Human Frontal–Subcortical Circuit and Asymmetric Belief Updating

How humans integrate information to form beliefs about reality is a question that has engaged scientists for centuries, yet the biological system supporting this process is not well understood. One of the most salient attributes of information is valence. Whether a piece of news is good or bad is critical in determining whether it will alter our beliefs. Here, we reveal a frontal–subcortical circuit in the left hemisphere that is simultaneously associated with enhanced integration of favorable information into beliefs and impaired integration of unfavorable information. Specifically, for favorable information, stronger white matter connectivity within this system, particularly between the left inferior frontal gyrus (IFG) and left subcortical regions (including the amygdala, hippocampus, thalamus, putamen, and pallidum), as well as insular cortex, is associated with greater change in belief. However, for unfavorable information, stronger connectivity within this system, particularly between the left IFG and left pallidum, putamen, and insular cortex, is associated with reduced change in beliefs. These novel results are consistent with models suggesting that partially separable processes govern learning from favorable and unfavorable information. SIGNIFICANCE STATEMENT Beliefs of what may happen in the future are important, because they guide decisions and actions. Here, we illuminate how structural brain connectivity is related to the generation of subjective beliefs. We focus on how the valence of information is related to people's tendency to alter their beliefs. By quantifying the extent to which participants update their beliefs in response to desirable and undesirable information and relating those measures to the strength of white matter connectivity using diffusion tensor imaging, we characterize a left frontal–subcortical system that is associated simultaneously with greater belief updating in response to favorable information and reduced belief updating in response to unfavorable information. This neural architecture may allow valence to be incorporated into belief updating.

[1]  Joseph E LeDoux.  Brain mechanisms of emotion and emotional learning , 1992, Current Biology.

[2]  Antonio Damasio,et al.  The somatic marker hypothesis: A neural theory of economic decision , 2005, Games Econ. Behav..

[3]  Stephen M. Smith,et al.  A global optimisation method for robust affine registration of brain images , 2001, Medical Image Anal..

[4]  O Salonen,et al.  Poststroke depression and lesion location revisited. , 2004, The Journal of neuropsychiatry and clinical neurosciences.

[5]  Michael J. Frank,et al.  Hold Your Horses: Impulsivity, Deep Brain Stimulation, and Medication in Parkinsonism , 2007, Science.

[6]  Heidi Johansen-Berg,et al.  Using diffusion imaging to study human connectional anatomy. , 2009, Annual review of neuroscience.

[7]  T. Sharot,et al.  How Robust Is the Optimistic Update Bias for Estimating Self-Risk and Population Base Rates? , 2014, PloS one.

[8]  Peter Bossaerts,et al.  The Neural Representation of Unexpected Uncertainty during Value-Based Decision Making , 2013, Neuron.

[9]  R. Dolan,et al.  Optimistic update bias increases in older age , 2013, Psychological Medicine.

[10]  R. J. Dolan,et al.  Depression is related to an absence of optimistically biased belief updating about future life events , 2013, Psychological Medicine.

[11]  R. Blair,et al.  Adapting to Dynamic Stimulus-Response Values: Differential Contributions of Inferior Frontal, Dorsomedial, and Dorsolateral Regions of Prefrontal Cortex to Decision Making , 2009, The Journal of Neuroscience.

[12]  John Ashburner,et al.  A fast diffeomorphic image registration algorithm , 2007, NeuroImage.

[13]  G. Rees,et al.  The structural basis of inter-individual differences in human behaviour and cognition , 2011, Nature Reviews Neuroscience.

[14]  N. Srinivasan,et al.  Role of affect in decision making. , 2013, Progress in brain research.

[15]  Geraint Rees,et al.  Relating Introspective Accuracy to Individual Differences in Brain Structure , 2010, Science.

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

[17]  Anne G E Collins,et al.  Opponent actor learning (OpAL): modeling interactive effects of striatal dopamine on reinforcement learning and choice incentive. , 2014, Psychological review.

[18]  W. K. Simmons,et al.  Circular analysis in systems neuroscience: the dangers of double dipping , 2009, Nature Neuroscience.

[19]  Self‐specific Optimism Bias in Belief Updating Is Associated with High Trait Optimism , 2015 .

[20]  George I. Christopoulos,et al.  Neural Correlates of Value, Risk, and Risk Aversion Contributing to Decision Making under Risk , 2009, The Journal of Neuroscience.

[21]  Justin M. Rao,et al.  The Good News-Bad News Effect: Asymmetric Processing of Objective Information about Yourself , 2011 .

