (1984) reported results consistent with this prediction. In their study, three groups of rats learned that a light stimulus (CS) predicted the onset of a food (US). Each group Chapter 1 42
experienced a different CS-US contingency. In one group, the CS reliably predicted the presence of a food reward (CS+). In the second group, the CS reliably predicted the absence of a food reward (CS-). In a third group, the CS was partially reinforced with the
US, such that the US occurred on some trials and not on other trials (CS+/-). It was observed that the orienting response to the unreliable predictor (CS+/-) was greater than orienting to either of the reliable predictors (CS+ and CS-). Thus, attention was greater to the unreliable than to the reliable predictors.
The finding by Kaye and Pearce (1984) has recently been replicated using a human associative learning procedure (Hogarth, Dickinson, Austin, Brown, & Duka, 2008). In their study, Hogarth and colleagues measured participants’ eye movements to verify the predictions of attention by the Pearce-Hall (1980) model. In their task, participants were required to learn three different cue-outcome relationships in a computer task. In a within- subject version of Kaye and Pearce’s study, two visual cues (A & B) were trained to be reliable predictors, one cue was associated with an outcome (A+), and the other cue was associated with no outcome (B-). A third visual cue (C) was trained as a partial, and thus unreliable, predictor of an outcome (C+/-). Hogarth and colleagues reported that eye gaze length was greater to the unreliable predictor (C) than to either of the reliable predictors (A and B). The consistency between the animal and human learning data supports the assumption that eyegaze in humans is analogous to the orienting response in animals. That is, both measures seem to index the same attentional construct. Eyegaze therefore, might provide an ideal way to measure salience changes in perceptual learning.
The Current Project
The overall aim of the current thesis is to investigate the attentional mechanisms involved in human perceptual learning. The first aim is to determine whether exposure to Chapter 1 43
similar stimuli increases attention to the distinguishing features. For example, exposure to two similar complex visual patterns should enhance the discriminability of these patterns compared to non-preexposed control patterns. The common assumption is that the difference in salience between the unique and common elements is greater for the preexposed than for the novel patterns. Observing how participants allocate their eyegaze to the preexposed and novel unique elements provides an independent validation of this assumption. In addition, attentional bias is thought to explain other perceptual learning effects such as the intermixed-blocked effect. The aim of Chapter 2 is to examine eye movements in some of the important established human perceptual learning findings (e.g.,
Lavis & Mitchell, 2006; Mundy et al., 2007).
Assuming that perceptual learning involves an attentional process, the subsequent question is to determine which mechanism (Hall, 2003; McLaren & Mackintosh, 2000;
Mitchell, Nash et al., 2008) best describes this process. This question is examined in
Chapters 3-5. The reverse habituation and unitization mechanisms predict that a bottom-up attentional process will affect eyegaze during both the phases of preexposure and test.
Thus, preexposure to AX and BX will increase the salience of the unique elements. Then, on test, AX will be discriminable from BX because A and B will capture more attention than X. According to this account, attention is stimulus-driven because A and B are perceived as more salient than X. Note, the term “attention”, in context of discussion of the
McLaren and Mackintosh model here, refers to an automatic consequence of stimulus salience. That is, increased attention to the unique elements through exposure is the consequence, not the cause, of increased salience (e.g., Mackintosh, 1975).
The short-term habituation mechanism (Mitchell, Nash et al., 2008) suggests that attention in preexposure is a bottom-up process. The common elements have suffered Chapter 1 44
greater habituation than either unique elements, and so attention is directed to the unique elements. However, continued attention to A and B in preexposure should reduce their bottom-up salience. Discrimination of AX from BX on test cannot rely on this bottom-up process. Instead, the model argues that exposure allows participants to encode a representation of A and B in memory. In test, a top-down attentional process is engaged to search for these elements. The eyegaze will be sensitive to either of these processes. Each model will also make different predictions regarding how attention should affect discrimination. The role of attention in perceptual learning will be investigated by comparing performance in the discrimination task and the eye movements made during that task.
Note, there are variations of the short-term habituation account (see also Honey &
Bateson, 1996; Mundy et al., 2007). All accounts use the same approach (i.e., Wagner,
1981) to describe how preexposure affects attention to the unique and common elements.
Each account however, differs as to how attention might be utilised for discrimination. For example, Honey and Bateson do not specify how increased attention to the unique elements in preexposure is maintained in the test phase. Similarly, Mundy and colleagues do not state that memory of the unique elements guides a search process to these elements for discrimination. Thus, the experiments in this thesis were designed to examine the predictions of the account proposed by Mitchell, Nash and colleagues (2008).
Chapters 3-5 will examine a variety of predictions from the models proposed by
McLaren and Mackintosh (2000), Hall (2003), and Mitchell, Nash and colleagues (2008).
Chapter 3 will focus on McLaren and Mackintosh’s unitization mechanism. According to unitization, stimulus salience is greatest when the stimulus is novel. Intermixed exposure can only attenuate the loss of salience to the unique elements. The unitization mechanism Chapter 1 45
therefore, predicts that patterns with novel features will be better discriminated than patterns with preexposed features, given that the salience of the background is equivalent.
Conversely, both the short-term and reverse habituation mechanisms predict the opposite result – intermixed exposure will render the unique elements more salient than novel features. The experiments in Chapter 4 and 5 will attempt to differentiate these two theories. Chapter 2 46
CHAPTER TWO
Chapter 2 aims to determine whether perceptual learning in humans is the result of an attentional process. In order to achieve this, participants’ eyegaze was monitored during both preexposure and test phases of a perceptual learning task. The stimuli were checkerboard stimuli similar to those used by Lavis and Mitchell (2006; see Figure 2.1).
These stimuli were chosen because the unique features A and B are placed in specific locations on the checkerboard background (the common element X). If preexposure enhances the salience of the unique features, it can be predicted that eyegaze towards the locations of the unique features will increase across preexposure, and will be higher on test for preexposed than for novel stimuli.
To anticipate, Experiment 1 showed a bias in attention, as measured by eyegaze, towards the unique features of the preexposed stimuli. Experiments 2 then examined whether this attentional process was the result of relative novelty of the unique and common features following preexposure. The prediction was tested that exposure to the common element (X) alone will enhance discrimination of AX and BX (Bennett et al.,
1994; Mundy et al., 2007) and also produce a bias to gaze at the unique features A and B on test. Experiment 3 examined Gibson’s (1969) suggestion that the opportunity for stimulus comparison, not simply relative novelty, is critical to the detection of the unique features.
The prediction was that attention would be greater to unique elements of stimuli preexposed on an intermixed than a blocked schedule.
Chapter 2 47
Figure 2.1. Example of a stimulus set used in Experiments 1 and 2. One of the backgrounds appears twice in the top row, the other background appears twice in the bottom row. The four unique elements are outlined in the solid black lines. The white dotted boxes indicate the approximate size of the area of interest (AOI) used to analyse the eyegaze data. Neither the black or white lines appeared in the experiments. The two common backgrounds and the four unique elements were randomly combined and assigned to the roles of X-Y and A-
D for each participant. Chapter 2 48
There are number of methods by which to measure eye movements, and the method often varies with the type of task. Eye movements comprise two key processes – saccades and fixations (Hollingworth & Henderson, 1999). Visual information about a scene is processed during an eye fixation. Saccades describe the movement of the eyes from one fixation point to another. No visual information is processed during these saccadic eye movements. In addition, during a fixation, a viewer will make a number of small saccadic eye movements within an area of the scene, called micro-saccades. The fixation point is calculated by finding the focal point of this cluster of gaze points. The size of the fixation area (the cluster of gaze points around the fixation point) will vary with the type of stimulus. For example, the fixation area for reading a word will be smaller than the fixation area for scene perception. The definition and calculation of a fixation point and fixation area varies with the type of cognitive task (Rayner, 2009). Consequently, there is no general guide to record and analyse eye movements for the present experiments.
Our aim for recording eye movements is to determine whether exposure changes the amount of attention paid to the unique elements A and B. Kruschke, Kappenman, and
Hetrick (2005) described a simple method to record and analyse dwell time that is ideally suited to the present purposes. In their experiment, an area of interest (AOI) was defined around each target stimulus (in this case a word stimulus). The AOI represented a fixation area, and any gaze points that landed in that area were recorded. Their eyetracker recorded the position of the eyes once every 17 milliseconds (or 60 samples per second). Each gaze point can be assumed to represent a dwell time of 17ms at that location. Total dwell time to a target stimulus is a product of the number of gaze points within the AOI and the duration each sample represents. This technique can be easily adapted to a perceptual learning Chapter 2 49
procedure. An AOI was liberally defined around each unique element and the eyetracker recorded the number of gaze points that landed within those areas.
Experiment 1
Experiment 1 was an attempt to replicate the basic perceptual learning effect. The experiment used a within-subjects design in which one pair of checkerboards, AX and BX, was preexposed and a second pair, CY and DY, was novel at the time of test. AX and BX were preexposed on an alternating schedule (AX, BX, AX, BX…) because, with the checkerboard stimuli and same-different task used here, this schedule is known to produce a strong perceptual learning effect (Lavis & Mitchell, 2006; Mitchell, Kadib et al., 2008). It was predicted that performance to AX and BX would be better than to CY and DY.
Participants’ eye movements were also measured during both the preexposure phase and the same-different task. It was predicted that participants would spend more time looking at
A and B than at C and D on test.
Method
Participants. Seventeen students (14 female and 3 male, with a mean age of 20.6) from the University of New South Wales (UNSW) participated in this experiment. Ten participants were first year psychology students who participated in exchange for course credit. Seven participants were recruited from the campus and they were paid ten dollars for their participation.
Apparatus and Stimuli. The experimental stimuli were 20 x 20 square checkerboards (see Figure 2.1). The two different backgrounds that served as X and Y elements were created by colouring 156 of the 400 squares green, red, yellow, purple, or blue. The remaining squares were grey. Each of the four unique features was an arrangement of six adjacent coloured squares. AX and BX patterns were created by Chapter 2 50
randomly selecting two unique elements and placing them on one of the two backgrounds.
CY and DY were created with the two remaining unique elements and the remaining background. Each unique feature replaced an area that was previously filled with grey squares. The location of a given unique feature was constant across exposures. Thus, features A-D differed both in colour and in location on the checkerboard. A custom program written in Revolution Studio 2.9 was used to control stimulus presentation on an
IBM-compatible PC.
Design. In the preexposure phase, all participants received alternating presentations of AX and BX (60 trials of each). In the subsequent test phase, there were four trial types:
(1) preexposed different (e.g., AX and BX), (2) preexposed same (e.g., AX and AX; BX and BX), (3) novel different (e.g., CY and DY), and (4) novel same (e.g., CY and CY; DY and DY). There were four blocks of 24 test trials, for a total of 96 trials. Within each 24 trial block on test, there were six trials of each type, and all 24 trials were presented in a random order. The assignment of the common background (X or Y) to the preexposure condition (preexposed versus novel) was counterbalanced between participants.
Eyegaze analysis. Participants sat in a position such that viewing distance was 60 cm from the participant to the screen. The use of a chin rest ensured the distance between the participant’s eyes to the screen was fixed throughout the experimental session. At this distance, each checkerboard subtended a visual angle of 12.23° x 12.23° and each unique element subtended a visual angle of 1.86° x 1.86°. An area of interest (AOI) with visual angle of 3.98° x 3.98° was defined around each unique element. The liberal definition prevented eyegaze points near and around the unique element from being missed.
Eye gaze was measured using a Tobii T60 eye tracking system. A 17-inch monitor with resolution of 1280 x 1024 showed the experiment stimuli. The Tobii eye tracking Chapter 2 51
system sampled the spatial location of an eyegaze every 1/60th seconds (approximately 17 ms). A custom written program calculated the number of these samples that fell within each
AOI, and from this the total gaze length in each AOI for all stimulus presentations within a test block. The eyegaze analysis only included gaze length to the AOI corresponding to the relevant unique features on each trial (i.e. A for AX).
The eyetracker was not correctly calibrated for some participants, and consequently, the eyetracker failed to record any eyegaze data on a number of preexposure and test trials.
Participants’ data were excluded from all analyses (i.e., discrimination performance and eyegaze) if more than 50% of their eyegaze data were missing. Specifically, data for one participant were excluded since the total number of recorded gaze points to the checkerboard pattern for that participant was fewer than 50% of total possible number of gaze points. This exclusion criterion was also applied in the subsequent experiments.
Procedure. The experiment started by calibrating eye movements using nine fixation points. Once the calibration process was completed, participants were told that the experiment was about to commence. They were instructed to pay attention to the presented stimuli and that any differences detected would be useful later in the experiment. Each stimulus was presented for 480 ms, followed by a 2000 ms inter-trial interval where the stimulus disappeared and only the black background was present.
Upon completion of the training phase, participants received another set of instructions detailing the requirements of the same-different task. Participants were told that two checkerboards would be presented in succession and that they must decide whether the two checkerboards were the same or different. Each stimulus was presented for 900ms and a grey square held the place of the stimulus during the 880ms inter-stimulus interval.
Participants pressed the A or 5 key on the keyboard to indicate whether the two stimuli Chapter 2 52
were the same or different. Participants made their response after the second stimulus was removed. The next test trial was initiated 1400 ms after this key response. Participants were not given any feedback (i.e. correct or incorrect) following their response.
Statistical Analysis. A set of planned contrasts using a multivariate, repeated measures model (O'Brien & Kaiser, 1985) were used to analyse the data from this and the subsequent experiments. A significance level of p < 0.05 was set for all of the statistical analyses. All experiments presented in this thesis used the same analytical approach.
Results
Participants were not required to make any response during stimulus presentation.
Consequently, no behavioural information, other than eyegaze data, was recorded in the preexposure phase. Analysis was only conducted on participants’ responses from the same- different task (test phase). The eyetracker measured participants’ eye movements during both the preexposure and test phases. Only eyegaze data from the test phase were subject to statistical analysis. The eyegaze data from preexposure were not analysed since there was not an appropriate comparison condition in this experiment. All participants experienced the same preexposure condition. However, there are some experiments in this thesis where analysis of the eyegaze data in preexposure was appropriate. In all experiments, eyegaze data from the preexposure and test phases were used to determine if a participant’s data were fit for inclusion in the analysis (the exclusion criterion is described in the Method section).
Chapter 2 53
Figure 2.2. The panel A shows mean proportion of correct responses within each test block on the four types of test trial in Experiment 1. The panel B shows mean sensitivity score (d’) for each preexposure condition across test blocks. The panel C shows mean c values across test blocks within each preexposure condition. A higher c value represents a stronger tendency to make the “same” response. The panel D shows the total gaze length to the two unique elements within each preexposure condition (A&B or C&D) across test blocks. Chapter 2 54
Same-different performance. Discriminability of the pair of patterns in each preexposure condition was determined by calculating the proportion of correct responses for the same and different trials in each test condition. A preliminary analysis was conducted to determine whether performance in the same-different task was affected by the assignment of the different backgrounds (X versus Y) to the preexposed and novel conditions. The analysis revealed a significant main effect of background, F(1, 14) = 6.00.
That is, overall performance was better when the lower patterns in Figure 2.1 were preexposed, than when the upper patterns were preexposed. However, neither the interaction between background and preexposure condition (preexposed versus novel), F(1,
14) = 2.65, nor the interaction between background and trial type (same versus different), F
< 1, were significant. The three-way interaction between these factors was also not significant, F(1, 14) = 1.90. Although overall performance was different across counterbalancing groups, the same pattern of data was observed in each group. Thus, the data were collapsed across the two groups for the remaining analysis.
Panel A of Figure 2.2 shows discrimination performance for the four types of test trial in the same-different task across the four test blocks. Performance was better on same than on different trials, F(1, 15) = 30.08. Participants did not show an overall improvement in accuracy across the task (a main effect of test block), F(1, 15) = 3.71, p = 0.07. Most importantly, accuracy for AX and BX was better than for CY and DY, F(1, 15) = 8.64. An interaction was also observed between trial type (same versus different), and preexposure condition (preexposed versus novel), F(1, 15) = 7.14. It would appear that the effect of preexposure was seen on different test trials, but not on same trials. There was no interaction between test block and preexposure condition, F(1, 15) = 1.64, or between test block and trial type (same versus different), F < 1. A significant three-way interaction, Chapter 2 55
however, between test block, preexposure condition, and trial type was observed, F(1, 15)
= 5.42. This interaction seems to suggest that the difference in accuracy between same and different trials was reduced across the test phase, but the rate of reduction was greater for the novel condition. This interaction prompted an examination of only the different test trials.
Accuracy on the different trials did not improve across test blocks, F(1, 15) = 2.29.
However, on these trials, participants were better at discriminating AX from BX than discriminating CY from DY, F(1, 15) = 8.29. The interaction between test block and preexposure condition was significant, F(1, 15) = 5.38. This interaction suggests that the difference between the preexposure conditions changed across test trials. In particular, performance improved for the novel condition, F(1, 15) = 4.96, but performance did not improve for the preexposure condition (F < 1). Simple effects analyses revealed a difference between the preexposure conditions on the first block of test trials, F(1, 15) =
11.36, that was not present on the final block of trials (F < 1).
A signal detection analysis was also conducted to measure participants’ sensitivity to detect differences between AX and BX, and between CY and DY. This analysis gives a clearer indication of the participant’s ability to detect the unique elements since it controls for the possibility that discrimination accuracy (proportions correct) might be inflated by response bias (e.g., responding ‘same’). Sensitivity scores, d’, were calculated for each participant. Hits (H in equation 2.1) were defined as the proportion of different trials on which a correct response (“different”) was given. False alarms (F in equation 2.1) were defined as the proportion of same trials on which an incorrect response was given (also
“different”). An unbiased proportion correct score ( ) was calculated for each participant using equation 2.1 (MacMillan & Creelman, 1991; Wickens, 2002). Scores of 0 Chapter 2 56
and 1 were adjusted using 1/2n (where n is the number of test trials for that block).
Sensitivity scores were then calculated using equation 2.2.
( ) (2.1)
√ ( ) (2.2)
Panel B of Figure 2.2 shows d’ score for each condition over the four test blocks.
Sensitivity scores were superior for AX and BX than for CY and DY, F(1, 15) = 7.91, thus participants’ ability to detect the unique elements was better for the preexposed patterns, than for the novel patterns. Sensitivity scores did not improve across test blocks in either preexposure condition, F(1, 15) = 2.80. The interaction between these two main factors was also not significant, F(1, 15) = 1.81. A simple effects analysis using t-tests showed that the difference in sensitivity was apparent in the first block of test trials, t(15) = 2.69, but this difference was not observed in the final block of test trials, t(15) = 1.74, p = 0.10.
An analysis of response bias was also conducted for discrimination performance in the same-different task. Equation 2.3 was used to calculate the criterion value (c). Larger positive scores indicate a bias toward responding “same”, whereas a score close to zero or smaller than zero indicates a bias toward responding “different”. A c estimate was calculated for each participant, a summary of the data is presented in panel C of Figure 2.2.
( ) (2.3)
An effect of preexposure was observed; c values were greater in the novel than in the preexposed condition, F(1, 15) = 7.46. This shows that participants had a stronger tendency to respond “same” in the novel condition than in the preexposed condition. Across Chapter 2 57
test blocks, c values did not change (F < 1). The interaction between these two factors however, was significant, F(1, 15) = 5.62.
Eyegaze. Panel D of Figure 2.2 shows mean gaze length to the unique elements A-
D during the same-different task. Gaze length for each condition is the time spent in each block looking at the AOI corresponding to the unique elements that were present (i.e. the time spent looking at A on AX trials and B on BX trials). Preliminary analyses of the eyegaze data included gaze length to the AOI’s for A-D on the trials in which they were absent (i.e. B region on AX trials and A region on AX trials.). In general, participants only attend to the AOIs when the unique elements are present in that region. Thus, gaze length to
A-D regions when these elements are not presented does not alter the conclusions offered by the existing inferential statistical analyses. Similar results, using this approach, were observed in Experiment 2 and 3. Consequently, this approach was abandoned.
Gaze length to the unique elements increased overall across the test phase, F(1, 15)
= 5.47. Gaze length to A and B was greater than gaze length to C and D, F(1, 15) = 7.78.
The test block by condition interaction was also significant, F(1, 15) = 4.67. A simple effects analysis of a linear trend across test blocks showed that gaze length increased for the novel condition, F(1, 15) = 7.44, but gaze length did not increase in the preexposure condition (F < 1). Lastly, the difference in gaze length between the two conditions was apparent in the first block of test trials, t(15) = 3.80, but this difference was not apparent in the final block of test trials, t(15) = 1.74, p = 0.10.
Correlation analysis. An additional analysis was conducted to examine the correlation between eyegaze and discrimination performance. For simplicity, this analysis used sensitivity scores (d’) to index discrimination performance. The analysis was conducted in the following manner. The difference in sensitivity between the AX/BX and Chapter 2 58
CY/DY test trials, averaged across test blocks, were calculated for each participant.
Similarly, the corresponding difference between gaze length to A/B and C/D in test was also calculated. These two difference scores were then used to analyse two correlations.
The first correlation examined the relationship between gaze length to A and B in preexposure and discrimination performance. The significant correlation between these two variables, r = 0.51, p < 0.05, suggests that increased gaze length to A and B in preexposure is correlated with larger effects of preexposure. The second correlation examined the relationship between the eyegaze and sensitivity in test. The significant correlation between these two variables, r = .83, p < 0.01, suggests that a large difference in gaze length between the preexposed and novel conditions is correlated with a large difference between the two conditions in discrimination performance. Unless stated otherwise, the correlation analyses for the subsequent experiments followed this procedure
Discussion
Preexposure to AX and BX enhanced discrimination of these patterns, as compared to the patterns that were novel at test, CY and DY. The advantage conferred by preexposure was apparent in the first block of test trials. Interestingly, the difference in performance between the two conditions was observed on the different trials but not on the same trials.
In general, participants performed very accurately on the same trials, and there was no difference between the two conditions on these trials. One possibility is that participants have a strong bias toward making the “same” response, and perhaps, this is unsurprising since the stimuli are very difficult to discriminate. In addition, participants showed a stronger tendency to respond “same” in the novel than in the preexposed condition.
The eyegaze measure showed that participants spent more time looking at the preexposed (A and B) than at the novel (C and D) unique elements. This difference was Chapter 2 59
apparent in the first block of test trials. Exposure to AX and BX is thought to increase the perceived salience of the unique elements, such that A and B are perceived as more salient than C and D (Gibson, 1969). These eyegaze data present the first direct support for this assumption.
The notion of relative novelty provides the simplest account of these results
(McLaren et al., 1989). Following preexposure, the common element X was more familiar and therefore less salient than the unique elements A and B. This would explain the same- different performance and the eyegaze data. Although the novel unique elements C and D were, in an absolute sense, more salient than A and B, they captured less attention than A and B because they were presented on the novel and very salient Y background. That is, attention to C and D was reduced because there was competition for attention from the novel Y background. This explanation suggests that exposures to X alone will also increase the discriminability of AX and BX over CY and DY. Experiment 2 examined this possibility.
