EFFECTS OF PERCEPTUAL LOAD ON 1

Running head: WHAT ARE THE EFFECTS OF PERCEPTUAL LOAD ON VISUAL TASKS OF SELECTIVE ATTENTION?

Selective Attention: Effects of perceptual load on visual tasks of attention

Adaline Rasmijn

Tilburg University, the Netherlands

Developmental Department

Masterthesis P & GG

ANR: 381938

Begeleider: Dr. J.G.M. Scheirs

Tweede beoordelaar: Dr. M.J.A. Feltzer

Datum: 25-08-2014

EFFECTS OF PERCEPTUAL LOAD ON ATTENTION 2

Abstract

In the present study, Lavie’s was tested to settle a long-standing debate between early selection theories, implying that only the attended stimuli are processed because of limited capacity, and late selection theories, implying that all information is fully processed but only selected stimuli are given access to further processing stages such as memory. According to

Lavie’s theory, it depends on perceptual load whether information is processed early or late. It was anticipated that during high perceptual load (difficult task), early selection would be the chosen strategy and during low perceptual load (easier task), the chosen strategy would be late selection. Participants consisted of 10 females and 10 males (age 18-23 years), who were undergraduate university students. Participants were tested individually using a computer- administered visual selection task with three levels of difficulty, defined by inter-stimulus interval (speed) and Target Type. Repeated measures MANOVA showed significant main effects of inter-stimulus interval and Target Type for all three assessment factors (reaction time, hit rate and false alarm rate). The hypothesis was confirmed, which supports Lavie’s theory that when a task is more difficult and there is only sufficient capacity to attend to relevant stimuli, early selection is used. However, when the task is easier and all information can be attended to, late selection is used.

EFFECTS OF PERCEPTUAL LOAD ON ATTENTION 3

Selective Attention: effects of perceptual load on visual tasks of attention

Attention is the cognitive process of selectively attending to particular and relevant aspects in the environment while ignoring other irrelevant aspects. A popular example is the , listening to what someone is saying while ignoring other conversations in the room except when those conversations contain your own name (Cherry, 1953). The topic of attention is one of the most intensely studied in psychology. The reason for all this focus on attention is because attention is necessary for a variety of cognitive purposes, for example attention is needed for , memory and language. Another reason why it is important to do research on the mechanisms of attention is because attention deficits are found in neurological and psychiatric disorders, such as ADHD, autism spectrum disorders or schizophrenia.

Accordingly, the studies of attention provide insight into clinical disorders. Another main goal of studying attention is to describe the factors of focused attention that allow people to ignore irrelevant distractions (Lavie, 2010).

In the 1950’s, Broadbent’s filter theory was the first to describe the human’s processing system using an information processing metaphor (Fernandez-Duque & Johnson, 1999). In his theory,

Broadbent (1958) proposed an early selection theory of attention, which states that humans process information with limited capacity and information to be processed is selected early. Due to our limited capacity, a selective filter is needed for information processing. According to

Broadbent, all stimuli are initially processed for basic, physical properties, such as pitch, color, loudness and direction. Thus, the selective filter allows for certain stimuli to pass through the filter for further processing, while the unattended stimuli are filtered out and lost. Broadbent emphasized the splitting of incoming stimuli to attended or unattended channels. The selection of a channel is guided through attention. Information selected to pass through the filter is then available for short-term memory and for manipulation, prior to storage in long-term memory. If EFFECTS OF PERCEPTUAL LOAD ON ATTENTION 4 someone is actively attempting to attend to a stimulus based on his intentions (current goals), then voluntary attention or top-down processing will be employed. However, if a sensory event catches one’s attention, then reflexive attention or bottom-up processing will be employed

(Lachter, Forster, & Ruthruff, 2004). Visual search experiments by Yantis and Jonides (1984) showed that when an item appears abruptly, it is always processed first. Broadbent’s filter theory then postulates that a selective filter is needed to cope with the overwhelming amount of information entering the channels, such that certain messages are filtered out or inhibited from the messages that were filtered for further processing. This filter theory reflects an early selection theory because only certain information is selected and attended to at an early stage of information processing (Broadbent, 1958).

