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Mechanisms of brightness perception

ProQuest Dissertations and Theses, 2009
Dissertation
Author: Alan Edward Robinson
Abstract:
A physically identical shade of gray on a black background appears lighter than on a white background. This tells us that apparent brightness is not simply a function of how many photons are reflected from a surface, but depends on the surrounding context. This dissertation investigates the mechanisms that underlie this dependence on context. Chapter 1 presents a computational model of apparent brightness, built out of neurally plausible components. This model uses spatial filtering with oriented difference of Gaussians at several different scales. The output of these spatial filters is locally reweighted to normalize the amount of energy within different scales and orientations. This model can account for a wide range of human brightness illusions, using only simple mechanisms. It suggests that brightness perception might be due to relatively early visual areas, and may not require more high-level calculations (such as inferring the 3d structure of the scene), that have been suggested by previous researchers. If brightness perception is due to early visual areas, then we would expect it to be quite fast. Chapter 2 presents evidence that this is correct. Perceived brightness was measured in human participants who viewed briefly presented stimuli which were then masked to limit the amount of perceptual processing. Subjects were able to report brightness percepts for very brief presentations (as little as 58ms). If brightness is computed in early visual areas, how is it represented? Chapter 3 asks if brightness is represented in a point-for-point neural map that is filled-in from the response of small, contrast sensitive edge detector cells. Subjects adapted to illusory flicker caused by a dynamic brightness induction stimulus, with a modulating surround and a constant center. Flicker sensitivity was reduced when the test region was the same size as the constant center, but not for smaller, inset regions. This suggests that brightness induction does adapt cells along the contrast edge, but that there is no filled-in population of brightness selective cells to adapt. This is compatible with the model presented in chapter 1, which does not require a filling-in mechanism.

iv TABLE OF CONTENTS Signature Page ......................................... iii Table of Contents ...................................... iv List of Figures and Tables ............................. vi Acknowledgements ....................................... vii Curriculum Vitae ....................................... ix Abstract of the Dissertation ........................... xi Chapter 1 .............................................. 1 1. Introduction ................................... 1 2. Illusions tested ............................... 4 3. The ODOG and UNODOG models ..................... 5 3.1. Modeling details ....................... 5 3.2. Results—ODOG and UNODOG ................ 5 4. Local normalization of ODOG .................... 8 4.1. Results and discussion ................. 8 5. FLODOG ......................................... 10 5.1. Results ................................ 11 6. Conclusions .................................... 13 Chapter 2 .............................................. 16 1. Introduction ................................... 16 2. Experiment 1 ................................... 17 2.1. Methods ................................ 17 2.1.1. Subjects ............................. 17 2.1.2. Apparatus ............................ 17 2.1.3. Stimuli and procedure ................ 18 2.1.4. Results .............................. 18 3. Experiment 2 ................................... 19 3.1. Methods ................................ 19 3.1.1. Subjects ............................. 19 3.1.2. Stimuli and procedure ................ 19 3.1.3. Results .............................. 19 4. Experiment 3 ................................... 19 4.1. Methods ................................ 22 4.1.1. Subjects ............................. 22 4.1.2. Stimuli and procedure ................ 22 4.1.3. Results .............................. 22 5. Experiment 4 ................................... 22 5.1. Methods ................................ 22 5.1.1. Subjects ............................. 22 5.1.2. Stimuli and procedure ................ 22 5.1.3. Results .............................. 22 6. General discussion ............................. 24 6.1. The speed of brightness processing ..... 24 6.2. Filling-in ............................. 26 7. Conclusions .................................... 26