Experiment 2
In this experiment, participants were presented with just the X background in preexposure. They then completed the same-different task used in Experiment 1, in which discrimination of AX and BX as well as CY and DY was tested. It was predicted that preexposure would reduce the salience of X and so allow A and B to capture more attention when the AX and BX compounds were presented on test. The resulting attentional bias towards A and B on AX and BX trials was predicted to enhance discrimination between
AX and BX relative to the novel CY and DY. As in Experiment 1, eyegaze was measured.
The relative novelty hypothesis predicts that, because A and B will capture more attention Chapter 2 60
than C and D, participants will spend more time looking at A and B than at C and D when these elements are presented on test.
Method
Experiment 2 only differed from Experiment 1 in the following respects.
Participants. Twenty-one (14 female and 7 male, mean age = 21.1) first year psychology students participated in the experiment in exchange for course credit.
Design. In the preexposure phase, all participants received 120 presentations of the common element X1. The same-different task described in Experiment 1 followed the preexposure phase.
1 At the start of the preexposure phase, participants were still instructed to look for differences between the patterns even though no differences actually appeared. Chapter 2 61
Figure 2.3. Panel A shows mean proportion of correct responses within each test block on the four types of test trial in Experiment 2. Panel B shows mean sensitivity score (d’) for each preexposure condition across test blocks. Panel C shows mean c values test blocks within each preexposure condition. Panel D shows the total gaze length to the two unique elements within each preexposure condition (A&B or C&D) across test blocks.
Chapter 2 62
Results
Data for two participants were excluded using the criterion described in Experiment
1.
Same-different performance. A preliminary analysis was conducted to determine whether performance in the same-different task was affected by the assignment of background stimuli (X versus Y) to the preexposure and novel conditions. The analysis revealed no main effect of background (F < 1), and no interaction between background and any other factor (all Fs < 1). The data were collapsed across the two counterbalancing groups for the remaining analysis.
Panel A of Figure 2.3 shows the proportion of correct responses for the four types of trial in the same-different task. As in Experiment 1, performance was better on the same trials than on the different trials, F(1, 18) = 12.46. The apparent increase in accuracy across test blocks approached significance; a marginal linear trend was observed, F(1, 18) = 3.62, p = 0.07. Most importantly, performance to AX and BX was better than to CY and DY,
F(1, 18) = 4.60. As in Experiment 1, the interaction between trial type (same versus different) and preexposure condition was significant, F(1, 18) = 7.36, as was the interaction between test trial type (same versus different) and test block, F(1, 18) = 7.36. However, there was no preexposure by test block interaction, F(1, 18) = 2.32. The three way interaction between the main factors of test block, test trial type, and preexposure condition was significant, F(1, 18) = 5.35.
The significant interaction of preexposure condition and trial type prompted an analysis of only the different trials. It showed that accuracy on these trials significantly increased across test blocks, F(1, 18) = 19.14, and that performance to AX and BX was better than to CY and DY, F(1, 18) = 6.37. The interaction between these two factors was Chapter 2 63
also significant, F(1, 18) = 5.14. This confirms the observation that the difference between the preexposure conditions, in performance across different trials, increased across test blocks. There was no difference between the preexposure conditions on the first block of different test trials (F < 1). In fact, if anything, performance to AX and BX was poorer than to CY and DY in this first block. A significant difference was observed, however, on the final block of test trials, F(1, 18) = 13.62. A final analysis of performance on different trials for the two conditions across test blocks showed that responses to AX and BX increased in accuracy, F(1,18) = 21.00, but responses to CY and DY did not, F < 1. Given that performance on the same trials was high and constant across conditions and test trial types, this analysis of the different trials suggests a particular interpretation of the three-way interaction. That is, the difference in accuracy between same and different trials reduced
(performance on different trials increased) across the test phase, and that the rate of this reduction was greater for AX and BX than for CY and DY.
Panel B of Figure 2.3 shows the sensitivity scores (d’) for the two preexposure conditions. Sensitivity scores for AX and BX were better than for CY and DY, F(1, 18) =
5.15. Participants were better able to detect the unique elements of AX and BX than those of CY and DY. Sensitivity scores overall did not increase across the test blocks, F(1, 18) =
2.13. The interaction between preexposure conditions and test block was marginally significant, F(1, 18) = 4.26, p = 0.054. Thus, the difference in sensitivity between the preexposure conditions increased across test blocks. A simple analysis for linear trend showed that sensitivity for discriminating AX from BX increased across test blocks, F(1,
18) = 6.33, but sensitivity for discriminating CY from DY did not increase (F < 1). T-tests revealed that there was no difference in sensitivity between the two preexposure conditions Chapter 2 64
in the first block of test trials (F < 1), but a difference was apparent in the final test block, t(18) = 2.72.
Panel C of Figure 2.3 shows c values across the same-different task. The c values for the preexposed condition were significantly lower than for the novel condition, F(1, 18)
= 7.29. That is, participants were more biased to respond “same” in the novel than in the preexposed condition. Across both test conditions, this response bias decreased across the test session, F(1, 18) = 7.33. The interaction between these two factors was also significant,
F(1, 18) = 5.17. The interaction effect confirms the trend observed in the figure that participants reduced their bias toward the “same” response faster in the preexposed than in the novel condition.
Eyegaze. Panel D of Figure 2.3 shows gaze length to the two unique elements within each condition during the same-different task. The figure shows that participants spend more time looking at A and B than at C and D. Gaze length to the unique elements also increased across the test phase. A statistical analysis confirmed a significant main effect of preexposure condition, F(1, 18) = 6.33, and a significant linear trend across test trials, F(1, 18) = 7.40. The interaction between these two main factors was not significant,
F(1, 18) = 1.75. However, a simple effects analysis confirmed that there was no difference in gaze length between the preexposure conditions on the first test block (t < 1), but a difference was present on the final test block, t(18) = 2.74. In addition, gaze length increased in the preexposure condition, F(1, 18) = 5.91, but not in the novel condition, F(1,
18) = 1.07.
Correlation analysis. Similar to the previous experiment, an analysis was conducted to examine the correlation between eyegaze and discrimination performance.
This analysis only considered the eyegaze data from the same-different task since no Chapter 2 65
unique elements were presented in preexposure. A significant positive correlation was observed between performance and eyegaze, r = 0.81, p < 0.01. Specifically, a large difference in eyegaze between the preexposed and novel conditions is correlated with a large difference between the two conditions in discrimination performance.
Discussion
Experiment 2 showed that exposure to the common element X enhanced discrimination between AX and BX, compared to the novel stimuli, CY and DY. This replicates previous findings in animals (Bennett et al., 1994; Mackintosh et al., 1991) and humans (Mundy et al., 2007) using flavour and face stimuli respectively. These findings are broadly consistent with the relative familiarity account of perceptual learning. Thus, in the current experiment, participants found it easier to discriminate AX and BX than CY and
DY because A and B were presented on the familiar, and therefore non-salient, X background. The eyegaze data support the idea that better discrimination of AX and BX is related to greater attention to the unique elements of these stimuli. Relative novelty, and its effects on attention, can also explain performance to AX and BX in Experiment 1.
Following AX/BX preexposure, although A and B are not novel, they are more novel than the X background. Therefore, they may capture more attention than the novel C and D presented on the equally novel Y background. Thus, it would appear that the relative novelty account provides a very parsimonious account of at least some perceptual learning effects in both humans and non-human animals.
There are some details of the current data pattern (in both the same-different responses and the eyegaze measure) that do not sit so comfortably with the relative novelty account. In particular, the superiority in performance to AX and BX over CY and DY is present at the start of testing in Experiment 1, but emerges only later in Experiment 2. This Chapter 2 66
might suggest that the perceptual learning effect is stronger in Experiment 1 than
Experiment 2. The unique features A and B are, however, more novel in Experiment 2 than in Experiment 1 (the X background is equally familiar in the two cases). It would appear from this cross-experimental comparison, therefore, that there is more than just relative novelty in operation in these experiments. A detailed analysis of this particular feature of the current data will be postponed until Chapter 3. Before that, the eyegaze measure will be applied to the third important finding from the existing literature, which also resists explanation in terms of relative novelty, the intermixed-blocked effect.
Broadly speaking, there are two explanations for the finding that intermixed exposure to AX and BX (AX, BX, AX, BX) will produce better discrimination on test than blocked exposure (all presentations of AX followed by all presentations of BX). The first is that some mechanism (over and above relative novelty) serves to maintain or enhance the salience of (or attention to) the unique features presented on the intermixed schedule
(Gibson, 1969; Hall, 2003; Mitchell, Nash et al., 2008; Mundy et al., 2007). The second explanation is that inhibitory links form between A and B on the intermixed but not on the blocked schedule, which serve to enhance discrimination between AX and BX (McLaren &
Mackintosh, 2000). Using the eyegaze measure, Experiment 3 examined the possibility that, consistent with the salience/attention-based explanations, eyegaze to the unique elements is greater following an intermixed than a blocked preexposure schedule.
Experiment 3
Lavis & Mitchell (2006) demonstrated the intermixed-blocked effect using the type of checkerboard stimuli used in Experiments 1 and 2 here. The present experiment used the same checkerboards to test whether the superiority of intermixed over blocked preexposure was also reflected in a difference in eyegaze to the unique stimulus features. Chapter 2 67
Method
The procedure differed from the previous experiments only in the following details.
Participants. Thirty students (23 female and 7 male; mean age = 19.1), from
UNSW participated in this experiment in exchange for course credit.
Design. There were two preexposure conditions. In the intermixed condition, AX was presented in alternation with BX, and in the blocked condition, a series of CX presentations was followed by a series of DX presentations. Thus, the same checkerboard background was used in both conditions. The presentation order of these two conditions was counterbalanced across participants. In the subsequent test phase, there were four trial types: (1) intermixed different (e.g., AX and BX), (2) intermixed same (e.g., AX and AX),
(3) blocked different (e.g., CX and DX), and (4) blocked same (e.g., CX and CX). Chapter 2 68
Figure 2.4. Panel A shows mean proportion of correct responses within each test block on the four types of test trial in Experiment 3. Panel B shows mean sensitivity score (d’) for each preexposure condition across test blocks. Panel C shows mean c values test blocks within each preexposure condition. Panel D shows the total gaze length to the two unique elements within each preexposure condition (A&B or C&D) across the blocks of preexposure trials. Panel E shows the total gaze length to A&B and C&D across the test blocks. Chapter 2 69
Results
Three participants were excluded from this analysis according to the criterion described in Experiment 1.
Same-different performance. A preliminary analysis was conducted to determine whether the order of preexposure (intermixed followed by blocked or vice versa) affected performance. This analysis revealed no main effect of preexposure order, F(1, 25) = 3.18, p
= 0.09. No significant interaction between preexposure order and prexposure condition
(intermixed versus block) was observed (F < 1). A significant interaction between trial type
(same versus different) and preexposure order was observed, F(1,25) = 7.07. Participants who received blocked exposure before intermixed exposure produced a smaller difference between the same and different test trials than participants who received the opposite arrangement. However, the more important effect of preexposure (intermixed versus blocked) was not affected by counterbalancing. The three-way interaction was also not significant (F < 1). The data were therefore collapsed across the two counterbalancing orders.
Panel A of Figure 2.4 shows the mean proportion of correct responses for the four different test conditions across the four blocks of test trials. As in the previous experiments, performance was more accurate on the same test trials than the different test trials, F(1, 26)
= 31.18. Most importantly, performance was better for AX and BX trials than for CX and
DX trials, F(1, 26) = 4.86. That is, discrimination was better following intermixed than blocked exposure. The interaction between these two main factors was also significant, F(1,
26) = 7.83. There was no general improvement in accuracy across the test trials (F < 1).
However, the effect of test blocks did interact with preexposure condition (intermixed versus blocked), F(1, 26) = 6.05. The interaction between test blocks and types of test trial Chapter 2 70
(same versus different) was marginally significant, F(1, 26) = 3.89, p = 0.06. There was a three-way interaction between test blocks, preexposure condition and types of test trial,
F(1, 26) = 12.29. This interaction seems to suggest that the difference in accuracy between same and different trials reduced across the test phase, but the rate of reduction was greater for the blocked condition.
Examination of the different test trials alone showed that there was a significant difference in accuracy between preexposure conditions, F(1, 26) = 6.53. A linear trend across test blocks was not observed, F(1, 26) = 1.35. The interaction between these factors was, however, significant, F(1, 26) = 11.25. This interaction implies that the difference in accuracy between intermixed and blocked conditions was greatest at the start of the test phase, but the difference was reduced with more test trials. T-tests revealed a difference between the preexposure conditions on the first block of test trials, t(26) = 4.27, but this difference was not present on the final block of trials (t < 1). A simple effects analysis of trend showed that performance for the difference trial increased in the blocked condition,
F(1, 26) = 11.61, but performance decreased for the intermixed condition, F(1, 26) = 5.04.
Panel B of Figure 2.4 shows sensitivity scores (d’) for the intermixed and blocked conditions across test blocks. Similar to the previous analysis, sensitivity was greater for
AX and BX than for CX and DX, F(1, 26) = 5.22. Participants were better able to detect the unique elements in the intermixed than in the blocked condition. Sensitivity scores overall did not increase across the test blocks (F < 1). However, the interaction between these two factors was significant, F(1, 26) = 6.11. Inspection of the figures suggests that sensitivity scores declined in the intermixed condition, but sensitivity scores increased in the blocked condition. T-tests revealed that there was a difference in sensitivity between the two conditions in the first block of test trials, t(26) = 3.49, but this difference disappeared in the Chapter 2 71
final block of test trials (t < 1). A simple effects analysis of linear trend however, showed that neither the linear trend for AX and BX (F(1, 26) = 2.93), nor the linear trend for CX and DX (F(1, 26) = 2.72) were significant.
Similar to the previous experiments, an analysis of response bias was conducted for discrimination performance in the same-different task. A summary of this pattern of data is presented in panel C of Figure 2.4. An effect of preexposure was observed as c values were greater for the blocked than for the novel condition, F(1, 26) = 7.82. The c values did not change across the same-different task, F(1, 26) = 3.57. The interaction between these two factors was significant, F(1, 26) = 12.21. The interaction effect suggests that participants had a stronger initial bias to respond “same” in the blocked than in the intermixed condition. However, this difference was reduced across test blocks.
Eyegaze. Panel D of Figure 2.4 shows gaze length to the unique elements in the preexposure phase. Each block represents fifteen presentations of each pattern. Gaze length was calculated by summing gaze length to the two unique elements within the intermixed or blocked condition. The analysis showed that gaze length was greater to A and B than to
C and D during the preexposure phase, F(1, 26) = 8.88. Thus, attention to the unique elements was greater in the intermixed than in the blocked condition. Gaze length to the unique elements (A-D) did not increase across the preexposure phase (F < 1). The interaction between these two main factors was also not significant, F(1, 26) = 2.77. T-tests showed that the effect of preexposure on eyegaze was apparent in the final block of preexposure trials, t(26) = 3.09, but there was no difference in the first block (t < 1).
Panel E of Figure 2.4 shows gaze length to the unique elements A-D in the same- different test. Gaze lengths to the unique elements did not increase across the test phase,
F(1, 26) = 1.30. Participants spent more time looking at the unique elements in the Chapter 2 72
intermixed condition (A and B) than in the blocked condition (C and D), F(1, 26) = 5.86.
The interaction between these two main factors was not significant, F(1, 26) = 1.55. The difference in gaze length between the two conditions was apparent in the first block of test trials, t(26) = 3.17.
Correlation analysis. In this analysis, a difference score between gaze length to
A/B and to C/D was calculated for both preexposure and test. The correlation of eyegaze in preexposure and difference in sensitivity was not significant, r = 0.20, p = 0.31. In the same-different task, a significant positive correlation was observed between eyegaze and performance, r = 0.66, p < 0.01. That is, a large difference in eyegaze between the intermixed and blocked conditions is correlated with a large difference between the two conditions in discrimination performance.
Discussion
Experiment 3 showed that intermixed presentations of AX and BX produced better discrimination than blocked presentations of CX and DX. This replicates previous demonstrations of the intermixed-blocked effect with both animal subjects (e.g. Symonds &
Hall, 1995), and human participants (Lavis & Mitchell, 2006; Mundy et al., 2007). Similar to Experiment 1, participants had a stronger bias to respond “same” following blocked than intermixed exposure. Experiment 3 also showed that intermixed exposure to AX and BX produced greater attention to the unique elements than blocked exposure to CX and DX, as measured by eyegaze. This supports the idea that intermixed preexposure is especially effective in directing attention to the unique stimulus features, and it is not simply the amount of exposure to AX and BX, or the relative novelty of the unique and common features, that determines the salience of these features.
Chapter 2 73
Chapter Summary and Discussion
Experiment 1 showed that alternating exposures to AX and BX enhanced later discrimination (Mackintosh et al., 1991). Experiment 2 showed that exposure to X alone also enhanced discrimination between AX and BX (Bennett et al., 1994). Experiment 3 showed that discrimination was superior following intermixed presentations of AX and BX than blocked presentations of CX and DX (Symonds & Hall, 1995). Across all three experiments, the conditions that produced the best discrimination performance on test were those for which eyegaze to the unique features was greatest. This finding is consistent with the idea that when two similar stimuli increase in discriminability as a consequence of exposure, there is an increase in attention to the unique features of those stimuli. The eyegaze data are important because they are evidence from a direct measure of attention, rather than an inference about the role of attention from discrimination performance (see Le
Pelley, 2010, for a similar argument in the context of associative learning).
Experiments 1 and 2, when taken alone, are consistent with McLaren and
Mackintosh’s (2000) relative novelty mechanism. In both cases, the unique features of the most easily discriminated stimulus pair (A and B) were more novel than the background on which they were presented (X), whereas the unique and common features of the control stimuli were equally novel. This mechanism can explain both the discrimination performance and the pattern of eyegaze. Experiment 3 however, is not consistent with the relative novelty explanation. McLaren and Mackintosh suggested that better discrimination performance following intermixed than blocked exposure is the consequence of inhibition between A and B in the intermixed condition. Associative inhibition was proposed to explain perceptual learning effects in which stimulus discriminability is measured by the generalisation of a conditioned response from one stimulus to another. Chapter 2 74
It is unclear as to how associative inhibition (McLaren & Mackintosh, 2000) applies to same-different judgements. One possible explanation is as follows. Following exposure to AX and BX, A and B will be more salient than X. The presence of the X-A and X-B excitatory associations mean that A and B will be activated on BX and AX trials respectively. Both A and B will now compete for attention on AX and BX presentations.
This can be avoided if inhibition developed between A and B since inhibition will negate the activation of either A or B by X. Inhibition between the unique elements however, is less likely to develop during blocked presentations of CX and DX, and this produces a competition for attention between C and D on CX:DX test trials. Consequently, participants will spend more time looking at A and B than at C and D in the same-different task. This explanation does not assume that salience of the unique elements is different between the two exposure conditions.
The problem with this explanation is that on AX:BX trials, for example, X will not only activate A and B, but also C and D. This leaves the possibility that four different unique elements (A-D) may be directly or associatively activated on any test trial.
However, it remains the case that A and B will not be associatively activated on BX and
AX trials since there is inhibition between A and B. In contrast, all four unique elements A-
D will be activated on CX and DX trials. Consequently, discrimination performance will be poorer in the blocked than in the intermixed condition. Furthermore, eyegaze will be less focussed on the unique features that are present in the blocked condition (C and D), because more elements will be associatively activated, and might therefore demand attention, on CX and DX than on AX and BX trials.
Although inhibition can account for the current data, perhaps a simpler explanation of Experiment 3 is to assume that intermixed and blocked exposure affected the salience of Chapter 2 75
the unique elements differently. As suggested by Gibson (1969), intermixed presentations of AX and BX allow the participants to compare the two patterns. Attention is increased to the unique elements because the process of comparison highlights the relative differences between the patterns. The opportunity for comparison is limited on the blocked schedule on which CX and DX are presented. Greater attention to the unique elements, and improved discrimination should, therefore, be observed in the intermixed but not the blocked condition. The finding that gaze length to A and B is greater than to C and D is consistent with this notion and is also consistent with evidence from animal perceptual learning studies (e.g., Blair & Hall, 2003).
In summary, Chapter 2 replicated the three key findings reported in the animal perceptual learning literature. In addition, eyegaze was used to show that human perceptual learning, as demonstrated in all three experimental designs, involves an attentional process.
In particular, in Experiments 1-3, stimulus discriminability showed the same pattern as gaze length to the unique elements. The intermixed-blocked effect also showed that at least part of the attentional mechanism is not the result of the relative novelty of the unique and common features. Three models (Hall, 2003; McLaren & Mackintosh, 2000; Mitchell,
Nash et al., 2008) can, however, account for these results. In fact, the McLaren and
Mackintosh model provides both an associative (inhibition) and attentional (unitization) explanation of the intermixed-blocked effect. Chapter 3 will attempt to examine the applicability of these two mechanisms. Chapter 3 76
CHAPTER THREE
Chapter 2 provided evidence in support of a long held assumption that exposure to two similar stimuli increases attention to their distinguishing features. This process of attention is not simply the result of relative novelty since attention to the unique features is greater following intermixed than blocked presentations to two similar patterns
(Experiment 3). A number of different mechanisms account for the intermixed-blocked effect (Hall, 2003; McLaren & Mackintosh, 2000; Mitchell, Nash et al., 2008). Chapter 3 aimed to investigate the predictions of the McLaren and Mackintosh model.
To briefly recap, the McLaren and Mackintosh model provides two explanations for the intermixed-blocked effect. Both mechanisms rely on the fact that, during intermixed exposure to AX and BX, the common element X will associatively activate the unique features A and B. Associative activation will then 1) allow inhibition to develop between A and B and, 2) reverse the habituation of A and B that would otherwise have taken place, by weakening the links between the various parts of these unique features. There is one aspect of Experiments 1 and 2, in particular the comparison of these two experiments that does not appear to be accounted for easily by the mechanisms proposed by McLaren and
Mackintosh.
In Experiments 1 and 2, preexposure to AX and BX (Experiment 1) and to X alone
(Experiment 2) enhanced discrimination of AX or BX compared to the novel stimuli CY and DY. On its own, these results are consistent with the notion of relative novelty proposed by McLaren and Mackintosh (2000). However, in Experiment 1, discrimination of the preexposed patterns was better than of the novel patterns in the first block of test trials. In contrast, no effect of preexposure was observed in the first block of test trials in Chapter 3 77
Experiment 2. This implies that discrimination of AX and BX is superior following AX/BX exposure than X alone exposure. Neither of the mechanisms proposed by McLaren and
Mackintosh (2000) can account for this difference.
The unitization mechanism for example, assumes that stimulus salience is greatest when the stimulus is novel. Salience is highest before any links have formed between the parts of a stimulus representation. Associative activation of A and B will only attenuate the loss of salience that occurs through exposure. As a consequence, the McLaren and
Mackintosh model predicts that the salience of the intermixed elements A and B will always be lower than that of the novel elements C and D. That is, discrimination of AX and
BX will be better following X alone exposure than following AX/BX exposure.