An important piece of evidence for Broadbent’s theory was acquired through a task. This task has been extensively used to test for attended and unattended information presented to a participant. During this task, participants were presented with different letters in each ear simultaneously and were asked to repeat in any order. What

Broadbent (1958) found was that participants repeated what they heard in one ear followed by the other ear (ear-by-ear), instead of in order of presentation. This led him to conclude that we can only pay attention to one channel at a time. In short, early selection theories suggest that attention can prevent irrelevant stimuli from reaching awareness (Broadbent, 1958), more specifically a selective filter in the brain rejects the irrelevant stimuli before its content is fully analyzed (Treisman & Geffen, 1967).

On the other hand, Deutsch and Deutsch (1963) were proponents of the late selection theory, which says that all stimuli are fully identified when perceived, but only the attended stimuli are given access to further stages, such as long-term memory and motor control. In other EFFECTS OF PERCEPTUAL LOAD ON ATTENTION 5 words, both believed there was a type of ‘central bottleneck’ and a selection device prior to it.

Their disagreement regarded the location of the selection device; did the selection occur before after perceptual awareness? According to Deutsch and Deutsch the selection occurred after physical and semantic analysis of the stimuli, while according to Broadbent the selection occurred before semantically analyzing the stimuli but after physical analysis of the stimuli (as shown in Figure 1).

Figure 1. A comparison between early (A) and late selection (B) theories. S stands for stimulus. According to early selection the stimuli are analyzed only on their physical properties before they are perceived into awareness and require a response. On the other hand, according to late selection the stimuli are analyzed both on their physical and semantic properties before they are perceived into awareness and require a response.

What Deutsch and Deutsch (1963) found during the dichotic listening task was that both channels are recognized but are quickly forgotten unless they have personal significance to the participant, such as his or her own name. In short, late selection theories suggest that all stimuli are fully analyzed and attention only affects later processes such as response selection and memory but does not affect perceptual awareness (Deutsch & Deutsch, 1963). EFFECTS OF PERCEPTUAL LOAD ON ATTENTION 6

There was substantial evidence that supported both the early selection and the late selection theory. Studies of attention that used the dichotic listening task (Treisman & Geffen, 1967) and the selective-looking task (Becklen & Cervone, 1983) demonstrated that unattended information typically goes unnoticed, which supports the early selection theory (Lavie et al., 2004). Becklen and Cervone (1983) explain that in a selective-looking task subjects are presented with two videotaped or filmed naturalistic episodes simultaneously, printed above one another, and are asked to attend selectively to one and to ignore the other. However, the late selection theory received support in later studies that used indirect measures of distractor perception in Stroop- like tasks (Eriksen & Eriksen, 1974). In a Stroop task subjects are asked to name the ink color of written color words (e.g., say blue to the word green written in blue ink). A compromise between these two theories seemed nearly impossible.

However, the load theory by Lavie resolves the early and late selection debate by the hypothesis that perceptual processing has limited capacity but proceeds automatically in an involuntary, mandatory manner on all information within its capacity (Lavie, 2005). In other words, the perceptual load theory incorporates both aspects of the early and late selection theories by stating that perceptual capacity is limited (early selection) and that all stimuli are processed automatically (late selection) until capacity runs out. Among Lavie’s main findings is that high perceptual load engages full capacity in relevant processing and hereby reduces irrelevant distractor perception (Lavie, 2010), which means that the relevant stimuli are selected early on in the process. In contrast, in situations of low perceptual load, spare capacity remaining beyond the task-relevant processing would involuntarily spill over to irrelevant distractor processing (Lavie, 2010), which leads to selection taking place later on in the process. According to this theory, it can be concluded that early or late selection seems to depend on whether EFFECTS OF PERCEPTUAL LOAD ON ATTENTION 7 perceptual load is high or low. While top-down instructions are necessary to distinguish between relevant and irrelevant information so that higher priority is given to the relevant information, prevention of capacity allocation (spillover) to the irrelevant information can only occur as a natural consequence of reduced availability of perceptual processing capacity under load (Lavie,

Beck, & Konstantinou, 2014).