v Chapter 3 .............................................. 29 Abstract .......................................... 30 Introduction ...................................... 31 Experiment 1: Adapt to induction, detect polarity . 38 Methods ...................................... 39 Subjects ..................................... 39 Apparatus .................................... 39 Stimuli and procedure ........................ 40 Analysis ..................................... 43 Results ...................................... 44 Experiment 2: Adapt to induction, detect cycle count 45 Methods ...................................... 46 Results ...................................... 48 Experiment 3: Jittering edges and slower flicker .. 49 Methods ...................................... 51 Results ...................................... 53 General Discussion ................................ 54 Conclusions ....................................... 56

vi LIST OF FIGURES AND TABLES Figure 1.1: Illusions Tested .............................. 2 Table 1.1: ................................................ 3 Figure 1.2: ODOG and LODOG models ......................... 6 Table 1.2: ................................................ 7 Figure 1.3: Model Predictions for WE-thick ................ 9 Figure 1.4: The FLODOG model .............................. 10 Table 1.3: ................................................ 11 Figure 1.5: Model Predictions for WE-thick ................ 12 Figure 2.1: Diagram of experimental paradigm .............. 18 Figure 2.2: Experiment 1 .................................. 19 Figure 2.3: Experiment 1 .................................. 20 Figure 2.4: Experiment 2 .................................. 21 Figure 2.5: Experiment 3 .................................. 23 Figure 2.6: Experiment 4 .................................. 24 Figure 2.7: Experiment 4 .................................. 24 Figure 2.8: Diagram of the latency of brightness processing 25 Figure 3.1: Brightness induction and spatial filtering .... 31 Figure 3.2: Experiment structure .......................... 40 Figure 3.3: Aligned and inset conditions .................. 42 Figure 3.4: Experiment 1: induced flicker adaptation ...... 45 Figure 3.5: Experiment 2: count the number of test flickers 48 Figure 3.6: Experiment 3: variable sized adapter .......... 53

vii ACKNOWLEDGEMENTS I would like to acknowledge Professor de Sa for her support as the chair of my committee. Her extensive advice has been quite helpful at every stage of this dissertation, from designing the experiments to writing the manuscripts describing the results. It has been a pleasure to collaborate with her. I would also like to acknowledge the critical role Paul Hammon played in research and development of the FLODOG model presented in chapter 1. The final result is vey much a collaborative effort, and is much stronger thanks to his involvement. I would like to acknowledge Professor MacLeod for the ideas for the flicker adaptation experiment that led to chapter 3 of this dissertation, and the helpful discussions we have had about the project during its development. On a more personal level, I would like to thank my parents, Howard and Barbara Robinson, for their encouragement to pursue a career in the sciences, rather than something more practical. I would also like to thank Jymm Gifford for the emotional support provided during the latter half of my PhD. Finally, I would like to acknowledge that this work was supported by the National Science Foundation under NSF Career Grant No. 0133996 to VR de Sa., and NSF IGERT Grant #DGE-0333451 to GW Cottrell/VR de Sa. Chapter 1, in full, is a reprint of the material as it appears in Robinson, AE, Hammon, PS, & de Sa, VR. (2007). Explaining brightness illusions using spatial filtering and local response normalization. Vision Research, 47, 1631-1644. The dissertation author was the primary investigator and author of this paper.

viii Chapter 2, in full, is a reprint of the material as it appears in Robinson, AE, & de Sa, VR. (2008). Brief presentations reveal the temporal dynamics of brightness induction and White's illusion. Vision Research, 48, 2370-2381. The dissertation author was the primary investigator and author of this paper. Chapter 3, in full, has been submitted for publication and is currently under review by the journal Vision Research. The dissertation author was the primary investigator and author of this paper.

