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Abstract:
Frustration is a widely experienced emotional state that has been linked to a wide range of societal and individual issues. Early research characterized a “frustration effect” whereby behavior is invigorated im- mediately subsequent to the non-delivery of an expected reward. Here we present an experimental approach that aimed to measure and model the effect of frustrative non-reward on motor vigor within a reinforcement learning framework. Subjects were instructed to earn rewards by squeezing a dynamometer handgrip at a specific force, while we surreptitiously recorded non-instrumental motor responses in between trials. We found that the non-instrumental motor responses were significantly predicted by a simple, parameter-free associative learning model that represented primary frustration. This trial-by-trial analysis allowed us to precisely quantify the conditions under which this classic frustration effect arises, thereby situating this sub- jective state within a mathematical framework. Unlike earlier work that employed one-shot extinction trials, our data point to a parametric effect of frustration on generalized motor output. This adds to the growing body of literature that relates reinforcement learning mechanisms to domains outside of choice, and provides a quantitative link between reward, emotion, and behavior. The dependence of frustration on reward history and its apparent Pavlovian effect on motor output also strongly suggest that frustration serves an adaptive role in behavior.