In the present study, the hypothesis that early selection takes place only in conditions of high perceptual load and that late selection takes place only in conditions of low perceptual load, was examined. In particular, early or late selections of visual stimuli were evaluated in the context of Lavie’s perceptual load theory. Here, the task used consisted of visual selection involving three conditions, which were color, category and conjunction (color and category).

Also, perceptual load was manipulated by varying the inter-stimulus interval (ISI). These conditions will be thoroughly explained in the Method section.

Based on the perceptual load theory (Lavie, 2005), it was anticipated that during conditions of high perceptual load (fast and medium ISI), the conjunction and category conditions will differ significantly from each other compared to during conditions of low perceptual load (slow ISI). It was also anticipated that selection based on color alone (early-sensory selection) would be superior to selection based on category alone (late-semantic selection) and that selection of the conjunction category would be in between these two conditions. The reason why it was anticipated that the RT of conjunction category will be in between the RT of the color and the category conditions is because during the conjunction condition it is expected that first early selection will take place followed by late selection. Early selection takes place because participants first verify if the color is correct and then proceed to verify whether it is the correct letter or number (late selection). EFFECTS OF PERCEPTUAL LOAD ON ATTENTION 8

Method

Participants

Participants consisted of 20 (10 females and 10 males) undergraduate university students, aged between 18 and 23 years (Mean age = 19.45 years, SD = 1.61), who volunteered online to fulfill a course requirement. Inclusion criteria required that participants were physically healthy, had normal or corrected-to-normal vision, had no color blindness, did not currently use psychotropic medications, had no personal history of a neurological or a psychiatric disorder, had no substance abuse problems and had no family history of major psychiatric and substance abuse disorders, as assessed by self-report on questionnaires and checklists (Van der Stelt, Kok,

Smulders, Snel, & Gunning, 1998). Twenty-four students were recruited but four students were eliminated because they did not conform to the inclusion criteria. All participants provided a written informed consent.

Materials

Participants were tested individually using a computer-administered visual selection task

(adapted from Van der Stelt et al., 1998). Each participant was seated in front of a computer screen, at a viewing distance of 55 cm, with his or her right index finger located on a serial response box button. Stimuli were single alphanumeric characters, consisting of letters (E, H, P, and U) and digits (3, 5, 8, and 9), all in digital-font format and having the same size (at a viewing angle of 2.2 x 1.8 degrees). Two identical sets of stimuli were used, one in the color red and the other in the color green. Using this stimulus material, the letter and digit stimuli that share the same color could not easily be distinguished on the basis of their visual surface features, yet they belonged to distinct alphanumeric, semantic categories. Accordingly, the stimuli presented varied on two binary-valued dimensions, namely a simple, distinguishable sensory dimension EFFECTS OF PERCEPTUAL LOAD ON ATTENTION 9

(color: red or green) and a more complex, abstract, or semantic dimension (category: letter or digit). The stimuli were presented successively at central fixation against a grey background for

50 milliseconds (ms) duration on the computer screen. A small white central fixation cross was present continuously except during the stimuli. Stimuli presentation and data acquisition were controlled by means of E-Prime 1.2 software.

Procedure

The task consisted of three stimuli-discrimination conditions, which were presented at three different ISI’s. The three conditions all involved the same visual stimuli and required the same simple motor response, while differing solely in terms of the target-defining properties of the stimuli to which the participants were instructed to respond. The order of the conditions was counterbalanced across participants. Each condition involved a random mixture of task-relevant, infrequent (33.3%) target stimuli that required a button-press response and irrelevant, frequent

(66.7%) non-target stimuli that did not demand a response. All the conditions demanded stimuli discrimination, but differed from each other in terms of the type and complexity/difficulty of the target discrimination involved. The target stimuli of the three different conditions were either defined by color alone (i.e. target were either the red stimuli or the green stimuli, irrespective of category) (color condition), by the conjunction of color and category (e.g. targets were the red- letter stimuli or the green-digit stimuli) (color & category condition), or by category alone (i.e. targets were either the letter stimuli or the digit stimuli, irrespective of color) (category condition). The color condition was considered the easiest of the three tasks, while the category condition was considered the most difficult one since it did not involve selection based on color