ix CURRICULUM VITAE Alan Robinson Doctor of Philosophy, Department of Cognitive Science, UC San Diego, 2009 Bachelors of Arts, Cognitive Science, Hampshire College, 2001. PUBLICATIONS 1.Robinson, AE, & Triesch, J. (2008). Task-specific modulation of memory for object features in natural scenes. Advances in Cognitive Psychology, 4, 1-14. 2.Robinson, AE, Manzi, A., & Triesch, J. (2008). Object perception is selectively slowed by a visually similar working memory load. Journal of Vision, 8(16):7, 1- 13. 3.Robinson, AE, & de Sa, VR. (2008). Brief presentations reveal the temporal dynamics of brightness induction and White's illusion. Vision Research, 48, 2370- 2381. 4.Robinson, AE, & de Sa, VR. (2008). Measuring brightness induction during brief stimulus displays. Poster at Vision Sciences Society, Naples, FL. 5.Robinson, AE, & de Sa, VR. (2007). Measuring White's illusion during brief stimulus displays. Poster at Fall Vision Meeting, Berkeley, CA. 6.Robinson, AE, Hammon, PS, & de Sa, VR. (2007). Explaining brightness illusions using spatial filtering and local response normalization. Vision Research, 47, 1631-1644. 7.Robinson, AE, Hammon, PS, & de Sa, VR. (2007). A filtering model of brightness perception using Frequency-specific Locally-normalized Oriented Difference-of-Gaussians (FLODOG). Poster at Vision Sciences Society, Sarasota, FL. 8.Robinson, AE, Hammon, PS, & de Sa, VR. (2006). A neurally plausible model of lightness illusions combining spatial filtering and local response normalization. Poster at 13th Joint Symposium on Neural Computation, San Diego, CA. 9.Robinson, A., J. Triesch, M. Hayhoe, J. Droll, & B. Sullivan. (2006). Change blindness during multiple interactions with a single object. Poster at the Vision Sciences Society, Sarasota FL. 10.Robinson, A., A. Manzi, & J. Triesch. (2005). The Costs of Visual Working Memory. Talk presented at the Vision Sciences Society, Sarasota FL. 11.Robinson, A. & J. Triesch. (2004). Visual memory for natural scenes: automatic +

x task dependent components. Poster at Vision Sciences Society, Sarasota FL. 12.Robinson, A., & J. Morris. (2002). Is the N170 ERP Component Face Specific? Poster at Psychonomic Society, Kansas City. 13.Robinson, A., & L. Spector. (2002). Using Genetic Programming with Multiple Data Types and Automatic Modularization to Evolve Decentralized and Coordinated Navigation in Multi-Agent Systems. Late-Breaking Papers of GECCO-2002, the Genetic and Evolutionary Computation Conference. Published by the International Society for Genetic and Evolutionary Computation. 14.Spector, L., & A. Robinson. (2002). Genetic Programming and Autoconstructive Evolution with the Push Programming Language. Genetic Programming and Evolvable Machines, 3, pp. 7-40. 15.Spector, L., & A. Robinson. (2002). Multi-type, Self-adaptive Genetic Programming as an Agent Creation Tool. Proceedings of the Workshop on Evolutionary Computation for Multi-Agent Systems, ECOMAS-2002, International Society for Genetic and Evolutionary Computation. 16.Spector, L., R. Moore, & A. Robinson. (2001). Virtual Quidditch: A Challenge Problem for Automatically Programmed Software Agents. In E.D. Goodman, editor, Late-Breaking Papers of GECCO-2001, the Genetic and Evolutionary Computation Conference. Published by the International Society for Genetic and Evolutionary Computation. 17.Robinson, A. (2001). Genetic Programming: Theory, Implementation, and the Evolution of Unconstrained Solutions. Hampshire College Division III (senior) thesis.

