Literature DB >> 27683899

Partial Adaptation of Obtained and Observed Value Signals Preserves Information about Gains and Losses.

Christopher J Burke1, Michelle Baddeley2, Philippe N Tobler3, Wolfram Schultz4.   

Abstract

UNLABELLED: Given that the range of rewarding and punishing outcomes of actions is large but neural coding capacity is limited, efficient processing of outcomes by the brain is necessary. One mechanism to increase efficiency is to rescale neural output to the range of outcomes expected in the current context, and process only experienced deviations from this expectation. However, this mechanism comes at the cost of not being able to discriminate between unexpectedly low losses when times are bad versus unexpectedly high gains when times are good. Thus, too much adaptation would result in disregarding information about the nature and absolute magnitude of outcomes, preventing learning about the longer-term value structure of the environment. Here we investigate the degree of adaptation in outcome coding brain regions in humans, for directly experienced outcomes and observed outcomes. We scanned participants while they performed a social learning task in gain and loss blocks. Multivariate pattern analysis showed two distinct networks of brain regions adapt to the most likely outcomes within a block. Frontostriatal areas adapted to directly experienced outcomes, whereas lateral frontal and temporoparietal regions adapted to observed social outcomes. Critically, in both cases, adaptation was incomplete and information about whether the outcomes arose in a gain block or a loss block was retained. Univariate analysis confirmed incomplete adaptive coding in these regions but also detected nonadapting outcome signals. Thus, although neural areas rescale their responses to outcomes for efficient coding, they adapt incompletely and keep track of the longer-term incentives available in the environment. SIGNIFICANCE STATEMENT: Optimal value-based choice requires that the brain precisely and efficiently represents positive and negative outcomes. One way to increase efficiency is to adapt responding to the most likely outcomes in a given context. However, too strong adaptation would result in loss of precise representation (e.g., when the avoidance of a loss in a loss-context is coded the same as receipt of a gain in a gain-context). We investigated an intermediate form of adaptation that is efficient while maintaining information about received gains and avoided losses. We found that frontostriatal areas adapted to directly experienced outcomes, whereas lateral frontal and temporoparietal regions adapted to observed social outcomes. Importantly, adaptation was intermediate, in line with influential models of reference dependence in behavioral economics.
Copyright © 2016 Burke et al.

Entities:  

Keywords:  adaptive coding; context; reference dependence; reward

Mesh:

Year:  2016        PMID: 27683899      PMCID: PMC5039252          DOI: 10.1523/JNEUROSCI.0487-16.2016

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


  40 in total

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Authors:  Kevin N Laland
Journal:  Learn Behav       Date:  2004-02       Impact factor: 1.986

2.  Segregated and integrated coding of reward and punishment in the cingulate cortex.

Authors:  Juri Fujiwara; Philippe N Tobler; Masato Taira; Toshio Iijima; Ken-Ichiro Tsutsui
Journal:  J Neurophysiol       Date:  2009-04-01       Impact factor: 2.714

3.  Context-dependent utility overrides absolute memory as a determinant of choice.

Authors:  Lorena Pompilio; Alex Kacelnik
Journal:  Proc Natl Acad Sci U S A       Date:  2009-12-04       Impact factor: 11.205

4.  Category-dependent and category-independent goal-value codes in human ventromedial prefrontal cortex.

Authors:  Daniel McNamee; Antonio Rangel; John P O'Doherty
Journal:  Nat Neurosci       Date:  2013-02-17       Impact factor: 24.884

5.  Mechanisms of social avoidance learning can explain the emergence of adaptive and arbitrary behavioral traditions in humans.

Authors:  Björn Lindström; Andreas Olsson
Journal:  J Exp Psychol Gen       Date:  2015-04-13

6.  BOLD subjective value signals exhibit robust range adaptation.

Authors:  Karin M Cox; Joseph W Kable
Journal:  J Neurosci       Date:  2014-12-03       Impact factor: 6.167

7.  Medial orbitofrontal cortex codes relative rather than absolute value of financial rewards in humans.

Authors:  R Elliott; Z Agnew; J F W Deakin
Journal:  Eur J Neurosci       Date:  2008-05       Impact factor: 3.386

8.  Spatial gradient in value representation along the medial prefrontal cortex reflects individual differences in prosociality.

Authors:  Sunhae Sul; Philippe N Tobler; Grit Hein; Susanne Leiberg; Daehyun Jung; Ernst Fehr; Hackjin Kim
Journal:  Proc Natl Acad Sci U S A       Date:  2015-06-08       Impact factor: 11.205

9.  The valuation system: a coordinate-based meta-analysis of BOLD fMRI experiments examining neural correlates of subjective value.

Authors:  Oscar Bartra; Joseph T McGuire; Joseph W Kable
Journal:  Neuroimage       Date:  2013-03-15       Impact factor: 6.556

10.  Is avoiding an aversive outcome rewarding? Neural substrates of avoidance learning in the human brain.

Authors:  Hackjin Kim; Shinsuke Shimojo; John P O'Doherty
Journal:  PLoS Biol       Date:  2006-07       Impact factor: 8.029

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  7 in total

1.  Partial Adaptation to the Value Range in the Macaque Orbitofrontal Cortex.

Authors:  Katherine E Conen; Camillo Padoa-Schioppa
Journal:  J Neurosci       Date:  2019-03-04       Impact factor: 6.167

Review 2.  The power of price compels you: Behavioral economic insights into dopamine-based valuation of rewarding and aversively motivated behavior.

Authors:  Erik B Oleson; Jonté B Roberts
Journal:  Brain Res       Date:  2018-12-11       Impact factor: 3.252

3.  The Effect of Counterfactual Information on Outcome Value Coding in Medial Prefrontal and Cingulate Cortex: From an Absolute to a Relative Neural Code.

Authors:  Doris Pischedda; Stefano Palminteri; Giorgio Coricelli
Journal:  J Neurosci       Date:  2020-03-10       Impact factor: 6.167

4.  Adaptive Value Normalization in the Prefrontal Cortex Is Reduced by Memory Load.

Authors:  L Holper; L D Van Brussel; L Schmidt; S Schulthess; C J Burke; K Louie; E Seifritz; P N Tobler
Journal:  eNeuro       Date:  2017-04-27

Review 5.  Multi-scale neural decoding and analysis.

Authors:  Hung-Yun Lu; Elizabeth S Lorenc; Hanlin Zhu; Justin Kilmarx; James Sulzer; Chong Xie; Philippe N Tobler; Andrew J Watrous; Amy L Orsborn; Jarrod Lewis-Peacock; Samantha R Santacruz
Journal:  J Neural Eng       Date:  2021-08-16       Impact factor: 5.043

6.  A transient dopamine signal encodes subjective value and causally influences demand in an economic context.

Authors:  Scott A Schelp; Katherine J Pultorak; Dylan R Rakowski; Devan M Gomez; Gregory Krzystyniak; Raibatak Das; Erik B Oleson
Journal:  Proc Natl Acad Sci U S A       Date:  2017-11-06       Impact factor: 11.205

7.  Reference-point centering and range-adaptation enhance human reinforcement learning at the cost of irrational preferences.

Authors:  Sophie Bavard; Maël Lebreton; Mehdi Khamassi; Giorgio Coricelli; Stefano Palminteri
Journal:  Nat Commun       Date:  2018-10-29       Impact factor: 14.919

  7 in total

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