Humans incorporate attention-dependent uncertainty into perceptual decisions and confidence

Significance We must routinely make decisions based on uncertain sensory information. Sometimes that uncertainty is related to our own cognitive state, such as when we are not paying attention. Do our decisions about what we perceive take into account our attentional state? Or are we blind to such internal sources of uncertainty, leading to poor decisions and overconfidence? We found that human observers take attention-dependent uncertainty into account when categorizing visual stimuli and reporting their confidence in a task in which uncertainty is relevant for performance. Moreover, they do so in an approximately Bayesian fashion. Human perceptual decision-making can therefore, at least in some cases, adjust in a statistically appropriate way to external and internal sources of uncertainty. Perceptual decisions are better when they take uncertainty into account. Uncertainty arises not only from the properties of sensory input but also from cognitive sources, such as different levels of attention. However, it is unknown whether humans appropriately adjust for such cognitive sources of uncertainty during perceptual decision-making. Here we show that, in a task in which uncertainty is relevant for performance, human categorization and confidence decisions take into account uncertainty related to attention. We manipulated uncertainty in an orientation categorization task from trial to trial using only an attentional cue. The categorization task was designed to disambiguate decision rules that did or did not depend on attention. Using formal model comparison to evaluate decision behavior, we found that category and confidence decision boundaries shifted as a function of attention in an approximately Bayesian fashion. This means that the observer’s attentional state on each trial contributed probabilistically to the decision computation. This responsiveness of an observer’s decisions to attention-dependent uncertainty should improve perceptual decisions in natural vision, in which attention is unevenly distributed across a scene.

[1]  C. Koch,et al.  Spatial attention increases performance but not subjective confidence in a discrimination task. , 2008, Journal of vision.

[2]  Michael Zehetleitner,et al.  Visibility Is Not Equivalent to Confidence in a Low Contrast Orientation Discrimination Task , 2016, Front. Psychol..

[3]  M. Carrasco.  Visual attention: The past 25 years , 2011, Vision Research.

[4]  Andrei Gorea,et al.  Upshifted decision criteria in attentional blink and repetition blindness , 2010 .

[5]  C. Tyler,et al.  Bayesian adaptive estimation of psychometric slope and threshold , 1999, Vision Research.

[6]  S. Walker.  Invited comment on the paper "Slice Sampling" by Radford Neal , 2003 .

[7]  D G Pelli,et al.  The VideoToolbox software for visual psychophysics: transforming numbers into movies. , 1997, Spatial vision.

[8]  Aki Vehtari,et al.  Efficient implementation of leave-one-out cross-validation and WAIC for evaluating fitted Bayesian models , 2015 .

[9]  Craig Leth-Steensen,et al.  Selective attention and subjective confidence calibration , 2010, Attention, perception & psychophysics.

[10]  Jochen Kaiser,et al.  The Influence of Endogenous and Exogenous Spatial Attention on Decision Confidence , 2017, Scientific Reports.

[11]  Pieter R. Roelfsema,et al.  Variance misperception explains illusions of confidence in simple perceptual decisions , 2014, Consciousness and Cognition.

[12]  Luigi Acerbi,et al.  On the Origins of Suboptimality in Human Probabilistic Inference , 2014, PLoS Comput. Biol..

[13]  Wei Ji Ma,et al.  Neural coding of uncertainty and probability. , 2014, Annual review of neuroscience.

[14]  W. Ma,et al.  Factorial comparison of working memory models. , 2014, Psychological review.

[15]  Hakwan Lau,et al.  A decisional account of subjective inflation of visual perception at the periphery , 2014, Attention, Perception, & Psychophysics.

[16]  Thomas Haarmeier,et al.  Selective Attention Increases Choice Certainty in Human Decision Making , 2012, PloS one.

[17]  B. Dosher,et al.  External noise distinguishes attention mechanisms , 1998, Vision Research.

[18]  H. Lau,et al.  Attention induces conservative subjective biases in visual perception , 2011, Nature Neuroscience.

[19]  H. Lau,et al.  Prestimulus hemodynamic activity in dorsal attention network is negatively associated with decision confidence in visual perception. , 2012, Journal of neurophysiology.

[20]  H. Lau,et al.  Low attention impairs optimal incorporation of prior knowledge in perceptual decisions , 2015, Attention, perception & psychophysics.

[21]  Michael N. Shadlen,et al.  Effects of Cortical Microstimulation on Confidence in a Perceptual Decision , 2014, Neuron.

