Spatiotemporal dynamics of random stimuli account for trial-to-trial variability in perceptual decision making

Decisions in everyday life are prone to error. Standard models typically assume that errors during perceptual decisions are due to noise. However, it is unclear how noise in the sensory input affects the decision. Here we show that there are experimental tasks for which one can analyse the exact spatio-temporal details of a dynamic sensory noise and better understand variability in human perceptual decisions. Using a new experimental visual tracking task and a novel Bayesian decision making model, we found that the spatio-temporal noise fluctuations in the input of single trials explain a significant part of the observed responses. Our results show that modelling the precise internal representations of human participants helps predict when perceptual decisions go wrong. Furthermore, by modelling precisely the stimuli at the single-trial level, we were able to identify the underlying mechanism of perceptual decision making in more detail than standard models.

[1]  M. Shadlen,et al.  Decision Making as a Window on Cognition , 2013, Neuron.

[2]  D. Wolpert,et al.  Changing your mind: a computational mechanism of vacillation , 2009, Nature.

[3]  M. Shadlen,et al.  A Neural Implementation of Wald’s Sequential Probability Ratio Test , 2015, Neuron.

[4]  Karl J. Friston,et al.  Bayesian model selection for group studies — Revisited , 2014, NeuroImage.

[5]  R. Duncan Luce,et al.  Response Times: Their Role in Inferring Elementary Mental Organization , 1986 .

[6]  Leslie G. Ungerleider,et al.  The neural systems that mediate human perceptual decision making , 2008, Nature Reviews Neuroscience.

[7]  Stefan J. Kiebel,et al.  Perceptual decision making: drift-diffusion model is equivalent to a Bayesian model , 2014, Front. Hum. Neurosci..

[8]  Bingni W. Brunton,et al.  Distinct relationships of parietal and prefrontal cortices to evidence accumulation , 2014, Nature.

[9]  Stanislas Dehaene,et al.  Accumulation of Evidence during Sequential Decision Making: The Importance of Top–Down Factors , 2010, The Journal of Neuroscience.

[10]  R. Plevin,et al.  Approximate Bayesian Computation in Evolution and Ecology , 2011 .

[11]  C. Summerfield,et al.  Robust averaging during perceptual judgment , 2011, Proceedings of the National Academy of Sciences.

[12]  Konrad Paul Kording,et al.  Temporal Integration of Olfactory Perceptual Evidence in Human Orbitofrontal Cortex , 2012, Neuron.

[13]  M. Shadlen,et al.  Decision-making with multiple alternatives , 2008, Nature Neuroscience.

[14]  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.

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

[16]  Scott D. Brown,et al.  Revisiting the Evidence for Collapsing Boundaries and Urgency Signals in Perceptual Decision-Making , 2015, The Journal of Neuroscience.

[17]  Bingni W. Brunton,et al.  Rats and Humans Can Optimally Accumulate Evidence for Decision-Making , 2013, Science.

[18]  James L. McClelland,et al.  The time course of perceptual choice: the leaky, competing accumulator model. , 2001, Psychological review.

[19]  Nicolas Chopin,et al.  Expectation Propagation for Likelihood-Free Inference , 2011, 1107.5959.

[20]  Timothy D. Hanks,et al.  Bounded Integration in Parietal Cortex Underlies Decisions Even When Viewing Duration Is Dictated by the Environment , 2008, The Journal of Neuroscience.

[21]  Rajesh P. N. Rao,et al.  Decision Making Under Uncertainty: A Neural Model Based on Partially Observable Markov Decision Processes , 2010, Front. Comput. Neurosci..

[22]  Jerome R. Busemeyer,et al.  Psychological models of deferred decision making , 1988 .

[23]  C. Summerfield,et al.  Expectation in perceptual decision making: neural and computational mechanisms , 2014, Nature Reviews Neuroscience.

[24]  C. Summerfield,et al.  Rhythmic Fluctuations in Evidence Accumulation during Decision Making in the Human Brain , 2012, Neuron.

[25]  A. Pouget,et al.  The Cost of Accumulating Evidence in Perceptual Decision Making , 2012, The Journal of Neuroscience.

[26]  K. H. Britten,et al.  A relationship between behavioral choice and the visual responses of neurons in macaque , 2008 .