(early-sensory selection) but based on semantics. The conjunction category is considered intermediate in difficulty level because its selection characteristics are in between the color and EFFECTS OF PERCEPTUAL LOAD ON ATTENTION 10 the category condition. The specific sensory selection cue (red vs. green) and semantic cue (letter vs. digit) defining the target stimuli were constant across the different conditions for a given participant but varied between participants. What varied across conditions where the three different categories of the ISI, namely, relatively slow (1600ms), medium (1200ms), and fast

(800ms).

In each condition, participants first received a practice block of 30 stimuli trials, during which verbal feedback on performance was given, before completing an experimental block of

120 stimuli trials. Participants were instructed to press the response button with the right index finger each time a target stimulus was presented, emphasizing both speed and accuracy of responding.

Performance measures consisted of the mean reaction time (RT; defined as the time between target onset and the button-press response), and two factors measuring error, which are

1. Omission error, not responding to target stimuli, which is calculated by subtracting the proportion of correctly responded target stimuli in relation to the total number of target stimuli presented (HR; hit rate) from 100% and 2. Commission error, incorrectly responding to non- target stimuli in relation to the total number of non-target stimuli presented, labeled as false alarm rate (FAR).

Design

Three repeated-measures two-factor MANOVA were carried out on two within-subjects factors; stimulus type (color, conjunction and category) and ISI (3 levels; 1600ms, 1200ms,

800ms) to analyze RT, HR and FAR. When there was a significant interaction effect or main effect of Target Type or ISI, follow-up tests consisted of pairwise comparisons using dependent samples t-tests to determine exactly which specific conditions differed from each other. Also, the EFFECTS OF PERCEPTUAL LOAD ON ATTENTION 11

Bonferroni correction procedure was used to control for false positive findings due to multiple testing. An α of .05 was used for all tests.

Results

Descriptive statistics were computed to describe the performance data for each task condition.

Reaction time

The results of the repeated measures MANOVA produced a significant main effect of ISI

2 (Wilks’ Lambda = .002, F(2,18) = 4241.8, p < .01, ηp = .998), and of Target Type (Wilks’

2 Lambda = .004, F(2,18) = 2450.3, p < .01, ηp = .996). A repeated measure MANOVA with

Target Type as within-subjects factor was conducted to determine effects of Target Type for each ISI condition. The results showed significant main effects of Target Type for the fast ISI

2 condition (Wilks’ Lambda = .018, F(2,18) = 482.4, p < .01, ηp = .982), for the medium ISI

2 condition (Wilks’ Lambda = .02, F(2,18) = 440.9, p < .01, ηp = .980), and for the slow ISI

2 condition (Wilks’ Lambda = .015, F(2,18) = 602.3, p < .01, ηp = .985). These main effects, however, should be stated more precisely because the MANOVA also produced a significant interaction effect between ISI and Target Type (Wilks’ Lambda = .130, F(4,16) = 26.9, p < .01,

2 ηp = .870). Pairwise comparisons showed that for each ISI condition, the color condition differed from the conjunction as well as the category conditions. The difference between the conjunction and category condition was significant for the fast and medium ISI condition (p <

0.000) but was not significant for the slow ISI condition (p = .148). These results are consistent with our hypothesis. The observation that the difference in performance in the conjunction and category conditions was significant in the fast and medium conditions, but not in the slow EFFECTS OF PERCEPTUAL LOAD ON ATTENTION 12 condition, indicates that participants used early selection in the fast and medium condition, but late selection in the slowest condition (see Table 1 and Figure 2).

Table 1. Performance data of Reaction Time (mean values and standard deviation) as a function of inter- stimulus interval (ISI) and Target Type (N=20) REACTION TIME (ms) ISI Slow Medium Fast Target Type Color 219 (18) 424 (16) 627 (17) Conjunction 371 (16) 570 (20) 777 (15) Category 370 (17) 586 (22) 819 (28)

900 800 700 600 500 RT color 400 RT conjunction 300 RT category 200 100 0 ISI slow ISI medium ISI fast

Figure 2. Mean reaction time in milliseconds on the vertical line for Target Type and ISI.

