Behavioural and neural characterization of optimistic reinforcement learning

By G. Lefebvre, M. Lebreton, Florent Meyniel, S. Bourgeois-Gironde, Stefano Palminteri. Nature Human Behaviour, 2017

243 citations recorded by the source corpus.

When forming and updating beliefs about future life outcomes, people tend to consider good news and to disregard bad news. This tendency is assumed to support the optimism bias. Whether this learning bias is specific to ‘high-level’ abstract belief update or a particular expression of a more general ‘low-level’ reinforcement learning process is unknown. Here we report evidence in favour of the second hypothesis. In a simple instrumental learning task, participants incorporated better-than-expected outcomes at a higher rate than worse-than-expected ones. In addition, functional imaging indicated that inter-individual difference in the expression of optimistic update corresponds to enhanced prediction error signalling in the reward circuitry. Our results constitute a step towards the understanding of the genesis of optimism bias at the neurocomputational level.

Subfields: Applied Psychology, Experimental and Cognitive Psychology, General Decision Sciences

View Paper: https://www.semanticscholar.org/paper/864e84bc37b880c143976c0a98fb3297c492c744

Citations in the corpus

• The Importance of Falsification in Computational Cognitive Modeling. (2017, unknown): “To provide a concrete example of this type of studies, we consider the case of researchers interested in determining whether humans preferentially learn from positive compared to negative feedback [25].”