Experiments 1 and 2 suggest the reverse pattern.
The inhibition mechanism also fails to predict an advantage for AX/BX over X alone preexposure. Thus, according to this account, any X-A and X-B links formed during preexposure will increase the similarity of AX and BX (decrease performance on the same- different test). Inhibition between A and B will somewhat reduce the associative activation of A on BX trials (and B on AX trials), and so will improve test performance. However, in
Experiment 2, elements A and B were not presented during preexposure. Therefore, no X-A and X-B links could have formed, and this will lead to very good performance in the same- different test. Importantly, the combination of the excitatory and inhibitory links that might have formed following AX/BX preexposure (Experiment 1) cannot produce better discrimination performance than that following X alone exposure (Experiment 2). At best, inhibition between A and B will eliminate the effect of X-A and X-B links following
AX/BX preexposure, and so produce equivalent performance to that seen following X alone preexposure. Chapter 3 78
In summary, the comparison of Experiments 1 and 2 in Chapter 2 seem incompatible with the McLaren and Mackintosh (2000) model. To directly assess this possibility, Experiment 4 compared the intermixed and the background-alone preexposure schedules described in Experiments 1 and 2.
Experiment 4
Participants in Experiment 4 received exposure to two presentation schedules; alternating presentations of AX and BX, as well as presentations of Y alone. On the same- different test, participants were asked to discriminate AX from BX, and CY from DY. The inhibition and unitization mechanisms described in McLaren and Mackintosh (2000) suggest that, if anything, performance to CY and DY will be better than to AX and BX.
Method
The procedure differed from the previous experiments only in the following details.
Participants. Thirty-two students (22 female and 10 male; mean age = 18.9), from
UNSW participated in this experiment in exchange for course credit.
Design. There were two preexposure conditions. In one condition, AX was presented in alternation with BX, and in the other condition, Y was presented alone. The order of presentation for these two preexposure conditions was counterbalanced across participants. This experiment used two different coloured background patterns that served as X and Y. Two unique elements were randomly selected and placed on one background, and the two remaining unique elements were placed on the other background. The assignment of each background to preexposure condition was counterbalanced between participants. In the subsequent test phase, there were four trial types: (1) intermixed different (e.g., AX and BX), (2) intermixed same (e.g., AX and AX), (3) common different
(e.g., CY and DY), and (4) common same (e.g., CY and CY). Chapter 3 79
Figure 3.1. Panel A shows mean proportion of correct responses within each test block on the four types of test trial in Experiment 4. Panel B shows mean sensitivity score (d’) for each preexposure condition across test blocks. Panel C shows estimates of response bias (c) for each preexposure condition across test blocks. Panel D shows the total gaze length to the two unique elements within each preexposure condition (A&B or C&D) across test blocks. Chapter 3 80
Results
Three participants were excluded from this analysis using the criterion described in
Experiment 1.
Same-different performance. A preliminary analysis was conducted to determine whether preexposure order (AX/BX followed by Y and vice versa) affected performance.
There was no main effect of preexposure order (F < 1). There was also no interaction between preexposure order and preexposure condition (intermixed versus common), (F <
1) or between preexposure order and trial type (same versus different), F < 1. In addition, performance was not affected by the assignment of the two background patterns to the preexposure conditions (F < 1). Neither of the interactions between background type and preexposure condition, (F < 1) and between background type and trial type (F < 1) were significant. The data were therefore collapsed across preexposure order and background type for the remaining analysis.
Panel A of Figure 3.1 shows the mean proportion of correct responses for the four different test conditions across the four blocks of test trials. As in previous experiments, performance was more accurate on the same test trials than on the different test trials, F(1,
28) = 58.32. There was no improvement in performance across test trials, F(1, 28) = 1.70.
Most importantly, performance was better for AX and BX than for CY and DY, F(1, 28) =
9.90. There was an interaction between test trial type (same versus different) and preexposure condition (intermixed versus commmon), F(1, 28) = 9.38. As in previous experiments, the effect of preexposure appears to be located on the different test trials only.
A significant interaction was observed between test trial type and test block, F(1, 28) =
18.40. This suggests that the difference in performance accuracy between same and Chapter 3 81
different trials was reduced across test blocks. The remaining interactions were not significant, both Fs < 1.
As in the previous experiments, an analysis that focussed on the different test trials was conducted. It showed that participants performed more accurately on the different trials as the test phase progressed, F(1, 28) = 8.21. Also, participants were more accurate in discriminating AX from BX than CY from DY, F(1, 28) = 10.58. These two factors did not, however, interact (F < 1).
Panel B of Figure 3.1 shows sensitivity scores (d’) for discrimination performance of the two preexposure conditions. Sensitivity scores for AX and BX were greater than for
CY and DY, F(1, 28) = 9.76. Participants were more sensitive to differences between AX and BX than between CY and DY. Sensitivity scores overall did not improve across the test blocks, F(1, 28) = 2.99. In addition, the interaction between preexposure condition and test blocks was not significant (F < 1).
An analysis of response bias was also conducted for discrimination performance in the same-different task. The scores for response bias (c values) are presented in panel C of
Figure 3.1. In both test conditions, participants’ bias toward the “same” response decreased across test blocks, F(1, 28) = 17.79. Participants showed a stronger bias toward responding
“same” in the common elements condition than in the intermixed condition, F(1, 28) =
9.53. The interaction between these two factors was not significant, F(1, 28) = 1.38.
Eyegaze. Panel D of Figure 3.1 shows gaze length to the unique elements A-D in the same-different test. Gaze lengths to the unique elements increased across the test phase
F(1, 28) = 6.65. In particular, participants spent more time looking at the unique elements
A and B than at C and D, F(1, 28) = 8.97. The interaction between preexposure condition and test blocks was not significant (F < 1). Chapter 3 82
Correlation analysis. Gaze length to A and B in preexposure did not correlate with the difference in sensitivity between the preexposure conditions (AX/BX versus CY/DY), r
= 0.29, p = 0.12. Similar to previous experiments, a significant positive correlation between the difference in sensitivity scores and the difference in gaze length was observed in the same-different task, r = 0.68, p < 0.01. That is, a large difference in gaze length to the unique elements between the two preexposure conditions is correlated with a large difference between these conditions in discrimination performance
Discussion
Experiment 4 showed that discrimination of AX and BX was better than of CY and
DY. In addition, participants spent more time looking at A and B than at C and D during the same-different task. These findings suggest that that salience of the intermixed unique elements (A and B) was greater than that of the novel unique elements (C and D). The current findings are consistent with the observations made between Experiments 1 and 2 (of
Chapter 2). In Experiment 1, exposure to AX and BX rendered these patterns more discriminable than control patterns (CY and DY) in the first test block. The corresponding difference was not observed following exposure to X alone in Experiment 2. This suggests that AX and BX became more discriminable following exposure to AX and BX than to X alone. The current findings confirm this cross-experimental comparison. Furthermore, the eyegaze analysis suggests that the unique elements are more salient following exposure in the intermixed than in the common element condition. An alternative possibility is that participants have learned to attend to the location of the unique elements, and it is this that controls eye movements. This issue will be examined in Chapter 5.
This conclusion is inconsistent with two mechanisms proposed by the McLaren and
Mackintosh (2000) model. Firstly, the unitization mechanism predicts that attention should Chapter 3 83
be greatest to the novel elements. Exposure to AX and BX will only attenuate the loss of salience, but it cannot render A and B more salient than C and D. In addition, associative inhibition between A and B cannot explain why AX and BX are most discriminable.
Inhibition only serves to cancel out the effect of the X-A and X-B excitatory links.
Discrimination of CY and DY are immune to the effects of associative activation since Y-C and Y-D links cannot form during Y alone exposure. According to associative inhibition,
CY and DY should, therefore, be more discriminable than AX and BX following preexposure.
The findings of the current experiment appear to support Gibson’s (1969) view of perceptual learning, as well as that of Mitchell, Nash, and colleagues (2008). An alternative possibility for Experiment 4 however, is that the salience of the unique features
(preexposed and novel) did not differ, but that, instead, there is a difference in the salience of the common background (X versus Y) between the two preexposure conditions. The common element may lose more salience during AX/BX exposure than during Y alone exposure. Mondragon and Hall (2002) showed evidence to suggest that the salience of the common element was greater following blocked than intermixed exposure. In their study, rats received exposure trials to two flavours, AX and BX, on either an intermixed or a blocked schedule. An aversion was then established to X by injecting the animals with LiCl following consumption of X. Mondragon and Hall found that extinction of the conditioned aversion was faster in the intermixed than in the blocked group. This suggests that a weaker
X-US association was formed in the intermixed than in the blocked group. A weaker X-US association in the intermixed group suggests that X lost more salience during intermixed than blocked exposure. Following Gibson’s (1969) notion of stimulus differentiation,
Mondragon and Hall suggested that X received less attention in the intermixed than in the Chapter 3 84
blocked schedule, because the unique element received more attention in the intermixed schedule.
A similar process may have occurred in Experiment 4. Although it seems counterintuitive, it is possible that the common background X lost more salience than Y following AX/BX and Y alone exposure. Superior discrimination of AX and BX than of
CY and DY might then reflect the greater salience of Y than X. One way to rule out this possibility is to present the preexposed and novel unique elements on the same common background in the same-different task. This possibility was examined in Experiment 5.
Experiment 5
In Experiment 5, participants received alternating exposure to AX and BX.
Discrimination of this pair was then measured in the same-different task. Two control patterns, CX and DX, were introduced in the same-different task. For these patterns, two novel elements, C and D, were presented in compound with the preexposed common element. Experiment 5 used the same stimuli that were described in Experiment 3. Any difference in discrimination performance between the two preexposure conditions cannot be attributed to a difference in the salience of the common element. According to McLaren and Mackintosh’s (2000) unitization mechanism, discrimination should be better for CX and DX than for AX and BX because C and D are novel, and therefore more salient than A and B following the exposure phase.
Method
The procedure differed from the previous experiments only in the following details.
Participants. Twenty-five students (17 female and 8 male; mean age = 20.6), from
UNSW participated in this experiment in exchange for course credit. Chapter 3 85
Design. In the preexposure phase, all participants received alternating presentations of AX and BX (60 trials of each). In the subsequent test phase, there were four trial types:
(1) preexposed different (e.g., AX and BX), (2) preexposed same (e.g., AX and AX), (3) novel different (e.g., CX and DX), and (4) novel same (e.g., CX and CX). Chapter 3 86
Figure 3.2. Panel A shows mean proportion of correct responses within each test block on the four types of test trial in Experiment 5. Panel B shows mean sensitivity score (d’) for each preexposure condition across test blocks. Panel C shows estimates of response bias (c) for each preexposure condition across test blocks. Panel D shows the total gaze length to the two unique elements within each preexposure condition (A&B or C&D) across test blocks. Chapter 3 87
Results
Two participants were excluded from the analysis according to the same criterion as described in Experiment 1.
Same-different performance. Panel A of Figure 3.2 shows the mean proportion of correct responses for the four types of test trial across the four blocks of test trial in the same-different task. As in the previous experiment, discrimination performance was more accurate on the same trials than on the different trials, F(1, 22) = 21.55. Most importantly, performance accuracy for AX and BX was better than for CX and DX, F(1, 22) = 7.42. The interaction effect of trial type (same versus different) and preexposure (preexposed versus novel) was significant, F(1, 22) = 8.63. This interaction confirms the observation that the effect of preexposure was observed on the different, but not on the same test trials. There was no general improvement in performance across the test blocks, F(1, 22) = 2.59. The effect of test blocks did not interact with either the main factors of preexposure condition
(preexposed versus novel), F < 1, or trial type (same versus different), F(1, 22) = 1.56. The three-way interaction between these three main factors was also not significant, F(1, 22) =
3.33.
The two-way interaction between preexposure condition and trial type prompted a simple effects analysis of only the different trials. This analysis showed that discrimination performance on the different trials was better for AX and BX than for CX and DX, F(1, 22)
= 8.38. Across both test conditions, performance accuracy did not improve over test blocks,
F(1, 22) = 2.66. The interaction between different trials and test blocks was also not significant, F(1, 22) = 2.05.
Panel B of Figure 3.2 shows the mean sensitivity scores (d’) for the two preexposure conditions across the test blocks. Sensitivity scores were greater for AX and Chapter 3 88
BX than for CX and DX, F(1, 22) = 7.36. Participants were better at detecting the differences between AX and BX than between CX and DX. Sensitivity scores for both conditions did not increase across test blocks, F(1, 22) = 2.50. The interaction between the factors of preexposure condition and test block was also not significant (F < 1).
Panel C of Figure 3.2 shows the estimates of response bias (c values) for the two conditions across the same-different task. Participants showed a stronger bias toward responding “same” in the novel condition than in the preexposure condition, F(1, 22) =
8.76. Overall, response bias in each test condition did not change across test blocks, F(1,
22) = 1.82. The interaction between these two factors was not significant, F(1, 22) = 3.24.
Eyegaze. Panel D of Figure 3.2 shows mean gaze length to the unique elements A-
D in the same-different task. Gaze length to the unique elements increased overall across test blocks, F(1, 22) = 6.65. Participants spent more time looking at the unique elements in the preexposure condition (A and B) than in the novel condition (C and D), F(1, 22) = 7.11.
The test block by preexposure condition interaction was not significant (F < 1).
Correlation analysis. Gaze length to A and B in preexposure did not correlate with the difference in sensitivity between the preexposure conditions (AX/BX versus CX/DX), r
= 0.30, p = 0.16. Similar to previous experiments, a significant positive correlation between the difference in sensitivity scores and the difference in gaze length was observed in the same-different task, r = 0.78, p < 0.01.
Discussion
Experiment 5 showed that discrimination was better for the preexposed patterns,
AX and BX, than for the novel patterns, CX and DX. Furthermore, participants spent more time looking at the preexposed elements (A and B) than at the novel elements (C and D).
The current findings are thus consistent with Experiment 4. Unlike the previous Chapter 3 89
experiment, all the unique elements appeared on the same common background in
Experiment 4. The consistency between Experiments 4 and 5 implies that superior discrimination of AX and BX than of CY and DY in Experiment 4 was not the result of a difference in salience between X and Y. Only the difference in the salience between the unique elements should account for any difference in performance between the two preexposure conditions. Both experiments suggest that intermixed exposure to AX and BX increased the salience of A and B such that they became more salient than the novel elements. This is a conclusion that is inconsistent with the unitization mechanism (McLaren
& Mackintosh, 2000).
It is premature, however, to rule out an explanation of Experiments 4 and 5 in terms of McLaren and Mackintosh’s (2000) model. Here are two reasons. Firstly, a slightly different analysis of the current experiments allows an account in terms of the relative novelty of the unique features. To address this possibility, a re-analysis of the eyegaze data from Experiments 4 and 5 was conducted (see below). Secondly, Experiment 4 was explicitly designed to test the unitization and inhibition mechanisms, but it is possible that the salience of the X and Y backgrounds influenced the results of that experiment. The current Experiment 5 was designed, therefore, to rule out an explanation in terms of background salience by using the same X background in both conditions. However, it will turn out that this change allows an explanation of the results in terms of inhibition. This possibility requires a further experiment, Experiment 6.
Re-analysis of the eyegaze data from Experiments 4 and 5.
Thus far, we have conceptualised our checkerboard stimuli as AX, BX, CY and DY
(and CX and DX in Experiment 5), but there are other possible descriptions. For example, a grey area remains in the place of A whenever A is removed from the background (e.g. Chapter 3 90
when BX is presented). Perhaps the absence of A should be thought of as a unique feature.
We shall call this A’. Thus, presentations of BX might be conceptualised as BA’X (B, X and the absence of A). Presentations of AX can be thought of as AB’X. Similarly, CY and
DY can now be thought of as CD’Y and DC’Y. The following pages will describe a new account of Experiments 4 and 5 in terms of relative novelty. There will be separate accounts for each experiment, starting with Experiment 4.
Following preexposure to AB’X and BA’X, the features A, B, A’ and B’ will be less familiar (more salient) than X, because X was presented on twice as many trials as the unique features, A, B, A’, and B’. Preexposure to Y alone can now be thought of as preexposure to C’D’Y, because every presentation of Y also features the absence of C and
D. Thus, at the end of preexposure, the three features C’, D’ and Y will be equally salient.
On test, there are then six unique features that are more salient than their background: A, B,
A’, B’, C and D. Therefore, AB’X and BA’X will possess two relatively salient unique features each, whereas CD’Y and DC’Y will possess only one salient unique feature (C or
D). If one assumes that test performance is determined simply by the number of salient features present, then AB’X and BA’X should be better discriminated than CD’Y and
DC’Y, as observed. Given certain assumptions, therefore, the relative novelty account can predict better discrimination performance to AX and BX than to CY and DY in Experiment
4. In fact, the assumptions on which this account is based can be tested by an analysis of the eyegaze data to the unique feature locations both when the features were present (A-D) and when they were absent (A’-D’). The result of this analysis is presented in Figure 3.3. Chapter 3 91
Figure 3.3. Panel A shows total gaze length, across the same-different task, to the AOI associated with the unique elements in Experiment 4. The two left bars show gaze length to the two relevant unique elements within each preexposure condition (i.e., A & B on AX &
BX trials, or C & D on CY & DY trials). The two right bars show gaze length to the two background regions that each unique element occupies (i.e., A’ & B’ on BX & AX trials, or
C’ & D’ on DY & CY trials). These regions appear when the unique element is absent.
Panel B shows the same analysis for Experiment 5. Chapter 3 92
The account above predicts that A, B, A’, B’, C and D are all more salient, and therefore will be associated with longer gaze times, than C’ and D’. Furthermore, eyegaze on test to the novel C and D should be substantially greater than to the very habituated C’ and D’. However, gaze towards A and B may not differ from that towards A’ and B’; these features are all equally familiar (and so equally salient). Panel A of Figure 3.3 shows participant’s gaze lengths (collapsed across test trials) to the AOIs associated with the present unique elements A-D and the absent unique elements A’-D’. The most obvious feature of the graph is that gaze length was greater to the locations of the unique features presented on X (A, B, A’ and B’) than to the locations of the features presented on Y (C, D,
C’ and D’), F(1, 28) = 5.25. Gaze overall to the present elements A-D did not, however, differ from that to the absent elements A’-D’, F(1, 28)= 3.35, p = 0.08. The interaction between these two factors, predicted by the relative novelty account outlined above, was not significant, F(1, 28) = 3.32, p = 0.08. One might describe this interaction as marginal.
Importantly, however, it is in the opposite direction to that predicted by the relative novelty account; if anything, the difference between the presence and absence of A and B was greater than the difference between the presence and absence of C and D. A simple effects analysis showed that participants gazed longer at A and B than at the equally familiar A’ and B’, F(1, 28) = 17.00, but did not gaze longer at the novel C and D than at the very familiar C’ and D’ (F < 1 ). Furthermore, gaze towards the relatively novel A’ and B’ was no greater than towards the very familiar C’ and D’, F(1, 28) = 2.54, p = 0.12.
A potential problem with this analysis is that gaze length to each location is not independent of each other. For example, on AX presentations, longer dwell time to A means shorter dwell time to the B region. Consequently, the analysis of these findings using ANOVA may be inappropriate. A Chi-Square Test of Independence was conducted Chapter 3 93
to verify this claim. This analysis examined whether the number of times participants looked at A&B and C&D was dependent on their presence. The relationship between the two factors (A&B/C&D vs. present/absent) was not dependent in Experiment 4 (χ2 < 1).
The corresponding relationship however, was dependent in Experiment 5, χ2 = 159.4, p <
0.05. For consistency, the previous analysis was also applied to the measurement of eyegaze in Experiment 5. This analysis is only reliable to indicate the processes involved.
In the same-different task, the presence of one unique element meant that the other three unique elements were absent since all four unique elements shared the common X background. For example, the presence of A on AX trials meant that B, C, and D were absent. Consequently, AX and BX are conceptualised as AB’C’D’X and BA’C’D’X, and the control patterns are conceptualised as CA’B’D’X and DA’B’C’X. This analysis examined gaze length to A’ and B’ on the AX and BX test trials, as well as the CX and DX test trials. Similarly, gaze length to C’ and D’ was averaged across both test conditions.
Panel B of Figure 3.3 shows participant’s gaze lengths (collapsed across test trials) to the
AOIs associated with the present unique elements A-D and the absent unique elements A’-
D’.
Similar to Experiment 4, gaze length to the present and absent unique elements in the preexposed condition (A, B, A’ & B’) was greater than to the unique elements in the novel condition (C, D, C’ & D’), F(1, 23) = 6.28. Gaze length to the present elements (A-
D) was also greater than to the absent elements (A’-D’), F(1, 22) = 26.25. According to the relative novelty explanation, this difference should be driven by the difference in salience between C/D and C’/D’ since A, B, A’, and B’ are equally familiar (and equally salient) following preexposure. However, the data shows the opposite pattern; the difference between the presence and absence of A and B appears greater than the difference between Chapter 3 94
the presence and absence of C and D. The interaction of absent/present and preexposure condition confirmed this observation, F(1, 22) = 7.57. This interaction effect is consistent with Experiment 4. A simple effects analysis showed that participants gazed longer at A and B than at the equally familiar A’ and B’, F(1, 22) = 13.67. There was no difference in gaze length to the novel C and D than to the familiar C’ and D’, F(1, 22) = 1.10.
Interestingly, participants spend more time looking at C’ and D’ than at A’ and B’, F(1, 22)
= 8.96. In sum, the preexposed A and B features were salient, but the other features were not. It is therefore unlikely that the results from Experiment 4 and 5 are consistent with a relative novelty explanation.
Although McLaren and Mackintosh’s (2000) relative novelty account cannot explain the results of Experiment 5, it is possible that associative inhibition can. The X-A and X-B excitatory associations, formed during preexposure to AX and BX, will impair discrimination on both AX:BX and on CX:DX test trials; the representations of A and B will be activated whenever X is present, and will increase (by association) the number of elements shared by all four stimuli AX-DX. However, intermixed exposure to AX and BX also allows the formation of inhibitory associations between the unique elements A and B.
This inhibition between the unique elements A and B will reduce the number of common elements present on AX:BX test trials; A will not be activated on BX trials and B will not be activated on AX trials. On CX:DX test trials, in contrast, activation of A and B by X will not be reduced through inhibition. Therefore, the number of common elements present on
CX and DX trials will be higher than that on AX and BX trials, leading to poorer discrimination on CX:DX trials, as observed.
This associative analysis can also explain the eyegaze findings. As suggested in
Chapter 2, the associatively activated A and B elements compete for attention, but this Chapter 3 95
competition is reduced by inhibition. On an AX trial, for example, the associatively activated B element might compete for attention with the A element that is presented.
