Literature DB >> 21697380

Neurobiology of value integration: when value impacts valuation.

Soyoung Q Park1, Thorsten Kahnt, Jörg Rieskamp, Hauke R Heekeren.   

Abstract

Everyday choice options have advantages (positive values) and disadvantages (negative values) that need to be integrated into an overall subjective value. For decades, economic models have assumed that when a person evaluates a choice option, different values contribute independently to the overall subjective value of the option. However, human choice behavior often violates this assumption, suggesting interactions between values. To investigate how qualitatively different advantages and disadvantages are integrated into an overall subjective value, we measured the brain activity of human subjects using fMRI while they were accepting or rejecting choice options that were combinations of monetary reward and physical pain. We compared different subjective value models on behavioral and neural data. These models all made similar predictions of choice behavior, suggesting that behavioral data alone are not sufficient to uncover the underlying integration mechanism. Strikingly, a direct model comparison on brain data decisively demonstrated that interactive value integration (where values interact and affect overall valuation) predicts neural activity in value-sensitive brain regions significantly better than the independent mechanism. Furthermore, effective connectivity analyses revealed that value-dependent changes in valuation are associated with modulations in subgenual anterior cingulate cortex-amygdala coupling. These results provide novel insights into the neurobiological underpinnings of human decision making involving the integration of different values.

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Year:  2011        PMID: 21697380      PMCID: PMC6623498          DOI: 10.1523/JNEUROSCI.4973-10.2011

Source DB:  PubMed          Journal:  J Neurosci        ISSN: 0270-6474            Impact factor:   6.167


  52 in total

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4.  Functional imaging of neural responses to expectancy and experience of monetary gains and losses.

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Journal:  Neuron       Date:  2001-05       Impact factor: 17.173

5.  Stimulation of medial prefrontal cortex decreases the responsiveness of central amygdala output neurons.

Authors:  Gregory J Quirk; Ekaterina Likhtik; Joe Guillaume Pelletier; Denis Paré
Journal:  J Neurosci       Date:  2003-09-24       Impact factor: 6.167

6.  Temporal difference models describe higher-order learning in humans.

Authors:  Ben Seymour; John P O'Doherty; Peter Dayan; Martin Koltzenburg; Anthony K Jones; Raymond J Dolan; Karl J Friston; Richard S Frackowiak
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7.  Extinction learning in humans: role of the amygdala and vmPFC.

Authors:  Elizabeth A Phelps; Mauricio R Delgado; Katherine I Nearing; Joseph E LeDoux
Journal:  Neuron       Date:  2004-09-16       Impact factor: 17.173

8.  The functional neuroanatomy of the placebo effect.

Authors:  Helen S Mayberg; J Arturo Silva; Steven K Brannan; Janet L Tekell; Roderick K Mahurin; Scott McGinnis; Paul A Jerabek
Journal:  Am J Psychiatry       Date:  2002-05       Impact factor: 18.112

9.  Dissociable roles of ventral and dorsal striatum in instrumental conditioning.

Authors:  John O'Doherty; Peter Dayan; Johannes Schultz; Ralf Deichmann; Karl Friston; Raymond J Dolan
Journal:  Science       Date:  2004-04-16       Impact factor: 47.728

10.  The prefrontal cortex regulates lateral amygdala neuronal plasticity and responses to previously conditioned stimuli.

Authors:  J Amiel Rosenkranz; Holly Moore; Anthony A Grace
Journal:  J Neurosci       Date:  2003-12-03       Impact factor: 6.167

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  51 in total

1.  Adaptive coding of reward prediction errors is gated by striatal coupling.

Authors:  Soyoung Q Park; Thorsten Kahnt; Deborah Talmi; Jörg Rieskamp; Raymond J Dolan; Hauke R Heekeren
Journal:  Proc Natl Acad Sci U S A       Date:  2012-02-27       Impact factor: 11.205

2.  Human mesostriatal response tracks motivational tendencies under naturalistic goal conflict.

Authors:  Tal Gonen; Eyal Soreq; Eran Eldar; Eti Ben-Simon; Gal Raz; Talma Hendler
Journal:  Soc Cogn Affect Neurosci       Date:  2016-02-01       Impact factor: 3.436

3.  Informatic parcellation of the network involved in the computation of subjective value.

Authors:  John A Clithero; Antonio Rangel
Journal:  Soc Cogn Affect Neurosci       Date:  2013-07-24       Impact factor: 3.436

4.  Neurocomputational mechanisms underlying immoral decisions benefiting self or others.

Authors:  Chen Qu; Yang Hu; Zixuan Tang; Edmund Derrington; Jean-Claude Dreher
Journal:  Soc Cogn Affect Neurosci       Date:  2020-05-11       Impact factor: 3.436

5.  The good, the bad and the brain: Neural correlates of appetitive and aversive values underlying decision making.

Authors:  Mathias Pessiglione; Mauricio R Delgado
Journal:  Curr Opin Behav Sci       Date:  2015-08-24

6.  Differential patterns of amygdala and ventral striatum activation predict gender-specific changes in sexual risk behavior.

Authors:  Elizabeth C Victor; Alexandra A Sansosti; Hilary C Bowman; Ahmad R Hariri
Journal:  J Neurosci       Date:  2015-06-10       Impact factor: 6.167

Review 7.  Categorization = decision making + generalization.

Authors:  Carol A Seger; Erik J Peterson
Journal:  Neurosci Biobehav Rev       Date:  2013-03-30       Impact factor: 8.989

8.  Pervasive competition between threat and reward in the brain.

Authors:  Jong Moon Choi; Srikanth Padmala; Philip Spechler; Luiz Pessoa
Journal:  Soc Cogn Affect Neurosci       Date:  2013-04-01       Impact factor: 3.436

9.  Stimulus value signals in ventromedial PFC reflect the integration of attribute value signals computed in fusiform gyrus and posterior superior temporal gyrus.

Authors:  Seung-Lark Lim; John P O'Doherty; Antonio Rangel
Journal:  J Neurosci       Date:  2013-05-15       Impact factor: 6.167

10.  Functional connectivity with ventromedial prefrontal cortex reflects subjective value for social rewards.

Authors:  David V Smith; John A Clithero; Sarah E Boltuck; Scott A Huettel
Journal:  Soc Cogn Affect Neurosci       Date:  2014-02-03       Impact factor: 3.436

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