A Physiologically Based Nonhomogeneous Poisson Counter Model of Visual Identification

A physiologically based nonhomogeneous Poisson counter model of visual identification is presented. The model was developed in the framework of a Theory of Visual Attention (Bundesen, 1990; Kyllingsbæk, Markussen, & Bundesen, 2012) and meant for modeling visual identification of objects that are mutually confusable and hard to see. The model assumes that the visual system’s initial sensory response consists in tentative visual categorizations, which are accumulated by leaky integration of both transient and sustained components comparable with those found in spike density patterns of early sensory neurons. The sensory response (tentative categorizations) feeds independent Poisson counters, each of which accumulates tentative object categorizations of a particular type to guide overt identification performance. We tested the model’s ability to predict the effect of stimulus duration on observed distributions of responses in a nonspeeded (pure accuracy) identification task with eight response alternatives. The time courses of correct and erroneous categorizations were well accounted for when the event-rates of competing Poisson counters were allowed to vary independently over time in a way that mimicked the dynamics of receptive field selectivity as found in neurophysiological studies. Furthermore, the initial sensory response yielded theoretical hazard rate functions that closely resembled empirically estimated ones. Finally, supplied with a Naka-Rushton type contrast gain control, the model provided an explanation for Bloch’s law.

[1]  Ronald S. Harwerth,et al.  Psychophysical evidence for sustained and transient channels in the monkey visual system , 1980, Vision Research.

[2]  I. Ohzawa,et al.  Contrast Gain Control in the Visual Cortex: Monocular Versus Binocular Mechanisms , 2000, The Journal of Neuroscience.

[3]  B. Breitmeyer,et al.  Temporal studies with flashed gratings: Inferences about human transient and sustained channels , 1977, Vision Research.

[4]  Claus Bundesen,et al.  Testing a Poisson counter model for visual identification of briefly presented, mutually confusable single stimuli in pure accuracy tasks. , 2012, Journal of experimental psychology. Human perception and performance.

[5]  S. Kyllingsbæk,et al.  Gaussian counter models for visual identification of briefly presented, mutually confusable single stimuli in pure accuracy tasks , 2017 .

[6]  Richard P. Heitz,et al.  Neurally constrained modeling of perceptual decision making. , 2010, Psychological review.

[7]  M. Nicolelis,et al.  Feature article: the structure and function of dynamic cortical and thalamic receptive fields. , 2001, Cerebral cortex.

[8]  G. Logan.  The CODE theory of visual attention: an integration of space-based and object-based attention. , 1996, Psychological review.

[9]  M M Sondhi,et al.  Model for visual luminance discrimination and flicker detection. , 1968, Journal of the Optical Society of America.

[10]  A. Watson.  Probability summation over time , 1979, Vision Research.

[11]  D. G. Albrecht,et al.  Nonlinear Properties of Visual Cortex Neurons: Temporal Dynamics, Stimulus Selectivity, Neural Performance , 2002 .

[12]  Philip L. Smith.  Bloch's law predictions from diffusion process models of detection , 1998 .

[13]  J. Roufs.  Dynamic properties of vision. IV. Thresholds of decremental flashes, incremental flashes and doublets in relation to flicker fusion. , 1974, Vision research.

[14]  J. Roufs,et al.  Dynamic properties of vision. VI. Stochastic threshold fluctuations and their effect on flash-to-flicker sensitivity ratio. , 1974, Vision research.

[15]  Alexander S. Ecker,et al.  Decorrelated Neuronal Firing in Cortical Microcircuits , 2010, Science.

[16]  R. Shepard.  Stimulus and response generalization: A stochastic model relating generalization to distance in psychological space , 1957 .

[17]  T. Zandt,et al.  Time-dependent Poisson counter models of response latency in simple judgment. , 2000, The British journal of mathematical and statistical psychology.

[18]  William Carlisle Thacker,et al.  The role of the Hessian matrix in fitting models to measurements , 1989 .

[19]  C. Bundesen,et al.  A neural theory of visual attention: bridging cognition and neurophysiology. , 2005, Psychological review.

[20]  J. Robson,et al.  Application of fourier analysis to the visibility of gratings , 1968, The Journal of physiology.

[21]  P. Gouras.  Identification of cone mechanisms in monkey ganglion cells , 1968, The Journal of physiology.

[22]  A. Gorea,et al.  New look at Bloch's law for contrast. , 1986, Journal of the Optical Society of America. A, Optics and image science.

[23]  G. Loftus,et al.  Sensory and cognitive components of visual information acquisition. , 1994, Psychological review.

