The visible persistence of illusory contours.

Meyer, Glenn E.; Ming, Chow Yu

doi:10.1037/h0084201

Cited by 24 publications

(18 citation statements)

References 49 publications

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“…The faster synchronization observed for real contours is due to the fact that layer 4 activity, which drives the activity of layer 2/3, is synchronous along the simulated contour. This result is consistent with data showing that illusory contours take longer to be perceived than real contours (Francis et al 1994;Meyer and Ming 1988). Fig.…”

Section: Gamma Band Oscillations Synchrony and Perceptual Groupingsupporting

confidence: 82%

“…The time at which the decay of an illusory contour starts to follow the same course as that of the real contour may be interpreted as the time required for contour completion. Based on this interpretation, Meyer and Ming (1988) found completion times nearing 250ms. Shape discrimination tasks have also been used in which stimuli were chosen so as to require boundary completion for proper discrimination.…”

Section: Model Connection Functional Interpretation Selected Referencesmentioning

confidence: 99%

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Running as fast as it can: How spiking dynamics form object groupings in the laminar circuits of visual cortex

2010

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How spiking neurons cooperate to control behavioral processes is a fundamental problem in computational neuroscience. Such cooperative dynamics are required during visual perception when spatially distributed image fragments are grouped into emergent boundary contours. Perceptual grouping is a challenge for spiking cells because its properties of collinear facilitation and analog sensitivity occur in response to binary spikes with irregular timing across many interacting cells. Some models have demonstrated spiking dynamics in recurrent laminar neocortical circuits, but not how perceptual grouping occurs. Other models have analyzed the fast speed of certain percepts in terms of a single feedforward sweep of activity, but cannot explain other percepts, such as illusory contours, wherein perceptual ambiguity can take hundreds of milliseconds to resolve by integrating multiple spikes over time. The current model reconciles fast feedforward with slower feedback processing, and binary spikes with analog network-level properties, in a laminar cortical network of spiking cells whose emergent properties quantitatively simulate parametric data from neurophysiological experiments, including the formation of illusory contours; the structure of non-classical visual receptive fields; and self-synchronizing gamma oscillations. These laminar dynamics shed new light on how the brain resolves local informational ambiguities through the use of properly designed nonlinear feedback spiking networks which run as fast as they can, given the amount of uncertainty in the data that they process.

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Section: Gamma Band Oscillations Synchrony and Perceptual Groupingsupporting

confidence: 82%

Section: Model Connection Functional Interpretation Selected Referencesmentioning

confidence: 99%

Running as fast as it can: How spiking dynamics form object groupings in the laminar circuits of visual cortex

2010

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“…In a study ofvisual persistence, Meyer and Ming (1988) note that illusory contour perception appears to lag behind the perception of real contours, while also observing that illusory contours persist much longer than their inducing elements, once the latter have been replaced by blank fields. Petry and Gannon (1987) had subjects rate brightness as a function oftime as masked figures were flashed, finding that illusory figure brightness lags behind real figure brightness but exhibits the same general time course plot.…”

Section: Psychophysical Similarities Between Real and Illusory Contoursmentioning

confidence: 99%

Illusory contours: Toward a neurally based perceptual theory

Lesher

1995

Psychon Bull Rev

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Although illusory contours were first described nearly a century ago, researchers have only recently begun to approach a consensus on the processes underlying their formation. Neurophysiological and psychophysical evidence indicate that neural mechanisms of the early visual cortex subserve illusory contour generation, although cognitive factors play important roles in determining the fmal percept. I summarize experiments concerning the determinants of illusory contour strength and form, concentrating on findings particularly relevant to modeling. After establishing arguments for the early generation of illusory contours, I provide an overview of formation theories, culminating with descriptions of neural models. The constraints that experimental data place on models are outlined, and neural models are evaluated with respect to these constraints. Throughout the review,I indicate where further experimental and modeling research are critical.Schumann introduced the first illusory contour, depicted in Figure 1a, in 1900, noting that a central "white rectangle with sharply defined contours appears, which objectively are not there." Schumann had discovered and noted two salient features of illusory figures: sharp edges in regions of homogeneous luminance, and a brightening within the figure. Perhaps because this stimulus failed to yield particularly convincing illusory contours, it was not until more than 50 years later that illusory contours became a topic for activeresearch. Although Ehrenstein (1941) demonstrated stimuli with salient illusory contours, it is remarkable that he failed to note their existence in the text of his article explicitly, commenting instead only on illusory brightening effects. Kanizsa's (1955) stunning figures, examples of which are depicted in Figures Iband 1c, produced extremely sharp, salient contours along with obvious brightening, initiating a wave of research.Although there have been a number ofexcellent illusory contour review papers Meyer & Petry, 1987;Parks, 1984), research advances in the psychophysical, neurophysiological, and modeling domains have been more than sufficient to mandate a new summary ofthe literature. Inthis review, I will not only present more recent data, but also employ an approach to the summaries that is markedly different from those previously taken. At the time of the earlier reviews, determination of the locus of formation of illusory contours was the central issue in illusory contour research, a fact reflected in the structure of the review papers. Experimental results were presented primarily in the historical context of supporting or refut-I would like to thank Ennio Mmgolla of Boston University for generously providing many helpful comments and suggestions during the development ofthis review.

