A non-monotonic code for event probability in the human brain

Cedric Foucault (), Tiffany Bounmy, Sébastien Demortain, Bertrand Thirion, Evelyn Eger and Florent Meyniel ()
Additional contact information
Cedric Foucault: University of Paris-Saclay, Cognitive Neuroimaging Unit, NeuroSpin (INSERM-CEA)
Tiffany Bounmy: University of Paris-Saclay, Cognitive Neuroimaging Unit, NeuroSpin (INSERM-CEA)
Sébastien Demortain: University of Paris-Saclay, Cognitive Neuroimaging Unit, NeuroSpin (INSERM-CEA)
Bertrand Thirion: University of Paris-Saclay, Inria, CEA
Evelyn Eger: University of Paris-Saclay, Cognitive Neuroimaging Unit, NeuroSpin (INSERM-CEA)
Florent Meyniel: University of Paris-Saclay, Cognitive Neuroimaging Unit, NeuroSpin (INSERM-CEA)

Nature Communications, 2025, vol. 16, issue 1, 1-18

Abstract: Abstract Assessing probabilities and predicting future events are fundamental for perception and adaptive behavior, yet the neural representations of probability remain elusive. While previous studies have shown that neural activity in several brain regions correlates with probability-related factors such as surprise and uncertainty, similar correlations have not been found for probability. Here, using 7 Tesla functional magnetic resonance imaging, we uncover a representation of the probability of the next event in a sequence within the human dorsolateral prefrontal and intraparietal cortices. Crucially, univariate and multivariate analyses revealed that this representation employs a highly non-monotonic code. Tuning curves for probability exhibit selectivity to various probability ranges, while the code for confidence accompanying these estimates is predominantly monotonic. Given such diversity in tuning curves, future studies should move from assuming monotonic or simple canonical forms of tuning curves to considering richer representations, and clarify why different types of code exist.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-65727-7 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-65727-7

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-65727-7

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().