Down Contrast Sensitivity Control
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Electrophysiological Events Related to Top- Down Contrast Sensitivity Control by Bratislav Mišić A thesis submitted in conformity with the requirements for the degree of Master of Arts Graduate Department of Psychology University of Toronto © Copyright by Bratislav Mišić, 2009 Electrophysiological Events Related to Top-Down Contrast Sensitivity Control by Bratislav Mišić Master of Arts, Graduate Department of Psychology University of Toronto, 2009 Abstract Stimulus-driven changes in the gain of sensory neurons are well-documented, but relatively little is known about whether analogous gain-control can also be effected in a top-down manner. A recent psychophysical study demonstrated that sensitivity to luminance contrast can be modulated by a priori knowledge (de la Rosa et al., in press). In the present study, event-related potentials were used to resolve the stages of information processing that facilitate such knowledge-driven adjustments. Groupwise independent component analysis identified two robust spatiotemporal patterns of endogenous brain activity that captured experimental effects. The first pattern was associated with obligatory processing of contextual information, while the second pattern was associated with selective initiation of contrast gain adjustment. These data suggest that knowledge-driven contrast gain control is mediated by multiple independent electrogenic sources. page ii Acknowledgments First and foremost, I would like to thank my mentor, Anthony R. McIntosh for his patience, support and guidance. I would also like to thank Bruce Schneider and Claude Alain for generously providing their time and expertise as members of my thesis committee; Natasa Kovacevic for her invaluable technical help and all of the members of the McIntosh Lab and the Rotman Research Institute ERP Lab for providing a stimulating environment. Finally, I would like to thank the University of Toronto Department of Psychology and the Government of Canada for their financial support during my studies. page iii Table of Contents INTRODUCTION 1 Neural Manifestations of Contrast Sensitivity 2 Temporal Properties 5 Attention 6 Top-Down Control: de la Rosa, Gordon & Schneider 7 Top-Down vs. Bottom-Up: Neural Manifestations 8 MATERIALS AND METHODS 13 Experiment I 13 ERP Recording and pre-processing 15 Experiment II 17 Data Analysis 17 RESULTS 20 Experiment I 20 Experiment II 21 PLS Results 21 DISCUSSION 30 Behavior 30 Event-Related Potentials 30 REFERENCES 38 page iv List of Figures Figure 1. Control flow of Experiment I. 14 Figure 2. Cap configuration for the 76-channel ActiveTwo cap. 16 Figure 3. Mean percent accuracy for low-contrast trials for the three conditions in Experiment I. 14 Figure 4. Task-specific grand average ERP amplitudes at four representative electrodes. 14 Figure 5. Amplitude distributions for the Baseline and Valid-Cue conditions and their difference. 24 Figure 6. Task-related components. 25 Figure 7. Average amplitudes for cue- and task-specific conditions at one representative electrode. 26 Figure 8. Cue-type components common to all conditions. 27 Figure 9. Cue-type components unique to the Valid-Cue condition. 28 page v Introduction Successful object perception partly depends on the ability of the observer to parse a visual scene into objects. Objects can often be separated from their background and from each other as a result of subtle luminance and color contrasts along edges and mutual borders. These properties of the environment are therefore significant sources of information and sensitivity to contrasts is an important feature of the visual system. Please note that this literature review and research proposal specifically concern luminance contrast and not color contrast. Unless stated otherwise, the term ‘contrast’ refers to luminance contrast. Regardless of their specialized function or their station in the hierarchy of visual processing, most cortical visual neurons are sensitive to the level of contrast within their receptive field (RF) and their response profile varies somewhat predictably as a monotonic, increasing function of contrast (Albrecht & Hamilton, 1982). Response rates are accelerated and the response function is characterized by an expansive nonlinearity over low levels of contrast. As contrast increases, the response function becomes approximately linear. At high levels of contrast, the response rates saturate such that the response function has a second nonlinear region. Namely, the response function becomes compressed and asymptotes to some maximal response level. Despite considerable heterogeneity across individual neurons, the contrast- response function (CRF) generally maintains this sigmoidal shape (Albrecht & Hamilton, 1982) and