xi ABSTRACT OF THE DISSERTATION Mechanisms of brightness perception by Alan Edward Robinson Doctor of Philosophy in Cognitive Science University of California, San Diego, 2009 Professor Virginia de Sa, Chair A physically identical shade of gray on a black background appears lighter than on a white background. This tells us that apparent brightness is not simply a function of how many photons are reflected from a surface, but depends on the surrounding context. This dissertation investigates the mechanisms that underlie this dependence on context. Chapter 1 presents a computational model of apparent brightness, built out of neurally plausible components. This model uses spatial filtering with oriented difference of Gaussians at several different scales. The output of these spatial filters is locally re-

xii weighted to normalize the amount of energy within different scales and orientations. This model can account for a wide range of human brightness illusions, using only simple mechanisms. It suggests that brightness perception might be due to relatively early visual areas, and may not require more high-level calculations (such as inferring the 3d structure of the scene), that have been suggested by previous researchers. If brightness perception is due to early visual areas, then we would expect it to be quite fast. Chapter 2 presents evidence that this is correct. Perceived brightness was measured in human participants who viewed briefly presented stimuli which were then masked to limit the amount of perceptual processing. Subjects were able to report brightness percepts for very brief presentations (as little as 58ms). If brightness is computed in early visual areas, how is it represented? Chapter 3 asks if brightness is represented in a point-for-point neural map that is filled-in from the response of small, contrast sensitive edge detector cells. Subjects adapted to illusory flicker caused by a dynamic brightness induction stimulus, with a modulating surround and a constant center. Flicker sensitivity was reduced when the test region was the same size as the constant center, but not for smaller, inset regions. This suggests that brightness induction does adapt cells along the contrast edge, but that there is no filled-in population of brightness selective cells to adapt. This is compatible with the model presented in chapter 1, which does not require a filling-in mechanism.

15 Chapter 1, in full, is a reprint of the material as it appears in Robinson, AE, Hammon, PS, & de Sa, VR. (2007). Explaining brightness illusions using spatial filtering and local response normalization. Vision Research, 47, 1631-1644. The dissertation author was the primary investigator and author of this paper.

28 Chapter 2, in full, is a reprint of the material as it appears in Robinson, AE, & de Sa, VR. (2008). Brief presentations reveal the temporal dynamics of brightness induction and White's illusion. Vision Research, 48, 2370-2381. The dissertation author was the primary investigator and author of this paper.

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Alan E. Robinson & Virginia R. de Sa

Department of Cognitive Science University of California, San Diego

Dynamic brightness induction causes flicker adaptation, but only along the edges: evidence against the neural filling-in of brightness

Keywords: Dynamic brightness induction; flicker adaptation; filling-in; brightness perception

Please address correspondence to:

Alan Robinson Department of Cognitive Science University of California, San Diego 9500 Gilman Drive La Jolla, California 92093-0515 USA

Phone: (858) 822 2421 FAX: (858) 534 1128 robinson@cogsci.ucsd.edu

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Abstract

Is brightness represented in a point-for-point neural map that is filled-in from the response of small, contrast sensitive edge detector cells? We tested for the presence of this filled-in map using flicker adaptation. Subjects viewed illusory flicker caused by a dynamic brightness induction stimulus, with a modulating surround and a constant center. Thereafter flicker sensitivity was reduced when our test region was the same size as the constant center, but not for smaller, inset regions. This suggests that brightness induction does adapt cells along the contrast edge, but that there is no filled-in population of brightness selective cells to adapt.

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Introduction

The perceived brightness of a surface depends on the brightness of the surfaces that surround it. This is known as brightness induction. One particularly well-known example of brightness induction is brightness contrast, in which the brightness of the surrounding surfaces induces a shift in the brightness of the center surface such that the apparent contrast is increased. Thus, a medium-gray square on a light background appears darker than a square with the same luminance on a dark background (Fig. 1a). Since the gray squares are physically identical (Fig. 1b), the response of photoreceptors in the retina must be the same. Since the appearance of the squares differ, however, there must be an area in the brain where that difference is represented. Even before information leaves the retina it is recoded in terms of contrast, rather than point-for-point luminance. While this would predict that the square’s edges would appear different as a function of b. Luminance profile of Fig. 1a a. Brightness induction c. Small filter response d. Large filter response Fig. 1: Brightness induction and spatial filtering