[27]  KongFatt Wong-Lin,et al.  Neural Circuit Dynamics Underlying Accumulation of Time-Varying Evidence During Perceptual Decision Making , 2007, Frontiers Comput. Neurosci..

[28]  V. Lamme,et al.  How Awareness Changes the Relative Weights of Evidence During Human Decision-Making , 2011, PLoS biology.

[29]  Raymond J. Dolan,et al.  Active Inference, Evidence Accumulation, and the Urn Task , 2015, Neural Computation.

[30]  K. Wiech,et al.  Influence of prior information on pain involves biased perceptual decision-making , 2014, Current Biology.

[31]  Bruno B. Averbeck,et al.  Theory of Choice in Bandit, Information Sampling and Foraging Tasks , 2015, PLoS Comput. Biol..

[32]  M. Bauer,et al.  Neural Characterization of the Speed–Accuracy Tradeoff in a Perceptual Decision-Making Task , 2011, The Journal of Neuroscience.

[33]  Timothy D. Hanks,et al.  Elapsed Decision Time Affects the Weighting of Prior Probability in a Perceptual Decision Task , 2011, The Journal of Neuroscience.

[34]  W. Newsome,et al.  A selective impairment of motion perception following lesions of the middle temporal visual area (MT) , 1988, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[35]  J. Movshon,et al.  The analysis of visual motion: a comparison of neuronal and psychophysical performance , 1992, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[36]  Karl J. Friston,et al.  Bayesian model selection for group studies , 2009, NeuroImage.

[37]  Xiao-Jing Wang,et al.  Probabilistic Decision Making by Slow Reverberation in Cortical Circuits , 2002, Neuron.

[38]  L. Pinneo.  On noise in the nervous system. , 1966, Psychological review.

[39]  Larissa Albantakis,et al.  The encoding of alternatives in multiple-choice decision-making , 2009, Proceedings of the National Academy of Sciences.

[40]  Roger Ratcliff,et al.  The Diffusion Decision Model: Theory and Data for Two-Choice Decision Tasks , 2008, Neural Computation.

[41]  Peter I. Frazier,et al.  Sequential Hypothesis Testing under Stochastic Deadlines , 2007, NIPS.

[42]  Michael N. Shadlen,et al.  Erratum: Corrigendum: Decision-making with multiple alternatives , 2008 .

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

[44]  A. Pouget,et al.  Efficient computation and cue integration with noisy population codes , 2001, Nature Neuroscience.

[45]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[46]  Christof Koch,et al.  Temporal Precision of Spike Trains in Extrastriate Cortex of the Behaving Macaque Monkey , 1999, Neural Computation.

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

[48]  Christof Koch,et al.  The Drift Diffusion Model Can Account for the Accuracy and Reaction Time of Value-Based Choices Under High and Low Time Pressure , 2010, Judgment and Decision Making.

[49]  Tom Minka,et al.  Expectation Propagation for approximate Bayesian inference , 2001, UAI.

[50]  M. Lee,et al.  Evidence accumulation in decision making: Unifying the “take the best” and the “rational” models , 2004, Psychonomic bulletin & review.

[51]  J. Gold,et al.  The neural basis of decision making. , 2007, Annual review of neuroscience.

[52]  K. H. Britten,et al.  A relationship between behavioral choice and the visual responses of neurons in macaque MT , 1996, Visual Neuroscience.

[53]  Lionel Rigoux,et al.  VBA: A Probabilistic Treatment of Nonlinear Models for Neurobiological and Behavioural Data , 2014, PLoS Comput. Biol..

[54]  R. Carpenter,et al.  The influence of urgency on decision time , 2000, Nature Neuroscience.

[55]  Marius Usher,et al.  The Timescale of Perceptual Evidence Integration Can Be Adapted to the Environment , 2013, Current Biology.

[56]  M. Shadlen,et al.  Response of Neurons in the Lateral Intraparietal Area during a Combined Visual Discrimination Reaction Time Task , 2002, The Journal of Neuroscience.

[57]  J. Movshon,et al.  A computational analysis of the relationship between neuronal and behavioral responses to visual motion , 1996, The Journal of neuroscience : the official journal of the Society for Neuroscience.