Neural Mechanisms for Adaptive Learned Avoidance of Mental Effort.

JOURNAL OF NEUROSCIENCE(2018)

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摘要
Humans tend to avoid mental effort. Previous studies have demonstrated this tendency using various demand-selection tasks; participants generally avoid options associated with higher cognitive demand. However, it remains unclear whether humans avoid mental effort adaptively in uncertain and nonstationary environments. If so, it also remains unclear what neural mechanisms underlie such learned avoidance and whether they remain the same regardless of cognitive-demand types. We addressed these issues by developing novel demand-selection tasks where associations between choice options and cognitive-demand levels change over time, with two variations using mental arithmetic and spatial reasoning problems (males/females: 29:4 and 18:2). Most participants showed avoidance, and their choices depended on the demand experienced on multiple preceding trials. We assumed that participants updated the expected cost of mental effort through experience, and fitted their choices by reinforcement learning models, comparing several possibilities. Model-based fMRI analyses revealed that activity in the dorsomedial and lateral frontal cortices was positively correlated with the trial-by-trial expected cost for the chosen option commonly across the different types of cognitive demand. Analyses also revealed a trend of negative correlation in the ventromedial prefrontal cortex. We further identified correlates of cost-prediction error at time of problem presentation or answering the problem, the latter of which partially overlapped with or were proximal to the correlates of expected cost at time of choice cue in the dorsomedial frontal cortex. These results suggest that humans adaptively learn to avoid mental effort, having neural mechanisms to represent expected cost and cost-prediction error, and the same mechanisms operate for various types of cognitive demand.
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关键词
avoidance learning,cognitive demand,decision making,mental effort,model-based fMRI,reinforcement learning
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