Fig. 1: Brightness induction and spatial filtering

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their surround (Fig. 1c), it does not necessarily explain why the whole square appears to change, because the size of early receptive fields are much too small for cells responding to the center of the square to be influenced by the surround (induction can be clearly seen for figures where the induced region has an extent of 14°(Cornelissen, et. al, 2006)). There are three commonly suggested explanations for how the change in appearance of the whole induced region is represented. The first is the large receptive field model: from the response of small receptive fields of early visual areas, larger, contrast sensitive receptive fields are constructed in later areas. If these later receptive fields are large enough to encompass the entire induced region and part of the surround, then their response would depend on both the luminance of the center and surround (Fig. 1d). The highly successful ODOG (Blakeslee & McCourt, 1999) and FLODOG (Robinson, Hammon, & de Sa, 2007) brightness perception models make use of such large-scale contrast sensitive filters, though they are agnostic as to how (or where) those large-scale filters are implemented. A second possibility is known as neural filling-in. According to this theory at some post-retinal stage in the visual system there exists a point-for-point, retinotopic map of the brightness across the entire visual field. This point-for-point representation is generated by analyzing all the contrast edges, as determined by relatively small contrast- sensitive filters, determining the brightness on either side of those edges, and then finally propagating that brightness via a spread of activation from edges outward until the whole point-for-point representation is complete. One of the best known versions of this theory is Grossberg ‘s FACADE model (e.g., Grossberg & Todorovic, 1988), which can account

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for a range of brightness illusions, including brightness induction. The filling-in model differs from the large receptive field model in several ways. Consider a small, uniformly colored rectangle which grows in size. As it gets bigger the amount of neurons which represent it in the filled-in map also grows. Meanwhile, the neurons which represented its filled-in brightness when it was smaller now just represent the brightness of the interior of the rectangle. In the large receptive field model, however, neurons respond to contrast at different scales. So, as the rectangle grows, the population that represents it shifts from neurons which respond to small spatial frequencies to those selective for large spatial frequencies. The third theory suggests there is no point-for-point filling-in, or large contrast- sensitive receptive fields. Rather, the local edge responses themselves represent the whole brightness of the induced region, and the visual system directly infers from this signal the overall brightness of each region. This has been called the symbolic filling-in theory, since the visual system infers the appearance of the induced square based on the low-level input, but does not then re-represent that appearance in an ‘analog’, or point- for-point retinotopic map. We are unaware of any concrete model of how this would be implemented in the brain, but the general idea has been suggested by several authors (e.g. Dennett, 1992). Most of the experiments conducted on this issue have focused on whether there is a retinotopic, point for point filled-in map of brightness. Rossi & Paradiso (1996) have argued that temporal limits on the perception of brightness induction reflect a point-for- point representation which takes a long time to fill-in. Their experiments showed that

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temporally modulating the surround only induced a change in the brightness of a center region when the modulation rate is relatively slow (< 5 Hz). Furthermore, for larger induced regions, the temporal modulation rate that causes visible induction is even lower, dropping to below 2Hz for sufficiently large induced regions. This is consistent with the neural filling-in theory, where the propagation, or filling-in, of brightness takes time which is dependent on the distance that the signal must travel from contrast edges. While this result is certainly compatible with filling-in, it may instead be due to some yet-as- unknown factor, as two recent studies (discussed below) have shown that induction is visible at much higher speeds than Rossi & Paradiso’s 1996 results suggest. Blakeslee & McCourt (2008) investigated the timecourse of brightness induction using the grating induction illusion. Subjects viewed a sinusoidal grating that was modulated in counterphase. This induced a modulating grating 180° out of phase in a medium-gray test stripe that bisected the modulating grating. They found little to no change in phase between the induced grating and the inducing grating as the modulation rate increased from 2Hz to 24Hz. This suggests that induction sufficient to drive the motion percept occurs at the same speed as the perception of the inducing grating. While this is not particularly compatible with Rossi & Paradiso’s theory of slow filling-in, it is compatible with filling-in, so long as filling-in is almost instantaneous. We (Robinson & de Sa, 2008) have also shown that brightness induction appears to happen much quicker than suggested by Rossi and Paradiso, even when using induction stimuli closely modeled on Rossi and Paradiso’s experiments. Instead of measuring induction strength while the surround was constantly modulated, however, we