Inhibition between A and B will block associative activation of B by X and this will remove the competition for attention between A and B. In contrast, associative activation of
A and B on the CX and DX trials will not be inhibited since the absence of A and B on these trials will not prevent X from associatively activating A and B. This might be enough to take some attention away from C and D on the CX:DX test trials. As a consequence, even though elements C and D may be more salient than A and B, interference from the associatively activated A and B elements might reduce attention (and eyegaze) to C and D.
This is one way in which the A and B elements might come to command more attention than C and D on test in Experiment 5. Experiment 6 examined this possibility.
Experiment 6
In Experiment 6, participants received alternating preexposure to AX and BX. On test, to prevent associative activation of A and B on the CX and DX trials, the A and B features were added to the CX and DX stimuli to create a pair of test stimuli, CABX and
DABX. A second pair of test stimuli was also created, in which the novel elements, C and
D, were added to the AX and BX test trials to create ACDX and BCDX. Adding a new set of common elements (AB and CD) to each set of test trials should reduce discriminability in both conditions. Importantly, however, the discriminability of these complex stimuli will depend on the difference in salience of the two sets of unique elements. According to
McLaren & Mackintosh (2000), C and D should be more salient than A and B following
AX/BX preexposure. Consequently, discrimination of CABX and DABX should be better than discrimination of ACDX and BCDX for two reasons. Firstly, the novel and salient unique features C and D (presented on CABX:DABX test trials) will be more easily Chapter 3 96
detected than will A and B (presented on ACDX:BCDX test trials). Secondly, generalisation between ACDX and BCDX will be greater than between CABX and DABX because the common elements added to the former pair, C and D, will be more salient than those added to the latter pair, A and B. Importantly, associative activation of A and B by X, and therefore inhibition between A and B, should not influence detection of C and D. This is because, when C and D serve as the unique features, the A and B elements are physically present.
The alternative hypothesis, consistent with Mitchell, Nash, and colleagues (2008), and the more straightforward interpretation of the previous experiments, is that the intermixed unique elements A and B are more salient than the novel unique elements C and
D. If true, discrimination of ACDX and BCDX should be better than of CABX and DABX.
Method
The procedure differed from the previous experiments only in the following details.
Participants. Thirty students (24 female and 6 male; mean age = 18.9), from
UNSW participated in this experiment in exchange for course credit.
Design. In the preexposure phase, all participants received alternating presentations of AX and BX for 60 trials of each. In the subsequent test phase, two novel elements, C and
D, were added to the AX and BX patterns in order to create the discrimination pair of
ACDX and BCDX. Similarly, A and B were added to the novel patterns, CX and DX, to create the discrimination pair, CABX and DABX. There were four trial types: (1) preexposed different (e.g., ACDX and BCDX), (2) preexposed same (e.g., ACDX and
ACDX), (3) novel different (e.g., CABX and DABX), and (4) novel same (e.g., CABX and
CABX). Chapter 3 97
Figure 3.4. Panel A shows mean proportion of correct responses within each test block on the four types of test trial in Experiment 6. Panel B shows mean sensitivity score (d’) for each preexposure condition across test blocks. Panel C shows response bias (c) for each preexposure condition across test blocks. Panel D shows the total gaze length to the two unique elements within each preexposure condition (A&B or C&D) across test blocks. Panel E shows mean gaze length to elements A-D across test blocks. The two left bars show gaze length to element A-D when they were presented as the unique elements. The two right bars show gaze length to A-D when the elements were presented as common elements. Chapter 3 98
Results
Five participants were excluded from this analysis according to the same criterion as described in Experiment 1.
Same-different performance. Panel A of Figure 3.4 shows the mean proportion of correct responses for the four test conditions across the four blocks of test trials. Similar to the previous experiments, performance was more accurate on the same trials than on the different trials, F(1,24) = 22.26. Most importantly, performance accuracy was greater for the ACDX and BCDX trials than for the CABX and DABX trials, F(1,24) = 6.64.
Participants did not show an overall improvement in performance accuracy across the test phase (main effect of test blocks), F(1, 24) = 2.81. A marginally significant interaction effect was observed between the factors of preexposure condition (preexposed versus novel) and trial type (same versus different), F(1, 24) = 3.72, p = 0.07. Although the interaction is not significant, the pattern of data is consistent with previous experiment. The effect of preexposure is stronger on the different than on the same trials. A significant interaction was observed between the factors of test blocks and trial type, F(1, 24) = 13.95.
This confirms the trend in the figure that the difference in performance accuracy between same and different trials was reduced across test blocks. The interaction between the factors of test blocks and preexposure condition was not significant, F(1, 24) = 1.36. The three- way interaction between test blocks, trial type, and preexposure was also not significant (F
< 1).
The interaction of trial type and test blocks was not significant, but a simple effects analysis of the different trials was conducted for consistency with previous experiments.
The analysis showed that participants were better at discriminating ACDX from BCDX than at discriminating CABX from DABX, F(1, 24) = 5.38. Participants showed a general Chapter 3 99
improvement on the different trials as accuracy in both test conditions improved across test blocks, F(1, 24) = 8.84. The interaction between test block and preexposure condition was not significant, F(1, 24) = 1.02.
An analysis of sensitivity (d’) was also conducted for the results in the same- different task. Panel B of Figure 3.4 shows sensitivity scores for each preexposure condition across the four blocks of test trials. Sensitivity scores were greater for the ACDX and BCDX trials than for the CABX and DABX trials, F(1 ,24) = 7.65. Across both test conditions, sensitivity scores did not improve over test blocks, F(1, 24) = 1.58. The interaction of preexposure (preexposed versus novel) and test blocks was also not significant (F < 1).
Panel C of Figure 3.4 shows estimates of participants’ response bias (c values) in the same-different task. In both test conditions, response bias of “same” was reduced across test blocks, F(1, 24) = 13.60. There was no difference in response bias between the two test conditions, F(1, 24) = 3.73. The interaction between these two factors was not significant
(F < 1).
Eyegaze. Panel D of Figure 3.4 shows mean gaze length to the unique elements A-
D in the same-different task. Only gaze length to the relevant unique element on each test trial was considered in this analysis (i.e., A on ACDX, and C on CABX trials). Gaze length to the unique elements in the preexposed condition (A and B) were greater than to the unique elements in the novel condition (C and D), F(1, 24) = 11.26. Gaze length to the unique elements did not increase across the test blocks (F < 1). The interaction effect of preexposure conditions and test blocks was also not significant, F(1, 24) = 2.03.
A further analysis examined how participants attended to A-D when they served as common elements. If A and B are more salient than C and D, then gaze length to the Chapter 3 100
common element AB (on CABX and DABX trials) should be greater than to the common element CD (on ACDX and BCDX trials). Gaze lengths for the common element AB and
CD were calculated by averaging gaze length to A and B (and to C and D) on each trial in which these elements were part of the common background (i.e., CD on ACDX:BCDX trials and AB on CABX:DABX trials). Gaze length for these elements was compared to gaze length to the unique elements A-D (this is the sum of gaze length to the unique elements in each preexposure condition as shown in the middle right panel of Figure 3.4).
Gaze length to the unique (A-D) and common elements (AB & CD) in the same-different task is shown panel E of Figure 3.4. Overall, gaze length was greater for the unique elements (A/B and C/D) than for the common elements (AB and CD), F(1, 24) = 23.29.
There was a main effect of preexposure as gaze length to A and B was greater than gaze length to C and D, F(1, 24) = 10.31. The interaction between element type and preexposure condition was not significant, F(1, 24) = 1.70. Thus, gaze length was greater to A and B than to C and D across all test conditions.
Correlation analysis. Gaze length to A and B in preexposure did not correlate with the difference in sensitivity between the preexposure conditions (ACDX:BCDX versus
CABX:DABX), r = 0.07, p = 0.73. Similar to previous experiments, a significant positive correlation between the difference in sensitivity and the difference in gaze length was observed in the same-different task, r = 0.86, p < 0.01.
Discussion
Experiment 6 showed that discrimination of ACDX and BCDX was better than of
CABX and DABX. Gaze length to A and B was also greater than to C and D regardless of whether A and B were presented as the unique or common elements. Participants spent more time looking at A and B than at C and D across both the ACDX/BCDX and Chapter 3 101
CABX/DABX test trials. These findings rule out the idea that C and D were more salient than A and B, but that the poorer discrimination of CX and DX than of AX and BX seen in
Experiment 5 was the result of associative activation of A and B on CX and DX trials (but not the AX and BX trials). That is, they show that the results of Experiment 5 are not due to inhibition between A and B. In Experiment 6, the ease with which the unique features were detected depended on the difference in salience between the preexposed and novel unique elements. The findings support the more straightforward conclusion from Experiments 4 and 5, that the intermixed unique elements were more salient than the novel unique elements in the same-different task.
Chapter Summary and Discussion
Experiment 4 showed that discrimination of two patterns was more accurate following intermixed exposure (AX/BX) than following exposure to just the common elements of two patterns (Y). Attention was also greater to the intermixed elements than to the novel elements as measured by the eyetracker. Experiment 5 showed that this effect of preexposure was not the result of a difference in the salience of the two common backgrounds. Participants spent more time looking at the intermixed (A and B) than at the novel (C and D) unique elements when these elements were presented on the same common background (X). Experiment 6 examined the possibility that associative activation of A and
B by X impaired discrimination of CX and DX (but not AX and BX) in Experiment 5.
However, Experiment 6 found no evidence to support this claim. All three experiments, therefore, suggest that intermixed exposure to AX and BX increases the salience of A and
B such that these cues become more salient than novel elements C and D. This conclusion contradicts the idea that a stimulus is most salient when it is novel, and so discrimination with novel unique elements should be best (McLaren & Mackintosh, 2000). Chapter 3 102
The current findings replicate one aspect of an experiment conducted by Mundy and colleagues (2007). In their experiment, intermixed preexposure to two similar faces (AX and BX) produced better performance on test than did preexposure to the average of two faces (the midpoint faces using a morphing procedure). If we assume that exposure to the average of the two faces is the equivalent of exposure to the Y background here, then their results matches ours. More importantly, Experiments 5 and 6 found an analogous effect while controlling for any influences of the difference in background (X and Y) salience.
Furthermore, there is no clear evidence in animal studies with the flavour aversion procedure that shows exposure to the common elements produces a stronger perceptual learning effect than intermixed exposure. In an experiment by Bennett and colleagues
(1994) for example, one group of rats received alternating trials to sucrose-lemon and saline-lemon (AX and BX), and a second group received trials to only the lemon flavour
(X). A third group experienced only water in preexposure. Bennett and colleagues observed that generalisation of the conditioned response from AX to BX was weaker in both the intermixed and common elements groups than in the control group. More importantly, there was no difference between the two preexposed groups. If fact, generalisation from AX to
BX appeared weakest in the intermixed group (see McLaren et al., 1991 for a similar conclusion). The animal studies seem to produce the same data pattern as the human studies. Thus, no experiment using rats or humans has shown poorer discrimination following intermixed exposure to AX and BX than following exposure to the background alone, as predicted by McLaren and Mackintosh (2000).
The short-term habituation mechanism proposed by Mitchell, Nash, and colleagues
(2008) can account for the findings from Experiments 4-6. Intermixed presentations to AX and BX allow the unique features to be well encoded into memory. Memory for A and B Chapter 3 103
will guide the participant’s attention to the unique elements during the same-different task.
The absence of a representation of C and D means that attention to these elements will be low. Consequently, A and B will receive more attention than C and D in the same-different task. Hall’s (2003) reverse habituation mechanism can also account for these findings. Like the unitization mechanism, Hall assumes that associative activation of A and B through the
X-A and X-B associations will restore salience to A and B that is otherwise lost in preexposure. Hall did not specify how much salience might increase as a consequence of associative activation. The current findings suggest that salience of, or attention to, the unique elements can increase beyond their original novel level. The subsequent chapters will attempt to identify the mechanisms underlying this change. Chapter 4 104
CHAPTER FOUR
Chapter 3 showed that intermixed exposure to two similar stimuli increased attention to the unique elements such that they became more salient than novel elements.
This is a finding that none of the three mechanisms proposed by the McLaren and
Mackintosh (2000) model, associative inhibition, relative novelty, or unitization, can accommodate. Two mechanisms, reverse habituation (Hall, 2003) and short-term habituation (Mitchell, Nash et al., 2008) can, however, provide straightforward explanations for the experiments in Chapter 3. The focus of Chapter 4 is to differentiate the reverse habituation and short-term habituation mechanisms of salience modulation.
Hall’s (2003) reverse habituation mechanism relies on the presence of X-A and X-B associations for the salience of A and B to increase. As described in Chapter 1, associative activation of A on BX trials via the X-A associations will increase the salience of A.
Similarly, the salience of B will increase on AX trials through the X-B association. Without these X-A and X-B associations, exposure to AX and BX will result in habituation and the loss of salience to A and B (as well as X). In contrast, the short-term habituation mechanism (Mitchell, Nash et al., 2008) does not require within-compound associations for intermixed exposure to increase the perceptual effectiveness of A and B. It is this difference between the two theories that will be the focus of the experiments presented in the present chapter.
According to Hall’s (2003) model, any situation that prevents or attenuates the formation of the X-A and X-B associations will reduce the discriminability of AX and BX.
One way to slow the formation of associative links is to preexpose a stimulus; non- reinforced exposure to a CS is known to impair the subsequent formation of a CS-US Chapter 4 105
association (Lubow, 1989). In perceptual learning, therefore, the formation of the X-A and
X-B associations on the AX and BX trials should also be impaired if X was presented prior to those trials. The reverse habituation mechanism then predicts that exposing X prior to
AX/BX trials (X_AX/BX) will retard the formation of X-A and X-B associations, and thus reduce the opportunity for reverse habituation of A and B. As a consequence, the discriminability of AX and BX will be reduced compared to a schedule in which only AX and BX are presented (AX/BX). Chapter 4 examines this prediction.
Of course, additional exposure to X might be expected to have two opposing effects on the discriminability of AX and BX. Firstly, following Hall, the salience of A and B might be reduced, which would reduce the discriminability of AX and BX. However, X will also be reduced in salience, through the normal process of habituation, which would be expected to increase the discriminability of AX and BX. A simple solution to this problem is to present X following intermixed exposure to AX and BX (AX/BX_X) in the control condition. Presenting X after the AX and BX trials should not impair the formation of the
X-A and X-B associations. Thus, the opportunity for reverse habituation to A and B to occur is better following AX/BX_X exposure than following X_AX/BX exposure. In fact, reverse habituation to A and B might occur on the X alone trials following AX/BX preexposure, and this might further increase the salience of A and B in the AX/BX_X condition. Consequently, Hall predicts that presenting X after AX and BX trials should increase the discriminability of AX and BX compared to presenting X prior to AX and BX trials.
The short-term habituation mechanism (Mitchell, Nash et al., 2008) predicts the opposite pattern of results. According to this model, the degree to which participants attend to A and B in the discrimination task depends on the strength of these elements in memory. Chapter 4 106
Furthermore, it is attention to the unique elements in preexposure that allows participants to encode these elements in memory. In fact, encoding of A and B on the AX and BX trials might be expected to be enhanced if X alone trials precede the AX and BX trials.
Participants should already have habituated to X following the X alone trials. On the AX and BX trials, A and B will then capture nearly all of the participant’s attention for encoding because the habituated X element does not need to be processed. Presenting X after the AX and BX trials will not benefit attention to A and B during AX/BX exposure.
Consequently, the short-term habituation mechanism predicts that the discriminability of
AX and BX will be greater following X_AX/BX than AX/BX_X exposure.
Experiment 7
All participants received alternating exposure to two patterns, AX and BX during the preexposure phase. Half of the participants received additional exposure to the common element X prior to the AX and BX trials (PRE). The remaining participants received additional exposure to the common element X after the AX and BX trials (POST). This between-subjects design was used because it was thought that presenting both exposure schedules to all participants would produce fatigue and boredom. As in Experiment 5, discrimination of AX and BX was measured in a same-different test and was compared to two novel patterns, CX and DX. Based on the findings in Experiment 5, it was predicted that discrimination of AX and BX should be better than that of CX and DX. According to
Mitchell, Nash, and colleagues (2008), exposure to X prior to AX/BX presentations should increase attention to A and B, resulting in better encoding in memory of A and B in the
PRE than in the POST group. The superiority in discrimination performance of AX:BX over CX:DX should, therefore, be greater in the PRE than in the POST group. In contrast,
Hall (2003) predicts that preexposure to X in the PRE group will attenuate associative Chapter 4 107
activation to A and B on the subsequent AX and BX trials, and the difference between the preexposure and novel test conditions should be greatest in the POST group.
Method
The procedure differed from the previous experiments only in the following details.
Participants. Thirty (14 female and 16 male; mean age = 19.7) first year psychology students, from UNSW participated in this experiment in exchange for course credit.
Design. Participants in the PRE group received 120 exposures to the common element X in phase one of preexposure. In the second phase of preexposure, AX and BX were presented for 30 trials of each on an intermixed schedule. Participants in the POST group experienced the two phases of preexposure in the opposite order. Half of the participants were randomly assigned to the PRE group, and the remainder were assigned to the POST group. In the same-different task, there were four trial types: (1) preexposed different (e.g., AX and BX), (2) preexposed same (e.g., AX and AX), (3) novel different
(e.g., CX and DX), and (4) novel same (e.g., CX and CX). Chapter 4 108
Figure 4.1. Panel A shows mean proportion of correct responses within each test block on the four types of test trial in Experiment 7. Panel B shows mean sensitivity score (d’) for each preexposure condition across test blocks. Panel C shows estimates of response bias (c values) for the two test conditions across test blocks. Error bars indicate standard error of the mean (SEM).
Chapter 4 109
Results and Discussion
Three participants were excluded from this analysis according to the same criterion as described in Experiment 1.
Same-different performance. Panel A of Figure 4.1 shows the mean proportion of correct responses for the four trial types in the PRE and POST groups. Overall, there was no difference in performance accuracy between the PRE and POST groups (F < 1).
Performance was more accurate on the same trials than on the different trials, F(1, 25) =
21.89. The difference in discrimination accuracy between the AX and BX trials and the CX and DX trials only approached significance (p < 0.05), F(1, 25) = 3.23, p = 0.08.
Performance accuracy overall did not improve across test blocks, F(1, 25) = 1.80. The interaction of preexposure condition (preexposed versus novel) and trial type (same versus different), F(1, 25) = 6.10, was significant. That is, the preexposure effect (AX/BX versus
CX/DX) was greater on the different than on the same trials. The preexposure effect was reduced across test blocks as the interaction of preexposure condition and test blocks, F(1,
25) = 11.20 was significant. A significant three-way interaction between preexposure condition, trial type and test blocks was observed, F(1, 25) = 5.82. This suggests that the difference in accuracy between same and different trials was reduced across the test phase, but the rate of reduction was greater in the preexposed condition than in the novel condition. The factor of experimental group (PRE versus POST) did not interact with the factors of preexposure condition, trial type, or test blocks (all Fs < 1).
The interaction of preexposure condition and trial type prompted an analysis of only the different trials. Again, overall accuracy did not differ between the PRE and POST groups (F < 1). The main effect of preexposure condition (AX/BX versus CX/DX) was significant, F(1, 25) = 4.67, but the main effect of test blocks was not significant, F(1, 25) = Chapter 4 110
2.46. The interaction of preexposure condition and test blocks was significant, F(1, 25) =
11.30. Neither the factors of preexposure nor trial type interacted with experimental groups
(all Fs < 1). The three-way interaction between experimental group, preexposure condition and test blocks was not significant, F(1,25) = 3.18, p = 0.09.
In sum, a preexposure effect was observed in the same-different task; discrimination of the preexposed patterns, AX and BX, was better than that of the novel patterns, CX and
DX. Critically, this preexposure effect was not greater in the PRE group than in the POST group. This preexposure effect appeared greatest in the first test block, but the effect was reduced across test blocks. Moreover, the effect of preexposure in the first test block appears to be greater in the PRE than in the POST group. Consequently, a simple effects analysis was conducted to examine discrimination performance in this block. This analysis focused on the different trials.
In the first test block, there was no overall difference in accuracy between the PRE and POST groups (F < 1). Performance accuracy was better for AX and BX than for CX and DX, F(1, 25) = 13.21. The interaction between these two factors approached significance, F(1, 25) = 3.58, p = 0.07. It would appear that the difference in performance between the preexposed and novel conditions was marginally greater in the PRE than in the
POST group. T-tests confirmed that a significant difference between the preexposed and novel conditions was present in the PRE group, t(12) = 3.65, but this difference was not present in the POST group, t(13) = 1.91, p = 0.08.
Panel B of Figure 4.1 shows the mean sensitivity scores (d’) for the two preexposure conditions across the four blocks of test trials. Sensitivity did not differ between the PRE and POST groups (F < 1). Sensitivity scores appeared to be greater for
AX and BX than for CX and DX, but this difference only approached significance, F(1, 25) Chapter 4 111
= 3.82, p = 0.06. Sensitivity overall did not improve across the test blocks, F(1, 25) = 3.06, p = 0.09. The interaction of preexposure condition and test block was significant, F(1, 25) =
9.99. The three-way interaction between preexposure condition, test blocks and experimental group (PRE versus POST) was marginally significant, F(1, 25) = 4.17, p =
0.052. That is, the difference in sensitivity between the preexposure and novel conditions was reduced across test blocks, but the rate of reduction was greater in the PRE than in the
POST group.
The marginal three-way interaction prompted a simple effects analysis of the first test block. Sensitivity scores for AX and BX were greater than for CX and DX, F(1, 25) =
13.85. There was no difference in overall sensitivity between the PRE and the POST groups
(F < 1). However, the interaction between these two factors was significant, F(1, 25) =
4.56. T-tests confirmed that the significant difference between the preexposed and novel trials was present in the PRE group, t(12) = 3.90, but the difference was not significant in the POST group, t(13) = 1.19. In the first test block, AX and BX were more discriminable than CX and DX only in the PRE group.
An analysis of response bias in the same-different test was also conducted. Panel C of Figure 4.1 shows estimates for response bias (c values) within each preexposure condition across the four test blocks. Overall, participants showed a stronger tendency toward responding “same” in the novel condition than in the preexposed condition, F(1, 25)
= 6.44. Response bias overall was not different between the PRE and POST groups (F < 1).
Responses bias also did not change across test blocks, F(1, 25) = 1.44. The significant interaction of preexposure condition and test blocks, F(1, 25) = 5.69, suggests that the difference in response bias between the two preexposure conditions was reduced across test blocks. No other interactions were significant (all Fs < 1). Chapter 4 112
Figure 4.2. Panel A shows the total gaze length to A and B in preexposure in the PRE and
POST groups in Experiment 7. Panel B shows gaze length to the two unique elements within each preexposure condition (A&B or C&D) across test blocks. Error bars indicate
SEMs. Chapter 4 113
Eyegaze. Panel A of Figure 4.2 shows mean gaze length to the unique elements A and B in the PRE and POST groups during the preexposure phase. Gaze length to A and B seems to be greater in the PRE than in the POST group. However, this difference only approached significance, F(1, 25) = 3.27, p = 0.08. Gaze length to A and B did not increase across the preexposure phase, F(1, 25) = 3.23, p = 0.08. The interaction between these two main factors was not significant (F < 1).
