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2023
- handle: 10670/1.8d20w9
- halshs: halshs-04409145
- doi: 10.1038/s41467-023-42589-5
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info:eu-repo/semantics/altIdentifier/doi/10.1038/s41467-023-42589-5
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Chih-Chung Ting et al., « Neural and computational underpinnings of biased confidence in human reinforcement learning », HAL-SHS : économie et finance, ID : 10.1038/s41467-023-42589-5
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While navigating a fundamentally uncertain world, humans and animals constantly evaluate the probability of their decisions, actions or statements being 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, we leverage 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, we 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.