Abstract

Learning how to gain rewards (approach learning) and avoid punishments (avoidance learning) is fundamental for everyday life. While individual differences in approach and avoidance learning styles have been related to genetics and aging, the contribution of personality factors, such as traits, remains undetermined. Moreover, little is known about the computational mechanisms mediating differences in learning styles. Here, we used a probabilistic selection task with positive and negative feedbacks, in combination with computational modelling, to show that individuals displaying better approach (vs. avoidance) learning scored higher on measures of approach (vs. avoidance) trait motivation, but, paradoxically, also displayed reduced learning speed following positive (vs. negative) outcomes. These data suggest that learning different types of information depend on associated reward values and internal motivational drives, possibly determined by personality traits.

Highlights

  • Much of human behaviour is directed towards maximizing rewards and minimizing punishments

  • As in previous studies [15,29], participants were divided into two groups based on whether they were better at selecting A than rejecting B during the test phase while avoidance learners (n = 13) were those displaying the opposite trend

  • For display purposes training performance was averaged across trials in ten sized bins because individuals differed in the number of trials needed to reach the criteria

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Summary

Introduction

Much of human behaviour is directed towards maximizing rewards (via approach behaviour) and minimizing punishments (via avoidance behaviour). While individuals display differences in the ability to learn from rewards (approach learning) and punishments (avoidance learning), the link between approach and avoidance learning and the general expression of approach and avoidance behaviours is not well established. A frequently used paradigm in the literature on approach and avoidance learning is the probabilistic selection task (PST; [1]), in which participants first learn reward probabilities (i.e. the frequency of positive and negative outcomes) associated with different symbols, and use the learned reward probabilities to guide decision making in a subsequent testing phase (i.e. the discrimination between novel pairs of symbols; [1]). PLOS ONE | DOI:10.1371/journal.pone.0166675 November 16, 2016

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