Cited by 59Neural and Behavioral Psychology StudiesNeurotransmitter Receptor Influence on BehaviorDecision-Making and Behavioral Economics

Sustained neural activity associated with cognitive control during temporally extended decision making

Yarkoni, Tal, Gray, Jeremy R., Chrastil, Elizabeth R., Barch, Deanna M., Green, Leonard, Braver, Todd S.·2005·Cognitive Brain Research

Abstract

Decision making has both cognitive and affective components, but previous neuroimaging studies in this domain predominantly have focused on affect and reward. The current study examined a decision-making paradigm that placed strong demands on cognitive control processes by making reward payoffs contingent upon decision-making history. Payoffs were maximized by choosing the option that, paradoxically, was associated with a lower payoff on the immediate trial. Temporal integration requirements were manipulated by varying, across conditions, the window of previous trials over which the reward function was calculated. The cognitive demands of the task were hypothesized to engage neural systems responsible for integrating and actively maintaining actions and outcomes over time and the top-down biasing of response selection. Brain activation was monitored with functional magnetic resonance imaging (fMRI) using a mixed-blocked and event-related design to extract both transient and sustained neural responses. A network of brain regions commonly associated with cognitive control functions, including bilateral prefrontal cortex (PFC), bilateral parietal cortex, and medial frontal cortex, showed selectively sustained activation during the task. Increasing temporal integration demands led to a shift from transient to sustained activity in additional regions, including right hemisphere dorsolateral and frontopolar PFC. These results demonstrate the contribution of cognitive control mechanisms to temporally extended decision-making paradigms and highlight the benefits of decomposing activation responses into sustained and transient components.

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Crossref · 10.1016/j.cogbrainres.2005.01.013

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  • Studies of reward learning in schizophrenia are consistent with the framework that patients have intact hedonic responses but impaired motivation and reward representation, leading to a failure to motivate behavior for rewards.

    “Overall, however, studies of reward learning in individuals with schizophrenia are consistent with the framework that patients with schizophrenia have intact hedonic responses but impaired motivation and reward representation, leading to a failure to motivate their behavior for rewards [415].”

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Authors
Yarkoni, T., Gray, J. R., Chrastil, E. R., Barch, D. M., Green, L., & Braver, T. S.
Affiliations
  • Washington University in St. Louis (US)
  • Yale University (US)
Corresponding author
Braver, Todd S.
Venue
Cognitive Brain Research
Publisher
Elsevier BV
Published
2005-04
Volume
23
Issue
1
Pages
71-84
ISSN
0926-6410 (ISSN-L)
OpenAlex
W2000917478
Cited by
59
References
67
Citations by year
2025: 1 · 2024: 3 · 2023: 2 · 2022: 2 · 2021: 2 · 2019: 2 · 2017: 1 · 2016: 3 · 2015: 4 · 2014: 2 · 2013: 5 · 2012: 5
FWCI
2.32
Type
journal article
Language
en
Open access
closed
Locations
  • Cognitive Brain Research (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
  • Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
Keywords
cognitive control, decision-making, temporal integration, sustained neural activity, prefrontal cortex, dorsolateral prefrontal cortex, parietal cortex, medial frontal cortex, response selection, reward contingency
MeSH terms
Adult, Brain, Choice Behavior, Cognition, Decision Making, Evoked Potentials, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Models, Neurological, Photic Stimulation, Time Factors, Linear Models

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