[22]  Aki Vehtari,et al.  Understanding predictive information criteria for Bayesian models , 2013, Statistics and Computing.

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

[24]  Barbara F. Händel,et al.  Cortical representations of confidence in a visual perceptual decision , 2014, Nature Communications.

[25]  M. Carrasco,et al.  Attention alters appearance , 2004, Nature Neuroscience.

[26]  Adam Binch,et al.  Perception as Bayesian Inference , 2014 .

[27]  Nathaniel D. Daw,et al.  Self-Evaluation of Decision-Making: A General Bayesian Framework for Metacognitive Computation , 2017, Psychological review.

[28]  M. Carrasco,et al.  On the automaticity and flexibility of covert attention: a speed-accuracy trade-off analysis. , 2009, Journal of vision.

[29]  Denis G. Pelli,et al.  ECVP '07 Abstracts , 2007, Perception.

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

[31]  Bahador Bahrami,et al.  Post-decisional accounts of biases in confidence , 2016, Current Opinion in Behavioral Sciences.

[32]  J. Reynolds,et al.  Attentional modulation of visual processing. , 2004, Annual review of neuroscience.

[33]  A Gorea,et al.  Failure to handle more than one internal representation in visual detection tasks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[34]  Bruce Luber,et al.  Direct injection of noise to the visual cortex decreases accuracy but increases decision confidence. , 2012, Journal of neurophysiology.

[35]  Peter E. Latham,et al.  Doubly Bayesian Analysis of Confidence in Perceptual Decision-Making , 2015, PLoS Comput. Biol..

[36]  D H Brainard,et al.  The Psychophysics Toolbox. , 1997, Spatial vision.

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

[38]  Radford M. Neal.  Slice Sampling , 2003, The Annals of Statistics.

[39]  Mikhail Katkov,et al.  Decision criteria in dual discrimination tasks estimated using external-noise methods , 2012, Attention, Perception, & Psychophysics.

[40]  Dov Sagi,et al.  Natural extinction: A criterion shift phenomenon , 2002 .

[41]  M. Carrasco,et al.  Covert attention affects the psychometric function of contrast sensitivity , 2002, Vision Research.

[42]  B. Dosher,et al.  PSYCHOLOGICAL SCIENCE Research Article NOISE EXCLUSION IN SPATIAL ATTENTION , 2022 .

[43]  Dov Sagi,et al.  Disentangling signal from noise in visual contrast discrimination , 2001, Nature Neuroscience.

[44]  M. Carrasco,et al.  Sustained and transient covert attention enhance the signal via different contrast response functions , 2006, Vision Research.

[45]  Luigi Acerbi,et al.  Bayesian comparison of explicit and implicit causal inference strategies in multisensory heading perception , 2017, bioRxiv.

[46]  Marisa Carrasco,et al.  Attentional enhancement of spatial resolution: linking behavioural and neurophysiological evidence , 2013, Nature Reviews Neuroscience.

[47]  Michael N. Shadlen,et al.  Effects of Cortical Microstimulation on Confidence in a Perceptual Decision , 2014, Neuron.

[48]  Nicolaas Prins,et al.  The psychometric function: the lapse rate revisited. , 2012, Journal of vision.

[49]  A. Gorea,et al.  Criteria interactions across visual attributes , 2005, Vision Research.

[50]  J P Thomas,et al.  Bandwidths of orientation channels in human vision. , 1979, Journal of the Optical Society of America.

[51]  Adam N. Sanborn,et al.  Uncovering mental representations with Markov chain Monte Carlo , 2010, Cognitive Psychology.

[52]  Tony Ro,et al.  Transcranial magnetic stimulation to visual cortex induces suboptimal introspection , 2017, Cortex.

[53]  M. Carrasco,et al.  Spatial covert attention increases contrast sensitivity across the CSF: support for signal enhancement , 2000, Vision Research.

[54]  A. Tolias,et al.  Trial-to-trial, uncertainty-based adjustment of decision boundaries in visual categorization , 2013, Proceedings of the National Academy of Sciences.

[55]  Pascal Mamassian,et al.  Visual Confidence. , 2016, Annual review of vision science.

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

[57]  Wei Ji Ma,et al.  Comparing Bayesian and non-Bayesian accounts of human confidence reports , 2018, PLoS Comput. Biol..

[58]  Preeti Verghese,et al.  The unique criterion constraint: a false alarm? , 2002, Nature Neuroscience.

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

[60]  Aki Vehtari,et al.  Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC , 2015, Statistics and Computing.