can be fitted with the Naka-Rushton function (Naka & Rushton, 1966): n n n f(x) = rmaxx /(x + x50 ) where x represents contrast, rmax represents the firing rate at which the response saturates and x50 represents the semisaturation constant: the point at which 50% of the maximal response is attained. The slope of the function, which corresponds to the rate of change of the response, is represented by the exponent n. As will shortly become clear, it is useful to parameterize experimental contrast-response data in this way because it allows for page 1 page 2 most commonly observed phenomena to be conveniently described in terms of simple parameter manipulations. Contrast increments over the intermediate segment of the CRF elicit the greatest changes in the response and this segment is often termed the ‘dynamic range’. Therefore, the dynamic range of a given neuron is the range of contrasts over which the response is informative, but this range is considerably narrower than the range of contrasts likely to be encountered in the environment (Albrecht & Hamilton, 1982; Frazor & Geisler, 2006). To maintain sensitivity, gain is controlled and continuously adjusted in response to fluctuations of local contrast (Ohzawa, Sclar & Freeman, 1982; 1985). The precise means of gain-control is still subject to debate, but data from most studies have converged on two possibilities, both of which involve multiplicative mechanisms. The first is that the contrast necessary to elicit a given response is multiplied, thus changing the contrast-gain. This serves to center the CRF on the average ambient level of contrast (e.g. Gardner et al., 2005; Ohzawa et al., 1982), effectively shifting the dynamic range. Graphically, this corresponds to a lateral shift of the CRF, which can be modeled by changing the value of x50.The second is that response-gain is adjusted through a multiplication of the response by a constant factor proportional to the average ambient contrast (e.g. Geisler & Albrecht, 1992). This is equivalent to scaling the CRF and can be modeled by manipulating the rmax term of the Naka-Rushton function. Neural Manifestations of Contrast Sensitivity Thus far, the precise definition of ‘response’ has been left intentionally ambiguous and little attempt has been made to describe the nature of the processing units or their location. As previously noted, contrast sensitivity and gain-control are near-ubiquitous properties of visual neurons and typical CRFs can be acquired using a variety of recording techniques. Using single-cell recordings, they have been observed in cat striate cortex (Albrecht & Hamilton, 1982; Ohzawa et al., 1982; 1985; Sclar, Ohzawa & Freeman, 1982), monkey primary visual cortex (Albrecht & Hamilton, 1982; Albrecht et al., 2002; Sclar, Maunsell & Lonnie, 1990), area V4 (Cheng et al., 1994) and middle temporal area page 3 (MT; Cheng et al., 1994; Kohn & Movshon, 2003; Sclar et al., 1990). In these studies, the ‘response’ is the firing rate of an individual cell. Neuroimaging techniques such as functional magnetic resonance imaging (fMRI) have also revealed contrast sensitivity and gain-control in the human visual cortex, including areas V1, V2, V3, V4 and MT (Avidan et al., 2001; Boynton et al., 1999; Gardner et al., 2005; Heeger et al., 2000; Tootell et al., 1995; 1997). In these studies, the ‘response’ variable is the hemodynamic or blood-oxygen-level dependent (BOLD) signal that depends on the activity of many neurons. In addition, visually evoked potentials (VEPs) measured using electroencephalography (EEG) over the occipital lobe are also sensitive to stimulus contrast and standard CRFs can be computed using VEP amplitude as an index of the response (Spekreijse, Van der Tweel & Zuidema, 1973). These cortical responses are also subject to contrast sensitivity control mechanisms (Porciatti et al., 2000). Interestingly, VEP latency also depends on contrast, with phase advances resulting from increasing stimulus contrast (Porticatti et al., 2000; Spekreijse et al., 1973). Moreover, the neurophysiological measures provided by various methods are remarkably consistent with psychophysical contrast detection thresholds. For example, detection thresholds can be reliably predicted from single-cell responses (Geisler & Albrect, 1997), VEPs (Cannon, 1983) and BOLD responses (Boynton et al., 1999). This suggests a relatively direct link between neural activity and perceived contrast. Despite their substantial dependence on the level of local contrast and their response saturation at higher contrasts, neurons in the visual cortices typically maintain their functional selectivity when contrast is varied. For example, orientation (Sclar & Freeman, 1982) and direction selectivity (Sclar et al., 1990; Tootell et al., 1995; 1997) remain invariant with stimulus contrast.