Organizing probabilistic models of perception

[1]  Wei Ji Ma,et al.  Probabilistic Computation in Human Perception under Variability in Encoding Precision , 2012, PloS one.

[2]  Wei Ji Ma,et al.  Variability in encoding precision accounts for visual short-term memory limitations , 2012, Proceedings of the National Academy of Sciences.

[3]  Klaus Pawelzik,et al.  Optimality of Human Contour Integration , 2012, PLoS Comput. Biol..

[4]  J. Bowers,et al.  Bayesian just-so stories in psychology and neuroscience. , 2012, Psychological bulletin.

[5]  A. Pouget,et al.  Not Noisy, Just Wrong: The Role of Suboptimal Inference in Behavioral Variability , 2012, Neuron.

[6]  Optimal inference of sameness and difference , 2012 .

[7]  Christopher R Fetsch,et al.  Neural correlates of reliability-based cue weighting during multisensory integration , 2011, Nature Neuroscience.

[8]  A. Pouget,et al.  Marginalization in Neural Circuits with Divisive Normalization , 2011, The Journal of Neuroscience.

[9]  Konrad Paul Kording,et al.  Sensory Cue Integration , 2011 .

[10]  A. Pouget,et al.  Behavior and neural basis of near-optimal visual search , 2011, Nature Neuroscience.

[11]  Eero P. Simoncelli,et al.  Cardinal rules: Visual orientation perception reflects knowledge of environmental statistics , 2011, Nature Neuroscience.

[12]  W. Geisler,et al.  Contributions of ideal observer theory to vision research , 2011, Vision Research.

[13]  Konrad Kording,et al.  Annals of the New York Academy of Sciences Bayesian Models: the Structure of the World, Uncertainty, Behavior, and the Brain , 2022 .

[14]  Eli Brenner,et al.  Temporal Uncertainty Separates Flashes from Their Background during Saccades , 2011, The Journal of Neuroscience.

[15]  James L. McClelland,et al.  Dynamic Integration of Reward and Stimulus Information in Perceptual Decision-Making , 2011, PloS one.

[16]  M. Sahani,et al.  Observers Exploit Stochastic Models of Sensory Change to Help Judge the Passage of Time , 2011, Current Biology.

[17]  Guan-Yu Chen,et al.  Three-Dimensional Reconstruction of Brain-wide Wiring Networks in Drosophila at Single-Cell Resolution , 2011, Current Biology.

[18]  Pietro Berkes,et al.  Fiser J: Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment , 2022 .

[19]  Simon Barthelmé,et al.  Flexible mechanisms underlie the evaluation of visual confidence , 2010, Proceedings of the National Academy of Sciences.

[20]  Wei Ji Ma,et al.  Signal detection theory, uncertainty, and Poisson-like population codes , 2010, Vision Research.

[21]  Adam N Sanborn,et al.  Exemplar models as a mechanism for performing Bayesian inference , 2010, Psychonomic bulletin & review.

[22]  J. Tenenbaum,et al.  Probabilistic models of cognition: exploring representations and inductive biases , 2010, Trends in Cognitive Sciences.

[23]  P. Berkes,et al.  Statistically Optimal Perception and Learning: from Behavior to Neural Representations , 2022 .

[24]  George A. Alvarez,et al.  Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model , 2009, NIPS.

[25]  Wei Ji Ma,et al.  No capacity limit in attentional tracking: evidence for probabilistic inference under a resource constraint. , 2009, Journal of vision.

[26]  M. Shadlen,et al.  Representation of Confidence Associated with a Decision by Neurons in the Parietal Cortex , 2009, Science.

[27]  Philip Holmes,et al.  Can Monkeys Choose Optimally When Faced with Noisy Stimuli and Unequal Rewards? , 2009, PLoS Comput. Biol..

[28]  Sethu Vijayakumar,et al.  Multisensory Oddity Detection as Bayesian Inference , 2009, PloS one.

[29]  Jeffrey S. Perry,et al.  Contour statistics in natural images: Grouping across occlusions , 2009, Visual Neuroscience.

[30]  Timothy D. Hanks,et al.  Probabilistic Population Codes for Bayesian Decision Making , 2008, Neuron.

[31]  Hatim A. Zariwala,et al.  Neural correlates, computation and behavioural impact of decision confidence , 2008, Nature.

[32]  G. DeAngelis,et al.  Neural correlates of multisensory cue integration in macaque MSTd , 2008, Nature Neuroscience.

[33]  Paul M Bays,et al.  Dynamic Shifts of Limited Working Memory Resources in Human Vision , 2008, Science.

[34]  M. Landy,et al.  Decision making, movement planning and statistical decision theory , 2008, Trends in Cognitive Sciences.

[35]  W. Richards,et al.  Perception as Bayesian Inference , 2008 .

[36]  W. Geisler.  Visual perception and the statistical properties of natural scenes. , 2008, Annual review of psychology.

[37]  M. Sahani,et al.  Implicit knowledge of visual uncertainty guides decisions with asymmetric outcomes. , 2008, Journal of vision.