Hit rate

The results of the repeated measures MANOVA produced a significant main effect of ISI

2 (Wilks’ Lambda = .011, F(2,18) = 778.6, p < .01, ηp = .989), and of Target Type (Wilks’

2 Lambda = .002, F(2,18) = 4917.1, p < .01, ηp = .998). A repeated measure MANOVA with

Target Type as within-subjects factor was conducted to determine effects of Target Type for each ISI condition. The results showed significant main effects of Target Type for the fast ISI

2 condition (Wilks’ Lambda = .040, F(2,18) = 215.6, p < .01, ηp = .960), for the medium ISI

2 condition (Wilks’ Lambda = .045, F(2,18) = 189, p < .01, ηp = .955), and for the slow ISI

2 condition (Wilks’ Lambda = .051, F(2,18) = 167.3, p < .01, ηp = .949). These main effects, EFFECTS OF PERCEPTUAL LOAD ON ATTENTION 13 however, should be stated more precisely because the MANOVA also produced a significant

2 interaction effect between ISI and Target Type (Wilks’ Lambda = .29, F(4,16) = 9.8, p < .01, ηp

= .711). Pairwise comparisons showed that for each ISI condition, the color condition differed from the conjunction as well as the category conditions. The difference between the conjunction and category condition was significant for the fast and medium ISI condition (p < 0.000) but was not significant for the slow ISI condition (p = 1.000). These results are consistent with our hypothesis. The observation that the difference in performance in the conjunction and category conditions was significant in the fast and medium conditions, but not in the slow condition, indicates that participants used early selection in the fast and medium condition, but late selection in the slowest condition (see Table 2 and Figure 3).

Table 2. Performance data for Hit Rate (mean values and standard deviation) as a function of inter-stimulus interval (ISI) and Target Type (N=20) HIT RATE (%) ISI Slow Medium Fast Target Type Color 100 (.5) 93 (.5) 86 (.5) Conjunction 96 (1.3) 88 (.8) 82 (.5) Category 96 (1.5) 90 (.5) 84 (.5)

120

100

80 HR color 60 HR conjunction 40 HR category

20

0 ISI slow ISI medium ISI fast

Figure 3. Mean hit rate in percentages on the vertical line for Target Type and ISI. EFFECTS OF PERCEPTUAL LOAD ON ATTENTION 14

False alarm rate

The results of the repeated measures MANOVA produced a significant main effect of ISI

2 (Wilks’ Lambda = .009, F(2,18) = 999.3, p < .01, ηp = .991), and of Target Type (Wilks’

2 Lambda = .001, F(2,18) = 15086.5, p < .01, ηp = .999). A repeated measure MANOVA with

Target Type as within-subjects factor was conducted to determine effects of Target Type for each ISI condition. The results showed significant main effects of Target Type for the fast ISI

2 condition (Wilks’ Lambda = .1, F(1,19) = 171, p < .01, ηp = .9), for the medium ISI condition

2 (Wilks’ Lambda = .1, F(1,19) = 171, p < .01, ηp = .9), and for the slow ISI condition (Wilks’

2 Lambda = .055, F(2,18) =154.5, p < .01, ηp = .945). These main effects, however, should be stated more precisely because the MANOVA also produced a significant interaction effect

2 between ISI and Target Type (Wilks’ Lambda = .229, F(3,17) = 19.1, p < .01, ηp = .771).

Pairwise comparisons showed that for each ISI condition, the color condition differed from the conjunction as well as the category conditions. The difference between the conjunction and category condition was significant for the fast and medium ISI condition (p < 0.000) but was not significant for the slow ISI condition (p = 1.000). These results are consistent with our hypothesis. The observation that the difference in performance in the conjunction and category conditions was significant in the fast and medium conditions, but not in the slow condition, indicates that participants used early selection in the fast and medium condition, but late selection in the slowest condition (see Table 3 and Figure 4).