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used brief, static presentations, followed by a noise mask. We found that induction was clearly visible and quite strong, even when the brief presentation was as short as 58ms, and there was no indication that the spatial scale of the induced region had any influence on the temporal limits of induction. These results differ from Rossi & Paradiso’s data across several dimensions, and suggest that the temporal properties measured by Rossi & Paradiso are actually due to some other factor, not yet identified. Note that our results, however, are still compatible with filling-in, so long as it happens on a sufficiently fast time-scale. Some psychophysics paradigms unrelated to brightness induction also support the hypothesis that brightness spreads from contrast edges at a relatively slow speed. Paradiso & Nakayama (1991) studied brightness percepts elicited by briefly presenting a large white disk and then masking it with a smaller pattern, such as a black circle with a white outline. At short inter-stimulus-intervals the mask made the white disk appear as though it had a dark hole in the center, suggesting that the addition of a contrast edge blocked the completion of a slow filling-in process. Paradiso & Hahn (1996) showed that steadily decreasing or increasing the luminance of a disk led to a slightly delayed change in the perceived brightness at the center of the disk. This lag is suggestive of a relatively slow filling-in process, though it is also compatible with the theory that the response of larger receptive fields in later visual areas are built from the response of smaller receptive fields in early visual areas. So long as people are conscious of the response in both early and later areas, the later areas would necessarily lag in time.

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fMRI experiments in humans have produced mixed evidence for the filling-in of brightness information from contrast edges. Cornelissen, et al, (2006) measured the retinotopic response to viewing a static 14° wide gray disk while the surround modulated in brightness. In both V1 and V2 they found a very strong response in the voxels corresponding to the retinotopic location of the contrast edge between the center and surround. As the retinotopic distance from the edge increased, the BOLD signal decreased, with a similar fall-off for the regions corresponding to the center and the surround. This fall-off was quite slow, suggesting a very spatially broad response to edges; broader than would be expected due to the blurring caused by the BOLD point- spread function. While this could be the signature of filling-in, they argue that it is actually only evidence of a spatially diffuse response to edges. This argument is supported by their findings that a similar broad edge response was found when subjects viewed a modulating checkerboard background (which induced no change in brightness of the center, static region). Furthermore, a broad edge response was found in the surround when the center disk modulated in luminance and the surround was held constant, even though in this situation the surround appears constant in brightness. Other studies that purport to have found fMRI evidence for filling-in may well have been measuring this broad edge response instead (e.g. Boyaci, et al, 2007). Pereverzeva & Murray (2008) followed up on Cornelissen’s experiment but with an interesting twist – they tested induction at different luminance’s for the central, static disk. When the luminance was low, little illusory change was seen in the central disk from modulating the surround. When the central disk had a higher luminance, however,

