Literature DB >> 26864879

Altered behavioral and neural responsiveness to counterfactual gains in the elderly.

Michael J Tobia1,2, Rong Guo3,4,5, Jan Gläscher3, Ulrike Schwarze3, Stefanie Brassen3, Christian Büchel3, Klaus Obermayer4, Tobias Sommer3.   

Abstract

Counterfactual information processing refers to the consideration of events that did not occur in comparison to those actually experienced, in order to determine optimal actions, and can be formulated as computational learning signals, referred to as fictive prediction errors. Decision making and the neural circuitry for counterfactual processing are altered in healthy elderly adults. This experiment investigated age differences in neural systems for decision making with knowledge of counterfactual outcomes. Two groups of healthy adult participants, young (N = 30; ages 19-30 years) and elderly (N = 19; ages 65-80 years), were scanned with fMRI during 240 trials of a strategic sequential investment task in which a particular strategy of differentially weighting counterfactual gains and losses during valuation is associated with more optimal performance. Elderly participants earned significantly less than young adults, differently weighted counterfactual consequences and exploited task knowledge, and exhibited altered activity in a fronto-striatal circuit while making choices, compared to young adults. The degree to which task knowledge was exploited was positively correlated with modulation of neural activity by expected value in the vmPFC for young adults, but not in the elderly. These findings demonstrate that elderly participants' poor task performance may be related to different counterfactual processing.

Entities:  

Keywords:  Aging; Fictive prediction error; Model-based fMRI; Reward prediction; Ventromedial PFC

Mesh:

Year:  2016        PMID: 26864879     DOI: 10.3758/s13415-016-0406-7

Source DB:  PubMed          Journal:  Cogn Affect Behav Neurosci        ISSN: 1530-7026            Impact factor:   3.282


  54 in total

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4.  Ventral striatal signal changes represent missed opportunities and predict future choice.

Authors:  Christian Büchel; Stefanie Brassen; Juliana Yacubian; Raffael Kalisch; Tobias Sommer
Journal:  Neuroimage       Date:  2011-05-15       Impact factor: 6.556

5.  Mental models and counterfactual thoughts about what might have been.

Authors:  Ruth M.J. Byrne
Journal:  Trends Cogn Sci       Date:  2002-10-01       Impact factor: 20.229

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7.  Age-related cognitive deficits mediated by changes in the striatal dopamine system.

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Journal:  Am J Psychiatry       Date:  2000-04       Impact factor: 18.112

8.  Neural correlates of behavioral preference for culturally familiar drinks.

Authors:  Samuel M McClure; Jian Li; Damon Tomlin; Kim S Cypert; Latané M Montague; P Read Montague
Journal:  Neuron       Date:  2004-10-14       Impact factor: 17.173

Review 9.  The ubiquity of model-based reinforcement learning.

Authors:  Bradley B Doll; Dylan A Simon; Nathaniel D Daw
Journal:  Curr Opin Neurobiol       Date:  2012-09-06       Impact factor: 6.627

10.  Of goals and habits: age-related and individual differences in goal-directed decision-making.

Authors:  Ben Eppinger; Maik Walter; Hauke R Heekeren; Shu-Chen Li
Journal:  Front Neurosci       Date:  2013-12-24       Impact factor: 4.677

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  3 in total

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Review 2.  Developmental Changes in Learning: Computational Mechanisms and Social Influences.

Authors:  Florian Bolenz; Andrea M F Reiter; Ben Eppinger
Journal:  Front Psychol       Date:  2017-11-23

3.  Increasing influenza and pneumococcal vaccine uptake in the elderly: study protocol for the multi-methods prospective intervention study Vaccination60.

Authors:  Cornelia Betsch; Constanze Rossmann; Mathias W Pletz; Horst C Vollmar; Antje Freytag; Ole Wichmann; Regina Hanke; Wolfgang Hanke; Dorothee Heinemeier; Philipp Schmid; Sarah Eitze; Winja Weber; Anne Reinhardt; Nora K Küpke; Christina Forstner; Carolin Fleischmann-Struzek; Anna Mikolajetz; Josephine Römhild; Julia Neufeind; Thorsten Rieck; Kasia Suchecka; Konrad Reinhart
Journal:  BMC Public Health       Date:  2018-07-16       Impact factor: 3.295

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