Stress, individual differences and norepinephrine in the prediction of mouse behavior using reinforcement learning

Abstract:
Individual behavioral performance is known to be affected by modulatory factors such as stress and motivation as well as by genetic predispositions that influence sensitivity to these factors. Despite numerous studies, no integrative framework is available for prediction of animal learning behaviour in a realistic situation. In this talk I will show how reinforcement learning models can be used to predict individual mouse behaviour in conditioning and spatial learning tasks. The model-based analysis of behaviour reveal correlations between reinforcement learning meta-parameters and modulatory factors such as genetic strain, affective phenotype, stress, motivation, recent performance feedback, and pharmacological manipulations of adrenergic alpha-2 receptors. We find that stress and motivation affect behavioral performance by altering the exploration-exploitation balance in a strain dependent manner. Our results also provide computational insights into how an inverted-U-shape relation between stress/arousal/norepinephrine levels and behavioral performance could be explained through changes in performance accuracy and future reward discounting. Finally I will discuss how similar behavioural prediction methods could be used for optimizing individual performance.

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