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the brightness illusion was clear and quite strong. They found the largest V1 BOLD modulation in this latter condition, suggesting that the BOLD signal corresponds at least in part to the perceived brightness. Like Cornelissen they measured BOLD modulation as a function of distance from the contrast edge, and found a similar falloff in signal change as the distance from the edge increased. Puzzlingly, however, this was true even in the low-luminance condition, where no brightness induction was seen, again raising the question of whether the BOLD signal corresponds to perceived brightness as the authors suggest, or just a broad edge response, as Cornelissen argues. In addition, it is difficult to eliminate the possibility that broad edge response found by both studies is actually an fMRI artifact. Single unit recordings have produced some evidence for filling-in, but at radically different speeds. Recordings in cat areas V1 and V2 suggest that a signal does propagate from contrast edges into the center of uniform regions (Hung, Ramsden, & Roe, 2007). Based on temporal correlation of spikes from pairs of neurons, they estimated that filling- in within V1 travels between 1300-2400°/s, and as fast as 4000°/s from V1 to V2, although aspects of their data led the authors to suggest the spreading activation in V1 was unrelated to perceived brightness. In contrast, Huang & Paradiso (2008) have found evidence of much slower filling-in in monkey V1. They found many cells fired much earlier to a contrast border than to the interior of a large uniform region. Based on these results the authors calculated that filling-in travels at about 270°/s. The wide difference between these two experiments may be due to the different measure used (spike-timing correlation, vs. change in mean firing rate), or the difference in species.

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In the present work we introduce a new psychophysical paradigm for measuring the presence of neural filling-in, based on flicker adaptation. Prolonged viewing of a high-contrast flickering spot causes reduced sensitivity to that flicker, and in particular, elevated contrast thresholds for low-contrast flicker (see Schietin and Spillman, 1987, for a brief review). This reduction in sensitivity may be due to neural fatigue of the cells which represent the flickering region, or to a process which calibrates sensitivity in order to reduce the firing rate to frequently encountered stimuli (the precise explanation does not matter for the purpose of our research). In our current study we explore if flicker adaptation is found for regions where illusory flicker is caused by brightness induction from actual flicker in the surround. We will show that induction does cause strong flicker adaptation, suggesting that it isolates a population of neurons which represent the change in appearance of the induced region. This reduction is strongest when the inner edges of the flickering inducer are aligned with the outer edges of the test region. We will also show that the strength of flicker adaptation is drastically reduced when the test region is shrunk (that is, inset) a few degrees, suggesting that the population of cells that are adapted are primarily or even exclusively along the contrast edges, and if there is a filled- in population that represents the brightness of the induced region, it is not susceptible to adaptation. Experiment 1: Adapt to induction, detect polarity

In this experiment we measure the strength of induction-derived flicker adaptation for two conditions: when the test region is exactly aligned with the inner edge of the

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inducer, and where the test region is significantly inset relative to the edges of the inducer (Fig. 3). In the aligned condition two forms of adaptation are possible: first, we expect some edge adaptation because the contrast modulation at the border between the inducer and the inducing region exactly aligns with the contrast modulation between the test region and the background. Second, if there is a neural filling-in of brightness then we would expect those neurons to be adapted across the entire region where the induced flicker is seen. In the inset condition the edges of the inducer and the test region do not overlap, so the only type of adaptation that can remain is to the posited filled-in brightness modulation. Thus, if there is neural filling-in we should expect to measure an effect of adaptation in both the aligned and inset conditions. Methods Subjects Two moderately psychophysically experienced subjects participated. Both were naïve to the purpose of the experiment. Apparatus Stimuli were presented on a 21” NEC FE2111SB CRT driven by an ATI RADEON 7000 VE video card at a refresh rate of 85Hz. Display luminance was linearized using a 256-entry color lookup table that drove a 10-bit DAC. A Cambridge Research Systems ColorCal colorimeter was used to select the appropriate lookup table values. The lookup table was apportioned so that the full resolution of the DAC was available for luminance values between 37 cd/m 2 and 65 cd/m 2 (in steps of 0.15 cd/m 2 ). The remaining entries covered the rest of the 0 to 102 cd/m 2 range in much coarser