[24]  D. G. Albrecht,et al.  Motion selectivity and the contrast-response function of simple cells in the visual cortex , 1991, Visual Neuroscience.

[25]  Koen Lamberts,et al.  The time course of categorization. , 1998 .

[26]  C. Bundesen.  A theory of visual attention. , 1990, Psychological review.

[27]  R. Nosofsky.  Attention, similarity, and the identification-categorization relationship. , 1986, Journal of experimental psychology. General.

[28]  Anders Petersen,et al.  The effect of exposure duration on visual character identification in single, whole, and partial report. , 2012, Journal of experimental psychology. Human perception and performance.

[29]  C. Bundesen,et al.  Recent developments in a computational theory of visual attention (TVA) , 2015, Vision Research.

[30]  Koen Lamberts,et al.  Categorization under time pressure. , 1995 .

[31]  Richard A. Heath,et al.  A general nonstationary diffusion model for two-choice decision-making , 1992 .

[32]  D H Kelly,et al.  Theory of flicker and transient responses. III. An essential nonlinearity. , 1978, Journal of the Optical Society of America.

[33]  G R Loftus,et al.  A theory of visual information acquisition and visual memory with special application to intensity-duration trade-offs. , 1994, Journal of experimental psychology. Human perception and performance.

[34]  B. A. Wandell,et al.  Two temporal channels in human V1 identified using fMRI , 2009, NeuroImage.

[35]  James T. Townsend,et al.  The Stochastic Modeling of Elementary Psychological Processes , 1983 .

[36]  D. Heeger.  Normalization of cell responses in cat striate cortex , 1992, Visual Neuroscience.

[37]  C. Bundesen,et al.  Principles of Visual Attention: Linking Mind and Brain , 2008 .

[38]  C Bundesen,et al.  Visual selection from multielement displays: measuring and modeling effects of exposure duration. , 1988, Journal of experimental psychology. Human perception and performance.

[39]  D. G. Albrecht,et al.  Striate cortex of monkey and cat: contrast response function. , 1982, Journal of neurophysiology.

[40]  Robert A. Frazor,et al.  Visual cortex neurons of monkeys and cats: temporal dynamics of the contrast response function. , 2002, Journal of neurophysiology.

[41]  James D. Hildreth.  Bloch’s law and a temporal integration model for simple reaction time to light , 1973 .

[42]  W. Levick,et al.  Properties of sustained and transient ganglion cells in the cat retina , 1973, The Journal of physiology.

[43]  Philip L. Smith.  Psychophysically principled models of visual simple reaction time. , 1995 .

[44]  D. Tolhurst,et al.  Psychophysical evidence for sustained and transient detectors in human vision , 1973, The Journal of physiology.

[45]  Philip L. Smith,et al.  An integrated theory of attention and decision making in visual signal detection. , 2009, Psychological review.

[46]  B G Breitmeyer,et al.  Implications of sustained and transient channels for theories of visual pattern masking, saccadic suppression, and information processing. , 1976, Psychological review.

[47]  Nicholas J. Priebe,et al.  The Emergence of Contrast-Invariant Orientation Tuning in Simple Cells of Cat Visual Cortex , 2007, Neuron.

[48]  R. Nosofsky,et al.  An exemplar-based random walk model of speeded classification. , 1997, Psychological review.

[49]  Claus Bundesen,et al.  Single-letter recognition as a function of exposure duration , 1999 .

[50]  R. Freeman,et al.  Orientation selectivity in the cat's striate cortex is invariant with stimulus contrast , 2004, Experimental Brain Research.

[51]  G. Awatramani,et al.  Origin of Transient and Sustained Responses in Ganglion Cells of the Retina , 2000, The Journal of Neuroscience.

[52]  Thomas J. Palmeri.  The time course of perceptual categorization , 2001, Similarity and Categorization.

[53]  T A Busey,et al.  Providing a sensory basis for models of visual information acquisition , 1993, Perception & psychophysics.

[54]  S. Kyllingsbæk.  Modeling visual attention , 2006 .

[55]  Claus Bundesen,et al.  The relationship between independent race models and Luce's choice axiom , 1993 .

[56]  Rafal Bogacz,et al.  Optimal Decision Making on the Basis of Evidence Represented in Spike Trains , 2010, Neural Computation.

[57]  D. Luce,et al.  Detection and Recognition " ' , 2006 .

[58]  B Rassow,et al.  Correlation of Optotypes with the Landolt Ring—A Fresh Look at the Comparability of Optotypes , 1994, Optometry and vision science : official publication of the American Academy of Optometry.