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首页> 外文期刊>Frontiers in Human Neuroscience >How Accumulated Real Life Stress Experience and Cognitive Speed Interact on Decision-Making Processes
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How Accumulated Real Life Stress Experience and Cognitive Speed Interact on Decision-Making Processes

机译：积累的现实生活中的压力体验和认知速度如何在决策过程中相互作用

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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.
机译：原理：神经计算建模的进步表明，一方面用于目标导向（协商）的评估系统，另一方面用于惯常（自动）决策的评估系统可能依赖于强化学习的不同计算策略，即无模型与模型基础的学习。作为一个重要的理论差异，基于模型的系统强烈要求认知功能基于环境的内部认知模型对行为进行前瞻性计划，而无模型系统中的评估则依赖于相当简单的学习规则，从操作条件到追溯性关联动作其结果，因此在认知上要求不高。众所周知，急性应力反应性会损害基于模型的行为，但不会损害无模型的选择行为，而更高的工作记忆容量可保护基于模型的系统免受急性应力的影响。然而，尚不清楚现实生活中累积的压力会对无模型和基于模型的决策系统产生何种影响，以及这种影响如何与认知能力相互作用。方法：我们使用顺序决策任务来区分这两种学习策略对选择行为的相对贡献，使用社会适应调整量表问卷评估累积的实际生活压力，并使用数字符号替代测试来测试95名健康受试者的认知速度。结果：那些报告了高压力暴露且认知速度较慢的个体显示出减少了基于模型的行为，但增加了无模型的行为控制。相反，以较高的认知速度暴露于累积的现实生活压力中的受试者表现出了更高的基于模型的性能，但减少了无模型控制。结论：这些发现表明，累积的真实生活压力暴露可以增强对基于模型的计算的认知速度的依赖，这最终可以保护基于模型的系统免受累积的真实生活压力的不利影响。但是，累积的现实生活压力暴露和较慢的信息处理能力的结合可能会支持无模型策略。因此，任何一个系统的效价和偏好在很大程度上取决于压力经历和个人认知能力。

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