Neural representation of valenced and generic probability and uncertainty.


Journal

The Journal of neuroscience : the official journal of the Society for Neuroscience
ISSN: 1529-2401
Titre abrégé: J Neurosci
Pays: United States
ID NLM: 8102140

Informations de publication

Date de publication:
12 Jun 2024
Historique:
received: 24 01 2024
revised: 28 05 2024
accepted: 31 05 2024
medline: 13 6 2024
pubmed: 13 6 2024
entrez: 12 6 2024
Statut: aheadofprint

Résumé

Representing the probability and uncertainty of outcomes facilitates adaptive behavior by allowing organisms to prepare in advance and devote attention to relevant events. Probability and uncertainty are often studied only for valenced (appetitive or aversive) outcomes, raising the question whether the identified neural machinery also processes the probability and uncertainty of motivationally neutral outcomes. Here, we aimed to dissociate valenced from valence-independent (i.e., generic) probability (p; maximum at p=1) and uncertainty (maximum at p=0.5) signals using human neuroimaging. In a Pavlovian task (n=41; 19 females), different cues predicted appetitive, aversive, or neutral liquids with different probabilities (p=0, p=0.5, p=1). Cue-elicited motor responses accelerated, and pupil sizes increased primarily for cues that predicted valenced liquids with higher probability. For neutral liquids, uncertainty rather than probability tended to accelerate cue-induced responding and decrease pupil size. At the neural level, generic uncertainty signals were limited to occipital cortex, while generic probability also activated anterior ventromedial prefrontal cortex. These generic probability and uncertainty signals contrasted with cue-induced responses that only encoded the probability and uncertainty of valenced liquids in medial prefrontal, insular and occipital cortices. Our findings show that the brain processes probability and uncertainty in a generic fashion. Moreover, the behavioral and neural dissociation of generic and valenced signals indicates that the brain keeps track of motivational charge and highlights the need and usefulness of characterizing the exact nature of learned representations.

Identifiants

pubmed: 38866483
pii: JNEUROSCI.0195-24.2024
doi: 10.1523/JNEUROSCI.0195-24.2024
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2024 Kim et al.

Auteurs

Jae-Chang Kim (JC)

Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, 8006 Zurich, Switzerland jaechang.kim@econ.uzh.ch phil.tobler@econ.uzh.ch.

Lydia Hellrung (L)

Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, 8006 Zurich, Switzerland.

Marcus Grueschow (M)

Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, 8006 Zurich, Switzerland.

Stephan Nebe (S)

Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, 8006 Zurich, Switzerland.

Zoltan Nagy (Z)

Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, 8006 Zurich, Switzerland.

Philippe N Tobler (PN)

Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, 8006 Zurich, Switzerland jaechang.kim@econ.uzh.ch phil.tobler@econ.uzh.ch.
Neuroscience Center Zurich, University of Zurich, Swiss Federal Institute of Technology Zurich, Switzerland.

Classifications MeSH