How Accumulated Real Life Stress Experience and Cognitive Speed Interact on Decision-Making Processes.

Eva Friedel

doi:10.3389/fnhum.2017.00302

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.

Highlights

Habitual responding to rewards and the pursuit of goals are key to human decision-making
In the linear model regressing cognitive speed, stress and their interaction on model-free control, no main effect was found for either stress (p = 0.16), nor cognitive speed (p = 0.30), while their interaction (β = 0.5, F(91) = 2.76, p = 0.022) was significantly associated with model-free behavior
Model-free control increased with increasing stress exposure when cognitive speed was low

Summary

Introduction

Habitual responding to rewards and the pursuit of goals are key to human decision-making Such habitual (automatic) vs goal-directed (planned) control of behavior is associated with distinct neural systems for valuation and decisionmaking (Dolan and Dayan, 2013). Computational attempts to understand these behavioral systems assume that both reflect different computational strategies during reinforcement learning, namely model-based (goal-directed) vs model-free (habitual) behavior (Daw et al, 2005, 2011; Dolan and Dayan, 2013). The conditions under which each of these systems controls behavior have been of particular interest in neuroscience and psychiatry, in part because the (im)balance between model-free and model-based behavior is believed to be a key factor in a number of psychiatric disorders. Among situational factors that influence reward-based decision making, stress is a key candidate for biasing the balance of the two systems toward more habitual decision making (Schwabe and Wolf, 2009, 2012) and might be of relevance for the development and maintenance of these disorders

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