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“…Within the BCS, these rebounds inhibit the persisting activations. The properties of these reset signals explain why persistence of static stimuli varies inversely with stimulus duration and luminance (e.g., Bowen et aI., 1974), why the persistence ofillusory contours is greater, and differently affected by stimulus duration, than luminance contours (Meyer & Ming, 1988), and how orientation-specific adaptation can increase or decrease persistence (Meyer, Lawson, & Cohen, 1975). Details of these properties are in Francis et al (1994); for the present analysis, a second mechanism, lateral inhibition, is more significant.…”

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confidence: 99%

Cortical dynamics of lateral inhibition: Visual persistence and ISI

Francis

1996

Perception & Psychophysics

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Psychophysical studies show that increasing the interstimulus interval (lSI) between two stimuli decreases persistence of the first stimulus. Whilesome researchers account for these results with interactions of transient and sustained inhibition, this paper describes an alternative explanation. In a neural-network model of boundary detection called the boundary contour system, persistence is the result of feedback-generated reverberations. Mechanisms to control these reverberations include lateral inhibition, which computer simulations show allows persistence in the network to qualitatively match the psychophysicaldata. Additionalsimulations predict that increasing the duration of the second stimulus should cause persistence to increase with lSI. The model links psychophysical data on visual persistence with computational requirements of spatial vision and properties of cells in visual cortex. Burr (1980) observed that moving stimuli were not as blurred as would be expected. Studies of visual persistence show that stimuli persist for hundreds of milliseconds (e.g., Bowen, Pola, & Matin, 1974), which would produce substantial blurring of moving stimuli. However, Burr suggested that mechanisms responsible for detecting motion inhibited persisting representations ofa stimulus to deblur the moving percept. Di Lollo and Hogben (1987) found an inverse proximity effect, where nearby stimuli that were spatially close and temporally contiguous reduced the persistence of nonmoving stimuli. This finding suggested that motion-detection mechanisms were not critical for deblurring moving stimuli, and that lateral inhibition interacting across space and time inhibited persisting representations of past stimuli. Farrell, Pavel, and Sperling (1990) showed similar results using stimuli that moved in apparent motion. They also developed a descriptive model of visual persistence that outlined how lateral inhibition from a new stimulus affects the decay of old stimulus representations. In this model, persistence is the result ofa fading trace, and lateral inhibition decreases the strength of the trace so that it more quickly decays below a constant threshold.While the idea of persistence as a fading trace is intuitively appealing, the mechanisms underlying the trace and their interaction with mechanisms oflateral inhibition appear to be more complicated than the Farrell et al. (1990) description. Di Lollo andHogben (1987) found that the effects of nearby stimuli were stronger with longer stimulus onset asynchronies (SOAs), and Castet (1994), using a method very similar to Farrell et al.'s, demonstrated that persistence ofa leading stimulus decreases as Correspondence should be addressed to the author at Department of Psychological Sciences, 1364 Psychological Science Building, Purdue University, West Layfayette, IN 47907-1364 (e-mail: gfrancis@psych. purdue.edu). the interstimulus interval (lSI) between it and a trailing stimulus increases. Figure la shows data for 2 subjects from Castet. Farrell et al.'s model did not consider the...

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The visible persistence of illusory contours.

Cited by 24 publications

References 49 publications

Running as fast as it can: How spiking dynamics form object groupings in the laminar circuits of visual cortex

Running as fast as it can: How spiking dynamics form object groupings in the laminar circuits of visual cortex

Illusory contours: Toward a neurally based perceptual theory

Cortical dynamics of lateral inhibition: Visual persistence and ISI

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