Panel B of Figure 4.2 shows mean gaze length to the unique elements A-D during the same-different task. Gaze length to the unique elements did not differ between the PRE and POST groups (F < 1). The difference in gaze length to the preexposed (A and B) and novel (C and D) unique elements only approached significance, F(1, 25) = 4.03, p = 0.06.
Gaze length, averaged across A-D, did not increase across test blocks, F(1, 25) = 1.92. The interaction of preexposure condition and test blocks was marginally significant, F(1, 25) =
4.17, p = 0.052. It appears that the difference in gaze length to the preexposed and novel elements was reduced across test blocks. The interaction of preexposure condition and experiment group (PRE versus POST) was not significant, F(1, 25) = 2.2.
Like the performance accuracy measure, the preexposure effect on eyegaze was greatest in the first test block. For consistency with the performance accuracy data, a simple effects analysis of gaze length was conducted for the first test block. This analysis revealed no significant difference in overall gaze length between the PRE and POST groups (F < 1).
Gaze length to A and B was significantly greater than to C and D, F(1, 25) = 8.91. The interaction between the two factors approached significance, F(1, 25) = 3.95, p = 0.06. T- tests showed that gaze length was greater to A and B than to C and D in the PRE group, t(12) = 3.05, but this difference was not present in the POST group, t < 1. That is, the Chapter 4 114
difference in gaze length between the preexposed and novel condition, on the first test block, was observed only in the PRE group.
Correlation analysis. Across both the PRE and POST groups, gaze length to A and
B in preexposure did not correlate with the difference in sensitivity between preexposure conditions (AX:BX versus CX:DX), r = 0.25, p = 0.2. Similar to previous experiments, a significant positive correlation between the difference in sensitivity and the difference in gaze length was observed in the same-different task, r = 0.80, p < 0.01. These correlations did not vary between the PRE and POST groups.
Summary
Experiment 7 showed that discrimination of AX and BX was better than of CX and
DX. Gaze length to the preexposed was greater than to the novel unique elements. The short-term habituation mechanism (Mitchell, Nash et al., 2008) predicts that the preexposure effect should be stronger in the PRE than in the POST group. This interaction effect of preexposure (preexposed versus novel) and experimental group (PRE versus
POST) was only observed in the first test block. The reliability of this effect is unknown since any difference between the preexposure conditions was washed out with additional test trials. Consequently, the subsequent experiment attempted to replicate the current findings with fewer test trials since the early test blocks are most sensitive to any effects of preexposure.
Chapter 4 115
Experiment 8
Experiment 8 used the same design as that of Experiment 7, but the number of test trials was halved in the same-different task.
Method
The procedure differed from the previous experiments only in the following details.
Participants. Forty-five (30 female and 15 male; mean age = 19.8), from UNSW participated in this experiment in exchange for course credit.
Design. Experiment 8 retained the same design from the previous experiment, except that only 48 test trials were administered. Chapter 4 116
Figure 4.3. Panel A shows mean proportion of correct responses within each test block on the four types of test trial in Experiment 8. Panel B shows mean sensitivity score (d’) for each preexposure condition across test blocks. Panel C shows estimates of response bias (c values) for the two test conditions across test blocks. Error bars indicate SEMs. Chapter 4 117
Results and Discussion
Six participants were excluded from this analysis according to the same criterion as described in Experiment 1.
Same-different performance. Panel A of Figure 4.3 shows the mean proportion of correct responses for the four trial types across the two blocks of test trials. There was no difference in overall performance accuracy between the PRE and POST groups (F < 1).
Similar to the previous experiment, performance accuracy was better for the same trials than for the different trials, F(1, 37) = 49.56. Performance accuracy for the AX and BX trials was not better than for the CX and DX trials, F(1, 37) = 1.25. In addition, accuracy overall did not improve across the two blocks of test trials, F(1, 37) = 1.43. The effect of preexposure (preexposed versus novel) did not interact with trial type (same versus different), F < 1, or with test blocks, F < 1. The interaction of trial type and test blocks was also not significant, F(1, 37) = 2.52. A three-way interaction between trial type, test blocks, and experimental groups (PRE versus POST) was significant, F(1, 37) = 8.35. This interaction suggests that the difference in performance accuracy between same and different trials was reduced across the test blocks, but reduction was greater in the PRE than in the POST group. No other interactions were significant (all Fs < 1).
Panel B of Figure 4.3 shows the sensitivity scores (d’) for the preexposure conditions in the same-different task. Similar to the previous analysis, no significant differences were observed in the factors of group (PRE versus POST), F < 1, preexposure condition (preexposed versus novel), F(1, 37) = 1.40, or test blocks, F < 1. No interactions were significant (all Fs < 1).
Panel C of Figure 4.3 shows estimates of response bias (c values) in the two test conditions across the two blocks of test trials. Response bias overall did not differ between Chapter 4 118
the PRE and POST groups (F < 1). Response bias also did not vary between the preexposed and novel conditions (F < 1), and bias did not change across test blocks, F(1, 37) = 2.53.
There was a significant interaction of test blocks and experimental group, F(1, 37) = 8.39.
The trend in the figure suggests that participants in the PRE group showed a stronger reduction in their responding bias toward “same” across the two test blocks than participants in the POST group. The interaction of preexposure condition and test blocks was not significant F(1, 37) = 2.45
Experiment 8 failed to show that exposure to AX and BX produced better discrimination of these patterns than of novel patterns, CX and DX. In addition, the effect of preexposure did not vary between the PRE and POST groups. Thus, discrimination performance in Experiment 8 did not replicate the pattern of findings observed in the early test trials of Experiment 7. Chapter 4 119
Figure 4.4. Panel A shows the total gaze length to A and B in preexposure in the PRE and
POST groups in Experiment 8. Panel B shows gaze length to the two unique elements within each preexposure condition (A&B or C&D) across test blocks. Error bars indicate
SEM. Chapter 4 120
Eyegaze. Panel A of Figure 4.4 shows gaze length to the two unique elements A and B during the preexposure phase. Gaze length to elements A and B was greater in the
PRE than in the POST group, F(1, 37) = 7.33. Gaze length to A and B did not increase across the two blocks of preexposure trials, F(1, 37) = 1.27. The interaction between these two factors was not significant, F(1, 37) = 1.67.
Panel B of Figure 4.4 shows gaze length to the unique elements A-D in the same- different task. Gaze length to the unique elements did not differ between the PRE and
POST groups, F(1, 37) = 1.32. Across both groups, gaze length was greater to preexposed unique elements (A and B) than to the novel unique elements (C and D), F(1, 37) = 4.33.
The interaction of preexposure condition (preexposed versus novel) and experimental group
(PRE versus POST) was not significant, F(1, 37) = 2.29. Gaze length to the unique elements overall did not increase across the two blocks of test trials (F < 1). The interaction of test blocks and preexposure condition was also not significant F(1, 37) = 3.33.
Correlation analysis. Across both the PRE and POST groups, Gaze length to A and B in preexposure correlated with the difference in sensitivity (AX:BX versus CX:DX), r = 0.47, p < 0.01. That is, greater gaze length to A and B is associated with larger differences between the preexposure conditions in test. However, this correlation was significant in the PRE group, r = 0.68, p < 0.01, but it was not significant in the POST group, r = -0.20, p = 0.40. Similar to previous experiments, a significant positive correlation between the difference in sensitivity and the difference in gaze length was observed in the same-different task, r = 0.83, p < 0.01. This correlation did not vary between the PRE and POST groups.
Chapter 4 121
Summary
Experiment 8 showed that participants, in preexposure, spent more time looking at
A and B in the PRE than in the POST group. This confirms the marginal effect that was observed in Experiment 7. In the same-different task, gaze length to the preexposed elements (A and B) was greater than to the novel elements (C and D). However, this difference in gaze length to the two sets of unique elements did not render AX and BX more discriminable than CX and DX. Furthermore, the preexposure effect (preexposed versus novel) did not vary between the PRE and the POST group in either the discrimination accuracy or eyegaze measures. In this respect, Experiment 8 failed to replicate the findings of Experiment 7. The two experiments however, do exhibit a similar pattern of results. For example, the difference in accuracy between the preexposed and the novel conditions was numerically greater in the PRE than in the POST group in both experiments. The inability to replicate Experiment 7 may reflect a lack of statistical power.
Chapter Summary and Discussion
The aim of Chapter 4 was to differentiate between the reverse habituation (Hall,
2003) and short-term habituation (Mitchell, Nash et al., 2008) mechanisms of salience modulation. Both mechanisms can account for the finding that discrimination of the preexposed patterns, AX and BX, was better than of the novel patterns, CX and DX, a finding that was first demonstrated in Chapter 3. The critical question was whether the discriminability of AX and BX was greater in the PRE or in the POST group. Hall predicts that exposure to X prior to the AX and BX preexposure trials will attenuate associative activation, and therefore the salience, of A and B on these trials. The pattern of eyegaze in preexposure does not support this prediction since, if anything, A and B received more Chapter 4 122
attention in the PRE group. This effect was marginal in Experiment 7, but it was confirmed in Experiment 8.
The short-term habituation mechanism (Mitchell, Nash et al., 2008) assumes that greater attention to the unique elements in preexposure leads to greater encoding of those elements. This means that A and B would be better encoded following preexposure in the
PRE than in the POST group if these elements were more salient in the PRE schedule.
Consequently, the difference in attention between the preexposed and novel unique elements in test should be greater in the PRE than in the POST group, and this will also lead to better discrimination of AX and BX in the PRE group. In both the eyegaze and discrimination performance measures, the interaction of preexposure condition (preexposed versus novel) and experimental group (PRE versus POST) was only observed in the first test block of Experiment 7. Additional test trials in the test phase washed out this interaction effect. Furthermore, Experiment 8 failed to replicate this interaction effect. In sum, attention to A and B in preexposure was greater in the PRE than in the POST group, but this did not lead to better discrimination of AX and BX in the PRE group.
One possible explanation is that the eyegaze measure underestimated the salience of
A and B in the POST schedule. The AX and BX trials occur at different times in the two schedules. In the POST group, X is novel on initial AX and BX trials, but in the PRE group, X is familiar on the AX and BX trials. Eyegaze towards A and B during preexposure may underestimate the salience of A and B relative to X at the time of test in the POST group. This is because, following AX/BX trials in the POST group, X will lose more salience during the X alone trials. Thus, A and B may be more salient than X during the AX and BX trials in PRE than in the POST group, but there might be no difference between the two groups at the end of the entire preexposure phase. Chapter 4 123
Nevertheless, it is still true that A and B received more attention during preexposure in the PRE than in the POST group. Increased attention to the unique elements in preexposure can lead to two possible outcomes for these elements. According to Mitchell,
Nash et al. (2008), this should lead to better encoding of the unique elements, and better discrimination of AX and BX in the PRE group. Conversely, increased attention to A and B might also have the opposite effect. Greater attention to the unique elements in preexposure might lead to greater unitization (or habituation) and lower the salience of these elements.
If so, discrimination of AX and BX would be best in the POST group. Importantly, both accounts predict that there should be some difference in the discriminability of AX and BX between the PRE and POST group. Experiments 7 and 8 however, failed to find any difference. It seems that additional preexposure to X might affect attention to the unique elements in the short-term, but it does not affect the long-term discriminability of AX and
BX. There are two possible explanations for this observation.
One possibility is that latent inhibition to X failed to develop during the X alone trials in Experiments 7 and 8. According to Hall (2003), preexposure to X may impair the formation of the X-A and X-B associations through the process of latent inhibition. This will reduce the opportunity for the process of reverse habituation in the PRE but not in the
POST group, and produce the best discrimination of AX and BX in the POST group.
However, no such difference would be expected if latent inhibition to X did not develop during preexposure. Latent inhibition has been shown to be difficult to obtain in human associative learning procedures (Lubow, 1989). It has been argued that the effect can only be demonstrated when there is incidental preexposure of the CS, and incidental pairings of the subsequent CS-US association (Lubow & Gewirtz, 1995). That is, participants must believe that the presence of the CS is irrelevant to their task. In the current procedures, Chapter 4 124
preexposure is not incidental since participants were instructed to look for differences between the patterns. In addition, latent inhibition is thought to be a transient effect (Hall,
1991). Thus, even if preexposure to X impaired the formation of the X-A and X-B associations on the first few AX and BX trials, strong X-A and X-B links may nevertheless have formed later in AX/BX preexposure.
Another explanation is that presenting X alone, either before or after AX/BX trials, does affect the discriminability of AX and BX differently, but that the specific parameters used in Experiments 7 and 8 were not appropriate to demonstrate this effect. Two
(unreported) experiments were conducted to further explore the experimental parameters of the current experiments. In one experiment, participants received 60 trials of AX and BX with 120 trials of X in the PRE and POST groups in preexposure. In preexposure, attention to A and B was greater in the PRE than in the POST group. In the same-different task, discrimination of AX and BX was better than of CX and DX. The PRE and POST groups did not, however, differ. Thus, preexposure to X increased attention to A and B in the PRE group, but 60 trials to AX and BX in each group negated this advantage. Another experiment reduced the number of AX and BX presentations to 15 trials of each, but the number of X trials was preserved. In this experiment, preexposure to AX and BX failed to render these patterns more discriminable than the control patterns, CX and DX, in the same-different task.
In sum, Experiments 7 and 8 failed to differentiate between the short-term habituation (Mitchell, Nash et al., 2008) and reverse habituation (Hall, 2003) mechanisms.
These experiments manipulated the order in which X was presented relative to the AX and
BX trials with the assumption that further exposures to X affected the discriminability of
AX and BX. In preexposure, exposure to X prior to the AX and BX trials appeared to Chapter 4 125
further increase gaze length to A and B compared to an exposure schedule in which the
AX/BX trials precede X alone trials. However, this did not further enhance the discrimination of AX and BX. Consequently, Chapter 5 used a variation on the current approach to test Hall’s (2003) model against that of Mitchell, Nash, and colleagues (2008). Chapter 5 126
CHAPTER FIVE
Chapters 3 and 4 concluded that exposure to AX and BX increases attention to the unique elements such that they become more salient than novel elements. Chapter 4 explored whether presenting X prior to the AX and BX trials in preexposure (PRE group) further enhanced the salience of the unique elements compared to a schedule in which X was presented after the AX and BX trials (POST group). In preexposure, A and B received more attention in the PRE than in the POST group. This result is not surprising since, in the
PRE group, AX/BX trials followed X alone trials, and Experiment 2 (of Chapter 2) showed that preexposure to X alone increases attention to A and B when AX and BX are presented.
The AX and BX test trials are similar to AX/BX exposure trials since participants are given some opportunity to compare the stimuli. This greater attentional bias towards A and B in the PRE group during preexposure however, did not translate into better performance on the same-different test.
It may be the case that, although prior exposure to X alone does lead to greater attention to A and B on subsequent AX/BX trials, this does not affect the discriminability of AX and BX on test. Experiment 9 used a simpler experimental design than those used in
Chapter 4 to examine this notion. Here, X alone presentations were given prior to AX/BX trials (X_AX/BX), and this condition was compared to another pair of intermixed patterns
(CY/DY). As described in the previous chapter, the Hall (2003) and the Mitchell, Nash et al. (2008) theories make different predictions with regard to the salience of the unique features in this experimental design. Hall predicts that X preexposure will prevent strong X-
A and X-B links from forming, will reduce the associative activation of A and B, and so reduce the salience of A and B relative to C and D. In contrast, Mitchell, Nash, and Chapter 5 127
colleagues predict that the salience of A and B will be higher than that of C and D on test;
A and B will stand out on the familiar X background and so be more easily encoded in memory.
The predictions outlined above are made on the basis of the salience of the unique elements. However, the two preexposure conditions also vary in the salience of their backgrounds since X is presented more often than Y. Thus, if performance was observed to be better for AX and BX than for CY and DY (as predicted by Mitchell, Nash, et al., 2008), this may reflect the fact that A and B are presented on the more habituated background, rather than that A and B are more salient than C and D. This issue will be addressed in
Experiment 10. The critical aspect of Experiment 9, however, is that only the reverse habituation mechanism (Hall, 2003) predicts that discrimination of CY and DY could be better than that of AX and BX following preexposure.
Experiment 9
In Experiment 9, all participants received intermixed exposure to two pairs of patterns (AX/BX and CY/DY) in separate blocks. In addition, exposure to X preceded presentations of AX/BX. Discriminability of each pair of patterns was then measured in a same-different task.
Method
The procedure differed from the previous experiments only in the following details.
Participants. Twenty-four students (17 female and 7 male; mean age = 19.8), from
UNSW participated in this experiment in exchange for course credit.
Design. In one preexposure condition, participants received exposure to 120 trials of X, and this was followed by alternating exposure to AX and BX for 30 trials of each. In the other preexposure condition, participants received alternating exposure to CY and DY Chapter 5 128
for 30 trials of each. The order of these two preexposure schedules was counterbalanced between participants. The stimuli used in this experiment were the same as those described in Experiment 2. The same-different task was constructed in the same way as described in previous experiments. Chapter 5 129
Figure 5.1. Panel A shows the mean proportion of correct responses within each test block on the four types of test trial in Experiment 9. Panel B shows mean sensitivity score (d’) for each preexposure condition across test blocks. Panel C shows estimates (c values) for response across test blocks. Panel D shows gaze length to the unique elements within each preexposure condition (A&B or C&D) across the preexposure phase. Panel E shows gaze length to the unique elements within each test condition across the same-different task. Chapter 5 130
Results
Two participants were excluded from this analysis according to the same criterion as described in Experiment 1.
Same-different performance. A preliminary analysis was conducted to determine whether performance in the same-different task was affected by the two counterbalancing factors. The order in which each preexposure condition was presented did not affect overall discrimination performance, F(1, 18) = 1.64. In addition, the assignment of background pattern to each preexposure condition also did not affect discrimination performance (F <
1). These two factors did not interact with the main effect of preexposure (both Fs < 1).
Consequently, the data were collapsed across the four counterbalancing groups for the remaining analyses.
Panel A of Figure 5.1 shows the mean proportion of correct responses for the four different test conditions across the four blocks of test trials. Performance accuracy was better for the same trials than for the different trials, F(1, 21) = 37.72. Importantly, there was an effect of preexposure, as performance accuracy was better for AX and BX than for
CY and DY, F(1, 21) = 10.88. The interaction between these two main factors was also significant, F(1, 21) = 7.15. This confirms the observation that the effect of preexposure was present on the different trials but not on the same trials. Performance accuracy across the two preexposure conditions did not increase across the test blocks (F < 1). The factor of test blocks did not interact with either preexposure (AX/BX versus CY/DY) or trial type
(same versus different; all Fs < 1). No other significant interactions were observed (all Fs <
1).
The interaction of trial type and preexposure condition prompted an analysis of only the different trials. This analysis showed that discrimination performance on the different Chapter 5 131
trials was better for AX and BX than for CY and DY, F(1, 21) = 10.51. Overall performance on the different trials did not improve across test blocks, F(1, 21) = 1.43. The interaction of preexposure and test blocks was not significant (F < 1).
Panel B of Figure 5.1 shows the mean sensitivity scores (d’) for the two preexposure conditions across the four test blocks. Similar to the previous analysis, sensitivity scores were greater for AX and BX than for CY and DY, F(1, 21) = 8.39.
Sensitivity scores did not increase across test blocks (F < 1), and the interaction of preexposure and test blocks was not significant (F < 1).
Panel C of Figure 5.1 shows the mean estimates of response bias (c values) in the same-different task. Across the four test blocks, c values were greater for the CY and DY trials than for the AX and BX trials, F(1, 21) = 6.68. This suggests that participants demonstrated a stronger tendency to respond “same” on the CY and DY trials than on the
AX and BX trials. Estimates for response bias did not change across the four test blocks,
F(1, 21) = 1.13. The interaction between these two factors was also not significant (F < 1).
Eyegaze. Panel D of Figure 5.1 shows gaze length to the four unique elements A-D during the preexposure phase. Overall gaze length toward the unique elements increased across the two blocks of preexposure trials, F(1, 21) = 6.54. Gaze length was greater to A and B than to C and D, F(1, 21) = 9.22. That is, exposure to X increased gaze length to A and B on the AX and BX preexposure trials. The interaction between these two main factors was not significant (F < 1).
Panel E of Figure 5.1 shows the gaze length to the four unique elements A-D during the same-different task. Gaze length was greater to the A and B than to the C and D,
F(1, 21) = 19.50. Gaze length did not increase overall across the four blocks of test trials, Chapter 5 132
F(1, 21) = 1.97. The interaction of preexposure condition and test blocks was not significant, F(1, 21) = 3.24, p = 0.09.
Correlation analysis. In this analysis, a difference score between gaze length to
A/B and to C/D was calculated for both preexposure and test. Gaze length in preexposure did not correlate with the difference in sensitivity (AX/BX versus CY/DY), r = 0.33, p =
0.14 Similar to previous experiments, a significant positive correlation between the difference in sensitivity and the difference in gaze length was observed in the same- different task, r = 0.47, p = 0.03.
Discussion
Experiment 9 showed that discrimination accuracy for AX and BX was better than for CY and DY. In addition, gaze length to A and B was greater than to C and D in both the preexposure phase and the same-different task. Thus, exposure to X prior to AX and BX trials increased attention to A and B, and this rendered AX and BX more discriminable than
CY and DY. These results are consistent with the short-term habituation mechanism
(Mitchell, Nash et al., 2008). The results provide no evidence to support Hall’s (2003) prediction that exposure to X will reduce the salience of A and B. However, as mentioned earlier, the results of the current experiment may be a consequence, not of differences in salience between the unique features A-D, but of differences between the backgrounds X and Y. This idea was tested in Experiment 10
Experiment 10
Experiment 10 explored whether the effect seen in Experiment 9 was driven by a difference in the salience between the unique elements (A/B versus C/D) or between the common elements (X versus Y). To start, let us assume that the preexposure effect observed in Experiment 9 resulted from a difference in the salience of the common Chapter 5 133
elements, and that there was no difference in the salience of the unique elements.
According to this analysis, elements C and D received less attention than A and B on test because they appeared on the more salient background. This suggests that C and D would be able to attract greater attention if they were presented on a less salient background.
Conversely, A and B would attract less attention if they were shifted to a more salient background. Experiment 10 examined this prediction by swapping the common and unique elements between the two preexposure conditions following preexposure. That is, participants discriminated AY from BY, and CX from DX in the same-different task following X_AX/BX_CY/DY preexposure.
Greater habituation to X than to Y following preexposure should render the discriminability of CX and DX better than of AY and BY in the same-different task. This preexposure effect should be further enhanced if, as Hall (2003) might predict, the preexposure schedule renders C and D more salient than A and B. Thus, placing the salient
C and D on the habituated X background during test provides the best opportunity for participants to detect these unique elements. Conversely, placing A and B on the Y background will reduce attention to the unique elements. That is, swapping the X and Y backgrounds in test should produce a preexposure effect in the opposite direction to that observed in Experiment 9.