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increments. This allowed us to adapt subjects with maximum contrast inducers, while still allowing us to also generate relatively low contrast test stimuli. A chinrest was used to maintain a viewing distance of 72cm. Stimuli were generated and displayed using Matlab running the Psychophysics Toolbox, version 2.54 (Brainard, 1997; Pelli, 1997) on a Windows XP computer. The experiment was run in a dark room and subjects adapted to the light level for 3 minutes before collecting data. The same apparatus was used in all experiments. Stimuli and procedure Our paradigm is outlined in Fig. 2 Each large rectangle represents a single screen shown to the subject, with the duration that screen was visible listed below it. The experiment started with the screen on the far left, and proceeded rightward. The arrows show loops in the time line, causing groups of screens to be repeated multiple times (such as the pre-trial flickers, which were shown a total of 120 times in the adapt trials, or 0 times in the no-adapt trials.) 153ms

153ms

120 (or 0) times

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200ms

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dot

onset

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Fig. 2: Experiment

<5s

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Fig. 2: Experiment

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Contrast thresholds were measured in two different kinds of sessions: adapt and no-adapt. In the adapt session subjects first view a fixation dot centered in a gray rectangle (50.62 cd/m 2 ) surrounded by a larger rectangle whose luminance is modulated by a 3.3Hz square-wave (153ms per frame) between 0 and 102 cd/m 2 . The luminance change of the surround induces a change in the apparent brightness of the physically static center. Subjects adapt to 120 cycles of flicker. This Pre-trial flicker is meant to build up a high level of adaptation before any measurements are made. Subjects are instructed to fixate the dot at the center of the screen during this, and all subsequent parts of the experiment. After the pre-trial flickers the measurement trials start. A trial in an adapt session consists of the following screens. First, a gray screen (50.62cd/m 2 ) is shown for 306ms, serving as a very brief inter-trial break, and then a fixation dot is drawn, which remains on the screen for the rest of the trial. After an additional 200ms delay subjects view 20 full-contrast adapting flickers, which are identical to the pre-trial flickers in all ways except in total number. These flickers serve to keep subjects in an adapted state. Then three test flickers are shown. The test flickers consist of a gray screen for 153ms, followed by a gray rectangle that is either a small increment or small decrement relative to the background luminance, for 153ms. On any given trial the test flickers are all either increments or decrements, and the subject’s task is to report this using the keyboard after the 3 rd flicker is shown (we will refer to this as the polarity task). The fixation dot disappears after the 3 rd flicker to indicate a response is required. Subjects are encouraged to respond quickly but are given up to 5 s to respond before the trial times out. After the subject responds a new trial begins.

Full document contains 72 pages
Abstract: A physically identical shade of gray on a black background appears lighter than on a white background. This tells us that apparent brightness is not simply a function of how many photons are reflected from a surface, but depends on the surrounding context. This dissertation investigates the mechanisms that underlie this dependence on context. Chapter 1 presents a computational model of apparent brightness, built out of neurally plausible components. This model uses spatial filtering with oriented difference of Gaussians at several different scales. The output of these spatial filters is locally reweighted to normalize the amount of energy within different scales and orientations. This model can account for a wide range of human brightness illusions, using only simple mechanisms. It suggests that brightness perception might be due to relatively early visual areas, and may not require more high-level calculations (such as inferring the 3d structure of the scene), that have been suggested by previous researchers. If brightness perception is due to early visual areas, then we would expect it to be quite fast. Chapter 2 presents evidence that this is correct. Perceived brightness was measured in human participants who viewed briefly presented stimuli which were then masked to limit the amount of perceptual processing. Subjects were able to report brightness percepts for very brief presentations (as little as 58ms). If brightness is computed in early visual areas, how is it represented? Chapter 3 asks if brightness is represented in a point-for-point neural map that is filled-in from the response of small, contrast sensitive edge detector cells. Subjects adapted to illusory flicker caused by a dynamic brightness induction stimulus, with a modulating surround and a constant center. Flicker sensitivity was reduced when the test region was the same size as the constant center, but not for smaller, inset regions. This suggests that brightness induction does adapt cells along the contrast edge, but that there is no filled-in population of brightness selective cells to adapt. This is compatible with the model presented in chapter 1, which does not require a filling-in mechanism.