State-dependent valuation and confirmatory biases in reward and punishment learning

Valence is a fundamental concept in the learning decision-making and emotion literature. However, in the reinforcement learning context, different facets of this psychological concept are often confounded and/or ill-defined. In the present talk we theoretically propose, mathematically formalize and experimentally investigate two different facets of valence: ‘contextual’ and ‘informational’. Combining imaging and modeling techniques in a series of studies, we found that these different aspects have dissociable computational and neural correlates, and exert powerful effects on human learning behavior.

Main references:
Palminteri S, Khamassi M, Joffily M, Coricelli G. Contextual modulation of value signals in reward and punishment learning. Nature Communications (2015).
Lefebvre G, Lebreton M, Meyniel F, Bourgeois-Gironde S, Palminteri S. Behavioural and neural characterization of optimistic reinforcement learning. Nature Human Behaviour (2017)