In contrast, Mitchell, Nash, and colleagues (2008) predict that swapping the common and unique elements will reduce, rather than enhance, the difference in discrimination performance between the two test conditions. In contrast, Mitchell, Nash, and colleagues (2008) predict that swapping the common and unique elements will reduce, rather than enhance, the difference in discrimination performance between the two test conditions. According to this model, X_AX/BX exposure provides the best opportunity for Chapter 5 134
participants to discriminate these patterns since A and B are more salient than C and D, and that A and B are presented on the less salient X background. Placing A and B on the more salient Y background, and C and D on the less salient X background will reduce the advantage in salience of A/B over C/D. That is, swapping the backgrounds will reduce, rather than enhance, the preexposure effect between these two test conditions. It is also possible that A and B will continue to receive more attention than C and D on test since they were better encoded into memory.
A study by Mitchell, Kadib, and colleagues (2008) found evidence to support this latter prediction. In this experiment, participants received intermixed and blocked exposure to two pairs of checkerboard patterns (AX/BX_CX_DX). In the same-different task, elements A and C were placed on the preexposed background X, and elements B and D were placed on a novel background Y. An intermixed-blocked effect was observed; participants showed better discrimination performance for AX (versus X) than for CX
(versus X). In addition, discrimination of BY (versus Y) was also better than of DY (versus
Y). That is, the difference in salience between B and D was maintained when these elements were shifted onto a novel background. A similar finding might be expected in
Experiment 10; the advantage in attention to A/B over C/D may be maintained when these elements are swapped to a different background. In sum, the short-term habituation mechanism predicts the preexposure effect (the advantage in discriminating stimuli with A and B features than those with C and D features) will be reduced in Experiment 10
(compared to Experiment 9), but it leaves open the possibility that AY and BY will still be more discriminable than CX and DX. The exact outcome depends on how well the unique elements are encoded into memory and how much salience each background lost through Chapter 5 135
preexposure. Only Hall’s (2003) model predicts that discrimination of CX and DX will be better than that of AY and BY.
Method
The procedure differed from the previous experiments only in the following details.
Participants. Twenty-four first-year psychology students (15 female and 9 male; mean age = 18.9), from UNSW participated in this experiment in exchange for course credit.
Design. The preexposure phase was unchanged from the previous experiment. In the same-different task, the common and unique elements were swapped between the two conditions. Elements A and B were placed on the background Y, to form the test pair patterns of AY and BY. Similarly, C and D were placed on the background X to form CX and DX. The unique elements remained in the same location on their new background. In the same-different task, there were four trial types: (1) AY:BY different (e.g., AY and BY),
(2) AY:BY same (e.g., AY and AY), (3) CX:DX different, (e.g., CX and DX), and (4)
CX:DX same (e.g., CX and CX). There were four blocks of 24 test trials, for a total of 96 trials. Within each 24 trial block on test, there were six trials of each type, and all 24 trials were presented in a random order. Chapter 5 136
Figure 5.2. Panel A shows mean proportion of correct responses within each test block on the four types of test trial in Experiment 10. Panel B shows mean sensitivity score (d’) for each preexposure condition across test blocks. Panel C shows estimates (c values) for response across test blocks. Panel D shows gaze length to the unique elements within each preexposure condition (A&B or C&D) across the preexposure phase. Panel E shows gaze length to the unique elements within each test condition across the same-different task. Chapter 5 137
Results
Five participants were excluded from this analysis according to the same criterion as described in Experiment 1.
Same-different performance. A preliminary analysis was conducted to determine whether performance in the same-different task was affected by the two counterbalancing procedures. The order in which each preexposure condition was presented did not affect overall discrimination performance (F < 1). The assignment of background pattern to each preexposure condition affected overall discrimination performance, F(1, 15) = 6.43.
However, background pattern did not interact with preexposure (F < 1). In addition, the order in which each condition was presented did not interact with preexposure (AY:BY versus CX:DX; F < 1). Consequently, the data were collapsed across all the counterbalancing groups for the remaining analysis.
Panel A of Figure 5.2 shows the mean proportion of correct responses for the four different test trials across the four test blocks. Overall, performance accuracy improved across test blocks, F(1, 18) = 11.59. Similar to the previous experiment, performance accuracy was better for the same trials than for the different trials, F(1, 18) = 28.65. There was no effect of preexposure as discrimination of AY and BY was not better than of CX and DX (F < 1). The interaction of trial type (same versus different) and preexposure was not significant, F(1, 18) = 1.01. There was a significant interaction of trial type and test blocks, F(1, 18) = 8.79. The figure shows that difference in performance accuracy between the same and different trials was reduced across test blocks. No other interactions were significant (all Fs < 1).
Panel B of Figure 5.2 shows the sensitivity scores (d’) for the two preexposure conditions across the four blocks of test trials. Overall, sensitivity scores increased across Chapter 5 138
the four test blocks, F(1, 18) = 11.49. There was no difference in sensitivity scores between the two preexposure conditions (F < 1). The interaction of preexposure and test blocks was not significant (F < 1).
Panel C of Figure 5.2 shows estimates for response bias in the same-different task (c values). Estimate of response bias for both conditions was reduced across test blocks, F(1,
18) = 8.44. This suggests that participants reduced their bias to responding “same” across test blocks. There was no difference in c values between the two test conditions, F(1, 18) =
1.07. The interaction of preexposure condition and test blocks was also not significant (F <
1).
Eyegaze. Panel D of Figure 5.2 shows gaze length to the unique elements A-D in the preexposure phase. Gaze length to A and B was greater than to C and D, F(1, 18) =
6.66. Gaze length to all four unique elements did not increase across preexposure blocks (F
< 1). The interaction of preexposure condition and preexposure blocks was not significant,
F(1, 18) = 1.22.
Panel E of Figure 5.2 shows gaze length to the unique elements A-D in the same- different task. Overall, gaze length to all four unique elements increased across test blocks,
F(1, 18) = 7.52. The difference in gaze length between the two preexposure conditions (A
& B versus C & D) was not significant, F(1, 18) = 1.88. The interaction of test condition and test blocks approached significance, F(1, 18) = 3.68, p = 0.07.
Correlation analysis. As per Experiment 9, a difference score between gaze length to A/B and to C/D was calculated for both preexposure and test. Gaze length in preexposure did not correlate with the difference in sensitivity (AX/BX versus CY/DY), r = 0.04, p =
0.88. Similar to previous experiments, a significant positive correlation between the Chapter 5 139
difference in sensitivity and the difference in gaze length was observed in the same- different task, r = 0.51, p = 0.03.
Discussion
Experiment 10 showed that gaze length to A and B was greater than to C and D in the preexposure phase. This replicates the finding from Experiment 9. In the same-different task, discrimination of AY and BY was not better than of CX and DX. Similarly, gaze length to the unique elements did not differ between the two test conditions. Swapping the backgrounds between the two preexposure conditions in the same-different task seemed to remove any advantage of attention to A and B over C and D. There was no difference between the two preexposure conditions in the first test block.
Taken on its own, the absence of a difference between the two test conditions in this experiment does not provide strong support for either mechanism of salience modulation
(Hall, 2003; Mitchell, Nash et al., 2008). Perhaps a better understanding of these findings can be obtained by viewing them in the context of the results of Experiment 9. Hall would have to argue that better discrimination of AX and BX than of CY and DY in Experiment 9 resulted from the fact that X was more familiar (and so lower in salience) than Y. Because, according to Hall, C and D will be, if anything, more salient than A and B, discrimination of CX and DX (Experiment 10) should be better than that of AX and BX (Experiment 9).
Conversely, Hall predicts that the poor performance to CY and DY seen in Experiment 9 will be made even poorer when we present the less salient A and B features on the Y background in Experiment 10. In sum, the advantage of the X background over the Y background seen in Experiment 9 should be enhanced by presenting the more salient C and
D elements on the X background in Experiment 10. This is the reverse of the pattern Chapter 5 140
observed; if anything, the advantage of AX and BX over CY and DY seen in Experiment 9 was reduced by swapping the unique elements in Experiment 10.
This observation however, is consistent with the short-term habituation mechanism
(Mitchell, Nash et al., 2008). According to this model, swapping the backgrounds has removed any advantage in salience that A/B has over C/D since A and B are presented on the more salient Y background, and C and D are presented on the less salient X background. This explains why no preexposure effect was observed in test. A cross- experimental analysis was conducted to verify the statistical validity of these observations.
Cross-Experiment Analysis of Experiment 9 and 10
For simplicity, the analysis only considered the sensitivity scores (d’) and eyegaze data from each experiment. There were three factors in this analysis; experiments
(Experiment 9 versus 10), background (X versus Y), and test blocks. The backgrounds factor compared discrimination performance for the patterns that shared the X background to the patterns that shared Y background (e.g., AX-DX versus AY-DY). The analysis showed that there was no overall difference in sensitivity scores between Experiments 9 and 10, F(1, 39) = 2.48. Similarly, sensitivity scores for X and Y background patterns were not different (F < 1). More importantly, the interaction of experiment and background was significant, F(1, 39) = 5.45. Thus, the difference in sensitivity scores between the preexposure conditions was greater in Experiment 9 than in Experiment 10. Averaged across the two experiments, sensitivity scores improved across test blocks, F(1, 39) = 9.15.
The interaction of test block and experiment was significant, F(1, 39) = 5.28. The interaction suggests that the improvement of sensitivity scores was greater in Experiment
10 than in Experiment 9. No other interactions were significant (all Fs < 1). Chapter 5 141
A similar analysis was conducted for the eyegaze results from the same-different task. The analysis showed that gaze length, averaged across all four unique elements, was greater in Experiment 9 than in Experiment 10, F(1, 39) = 4.26. This suggests that swapping the backgrounds reduced average gaze length to all four unique elements. In addition, participants on average, spent more time looking at the unique elements (A-D) when they were presented on X than on the Y background, F(1, 39) = 4.70. However, the interaction of experiment and background was also significant, F(1, 39) = 16.65. Similar to discrimination performance, the interaction suggests that the difference in gaze length to the unique elements on the X and Y backgrounds was reduced when A and B were shifted from X to Y in Experiment 10. Gaze length to the unique elements increased across test blocks, F(1, 39) = 6.53. The interaction of experiment and test blocks was not significant (F
< 1).
The interaction of experiments (Experiment 9 versus 10) and background (X versus
Y) on both the discrimination performance and eyegaze measures, suggests that the preexposure effect in Experiment 9 (AX:BX versus CY:DY) was removed once A and B were shifted from X to Y in Experiment 10. This finding is consistent with the short-term habituation mechanism (Mitchell, Nash et al., 2008) since it implies that A and B received more attention than C and D across both experiments. Conversely, Hall’s (2003) model predicted the opposite effect; swapping the unique features in Experiment 10 should further enhance the advantage of the X over the Y background.
Another very interesting result from the cross-experimental analysis was that gaze length to A-D on test was greater in Experiment 9 than in Experiment 10. One explanation is that participants learned to attend to the specific regions where the unique elements appeared on X and on Y. That is, participants, in Experiment 9, were looking at the A and Chapter 5 142
B locations on X, and the C and D locations on Y, to discriminate the test patterns.
Swapping the backgrounds in Experiment 10 would result in participants looking to the incorrect locations in test. Consequently, this lowers gaze length to the correct position of all four unique elements. Experiment 11 examined the possibility that, in these procedures, the unique elements’ spatial location was of most importance for discrimination.
Experiment 11
A key aspect of the short-term habituation mechanism (Mitchell, Nash et al., 2008) is that discrimination in the same-different task is driven by memory for the unique elements. Thus far, the experiments have not been concerned with which aspects of the unique elements are encoded during preexposure. One might assume that participants encode the physical appearance of the unique elements (see Lavis, Kadib, Hall, & Mitchell,
2011 for related argument). On test, this allows participants to search for features that match their memory of the unique elements. However, the stimuli used throughout this thesis allow that discrimination can be based simply on the presence of bright colour in a specific location. Each unique element (A-D) appears in a specific quadrant of the common element on which it is placed (X or Y), and it will remain in that position throughout preexposure. Thus, participants do not need to encode the physical properties of the unique elements (their shape and colour) in order for them to discriminate the patterns. Participants can simply encode and attend to the spatial locations of the unique elements for discrimination.
Experiment 11 investigated whether participants utilise the physical identities or the spatial locations of the unique elements for discrimination in the same-different task. Let us assume that discrimination of AX from BX, following preexposure, is contingent upon attention to the spatial locations of A and B. If so, then shifting the unique elements from Chapter 5 143
their locations in preexposure to novel locations in test should then impair their detection.
The cross-experimental analysis of the eyegaze data from Experiments 9 and 10 is consistent with this prediction. In the current experiment, participants received intermixed presentations of AX and BX in preexposure. Discrimination of this pair was then measured in the same-different task, along with a novel pair of patterns (CX and DX). Given the results of previous experiments, discrimination of AX and BX should be better than of CX and DX. Two additional test conditions were introduced in which the locations of the preexposed and novel unique elements were swapped. In one condition, A and B were placed in the locations that C and D occupied on the CX and DX trials. These patterns are called ATX and BTX (the T superscript represents the notion that the location of A has been transposed from the AX pattern to the ATX pattern). In another condition, C and D were placed in the locations that A and B originally occupied during preexposure. These patterns are called CTX and DTX. For reference, the locations that A and B occupied on AX and BX trials are called the attended locations since, if location is important, it is these locations to which attention should increase. The locations that C and D occupied on the CX and DX trials are called the unattended locations.
The critical comparison in this experiment is between the ATX:BTX and CTX:DTX trials. If participants make their discriminations based on the physical identity of the unique elements, then discrimination for ATX and BTX should be better than for CTX and DTX.
That is, participants should find A and B easier to detect than C and D regardless of where these elements appear. Conversely, discrimination of CTX and DTX would be expected to be better than of ATX and BTX if participants use the locations of the unique elements for discrimination.
Chapter 5 144
Method
The procedure differed from the previous experiments only in the following details.
Participants. Twenty-four (11 female and 13 male; mean age = 18.8), from UNSW participated in this experiment in exchange for course credit.
Design. In the preexposure phase, all participants received alternating trials to AX and BX for 60 trials of each. Discrimination of this pair was then measured in the same- different task, along with the novel pair, CX and DX. For the ATX and BTX test trials, A appeared in C’s usual location, and B appeared in D’s usual location. For the CTX and DTX trials, C and D appeared in the locations usually occupied by A and B respectively. In the same-different task, there were eight types of test trial. In this experiment, the terms preexposed and novel unique elements refer to the physical appearance of A-D (colour and shape). There were four trial types in which the unique elements appeared in their original locations: (1) preexposed original different (e.g., AX and BX), (2) preexposed original same (e.g., AX and AX), (3) novel original different (e.g., CX and DX), and (4) novel original same (e.g., CX and CX). There were four types of test trial in which the locations of the unique elements were swapped: (5) preexposed swapped different (e.g., ATX and
BTX), (6) preexposed swapped same (e.g., ATX and ATX), (7) novel swapped different
(e.g., CTX and DTX), and (8) novel swapped same (e.g., CTX and CTX). There were two blocks of 48 test trials, for a total of 96 trials. Within each 48 trial block on test, there were six trials of each type, and all 48 trials were presented in a random order.
Chapter 5 145
Figure 5.3. The original trials refer to the trials in which the unique elements A-D
(represented by only their shape and colour) appeared in their original locations (i.e., AX-
DX trials). The swapped trials refer to the trials in which the locations for A-D were swapped between preexposure conditions (i.e., ATX - DTX trials). Panel A shows mean proportion of correct responses for the different test conditions in Experiment 11. Panel B shows mean sensitivity scores (d’) for each test type. Panel C shows mean estimates for response bias (c values). Panel D shows gaze length to the unique elements in their original or swapped locations in the same-different task. Chapter 5 146
Results
Three participants were excluded from this analysis according to the same criterion as described in Experiment 1.
Same-different performance. Panel A of Figure 5.3 shows the mean proportion of correct responses for the eight different test conditions in the same-different task. Overall accuracy was marginally better when the unique elements (A-D) appeared in their original locations than in their swapped locations, F(1, 20) = 3.72, p = 0.07. Performance accuracy was again better for same trials than for different trials, F(1, 20) = 35.14. Performance accuracy did not improve across test blocks, F(1, 20) = 1.15. There was no effect of preexposure as discrimination performance for patterns in which A and B were unique was not better than for patterns in which C and D were unique, F(1, 20) = 1.80. That is, familiarity/novelty of the unique elements’ physical characteristics did not affect discrimination performance. The interaction of preexposure condition (preexposed versus novel) and trial type (same versus different) was not significant (F < 1). The interaction of preexposure (preexposed versus novel) and location (original versus swapped) was significant, F(1, 20) = 16.40. This interaction confirms the observation that discrimination of AX/BX was greater than of CX/DX, but swapping the locations of A-D reversed the direction of this effect. A three-way interaction of preexposure, location, and trial type, F(1,
20) = 14.31, suggested that the interaction of preexposure and location was observed on the different test trials, but not on the same test trials. No other significant interactions were observed (all Fs < 1).
The interaction of preexposure and location prompted a simple effects analysis. In their original locations, discrimination performance for AX and BX was better than for CX and DX, F(1, 20) = 6.37. Swapping the locations of the unique elements rendered CTX and Chapter 5 147
DTX more discriminable than ATX:BTX, F(1, 20) = 14.84. These comparisons show that spatial location is most important for discrimination.
The previous analysis does not tell us whether or not changes in the salience of the unique elements’ physical properties affected discrimination performance. This requires an analysis comparing trials in which the preexposed (A/B) and novel (C/D) unique elements appear in the attended or the unattended location. With attention to spatial location controlled in this way, an effect of preexposure would indicate differences in the effectiveness of other aspects of the cues (their shape and colour) in determining discrimination performance. A simple effects analysis revealed that, in the unattended locations, the difference in discrimination performance between the patterns with novel
(CX and DX) and preexposed elements (ATX and BTX) was significant, F(1, 20) = 4.78, p
= 0.04. There was no difference in discrimination performance for the patterns with the novel and preexposed elements was observed when A-D appeared in the attended locations
(AX:BX versus CTX:DTX), F < 1.
Panel B of Figure 5.3 shows mean sensitivity scores (d’) for the four test conditions.
Overall, there was no difference in sensitivity scores for trials on which the unique elements appeared in their original or swapped locations, F(1, 20) = 2.09. Sensitivity scores for the preexposed patterns were not better than for the novel patterns, F(1, 20) = 2.44.
However, there was a significant interaction between location and preexposure, F(1, 20) =
15.15. That is, sensitivity scores for AX and BX were greater than for CX and DX, but the direction of this difference was reversed after the unique elements swapped locations. This interaction prompted a simple effects analysis to compare the sensitivity to detect the preexposed and novel unique elements in either their original or swapped locations.
Sensitivity scores for AX and BX trials were better than of CX and DX trials, F(1, 20) = Chapter 5 148
5.52. Similarly, discrimination of CTX and DTX was better than of ATX and BTX, F(1, 20)
= 15.95. In addition, an analysis was conducted to compare sensitivity when A-D appeared in either the attended or unattended locations. The analysis showed that, when the unique elements appeared in the unattended locations, the difference in sensitivity for CX and DX and for ATX and BTX was significant, F(1, 20) = 4.63, p = 0.04. No such difference was observed when the unique elements appeared in the attended locations (F < 1).
Panel C of Figure 5.3 shows the mean estimates of response bias (c values) in the same-different task. Response bias did not vary between the preexposed and novel conditions (F < 1), or between the trials in which the locations of the unique elements were swapped (F < 1). However, the interaction between these two factors was significant, F(1,
20) = 14.04. Response bias also did not change across test blocks (F < 1). The interaction of preexposure and spatial location (original versus swapped) prompted a simple effects analysis for a closer examination of the data. The simple effects analysis showed that response bias for CX and DX was greater than for AX and BX, F(1, 20) = 14.58. Estimates of bias for the ATX:BTX trials was also higher than for the CTX and DTX trials, F(1, 20) =
6.79. Overall, participants showed a stronger bias toward responding “same” when the unique elements appeared in the locations that participants did not attend to in preexposure.
Eyegaze. Panel D of Figure 5.3 shows gaze length to the four unique elements A-D in both the original and swapped locations in the same-different task. Overall gaze length to all unique elements was not affected by the swapping of location (F < 1). Gaze length to the preexposed element A and B was not greater than to C and D across the original and swapped test trials (F < 1). Importantly, however, the interaction of preexposure condition
(preexposed versus novel) and location type (original versus swapped) was significant, F(1,
20) = 11.97. A simple effects analysis confirmed the observation that gaze length to A and Chapter 5 149
B was greater than to C and D when these unique elements appeared in their original locations, F(1, 20) = 10.17. Conversely, gaze length to CT and DT was greater to AT and BT after their locations were swapped, F(1, 20) = 11.42. In other words, gaze lengths towards the attended locations (where A and B appeared in preexposure) were longer than to the unattended locations.
Correlation analysis. In this experiment, separate difference scores in sensitivity were calculated for the test trials in which A-D appeared in either their original and their swapped locations. That is, difference scores were calculated between the AX/BX and
CX/DX test trials, and between the ATX/ BTX and CTX/ DTX test trials. Similarly, the corresponding difference scores for gaze length in test were also calculated. The analysis first examined the relationship between eyegaze in preexposure and performance. Gaze length to A and B in preexposure did not correlate with either of the two difference scores for sensitivity, r = 0.16, p = 0.49 and r = -0.23, p = 0.32. In the same-different task, significant positive correlations between sensitive and gaze length were observed for the trials in which A-D appeared in their original, r = 0.68, p < 0.01, and swapped locations, r
= 0.55, p = 0.01.
The correlations, in all experiments, between eyegaze in preexposure and discrimination performance were positive, and, in some cases, marginally significant. A combined analysis was conducted to observe whether this relationship (eyegaze in preexposure vs. discrimination performance) is consistent across experiments. A linear regression model was fitted to investigate whether gaze length in preexposure predicted discrimination performance. Eyegaze was defined as the difference in gaze length to the unique elements between the preexposure conditions prior to the same-different task.
Similarly, discrimination performance was defined as the difference in d’ scores between Chapter 5 150
test conditions. For example, the difference in Experiment 1 was observed AX:BX and
CY:DY trials . Ten additional predictors, coded for each experiment, were included in the regression model. In this analysis, eyegaze in preexposure was a significant predictor of discrimination performance, t = 2.05. No other predictors were significant (largest t =
1.191).
Discussion
Discrimination performance for the preexposed patterns, AX and BX, was better than for the novel patterns, CX and DX. More importantly, discrimination accuracy for the
CTX:DTX trials was better than for the ATX:BTX trials. That is, when C and D were placed in the locations occupied by A and B during preexposure, they were detected very easily, whereas A and B were very difficult to detect when presented in a new location. In the eyegaze measure, participants spent more time looking at A and B than at C and D when these elements appeared in their original locations. Consistent with the behavioural data, participants spent more time looking at CT and DT than at AT and BT when the locations of these elements were swapped.