[38]  Eli Brenner,et al.  If I saw it, it probably wasn't far from where I was looking. , 2008, Journal of vision.

[39]  Sophie Denève,et al.  Bayesian Spiking Neurons I: Inference , 2008, Neural Computation.

[40]  Kazuyuki Aihara,et al.  Bayesian Inference Explains Perception of Unity and Ventriloquism Aftereffect: Identification of Common Sources of Audiovisual Stimuli , 2007, Neural Computation.

[41]  Konrad P. Kording,et al.  Decision Theory: What "Should" the Nervous System Do? , 2007 .

[42]  Konrad Paul Kording,et al.  Decision Theory: What "Should" the Nervous System Do? , 2007, Science.

[43]  Konrad Paul Kording,et al.  Causal Inference in Multisensory Perception , 2007, PloS one.

[44]  Peter Dayan,et al.  Fast Population Coding , 2007, Neural Computation.

[45]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[46]  Barry Stein,et al.  A Bayesian model unifies multisensory spatial localization with the physiological properties of the superior colliculus , 2007, Experimental Brain Research.

[47]  Wei Ji Ma,et al.  Bayesian inference with probabilistic population codes , 2006, Nature Neuroscience.

[48]  Jonathan D. Cohen,et al.  The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks. , 2006, Psychological review.

[49]  Peter B. Delahunt,et al.  Bayesian model of human color constancy. , 2006, Journal of vision.

[50]  J. Tenenbaum,et al.  Optimal Predictions in Everyday Cognition , 2006, Psychological science.

[51]  Neil W. Roach,et al.  Resolving multisensory conflict: a strategy for balancing the costs and benefits of audio-visual integration , 2006, Proceedings of the Royal Society B: Biological Sciences.

[52]  Anthony J. Movshon,et al.  Optimal representation of sensory information by neural populations , 2006, Nature Neuroscience.

[53]  Eero P. Simoncelli,et al.  Noise characteristics and prior expectations in human visual speed perception , 2006, Nature Neuroscience.

[54]  D. Burr,et al.  Visual Clutter Causes High-Magnitude Errors , 2006, PLoS biology.

[55]  A. Mizuno,et al.  A change of the leading player in flow Visualization technique , 2006, J. Vis..

[56]  Ulrik R Beierholm,et al.  Sound-induced flash illusion as an optimal percept , 2005, Neuroreport.

[57]  M. Shadlen,et al.  Neural Activity in Macaque Parietal Cortex Reflects Temporal Integration of Visual Motion Signals during Perceptual Decision Making , 2005, The Journal of Neuroscience.

[58]  Johan Wagemans,et al.  Texture and haptic cues in slant discrimination: reliability-based cue weighting without statistically optimal cue combination. , 2005, Journal of the Optical Society of America. A, Optics, image science, and vision.

[59]  K. Fujii,et al.  Visualization for the analysis of fluid motion , 2005, J. Vis..

[60]  W. Ma,et al.  A detection theory account of change detection. , 2004, Journal of vision.

[61]  D. Burr,et al.  The Ventriloquist Effect Results from Near-Optimal Bimodal Integration , 2004, Current Biology.

[62]  A. Yuille,et al.  Object perception as Bayesian inference. , 2004, Annual review of psychology.

[63]  Robert J. van Beers,et al.  How humans combine simultaneous proprioceptive and visual position information , 1996, Experimental Brain Research.

[64]  Rajesh P. N. Rao.  Bayesian Computation in Recurrent Neural Circuits , 2004, Neural Computation.

[65]  J. Saunders,et al.  Do humans optimally integrate stereo and texture information for judgments of surface slant? , 2003, Vision Research.

[66]  Robert A Jacobs,et al.  Bayesian integration of visual and auditory signals for spatial localization. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

[67]  P. Lennie.  The Cost of Cortical Computation , 2003, Current Biology.

[68]  David C Knill,et al.  Mixture models and the probabilistic structure of depth cues , 2003, Vision Research.

[69]  Gordon E Legge,et al.  Preneural limitations on letter identification in central and peripheral vision. , 2002, Journal of the Optical Society of America. A, Optics, image science, and vision.

[70]  Preeti Verghese,et al.  Comparing integration rules in visual search. , 2002, Journal of vision.

[71]  J. Elder,et al.  Ecological statistics of Gestalt laws for the perceptual organization of contours. , 2002, Journal of vision.

[72]  Edward H. Adelson,et al.  Motion illusions as optimal percepts , 2002, Nature Neuroscience.

[73]  M. Ernst,et al.  Humans integrate visual and haptic information in a statistically optimal fashion , 2002, Nature.

[74]  Aapo Hyvärinen,et al.  Interpreting Neural Response Variability as Monte Carlo Sampling of the Posterior , 2002, NIPS.

[75]  S. Laughlin,et al.  An Energy Budget for Signaling in the Grey Matter of the Brain , 2001, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[76]  Jacob feldman,et al.  Bayesian contour integration , 2001, Perception & psychophysics.