[22]  T. Robbins,et al.  Defining the Neural Mechanisms of Probabilistic Reversal Learning Using Event-Related Functional Magnetic Resonance Imaging , 2002, The Journal of Neuroscience.

[23]  P. Bossaerts.  What Decision Neuroscience Teaches Us About Financial Decision Making , 2009 .

[24]  Stephen M. Smith,et al.  Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference , 2009, NeuroImage.

[25]  N. Kanwisher.  Functional specificity in the human brain: A window into the functional architecture of the mind , 2010, Proceedings of the National Academy of Sciences.

[26]  J. Hodges.  Memory, Amnesia and the Hippocampal System , 1995 .

[27]  Richard C. Clarke,et al.  Human development of the ability to learn from bad news , 2013, Proceedings of the National Academy of Sciences.

[28]  R. Dolan,et al.  Losing the rose tinted glasses: neural substrates of unbiased belief updating in depression , 2014, Front. Hum. Neurosci..

[29]  Michael X. Cohen,et al.  Hippocampal-Prefrontal Connectivity Predicts Midfrontal Oscillations and Long-Term Memory Performance , 2011, Current Biology.

[30]  A. Damasio,et al.  Subcortical and cortical brain activity during the feeling of self-generated emotions , 2000, Nature Neuroscience.

[31]  Derek K. Jones,et al.  Virtual in Vivo Interactive Dissection of White Matter Fasciculi in the Human Brain , 2002, NeuroImage.

[32]  Christoph W Korn,et al.  How unrealistic optimism is maintained in the face of reality , 2011, Nature Neuroscience.

[33]  W. Schultz,et al.  The Human Brain Encodes Event Frequencies While Forming Subjective Beliefs , 2013, The Journal of Neuroscience.

[34]  Jean-Luc Anton,et al.  Region of interest analysis using an SPM toolbox , 2010 .

[35]  T. Robbins,et al.  Inhibition and the right inferior frontal cortex , 2004, Trends in Cognitive Sciences.

[36]  M S Mega,et al.  Frontal-subcortical circuits and neuropsychiatric disorders. , 1994, The Journal of neuropsychiatry and clinical neurosciences.

[37]  M. Raichle,et al.  Tracking neuronal fiber pathways in the living human brain. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[38]  Michael X. Cohen,et al.  Connectivity-based segregation of the human striatum predicts personality characteristics , 2009, Nature Neuroscience.

[39]  J. Cummings,et al.  Frontal-subcortical neuronal circuits and clinical neuropsychiatry: an update. , 2002, Journal of psychosomatic research.

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

[41]  Christopher K. Hsee,et al.  Risk as Feelings , 2001, Psychological bulletin.

[42]  Timothy Edward John Behrens,et al.  Characterization and propagation of uncertainty in diffusion‐weighted MR imaging , 2003, Magnetic resonance in medicine.

[43]  Michael X. Cohen,et al.  Connectivity-based segregation of the human striatum predicts personality characteristics , 2009, Nature Neuroscience.

[44]  Mark W. Woolrich,et al.  Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? , 2007, NeuroImage.

[45]  Stephen M. Smith,et al.  Permutation inference for the general linear model , 2014, NeuroImage.

[46]  Marlies E. Vissers,et al.  Subcortical, modality-specific pathways contribute to multisensory processing in humans. , 2014, Cerebral cortex.

[47]  Brian Knutson,et al.  Frontostriatal White Matter Integrity Mediates Adult Age Differences in Probabilistic Reward Learning , 2012, The Journal of Neuroscience.

[48]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[49]  H. Mayberg.  Limbic-cortical dysregulation: a proposed model of depression. , 1997, The Journal of neuropsychiatry and clinical neurosciences.

[50]  N. Daw,et al.  Differential roles of human striatum and amygdala in associative learning , 2011, Nature Neuroscience.

[51]  Geraint Rees,et al.  Selectively altering belief formation in the human brain , 2012, Proceedings of the National Academy of Sciences.

[52]  Zhong-Lin Lu,et al.  Neural correlates of risk prediction error during reinforcement learning in humans , 2009, NeuroImage.

[53]  Jörg Rieskamp,et al.  Neural correlates of informational cascades: brain mechanisms of social influence on belief updating. , 2015, Social cognitive and affective neuroscience.

[54]  M. Delgado,et al.  How instructed knowledge modulates the neural systems of reward learning , 2010, Proceedings of the National Academy of Sciences.

[55]  Joseph E LeDoux.  The Emotional Brain, Fear, and the Amygdala , 2003, Cellular and Molecular Neurobiology.

[56]  Michael X. Cohen,et al.  Neurocomputational models of basal ganglia function in learning, memory and choice , 2009, Behavioural Brain Research.