Interestingly, Experiment 11 also showed that when A-D appeared in the unattended locations, discrimination was better for patterns with novel elements (CX and DX) than patterns with preexposed elements (ATX and BTX). Only the physical identities of the unique elements differed on these trials. This effect of novelty, however, did not appear in the eyegaze measure as gaze length to C and D was not greater than to AT and BT.
Furthermore, this effect of novelty was not observed between the AX/BX and CTX/DTX test trials. These are the trials in which A-D appeared in the attended location. This implies that participants can readily discriminate AX from BX (or CTX from DTX) after they have Chapter 5 151
attended to the relevant locations. Consequently, increasing the novelty of the unique elements’ physical features does not further increase the discriminability of these patterns.
In summary, Experiment 11 showed that spatial location was more important for discrimination than the physical identities of the unique elements. However, the unique elements’ physical identity may control discrimination performance if they appear outside the attended locations.
Chapter Summary and Discussion
Experiment 9 showed that presenting the common element X prior to intermixed trials of AX and BX enhanced the discriminability of AX and BX compared to two intermixed patterns, CY and DY. In the preexposure phase of this experiment, gaze length to A and B was greater than to C and D. This replicates the findings from Experiment 7 and
8 (of Chapter 4) in which gaze length to A and B during preexposure was greater in the
PRE than in the POST group. Experiment 10 suggested that the difference in background novelty does not account for the preexposure effect in Experiment 9. Following
X_AX/BX_CY/DY preexposure, A and B were placed on Y and C and D were placed on
X. A preexposure effect between AY/BY and CX/DX was not observed when the unique and common backgrounds were swapped on test. A cross-experimental analysis of
Experiments 9 and 10 confirmed that the difference in the discriminability of AX/BX and of CY/DY was removed after A and B were shifted from X to Y in Experiment 10. In addition, the shift in background also removed the difference in gaze length between A/B and C/D.
Only the short-term habituation mechanism (Mitchell, Nash et al., 2008) predicts that A and B will become more salient than C and D following preexposure in Experiment
9. According to this theory, exposure to X allows participants to reduce the processing of Chapter 5 152
the common element through habituation. The habituated element X will demand less attention for processing on the AX and BX preexposure trials. This will increase the amount of attention given to A and B for processing. On the initial CY and DY preexposure trials, participants must still process Y since it is novel. Consequently, participants will encode a stronger representation of A and B than of C and D, since A and B received more attention for processing during preexposure. In Experiment 9, stronger memory of A and B than of C and D leads to better discrimination of AX and BX than of CY and DY. The model also accounts for why discrimination of AY and BY was not greater than of CY and
DY in Experiment 10. Placing A and B on the more salient Y background, and placing C and D on the less salient X background removed any advantage in salience of A and B over
C and D.
Mitchell, Nash, and colleagues (2008) never specified how memory of the unique elements might control attention in the same-different task. Experiment 11 showed that participants encoded the spatial location of the unique elements in preexposure, and they continued to attend to these locations for discrimination. In this experiment, discrimination of AX and BX, following preexposure to these patterns, was better than that of CX and DX.
After the locations of the preexposed and novel elements were swapped, however, discrimination of CTX and DTX was better than of ATX and BTX. That is, C and D received more attention and were better detected than A and B when they appeared in the locations that A and B occupied during preexposure (the attended locations).
This spatial location account does not need to assume that participants have encoded the physical identities of the unique elements. For example, a participant may have learned that A and B were areas of added brightness that appeared in the top left and bottom right quadrants of the X background during AX and BX exposure. The participant can Chapter 5 153
discriminate AX from BX by attending to the presence of added brightness in the learned locations. This explanation however, does not rule out that the participant may still have encoded the physical appearance of A and B during preexposure. Experiment 11 only examined which perceptual dimension was most important for discrimination, and it did not explicitly measure for memory of the unique elements’ appearance. Thus, participants may have encoded both spatial location and physical identity during preexposure. These issues will be further discussed in the General Discussion (Chapter 6).
Furthermore, physical identity of the unique elements may affect discrimination performance when the unique elements do not appear in the attended locations. In
Experiment 11, there were test trials in which the preexposed and novel unique elements appeared in the unattended locations. On these trials, novelty of physical identity appeared to control discrimination performance. That is, discrimination of CX and DX was better than of ATX:BTX. The short-term habituation mechanism (Mitchell, Nash et al., 2008) does not predict this effect of novelty, rather it predicts that discrimination should be superior for elements that are better encoded in memory (Hall, 2003; McLaren & Mackintosh, 2000).
The effect of novelty (seen in the unattended locations in Experiment 11) highlights how two different attentional processes may be utilised for discrimination in the same- different task. The findings suggest that participants utilised one attention process to encode the locations that contain the unique elements during preexposure (Mitchell, Nash et al.,
2008). On test, participants then engaged a top-down process to deliberately attend to these locations with the knowledge that a unique element should appear in these locations.
Conversely, the novel unique elements may have captured attention through a bottom-up process (Hall, 2003; McLaren & Mackintosh, 2000) because they possessed higher salience than their background. The failure to detect an effect of novelty in previous experiments Chapter 5 154
may have resulted from top-down attention to the spatial locations overriding the influence of the bottom-up process. Thus, bottom-up attention may only operate when strategic attention is not directed towards the locations in which the unique features appear. For example, novel elements were not better detected than preexposed elements when they appear in the attended locations (AX:BX versus CTX and DTX).
The effect of novelty does not appear on the eyegaze analysis. That is, gaze length to A and B does not differ from C and D when these elements appear in the unattended locations. One possibility for this dissociation between discrimination performance and eyegaze is that eye movements, in the same-different task, are controlled by a top-down attentional process. Eye movements, in this procedure, may be less sensitive to the influence of a bottom-up process.
In summary, Chapter 5 has shown that preexposure to X prior to intermixed trials of
AX and BX increased the discriminability of AX and BX compared to two intermixed patterns, CY and DY. The results of Experiment 10 suggest that this effect is better accounted for by the short-term habitation mechanism (Mitchell, Nash et al., 2008) than by the reverse habituation (Hall, 2003) mechanism. That is, preexposure allowed participants to form a stronger memory of A and B than of C and D. The final experiment showed that the critical feature of the unique elements encoded by participants during preexposure is their spatial location rather than their other physical properties such as colour or shape. Chapter 6 155
CHAPTER SIX
A commonly held assumption in perceptual learning is that exposure to two similar stimuli increases attention to the unique elements (Gibson, 1969; Hall, 1991). Few studies however, have provided direct evidence that validates this assumption (Blair & Hall, 2003;
Lavis & Mitchell, 2006). A number of studies have shown that eye movements provide a reliable index of attention (Hogarth et al., 2008; Kruschke et al., 2005). The current experiments measured eye movements to explore the attentional processes involved in human perceptual learning.
Summary of Experimental Findings
Attention and Perceptual Learning
Experiment 1 demonstrated the basic perceptual learning effect; preexposure to two similar visual patterns, AX and BX, enhanced their discrminability relative to novel control stimuli, CY and DY. Participants also spent more time looking at the preexposed unique elements (A and B), than at the novel elements (C and D). The simplest explanation for this effect is to assume that X lost more salience than A and B during preexposure because it was presented twice as often as either A or B (McLaren et al., 1989). As a consequence, the more salient A and B captured more attention than X in the same-different task. In the control condition, there was greater competition for attention to C and D since both the common and unique elements were novel. Experiment 2 showed that exposure to X alone also rendered AX and BX more discriminable than CY and DY. Consistent with the relative novelty explanation, A and B attracted more attention than C and D because they were presented on the familiar background. Gaze length to A and B was also greater than to
C and D. Chapter 6 156
Experiment 3 showed that the schedule in which the patterns are presented in preexposure also affected their discrimination. Intermixed presentations of AX and BX produced better discrimination of these patterns than blocked presentations of CX and DX.
A common explanation for the intermixed-blocked effect is that the unique elements become more salient following intermixed than blocked exposure. The eyegaze measure confirmed this as gaze length to A and B was greater than to C and D in both the preexposure phase and the same-different task. The three experiments in Chapter 2 are consistent with the corresponding results from animal studies (McLaren et al., 1991;
Bennett et al., 1994; Symonds & Hall, 1995; Blair & Hall, 2003). This was taken as evidence that the models used to describe perceptual learning in animals may be applicable to human perceptual learning (Hall, 2003; McLaren & Mackintosh, 2000; Mitchell, Nash et al., 2008). Chapters 3-5 focussed on differentiating between these three accounts.
Salience Modulation via Unitization
Three experiments in Chapter 3 examined the predictions of McLaren and
Mackintosh’s (2000) unitization mechanism. One critical assumption of this mechanism is that a stimulus is most salient when it is novel and that intermixed exposure to AX and BX can only attenuate the loss of salience to A and B. Contrary to this assumption, Experiment
4 showed that elements preexposed on an intermixed schedule received more attention than novel elements. Following exposure to AX/BX and to Y alone, discrimination of AX and
BX was better than of CY and DY. Participants also spent more time looking at A and B than at C and D. According to McLaren and Mackintosh (2000), intermixed exposure to
AX and BX, and the associative activation of A and B that results, may completely reverse the unitization of A and B that would otherwise have occurred through exposure. This Chapter 6 157
reverse unitization process cannot, however, render A and B more salient than the novel elements, C and D.
Two additional experiments in Chapter 3 support this conclusion. Experiment 5 showed that discrimination of AX and BX was better than of CX and DX following
AX/BX exposure. This rules out the possibility that a difference in the salience of X and Y affected discrimination performance in Experiment 4. Similarly, Experiment 6 showed that discrimination of ACDX and BCDX was better than of CABX and DABX following
AX/BX exposure. This experiment controlled for any associative mechanisms (McLaren &
Mackintosh, 2000) that may have affected discrimination performance in Experiment 5.
Placing A and B on the CX and DX trials should negate the possible activation of these elements through the X-A and X-B associations. These excitatory associations will activate
A and B on BX and AX trials respectively, but the presence of inhibition between A and B will prevent such activations. Thus, Experiment 6 provided a direct comparison of the salience of the preexposed and novel unique elements.
In three experiments, Chapter 3 showed that intermixed exposure to AX and BX increased the salience of A and B in such a way that they became more salient than novel elements. None of the mechanisms proposed in McLaren and Mackintosh’s (2000) model
(associative inhibition, latent inhibition, and unitization) can account for these findings.
However, they are consistent with Hall’s (2003) reverse habituation mechanism. Like the reverse unitization process, Hall assumes that associative activation of A and B will increase their salience, but Hall does not place a limit on the extent to which salience can increase through exposure. The short-term habituation mechanism can also account for these findings (Mitchell, Nash et al., 2008). According to this model, attention to the unique elements in preexposure allows participants to encode these elements into memory. This Chapter 6 158
memory then guides attention for discrimination in test. Consequently, A and B should receive more attention than C and D since participants have memory for the former but not the latter. Chapters 4 and 5 aimed to differentiate these two mechanisms.
Comparing the Reverse Habituation and Short-term Habituation Models
Chapter 4 presented a series of experiments to examine Hall’s (2003) model. This model predicts that exposure to X prior to AX/BX preexposure might reduce the strength of
X-A and X-B associations, and therefore prevent associative activation and dishabituation of A and B. In two experiments, X alone trials were presented either before or after AX/BX intermixed trials (PRE versus POST group). Contrary to Hall’s account, Experiments 7 and
8 showed that gaze length to A and B, in preexposure, was greater in the PRE than in the
POST group. However, greater attentional bias to A and B in the PRE schedule did not render AX and BX more discriminable in the PRE than in the POST group. In Chapter 5, a different approach was taken to the same question. Experiment 9 showed that exposing X prior to the AX/BX trials enhanced the discrimination of these patterns compared to two intermixed patterns, CY and DY. In both the preexposure and test phases, gaze length to A and B was greater than to C and D. If anything, this result suggests that A and B were more salient than C and D on test. This is the opposite of the result predicted by Hall (2003).
Experiment 9 however, is not theoretically decisive as the result may be explained in terms of background salience, not the salience of the unique features. That is, A and B may have attracted more attention than C and D because they appeared on the more familiar X background. Experiment 10 explored this notion by swapping the common backgrounds between the two preexposure conditions from the previous experiment.
According to Hall (2003), elements C and D should be more salient than A and B following preexposure. In the same-different task, elements C and D now appeared on X, a less Chapter 6 159
salient background than in preexposure. Features A and B appeared on Y, a more salient background than in preexposure. Thus, Hall predicts a bigger difference between X and Y
(test conditions) in Experiment 10 than in Experiment 9. No difference however, was observed between the two test conditions in the experiments.
Experiments 9 and 10 are consistent with the short-term habituation account
(Mitchell, Nash et al., 2008). Habituation to X on the X alone trials will reduce the amount of attention that X could demand on the AX and BX trials. The novel Y element will demand attention for processing on the initial CY and DY trials. Encoding of A and B should, therefore, be better than of C and D because more attentional resources are available to process A and B. Consequently, superior memory of A and B will lead to better discrimination of AX and BX than of CY and DY on test in Experiment 9. The model also accounts for why discrimination of AY and BY was not greater than of CX and DX in
Experiment 10. Specifically, placing the more salient A and B on the more salient Y background, and placing less salient C and D on the less salient X background rendered all stimuli equally discriminable. Furthermore, the cross-experimental analysis confirmed that attention to A and B was greater than to C and D when they appeared on either the X or Y backgrounds. In addition, this analysis contradicts Hall’s prediction that swapping the backgrounds in Experiment 10 should increase attention to C and D.
In sum, the short-term habituation mechanism (Mitchell, Nash et al., 2008) provides the best account of the current series of experiments. The final experiment of this thesis examined which aspects of the unique elements are being encoded into memory and how this may affect discrimination performance in test.
Chapter 6 160
Encoding of Spatial Locations
In the procedure used here, three perceptual dimensions or properties, colour, shape and location, define each unique element. Each element is a specific combination of adjacent coloured squares that are not repeated in the background. These coloured squares also occupy a specific location on each checkerboard pattern. One possibility is that participants encode the physical appearance of the unique elements (shape/colour) in preexposure and search for this feature on test. Alternatively, participants might discriminate two patterns by attending to the location in which changes occur (where the unique features appear), but without encoding the physical appearance of the unique features. Experiment 11 examined which of these dimensions is of primary importance for discrimination.
In Experiment 11, participants received intermixed exposure to AX and BX. In the same-different task, discrimination of AX and BX was better than a pair of novel patterns,
CX and DX. Critically, this pattern of results was reversed after the locations of the unique elements were swapped between the two preexposure conditions. That is, participants showed better discrimination performance for the patterns in which C and D appeared in the original locations of A and B (CTX and DTX) than for the patterns in which A and B appeared in the original locations of C and D (ATX and BTX). Spatial location was therefore most important for discrimination in test. The eyegaze data confirmed this conclusion as participants spent more time looking at the original locations of A and B than at the original locations of C and D regardless of which stimuli (A-D) had appeared in these locations.
Experiment 11 also provided an opportunity to compare the salience of the preexposed and novel elements on trials in which A-D appeared in the same locations. On Chapter 6 161
the AX/BX and CTX/DTX test trials for example, all unique features A-D appeared in locations that contained A and B in preexposure. Stimulus salience does not appear to affect performance on these trials as no difference in discrimination performance was observed. This might represent a ceiling effect as participants can accurately discriminate the test patterns once they have attended to the correct locations. Novelty however, seems to affect performance on trials in which A-D appear in the unattended locations. That is, discrimination of CX and DX was better than of ATX and BTX. This novelty effect was, however, weak and it did not appear in the eyegaze measure.
Summary
The current experiments showed that human perceptual learning involves an attentional process. The eyegaze analysis showed that exposure to two similar stimuli (i.e.
AX and BX) increased attention to the distinguishing features. The short-term habituation theory (Mitchell, Nash et al., 2008) provides the best account of this attentional process.
This model suggests that two attentional processes may be involved in these perceptual learning procedures. One process detects and encodes the unique elements during preexposure. Once detected, another process, driven by spatial location of the unique elements, guides attention for discrimination on test. Mitchell, Nash et al. (2008) suggested that the first process, attention for detection, is exogenous or stimulus-driven; attention is automatically prioritised to the most salient stimuli in the environment. The second process, however, attention for discrimination, is a goal-oriented, endogenous process (see also
Mundy et al., 2007).
In fact, this distinction between exogenous and endogenous attention may be unnecessary. For instance, participants may have engaged an endogenous attention process in preexposure since they were explicitly instructed to look for differences between the Chapter 6 162
preexposed patterns. That is, the mechanisms that control attention (and eye movements) in both the preexposure and test phase may be goal-oriented. Consequently, I will review whether the current experiments provided any evidence for an exogenous attention process.
Finally, the suggestion that attention in human perceptual learning is goal-oriented deviates from the notion of attention used to describe animal perceptual learning
(Mackintosh, 2009). The implication is that the current series of experiments may fall outside the scope of the mechanisms proposed by McLaren and Mackintosh (2000), and
Hall (2003). This issue will be discussed towards the end of the General Discussion.
The Role of Salience in Stimulus Detection
The eyetracking analysis shows that participants increase their attention to the unique elements through exposure. It is unclear however, as to how participants first come to detect and attend to the unique elements in preexposure. Eyegaze can be thought to reflect the strength of the orienting response to the visual stimuli (Hogarth et al., 2008; Le
Pelley, Beesley, Griffiths, 2011). The animal models of perceptual learning (McLaren &
Mackintosh, 2000; Hall, 2003; Honey & Bateson, 1996) assume that stronger orienting response to the unique elements in preexposure is associated with the ability of these elements to capture attention. This however, it not necessarily true, since participants were instructed to look for differences between patterns in preexposure. In this case, gaze length may reflect the ease with which participant can search and find the unique elements. Each of these two possibilities is further discussed in the section below.
Attentional Capture and Relative Novelty
The general assumption of attention in perceptual learning is that the subject orients to the more salient stimuli (Hall, 1991). For example, exposure to AX and BX renders A and B more salient than X, and participants perceive A and B to have higher physical Chapter 6 163
intensity than X. Attention to the more salient stimuli is thought to reflect an exogenous, bottom-up process of attention (e.g., Theeuwes, 2010). One interpretation of bottom-up salience is that it can automatically capture the participants’ attention. That is, participants automatically orient to the more salient unique elements. However, the automaticity of bottom-up salience is debatable (e.g., Yantis & Jonides, 1990). Instead, bottom-up salience may serve to reduce the participants’ threshold to detect the unique features. Both interpretations predict that attention should increase to more salient stimuli.
A prediction of exogenous attention is that greater difference in salience between the unique and common elements should lead to greater attentional capture by the unique elements. In Experiment 9 for example, participants received intermixed exposure to two pairs of patterns, AX/BX and CY/DY. Exposure to the AX and BX was also preceded by X alone trials. This meant that elements A and B appear on a habituated background but C and D appear on a novel background. Thus, the competition for attention between the unique and common elements is greater on the CY/DY than on the AX/BX trials since C,
D, and Y are novel. Elements A and B will capture attention more readily on the initial
AX/BX trials than C and D on the initial CY/DY trials. Consistent with this prediction, participants spent more time looking at A and B than at C and D in preexposure, and this difference was observed in the first preexposure block.
There are number of issues to consider, however, before one can conclude that participants detected the unique elements through a bottom-up process. One issue is that each preexposure block represents gaze length to the unique elements across fifteen trials of
AX and BX (or CY and DY). Presumably the unique elements will only capture attention on the first AX and BX trials. Gaze length to A and B on the subsequent AX/BX trials reflects the participants’ ability to maintain attention to these elements. A second issue is Chapter 6 164
that participants were explicitly instructed to look for differences between the patterns in preexposure. Participants are, therefore, motivated to search for the unique elements in preexposure, and so greater attention to A and B than to C and D in Experiment 9 does not necessarily reflect a process of attentional capture. Rather, familiarity with X might increase the efficiency of the search for unique elements. The eyegaze findings, even with a deeper level of analysis, cannot differentiate between these two processes, attentional capture and deliberate search, because both processes produce the same outcome. The next section will discuss how deliberate search might operate in these procedures.
Role of Memory in Deliberate Search
The notion of deliberate search implies that participants have a particular target in mind and they scan through the visual pattern until the target is found (Shiffrin &
Schneider, 1977; Wolfe, 1994). In exposure to AX and BX for example, the targets are coloured squares that are unique to each pattern. One might assume that this search process is not random since participants should not search in areas that they have previously attended to. A mechanism must be available to control this search process. One possibility is that search is guided by the memory of the common background.
In preexposure to AX and BX for example, participants are trying to find a
“mismatch” between the two patterns. The complexity of each checkerboard pattern only allows participants to attend to and process a small area of each pattern. Each trial gives participants an opportunity to encode the attended area into working memory (or a short- term store). On the subsequent trial, participants can judge whether the corresponding area has changed. Consequently, participants should systematically attend to different areas of
AX and BX until A and B are found. Once detected, participants should process and encode the physical features associated with A and B (colour, shape and location) since they were Chapter 6 165
told that the differences will be useful later in the experiment. At this point, participants should not return and attend to areas that did not contain differences. That is, attention to various areas of X will decrease once A and B have been detected.
Detecting the “mismatch” might be enhanced when participants are familiar with the background prior to the AX and BX trials. Exposure to X alone may bring long-term changes to the representation of X. Previously, I suggested that participants encode different areas of X when they are searching for the unique elements during AX and BX exposure. This search process terminates after participants have detected A and B.
Termination of search reduces attention to X, and this prevents an opportunity for participants to encode X into long-term memory. In a presentation schedule of X alone trials however, search for the unique elements should not terminate, and participants would return to previously attended areas of X. This provides an opportunity for participants to rehearse and encode X into long-term memory. The representation of X may be organised in a manner consistent with the notion of unitization (McLaren & Mackintosh, 2000). The various parts of X, through repeated X alone trials, will link together to form a unitized representation of X. Attention to one area of X allows participant to associatively activate other parts of X. In Experiment 9, the activation of X into working memory will increase participants’ sensitivity to the changes between the novel (AX/BX) and familiar (X) patterns. Specifically, participants can compare more of the checkerboard, through the unitized representation, on each trial. Thus, increasing the ease with which A and B are detected.
