title: Altered behavioral and neural responsiveness to counterfactual gains in the elderly.
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.
authors: Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany. mtobia@hmc.psu.edu.|Center for NMR Research, Department of Radiology, Pennsylvania State University College of Medicine, Hershey, PA, USA. mtobia@hmc.psu.edu.
authors: Michael J
authors: MJ
authors: Tobia
authors: Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.|Department of Software Engineering and Theoretical Computer Science, School IV Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany.|Bernstein Center for Computational Neuroscience, Berlin, Germany.
authors: Rong
authors: R
authors: Guo
authors: Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
authors: Jan
authors: J
authors: Gläscher
authors: Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
authors: Ulrike
authors: U
authors: Schwarze
authors: Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
authors: Stefanie
authors: S
authors: Brassen
authors: Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
authors: Christian
authors: C
authors: Büchel
authors: Department of Software Engineering and Theoretical Computer Science, School IV Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany.
authors: Klaus
authors: K
authors: Obermayer
authors: Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
authors: Tobias
authors: T
authors: Sommer
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000375:Aging; D001931:Brain Mapping; D002755:Choice Behavior / Q000502:physiology*; D003657:Decision Making / Q000502:physiology*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D007858:Learning / Q000502:physiology*; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D012201:Reward; D055815:Young Adult
keywords: Aging; Fictive prediction error; Model-based fMRI; Reward prediction; Ventromedial PFC