[77]  M S Landy,et al.  Ideal cue combination for localizing texture-defined edges. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[78]  P. Verghese.  Visual Search and Attention A Signal Detection Theory Approach , 2001, Neuron.

[79]  Jeffrey S. Perry,et al.  Edge co-occurrence in natural images predicts contour grouping performance , 2001, Vision Research.

[80]  J. Gold,et al.  Neural computations that underlie decisions about sensory stimuli , 2001, Trends in Cognitive Sciences.

[81]  A. M. Smith.  Alhacen's Theory of Visual Perception: A Critical Edition, with English Translation and Commentary, of the First Three Books of Alhacen's "De aspectibus", the Medieval Latin Version of Ibn al-Haytham's "Kitab al-Manazir": Volume One , 2001 .

[82]  Thomas J. Anastasio,et al.  Using Bayes' Rule to Model Multisensory Enhancement in the Superior Colliculus , 2000, Neural Computation.

[83]  J. P. Thomas,et al.  A signal detection model predicts the effects of set size on visual search accuracy for feature, conjunction, triple conjunction, and disjunction displays , 2000, Perception & psychophysics.

[84]  E. Mach,et al.  Contributions to the Analysis of the Sensations , 1998 .

[85]  Pascal Mamassian,et al.  Observer biases in the 3D interpretation of line drawings , 1998, Vision Research.

[86]  M. Eckstein.  The Lower Visual Search Efficiency for Conjunctions Is Due to Noise and not Serial Attentional Processing , 1998 .

[87]  Alexandre Pouget,et al.  Probabilistic Interpretation of Population Codes , 1996, Neural Computation.

[88]  T. Sanger,et al.  Probability density estimation for the interpretation of neural population codes. , 1996, Journal of neurophysiology.

[89]  Wendy L. Braje,et al.  Human efficiency for recognizing 3-D objects in luminance noise , 1995, Vision Research.

[90]  David C. Knill,et al.  Object classification for human and ideal observers , 1995, Vision Research.

[91]  P. Földiák,et al.  The ‘Ideal Homunculus’: Statistical Inference from Neural Population Responses , 1993 .

[92]  J. Palmer.  Attentional limits on the perception and memory of visual information. , 1990, Journal of experimental psychology. Human perception and performance.

[93]  W. Geisler.  Sequential ideal-observer analysis of visual discriminations. , 1989, Psychological review.

[94]  A E Burgess,et al.  Visual signal detection. IV. Observer inconsistency. , 1988, Journal of the Optical Society of America. A, Optics and image science.

[95]  Norma Graham,et al.  Signal-detection models for multidimensional stimuli: probability distributions and combination rules , 1987 .

[96]  G. Legge,et al.  Contrast discrimination in noise. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[97]  D G Pelli,et al.  Uncertainty explains many aspects of visual contrast detection and discrimination. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[98]  R. F. Wagner,et al.  Efficiency of human visual signal discrimination. , 1981, Science.

[99]  H. Barlow.  The efficiency of detecting changes of density in random dot patterns , 1978, Vision Research.

[100]  Roger Ratcliff,et al.  A Theory of Memory Retrieval. , 1978 .

[101]  H. McGurk,et al.  Hearing lips and seeing voices , 1976, Nature.

[102]  T. Cohn,et al.  Detectability of a luminance increment: effect of spatial uncertainty. , 1974, Journal of the Optical Society of America.

[103]  H B Barlow,et al.  Single units and sensation: a neuron doctrine for perceptual psychology? , 1972, Perception.

[104]  H B Barlow,et al.  PATTERN RECOGNITION AND THE RESPONSES OF SENSORY NEURONS * , 1969, Annals of the New York Academy of Sciences.

[105]  David Jaarsma,et al.  More on the Detection of One of M Orthogonal Signals , 1967 .

[106]  D. M. Green,et al.  Signal detection theory and psychophysics , 1966 .

[107]  Hermann von Helmholtz,et al.  Treatise on Physiological Optics , 1962 .

[108]  H. Barlow.  Retinal noise and absolute threshold. , 1956, Journal of the Optical Society of America.

[109]  J. Swets,et al.  A decision-making theory of visual detection. , 1954, Psychological review.

[110]  W. W. Peterson,et al.  The theory of signal detectability , 1954, Trans. IRE Prof. Group Inf. Theory.

[111]  E. Lamar,et al.  Quanta and Vision , 1948 .

[112]  J. Wolfowitz,et al.  Optimum Character of the Sequential Probability Ratio Test , 1948 .

[113]  A. Rose.  The sensitivity performance of the human eye on an absolute scale. , 1948, Journal of the Optical Society of America.

[114]  H. Vries.  The quantum character of light and its bearing upon threshold of vision, the differential sensitivity and visual acuity of the eye , 1943 .

[115]  S. Hecht,et al.  ENERGY, QUANTA, AND VISION , 1942, The Journal of general physiology.