Table 3. Performance data for False Alarm Rate (mean values and standard deviation) as a function of inter- stimulus interval (ISI) and Target Type (N=20) FALSE ALARM RATE (%) ISI Slow Medium Fast Target Type Color 0 (0) 3.5 (.5) 7.5 (.5) Conjunction 1.5 (.5) 5 (.5) 9 (0) Category 1.5 (.5) 6 (0) 10 (0)

EFFECTS OF PERCEPTUAL LOAD ON ATTENTION 15

12

10

8 FAR color 6 FAR conjunction 4 FAR category

2

0 ISI slow ISI medium ISI fast

Figure 4. Mean false alarm rate in percentages on the vertical line for Target Type and ISI.

Discussion

In the present study, the most important hypothesis is confirmed. The conjunction and category condition differed from each other only during high perceptual load (fast and medium

ISI) and not during low perceptual load (slow ISI). These results indicate that the conjunction condition started to utilize early selection, as predicted. Furthermore, the hypothesis was confirmed that the RT of the conjunction condition would be in between the RT of the color condition and the RT of the category condition.

These results provide support for Lavie’s perceptual load theory (Lavie, 2005), which states that when the task performed exhausts perceptual capacity, early selection is being utilized. However, this support is provided indirectly through the analysis of task RT. According to Lavie, Beck &

Konstantinou (2014), these results cannot lead to direct conclusions about the effects of perceptual load on conscious perception because the RT effects are open to alternative interpretations that do not include a role for perceptual load. Priming is one example of an alternative interpretation of the elimination of distractor effects on RT. Priming can result from unconscious processing of the distractors (Dehaene et al., 2001) raising the possibility that both EFFECTS OF PERCEPTUAL LOAD ON ATTENTION 16 the distractor interference effects in low load and their reduction under high load may reflect various degrees of unconscious processing (Lavie, Beck & Konstantinou, 2014). Studies using

EEG demonstrate that under conditions of high perceptual load, visual processing is enhanced at a task-relevant location but is suppressed in the space immediately surrounding that location

(Parks, Hilimire, & Corballis, 2011). These types of studies provide a more direct measure of the effects of perceptual load on selective attention and hereby provide stronger support for Lavie’s perceptual load theory.

The limitations of the present study are the following: the study included 20 undergraduate students (ages between 18 and 23) at a university in the Netherlands, which means the results cannot be generalized to the population at large. If the study had included more than 20 participants, the significance effects might have been greater. The task used in the present study is commonly referred to as a response-competition task and although it’s intent is to provide non- targets entirely unrelated to the response task in order to recreate the kind of interference often experienced in daily life, the non-targets are not entirely unrelated to the task (Forster, 2013).

The use of a response-competition task is a limitation of the present study. In order for the non- targets to be considered entirely task-irrelevant, distractors must be unrelated to any task responses, presented in an irrelevant location, visually dissimilar from the search stimuli and irrelevant to any attentional settings for the current task (Forster, 2013). Forster and Lavie

(2008a, b, 2011) introduced a new perceptual load measure task which included the aforementioned criteria and the task is known as the irrelevant distractor task. The use of indirect measures to provide support for Lavie’s perceptual load theory is also a limitation of this study. A suggestion for future studies is to incorporate the 3x3 design of the present study with the use of EEG measurement and the irrelevant distractor task, including more participants with EFFECTS OF PERCEPTUAL LOAD ON ATTENTION 17 also a greater variance in age and educational level, and residing in different countries. The strength of the present study lies in its 3x3 design, which includes the presentation of target stimuli using 3 different levels of ISI (1600ms, 1200ms and 800ms) and 3 different conditions

(color, category and conjunction). It can be concluded that in the present study the conjunction and category condition differed significantly from each other but only during high perceptual load (fast and medium ISI), which means that when the perceptual load is high participants started employing early selection to reap the benefits (faster RT) in comparison with the category condition. The results of the study provide indirect support for Lavie’s perceptual load theory.

EFFECTS OF PERCEPTUAL LOAD ON ATTENTION 18

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