Multiple brain networks contribute to the acquisition of bias in perceptual decision-making

Bias occurs in perceptual decisions when the reward associated with a particular response dominates the sensory evidence in support of a choice. However, it remains unclear how this bias is acquired and once acquired, how it influences perceptual decision processes in the brain. We addressed these questions using model-based neuroimaging in a motion discrimination paradigm where contextual cues suggested which one of two options would receive higher rewards on each trial. We found that participants gradually learned to choose the higher-rewarded option in each context when making a perceptual decision. The amount of bias on each trial was fit well by a reinforcement-learning model that estimated the subjective value of each option within the current context. The brain mechanisms underlying this bias acquisition process were similar to those observed in reward-based decision tasks: prediction errors correlated with the fMRI signals in ventral striatum, dlPFC, and parietal cortex, whereas the amount of acquired bias correlated with activity in ventromedial prefrontal (vmPFC), dorsolateral frontal (dlPFC), and parietal cortices. Moreover, psychophysiological interaction analysis revealed that as bias increased, functional connectivity increased within multiple brain networks (dlPFC-vmPFC-visual, vmPFC-motor, and parietal-anterior-cingulate), suggesting that multiple mechanisms contribute to bias in perceptual decisions through integration of value processing with action, sensory, and control systems. These provide a novel link between the neural mechanisms underlying perceptual and economic decision-making.

[1]  C. White,et al.  Decomposing bias in different types of simple decisions. , 2014, Journal of experimental psychology. Learning, memory, and cognition.

[2]  J. Gold,et al.  The Basal Ganglia’s Contributions to Perceptual Decision Making , 2013, Neuron.

[3]  Joseph W. Kable,et al.  The valuation system: A coordinate-based meta-analysis of BOLD fMRI experiments examining neural correlates of subjective value , 2013, NeuroImage.

[4]  Jane R. Garrison,et al.  Prediction error in reinforcement learning: A meta-analysis of neuroimaging studies , 2013, Neuroscience & Biobehavioral Reviews.

[5]  J. Gold,et al.  How mechanisms of perceptual decision-making affect the psychometric function , 2013, Progress in Neurobiology.

[6]  M. Philiastides,et al.  Temporal Characteristics of the Influence of Punishment on Perceptual Decision Making in the Human Brain , 2013, The Journal of Neuroscience.

[7]  Jonathan D. Nelson,et al.  How Embodied Is Perceptual Decision Making? Evidence for Separate Processing of Perceptual and Motor Decisions , 2013, The Journal of Neuroscience.

[8]  J. Gottlieb Attention, Learning, and the Value of Information , 2012, Neuron.

[9]  H. Seo,et al.  Neural basis of reinforcement learning and decision making. , 2012, Annual review of neuroscience.

[10]  Christopher Summerfield,et al.  Building Bridges between Perceptual and Economic Decision-Making: Neural and Computational Mechanisms , 2012, Front. Neurosci..

[11]  R. Ratcliff,et al.  Bias in the Brain: A Diffusion Model Analysis of Prior Probability and Potential Payoff , 2012, The Journal of Neuroscience.

[12]  Abraham Z. Snyder,et al.  Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion , 2012, NeuroImage.

[13]  J. Haynes,et al.  Perceptual Learning and Decision-Making in Human Medial Frontal Cortex , 2011, Neuron.

[14]  T. Robbins,et al.  Decision Making, Affect, and Learning: Attention and Performance XXIII , 2011 .

[15]  Nathaniel D. Daw,et al.  Trial-by-trial data analysis using computational models , 2011 .

[16]  Rafal Bogacz,et al.  Integration of Reinforcement Learning and Optimal Decision-Making Theories of the Basal Ganglia , 2011, Neural Computation.

[17]  P. Glimcher Understanding dopamine and reinforcement learning: The dopamine reward prediction error hypothesis , 2011, Proceedings of the National Academy of Sciences.

[18]  J. Gold,et al.  Caudate Encodes Multiple Computations for Perceptual Decisions , 2010, The Journal of Neuroscience.

[19]  C. Summerfield,et al.  Economic Value Biases Uncertain Perceptual Choices in the Parietal and Prefrontal Cortices , 2010, Front. Hum. Neurosci..

[20]  Scott D. Brown,et al.  Cortico-striatal connections predict control over speed and accuracy in perceptual decision making , 2010, Proceedings of the National Academy of Sciences.

[21]  Takeo Watanabe,et al.  Temporally Extended Dopamine Responses to Perceptually Demanding Reward-Predictive Stimuli , 2010, The Journal of Neuroscience.

[22]  P. Dayan,et al.  States versus Rewards: Dissociable Neural Prediction Error Signals Underlying Model-Based and Model-Free Reinforcement Learning , 2010, Neuron.

[23]  Steen Moeller,et al.  Multiband multislice GE‐EPI at 7 tesla, with 16‐fold acceleration using partial parallel imaging with application to high spatial and temporal whole‐brain fMRI , 2010, Magnetic resonance in medicine.

[24]  M. Sahani,et al.  Effects of Category-Specific Costs on Neural Systems for Perceptual Decision-Making , 2010, Journal of neurophysiology.

