New publication in the journal "Nature Communications"
9 November 2023, by Celestina Hermida da Costa
Ting, C.C., Post-Doc for General Psychology at the University of Hamburg, and Salem-Garvia, N., Palminteri, S., Engelmann, J. B. and Lebreton, M. have published the article "Neural and computational underpinnings of biased confidence in human reinforcement learning" in the journal 'Nature Communications'.
Humans and animals operate in a fundamentally uncertain world and are constantly evaluating the probability that their decisions, actions or statements are correct. When explicitly elicited, these confidence estimates typically correlates positively with neural activity in a ventromedial-prefrontal (VMPFC) network and negatively in a dorsolateral and dorsomedial prefrontal network. Here, combining fMRI with a reinforcement-learning paradigm, they leveraged the fact that humans are more confident in their choices when seeking gains than avoiding losses to reveal a functional dissociation: whereas the dorsal prefrontal network correlates negatively with a condition-specific confidence signal, the VMPFC network positively encodes task-wide confidence signal incorporating the valence-induced bias. Challenging dominant neuro-computational models, they found that decision-related VMPFC activity better correlates with confidence than with option-values inferred from reinforcement-learning models. Altogether, these results identify the VMPFC as a key node in the neuro-computational architecture that builds global feeling-of-confidence signals from latent decision variables and contextual biases during reinforcement-learning.