Experiment 2 showed that exposure to X enhanced detection of the novel unique elements on test. In this experiment, exposure to X alone rendered AX and BX more discriminable than two completely novel patterns, CY and DY. Thus, participants appeared Chapter 6 166
to encode X into long-term memory during the X alone trials. Unlike Experiment 9, where a difference in gaze length to A/B and to C/D was observed in preexposure, the difference between the preexposure conditions was not observed in the first block of test trials. In
Experiment 2, participants received AX/BX trials with CY/DY trials in the same-different task. In Experiment 9 however, AX/BX trials and CY/DY trials were presented in separate preexposure blocks; the X_AX/BX preceded the CY/DY schedule or vice versa. Thus, presenting the AX/BX and CY/DY test trials together in Experiment 2 might increase the difficulty of detecting the unique features A and B. Again, let us assume that participants are detecting a “mismatch” between the test patterns. Both the X and Y backgrounds are complex visual patterns, and processing both backgrounds in quick succession may induce high perceptual load in working memory (e.g., Lavie, 2006). This reduces the amount of information about X and Y that participants can retain in working memory. In addition, high perceptual load might reduce the number of areas that can be associatively activated to form the unitized representation of X. Consequently, the efficiency with which A and B are detected may be reduced. Nevertheless, the familiar X background allows participants to improve discrimination of AX and BX relative to CY and DY.
The notion of deliberate search provides an alternative explanation for the role of attention in preexposure, without reference to attentional capture. The memory for the background guides a search process for the unique elements, and this process becomes more efficient if the background is well encoded. This process of attention is thought to reflect a top-down or endogenous process (Theeuwes, 2010). As stated earlier, the eyegaze analysis is unable to distinguish this process from one of attentional capture by A and B, since both attentional processes produce the same outcome, and both processes can control Chapter 6 167
eye movements (Rayner, 2009). Consequently, the current procedures offer no method to test between these two mechanisms.
The only way to differentiate the top-down and bottom-up processes of attention is to place each process in competition with each other (Theeuwes, 1992). Experiment 11 is the only experiment in the current thesis that fits this criterion. In this experiment, participants learned to attend to the locations for A and B following exposure to AX and
BX. In the same-different task, participants showed good discrimination performance on test trials in which the unique elements appeared in the attended locations (AX/BX and
CTX/DTX test trials). Attention to spatial location in the same-different task can be considered an endogenous process since attention may be controlled by memory of the location. The endogenous process however, does not aid discrimination on test trials in which A-D appear in the unattended locations (CX/DX and ATX/BTX test trials), and so performance is generally poorer on these trials. The critical observation is that, on these trials, patterns with novel unique elements were better discriminated than patterns with preexposed unique elements; performance with CX and DX was better than that with ATX and BTX. One possible explanation is that exogenous attention, based on novelty, controls discrimination performance in situations where the endogenous attention process fails.
Exogenous attention however, did not affect eyegaze as this effect of novelty was not observed in the eyegaze measure. This issue is further discussed later.
Experiment 11 shows that bottom-up salience can affect detection of the unique elements in the same-different task. Bottom-up salience may have a similar effect in preexposure, but it is impossible to determine within the current procedures. The question of how much influence, if any, bottom-up salience has on attention in preexposure is unresolved. The visual search literature may provide some insight to resolving this Chapter 6 168
question. The manner in which attention is utilised to detect the unique elements in preexposure is similar to how attention is utilised in visual search tasks.
Attention in Visual Search
In a visual search procedure, a target can capture attention if it is not perceptually similar to its distracters. For example, participants can quickly detect a circle shape within a display array of triangle-shaped distracters (Treisman & Gelade, 1980). Increasing the number of triangle-shaped distracters does not affect the reaction time to detect the circle- shaped target. The target is said to automatically “pop-out” and capture attention since reaction time does not change with increased number of distracters. The target however, will no longer “pop-out” if it does not contain a perpetual feature unique from its distracters. For instance, finding a green circle in a distracter field of green triangles and red circles becomes effortful since the target is a conjunction of two features shared by the distracters (colour and shape). In this case, reaction time for target search will increase with additional number of distracters. To find the target, participants must process each stimulus in the search array until the target is found (Treisman & Gelade, 1980; Wolfe, 1994).
Consequently, a target, defined by a conjunction of features, does not have the ability to automatically capture the participant’s attention. In the current experiments, the unique elements share the same colours and shapes as squares in the common background. Finding the unique elements during preexposure is analogous to the conjunctive search process in visual search.
Visual search is sensitive to differences in novelty between the target and distracters. For example, Wang, Cavangah, and Green (1994; see also Lubow & Kaplan,
1997) asked participants to detect an unfamiliar target (e.g., an inverted N letter) in an array of familiar distracters (e.g., upright N letters). On other trials, participants were asked to Chapter 6 169
find a familiar target (an upright N letter) in an array of unfamiliar distracters (inverted N letters). Wang and colleagues reported that mean reaction time was shorter for detecting the unfamiliar target than the familiar target. Moreover, reaction time for detecting the unfamiliar target did not increase with greater number of distracters. That is, an unfamiliar target, when presented in an array of familiar distracters, appears to “pop-out” and capture attention. Wang and colleagues employed relatively simple stimuli in their search task, and it is unclear if the same finding could be replicated with more complex stimuli.
Specifically, these complex stimuli are defined by a conjunction of different perceptual features (e.g., colour and shape). Such finding would imply that unique features, when presented on a less familiar background, can capture attention in the current experiments.
In summary, participants are able to detect the unique elements in preexposure through either an exogenous or endogenous process of attention. The more salient unique elements may capture attention in preexposure. Alternatively, participants can search for the unique elements guided by their memory of the common background. Both suggestions provide adequate accounts of the current findings. The subsequent question is how detection of the unique elements translates to discrimination performance in the same- different task. This will be reviewed in the following section.
Attention for Discrimination
Thus far, I have reviewed how attention might be utilised for detecting the unique elements in preexposure. Little has been mentioned as to how attention might be utilised for discrimination in the same-different task. The animal models of perceptual learning (Hall,
2003; McLaren & Mackintosh, 2000) assume that attentional capture allows participants to detect the unique elements in both preexposure and in the same-different task. This may not be a feasible suggestion since continued attention to the unique elements in preexposure Chapter 6 170
should reduce their salience, and, in turn, reduce the discriminability of the preexposed patterns. Mitchell, Nash, and colleagues (2008) suggested that, once detected, attention to the unique elements in preexposure allows participant to encode these elements in memory.
This memory then drives a top-down search process in the same-different task. The findings from Experiment 11 show that memory for the spatial location guides attention for discrimination.
Contextual Cueing
Utilizing location for discrimination mirrors a visual search effect called contextual cueing. This effect relates to how memory for a visual context guides attention in visual search (Chun & Jiang, 1998; Chun, 2000). There are many features or objects that tend to co-exist in certain scenes. In a scene of a park for example, a viewer, from experience, knows that grass occurs in lower regions of the scene, and that people or animals might be walking on the grass. Trees or the skyline occur in the upper regions of the scene. The spatial relationships between these features constitute a global visual context. If a viewer were instructed to find a person in this scene, they would look at the lower, and not the upper region, of the scene. Thus, memory for this particular visual context has increased the efficiency for target search. In the current procedures, a similar process, based on the spatial relationship between the common and unique elements, appears to control attention in the same-different task.
Chun and Jiang (1998) argued that global context is relatively difficult to control in real-world scenes since different people will have different memories for any particular scene. Consequently, they devised an experimental paradigm to examine the influence of global context on attention. In their procedure, participants search for a target stimulus (T shape) that is placed in a field of distracter stimuli (rotated L shapes). Their task was to Chapter 6 171
report the orientation of the T shape (e.g., the T shape is rotated to either the left or the right). In the invariant trials, the spatial relationship between the target and distracters is fixed. A global context is created since the target and distracters appear in the same location during these invariant trials. Note, participants may be exposed to a different number of global contexts, each having a different fixed configuration between the target and distracters. In the novel trials, there is no fixed spatial relationship between the target and distracters. A contextual cueing effect is observed when reaction time for target detection is faster on the invariant than on the novel trials. This procedure prevents participants from learning to attend to one location since the target has an equal chance of appearing in any given location across both the novel and invariant trials.
One explanation for contextual cueing (Brady & Chun, 2007) suggests that the search array can be internally represented as a matrix of all possible locations for each stimulus (target and distracter). This model calculates the probability that the target will appear in a particular location. In turn, attention is preferentially directed to the location with the highest probability value. In order to calculate these values, the model attempts to find consistent spatial relationships between the distracters and the target. In particular, the model learns that the presence of a distracter at one location is associated with the target at another location. Exposure to trials in which the locations of the distracter and target are fixed will strengthen this target-distracter association. The strength of this association modulates the probability value of the target. In the invariant trials, this probability weight is further increased since the probability weight of each target-distracter association is summed across the multiple distracters. Consequently, the model, with a high degree of probability, can determine the location of the target in an invariant search context. Target- distracter associations cannot form on the novel trials since the target and distracters appear Chapter 6 172
in different locations on each trial. Consequently, the distracters will readily cue attention to target on invariant trials and not on the novel trials.
Brady and Chun’s (2007) model explains how spatial location guides attention for discrimination in the current perceptual learning experiments. Each checkerboard pattern can be conceived as different clusters of coloured squares that occupy different locations. In visual search terms, each unique element is the target, and the different coloured clustered squares of the common background are distracters. Brady and Chun’s model may be applied to the basic perceptual learning effect in the following way. After participants have detected A and B, exposure to AX and BX allows participants to form associations between the target (A or B) and the various parts of X. Viewing any part of X in the same-different task should cue participants’ attention to the locations for A and B. Discrimination of two novel patterns (e.g., CY and DY) will be poor since there are no target-distracter associations to cue attention to C and D.
The location of unique elements relative to their background seems to be critical for attention in the same-different task. Disrupting this relationship would, therefore, reduce attention to the unique elements. For example, Experiment 9 showed that exposure to X prior to the AX and BX trials render AX and BX more discriminable than two intermixed patterns, CY and DY. In Experiment 10, the two backgrounds were swapped between the two preexposure conditions in the same-different task. No difference in performance was then observed between the AY/BY and CX/DX test trials. According to Brady and Chun, the X background would cue attention to the locations for A and B, and background Y would cue attention to the locations for C and D. That is, the background cues attention to the irrelevant location, and this impairs discrimination performance in test.
Chapter 6 173
Contextual Cueing and Eyegaze
The premise of the previous section is that participants, in test, discriminate the visual patterns in a manner similar to the contextual cueing effect. The current experiments assume that a single attentional process controls both discrimination performance and eye movements in the same-different task. Consequently, the manner in which eyegaze is deployed in test may be similar to how contextual cueing affects eyegaze in visual search.
Peterson and Kramer (2001) investigated how contextual cueing affected eye movements in visual search. They observed that participants required fewer fixations before fixating on the target on invariant trials than on novel trials. In turn, participants were faster to detect the target on the invariant than on the novel trials. Presumably, a similar process is occurring in the current perceptual learning procedures. Faster detection of the unique elements in the same-different task allows participants to spend more time looking at the unique elements. Thus, the ease with which participants can detect the unique elements is reflected in their gaze length to those elements, and discrimination accuracy for those patterns.
In their study, Peterson and Kramer (2001) also investigated whether the contextual cueing effect was affected by low-level salience (Yantis and Jonides (1984) showed that presenting an object abruptly can induce attentional capture in a visual search task). In the
Yantis and Jonides experiment, participants were asked to find a target letter within a field of four letters. Prior to each trial, the location of three letters was signalled to the participants by the presence of a visual mask (figure-eight shape). Subsequently, a four- letter search array was displayed after the masks were removed. Thus, one letter (target or distracter) was abruptly presented since its location was not signalled by a mask. Yantis and
Jonides observed that reaction time was faster when the target was presented as an abrupt Chapter 6 174
object than as a signalled object. Abrupt onset of the target appears to capture attention automatically since reaction time does not change across search set size (see Yantis &
Jonides, 2000 for review). It might be the case that the sudden appearance of a distracter stimulus in a contextual cueing procedure might direct attention away from the target
(Peterson & Kramer, 2001). This might provide some insight to how novel elements, in perceptual learning, affect attention in the same-different task.
Following Yantis and Jonides (1984), Peterson and Kramer (2001) presented visual masks to signal the locations of the target and distracters prior to each visual search trial.
On the onset trials, one distracter appeared in a location that was not signalled by a mask. A difference in reaction time between the onset and non-onset trials was only observed for the novel trials and not for the invariant trials. That is, abrupt onset only affected reaction time for trials in which the spatial relationships between the target and distracters were not fixed.
Similarly, eyegaze was also largely unaffected by the abrupt distracter in the invariant trials. Thus, the abrupt distracter only captured attention on trials in which the global context was novel. Peterson and Kramer suggested that there is a competition for attention between cued attention to target and low-level attentional capture on the invariant trials.
Participants might process the global context prior to the abrupt distracter, and this will cue attention to the target. Low-level attentional capture cannot then control attention once the previous process has been initiated. The absence of this competition on the novel trials allows the abrupt distracter to exert a greater influence on attention.
The aforementioned account might explain for why the effect of novelty in
Experiment 11 was observed in the accuracy and not in the eyegaze measure. To recap,
Experiment 11 presented test trials in which preexposed (A & B) and novel (C & D) unique elements were placed in locations that were unattended in preexposure. For these trials, Chapter 6 175
discrimination was better for patterns with novel (CX/DX) than for patterns with preexposed (ATX/BTX) unique elements, but no difference in gaze length was observed. On each test trial, it can be assumed that the X background cued attention to the original locations for A and B. This however, will not aid discrimination for trials in which A-D appeared in the unattended locations, and indeed, in this case, the novelty of the unique elements appeared to control discrimination performance. According to Peterson and
Kramer (2001), the low-level properties of the unique elements may only affect attention after the cued attention to the known (“attended”) locations has terminated. This provides participants with a limited window to detect the unique elements, and it leaves little scope to observe differences in gaze length between the two test conditions (novel and familiar features in the unattended locations). This might explain why participants detected the novel elements more readily than the preexposed elements, but did not spend more time looking at them.
In sum, the contextual cueing effect provides a description of how memory for the locations of the unique elements might control attention for discrimination. This conclusion is consistent with Mitchell, Nash et al.’s (2008) suggestion that participants search for the unique elements in the same-different task based on memory for those elements.
Role of Physical Identity in Perceptual Learning
Although spatial location of the unique elements is primary for discrimination, it does not necessarily mean that participants do not encode the appearance of the unique elements during preexposure. Recently, Lavis and colleagues (2011; see also de Zilva &
Mitchell, 2012) showed that superior detection of the unique elements in preexposure is associated with improved memory for these elements. In their experiment, participants received intermixed and blocked exposure (AX/BX_CX_DX) to two pairs of visual Chapter 6 176
checkerboard patterns. Participants showed better discrimination of the intermixed than of the blocked patterns. Participants also completed a memory task in which they were asked to select the colour of the unique feature when presented with the outline of that feature.
Memory of the intermixed unique features was better than of the blocked unique features as selection accuracy was greater for intermixed features.
In this thesis, Experiment 3 showed that gaze length to the unique elements was greater during intermixed than blocked exposure. Together, these two experiments
(Experiment 3 and Lavis et al., 2011) are consistent with the notion that attention in preexposure allows participants to encode the unique features into memory (Mitchell, Nash et al., 2008). The strength of this representation depends on the amount of attention the feature received in preexposure. Moreover, the two experiments show that this representation might encompass both the spatial location and physical identity of each unique feature. The two perceptual dimensions are related since participants may only encode the physical features (colour and shape) after they have found the spatial location of each unique element (e.g., Treisman & Gelade, 1980; Treisman, 1998). Nevertheless, participants are more likely to utilise spatial location rather than physical identity for discrimination.
Perceptual Learning as Mere Exposure
The current thesis began with the assumption that that perceptual learning in animals and humans shares a common mechanism, and thus the mechanisms derived from animal studies (Hall, 2003; Honey & Bateson, 1996; McLaren & Mackintosh, 2000) may account for human perceptual learning. However, there are certain aspects of the current human perceptual learning procedure that question this assumption. In animals, perceptual learning is thought to result from mere stimulus exposure – learning in which no Chapter 6 177
reinforcement is given, or which is unsupervised. The McLaren and Mackintosh model was designed to account for this type of perceptual learning. The inability to find evidence to support this model in the current experiments may reflect the fact that the current perceptual learning effects are not the result of mere exposure.
Mackintosh (2009) argued that human studies of perceptual learning involve some element of supervision (e.g., Lavis & Mitchell, 2006; Mundy et al., 2007). In these experiments, participants are instructed to actively look for differences between the experimental stimuli. Mackintosh argued that when participants are deliberately searching for the unique elements in preexposure, once those elements are found, they will receive reinforcement through “self-supervision”. This is because they have succeeded at the task and success is reinforcing. As a consequence of this “self-supervised” learning process, attention to the differences (e.g., A and B), whose detection led to reinforcement, will increase. For this reason, the effects observed in human perceptual learning fall outside the scope of the McLaren and Mackintosh (2000) model.
This suggestion implies that attention in these experiments may be controlled by the processes proposed by Mackintosh’s (1975) in his theory of associative learning. In this case, there is an increase in attention to a stimulus that reliably predicts some kind of outcome. Imagine a scenario in which an animal is trained to learn that two stimuli, AX and
BX, are followed by food and no food respectively. The animal will learn that A and not B signals food reward and the animal will subsequently increase attention to A, and decrease attention to B. Mackintosh (2009) argued that this theory of attentional learning also applies to perceptual learning procedures in which the participants are instructed to look for differences. According to Mackintosh, participants will first search for the unique elements in preexposure, and once found, this behaviour is reinforced through “self-supervision”. Chapter 6 178
Reinforcement will then increase the salience of the discriminating features. This explanation accounts for why preexposed features received more attention than novel features in Chapter 3.
Mackintosh (2009) argued that true examples of perceptual learning occur under conditions with no supervision, or when deliberate search is not possible. According to this rule, most of the current experiments fall outside the scope of the McLaren and Mackintosh
(2000) model. There are perhaps two exceptions to this. Firstly, Experiment 2, in which only X was presented in preexposure, might be considered an example of true unsupervised perceptual learning. Although participants were asked to look for stimulus differences during preexposure, no differences were present, and so there was no opportunity for self- reinforcement. Thus, only Experiment 2 provides a direct examination of the McLaren and
Mackintosh model. The results of this experiment suggest that participants did not immediately detect the unique features on the familiar X background at the start of testing.
That is, the novel unique features failed to “capture” attention, as would be predicted by the relative novelty hypothesis (McLaren & Mackintosh, 2000). Rather, those features were more quickly learned about than the unique features presented on the novel Y background.
However, this is a particular interpretation of the McLaren and Mackintosh model. Within an alternative interpretation, the novel unique elements, instead of attentional capture, may increase the probability of detection. For example, in combination with the deliberate attentional processes involved in the search for differences on test (see Mackintosh, 2009;
McLaren, Graham & Wills, 2010), A and B may have been more likely than C and D to reach a threshold for detection on any given test trial due to their greater novelty relative to the background on which they were presented. In addition, an unitized representation of X, formed during X alone trials, may also facilitate detection of the unique features in Chapter 6 179
combination with the deliberate search process. A description of this analysis was provided earlier in this chapter.
In light of this interpretation of Experiment 2, cross-experimental comparisons of discrimination performance in Experiment 2 to other experiment in this thesis (e.g.,
Experiment 1) may be difficult to interpret since participants engage in different processes to detect the unique features. In Experiment 1 for example, participants have developed bias to attend to the locations of A and B following preexposure. However, the opportunity to develop this bias prior to test is unavailable to participants in Experiment 2.
The second exception is Experiment 11. To recap, Experiment 11 presented test trials in which preexposed (A & B) and novel (C & D) unique elements were placed in locations that were unattended in preexposure. For these test trials, discrimination was better for patterns with novel (CX/DX) than for patterns with preexposed (ATX/BTX) unique elements. Attention to these unattended locations in preexposure should not be self- reinforced, and in turn, this prevents participants from deliberately attending to these areas in test. By directing deliberate search away from the unique features, Experiment 11 provides support for relative novelty explanation of perceptual learning (McLaren and
Mackintosh, 2000). In conclusion, Experiments 2 and 11 provide evidence consistent with the McLaren and Mackintosh model. However, in order to investigate the mechanisms of this model one must first direct deliberate search elsewhere.
Future Research
Mackintosh’s (2009) criticism of human perceptual learning procedures, such as the current experiments, provides an obvious direction for future research. Specifically, future research should investigate whether the current findings can be replicated in procedures where exposure to the to-be-discriminated stimuli is incidental, perhaps by reducing the Chapter 6 180
motivation for participants to deliberately search for the unique elements in preexposure.
One possibility is to reinforce participants for attending to irrelevant features in preexposure, whilst the target unique features are presented concurrently. That is, participants should be motivated to search for the to-be-discriminated unique features. A number of studies (Goldstone, 1994; Goldstone & Steyvers, 2001; McLaren, Leevers, &
Mackintosh, 1994; Welham & Wills, 2011; Wills & McLaren, 1998) have used variations of this procedure to show that exposure to the common element enhances discrimination of patterns that share this common element. These studies have not shown whether the schedule of presentation of the unique features affects subsequent discrimination performance.
Future research might also explore how the current findings might fit with the attention literature. For example, the current experiments show that attention in the same- different task may be analogous to the contextual cueing procedure (Chun & Jiang, 1998).
Moreover, prior exposure to the common background facilitates detection of the unique features. There are unresolved arguments in the contextual cueing literature about what is actually learned that facilitates performance in contextual cueing (Brady & Chun, 2007;
Jiang & Wagner, 2004). It is possible some information is encoded about the background
(Kunar et al., 2007), analogous to a process of unitization. Another possible direction for future research is to link the current findings with the perceptual expertise literature. For example, experts, compared to novices, demonstrate a different utilisation of attention to relevant and important features in domain-specific tasks (Gauthier & Tarr, 2002). The literature has focused on describing the mechanisms that differentiate experts and novices.
The current findings might contribute to understanding how experts might acquire and develop such finely tuned attentional processes. Chapter 6 181
Concluding Comments
The aim of the thesis was two-fold. The first aim was to determine whether an attentional process was involved in human perceptual learning by recording participants’ eye movements during the preexposure phase and the same-different task. For example, exposure to AX and BX increased gaze length to the unique elements. The subsequent experiments attempted to determine which mechanism best accounted for the effect of attention. Together, the evidence supports Mitchell, Nash, and colleagues’ (2008) short- term habituation mechanism. This model states an exogenous attention process allows detection of the unique elements in preexposure. Following detection, an endogenous process maintains attention to the unique elements in the same-different task. The findings support these notions, but do not provide unique support for them. A combination of endogenous and exogenous processes might be involved in detection and discrimination.
The perceptual learning observed here reflects a goal-oriented process, in that participants are actively seeking the unique elements. The current findings could therefore be argued to fall outside the scope of accounts (e.g., McLaren and Mackintosh, 2000) that seek to explain the perceptual learning that results from mere exposure. Nevertheless, there are a wide range of examples of perceptual learning that are goal-oriented. For example, it is the goal of an oncologist to search and find differences between pictures of healthy and cancerous tissue. An expert oncologist, through experience, has honed their attentional set to identify distinguishing features associated with cancerous tissue. The current findings contribute to the understanding of the psychological processes involved in goal-oriented perceptual learning. References 182
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