[25]  James L. McClelland,et al.  Integration of Sensory and Reward Information during Perceptual Decision-Making in Lateral Intraparietal Cortex (LIP) of the Macaque Monkey , 2010, PloS one.

[26]  Jonathan D. Cohen,et al.  Reward rate optimization in two-alternative decision making: empirical tests of theoretical predictions. , 2009, Journal of experimental psychology. Human perception and performance.

[27]  Klaus Wunderlich,et al.  Neural computations underlying action-based decision making in the human brain , 2009, Proceedings of the National Academy of Sciences.

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

[29]  John T Serences,et al.  Value-Based Modulations in Human Visual Cortex , 2008, Neuron.

[30]  L. Stone,et al.  Effects of Prior Information and Reward on Oculomotor and Perceptual Choices , 2008, The Journal of Neuroscience.

[31]  C. Law,et al.  Neural correlates of perceptual learning in a sensory-motor, but not a sensory, cortical area , 2008, Nature Neuroscience.

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

[33]  Sabrina M. Tom,et al.  The Neural Basis of Loss Aversion in Decision-Making Under Risk , 2007, Science.

[34]  P. Glimcher,et al.  JOURNAL OF THE EXPERIMENTAL ANALYSIS OF BEHAVIOR 2005, 84, 555–579 NUMBER 3(NOVEMBER) DYNAMIC RESPONSE-BY-RESPONSE MODELS OF MATCHING BEHAVIOR IN RHESUS MONKEYS , 2022 .

[35]  M. Shadlen,et al.  The effect of stimulus strength on the speed and accuracy of a perceptual decision. , 2005, Journal of vision.

[36]  S. E. Bialkova,et al.  Cognitive control in Task switching , 2003 .

[37]  J. Lewin Functional MRI: An introduction to methods , 2003 .

[38]  Jonathan D. Cohen,et al.  Mechanisms underlying dependencies of performance on stimulus history in a two-alternative forced-choice task , 2002, Cognitive, affective & behavioral neuroscience.

[39]  W Todd Maddox,et al.  Toward a unified theory of decision criterion learning in perceptual categorization. , 2002, Journal of the experimental analysis of behavior.

[40]  P. Montague,et al.  Neural Economics and the Biological Substrates of Valuation , 2002, Neuron.

[41]  P. Montague,et al.  Activity in human ventral striatum locked to errors of reward prediction , 2002, Nature Neuroscience.

[42]  Keith J. Worsley,et al.  Statistical analysis of activation images , 2001 .

[43]  W. Newsome,et al.  Neural basis of a perceptual decision in the parietal cortex (area LIP) of the rhesus monkey. , 2001, Journal of neurophysiology.

[44]  V. Carey,et al.  Mixed-Effects Models in S and S-Plus , 2001 .

[45]  D. Bates,et al.  Mixed-Effects Models in S and S-PLUS , 2001 .

[46]  K. H. Britten,et al.  Responses of neurons in macaque MT to stochastic motion signals , 1993, Visual Neuroscience.

[47]  Peter Dayan,et al.  Q-learning , 1992, Machine Learning.

[48]  Neil A. Macmillan,et al.  Detection Theory: A User's Guide , 1991 .

[49]  K. H. Britten,et al.  Neuronal correlates of a perceptual decision , 1989, Nature.

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

[51]  H. Akaike A new look at the statistical model identification , 1974 .

[52]  Michael D. Geurts,et al.  Time Series Analysis: Forecasting and Control , 1977 .

[53]  W. Edwards Optimal strategies for seeking information: Models for statistics, choice reaction times, and human information processing ☆ , 1965 .

[54]  Haynes John-Dylan,et al.  Perceptual learning and decision making in human medial frontal cortex , 2012 .

[55]  A. Cooper,et al.  Predictive Reward Signal of Dopamine Neurons , 2011 .

[56]  P. Dayan,et al.  NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript NIH Public Access Author Manuscript Neuron. Author manuscript. , 2011 .

[57]  Jonathan D. Cohen,et al.  Between-Task Competition and Cognitive Control in Task Switching , 2006, The Journal of Neuroscience.

[58]  Tobias Egner,et al.  Cerebral Cortex doi:10.1093/cercor/bhi129 Mistaking a House for a Face: Neural Correlates of Misperception in Healthy Humans , 2005 .

[59]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[60]  Neil A. Macmillan,et al.  Detection theory: A user's guide, 2nd ed. , 2005 .

[61]  D. F. H. A. N. H. Azrin,et al.  JOURNAL OF THE EXPERIMENTAL ANALYSIS OF BEHAVIOR , 2005 .

[62]  Peter Dayan,et al.  Technical Note: Q-Learning , 2004, Machine Learning.

[63]  Karl J. Friston,et al.  Modeling regional and psychophysiologic interactions in fMRI: the importance of hemodynamic deconvolution , 2003, NeuroImage.

[64]  Corey J. Bohil,et al.  Base-rate and payoff effects in multidimensional perceptual categorization. , 1998, Journal of Experimental Psychology. Learning, Memory and Cognition.

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

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

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

[68]  Frontiers in Computational Neuroscience , 2022 .