How accumulated real life stress experience and cognitive speed interact on decision-making processes

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

  • Eva Friedel - , Charité – Universitätsmedizin Berlin, Berlin Institute of Health at Charité (Author)
  • Miriam Sebold - , Charité – Universitätsmedizin Berlin, University of Potsdam (Author)
  • Sören Kuitunen-Paul - , Chair of Clinical Psychology and Psychotherapy (Author)
  • Stephan Nebe - , Institute of Clinical Psychology and Psychotherapy, Neuroimaging Center (Author)
  • Ilya M. Veer - , Charité – Universitätsmedizin Berlin (Author)
  • Ulrich S. Zimmermann - , TUD Dresden University of Technology (Author)
  • Florian Schlagenhauf - , Charité – Universitätsmedizin Berlin, Max Planck Institute for Human Cognitive and Brain Sciences (Author)
  • Michael N. Smolka - , Department of Psychiatry and Psychotherapy, Neuroimaging Center (Author)
  • Michael Rapp - , University of Potsdam (Author)
  • Henrik Walter - , Charité – Universitätsmedizin Berlin, Berlin Institute of Health at Charité (Author)
  • Andreas Heinz - , Charité – Universitätsmedizin Berlin, Berlin Institute of Health at Charité (Author)

Abstract

Rationale: Advances in neurocomputational modeling suggest that valuation systems for goal-directed (deliberative) on one side, and habitual (automatic) decision-making on the other side may rely on distinct computational strategies for reinforcement learning, namely model-free vs. model-based learning. As a key theoretical difference, the model-based system strongly demands cognitive functions to plan actions prospectively based on an internal cognitive model of the environment, whereas valuation in the model-free system relies on rather simple learning rules from operant conditioning to retrospectively associate actions with their outcomes and is thus cognitively less demanding. Acute stress reactivity is known to impair model-based but not model-free choice behavior, with higher working memory capacity protecting the model-based system from acute stress. However, it is not clear which impact accumulated real life stress has on model-free and model-based decision systems and how this influence interacts with cognitive abilities. Methods: We used a sequential decision-making task distinguishing relative contributions of both learning strategies to choice behavior, the Social Readjustment Rating Scale questionnaire to assess accumulated real life stress, and the Digit Symbol Substitution Test to test cognitive speed in 95 healthy subjects. Results: Individuals reporting high stress exposure who had low cognitive speed showed reduced model-based but increased model-free behavioral control. In contrast, subjects exposed to accumulated real life stress with high cognitive speed displayed increased model-based performance but reduced model-free control. Conclusion: These findings suggest that accumulated real life stress exposure can enhance reliance on cognitive speed for model-based computations, which may ultimately protect the model-based system from the detrimental influences of accumulated real life stress. The combination of accumulated real life stress exposure and slower information processing capacities, however, might favor model-free strategies. Thus, the valence and preference of either system strongly depends on stressful experiences and individual cognitive capacities.

Details

Original languageEnglish
Article number302
JournalFrontiers in human neuroscience
Volume11
Publication statusPublished - 8 Jun 2017
Peer-reviewedYes

External IDs

ORCID /0000-0001-5398-5569/work/161890772

Keywords

Keywords

  • Chronic stress, Cognitive speed, Decision making, Model-based learning, Model-free learning, Real-life events