Orbitofrontal noradrenaline supports adaptive learning-rate adjustment in probabilistic reversal learning

H Hadrien Plat (University of Bordeaux, Institut de Neurosciences Cognitives et Intégratives d‘Aquitaine, UMR 5287 CNRS) C Coline Chevallier (University of Montpellier, Institut de Génomique Fonctionnelle, UMR 5203 CNRS, U 1191 INSERM) A Alessandro Piccin (University of Bordeaux, Institut de Neurosciences Cognitives et Intégratives d‘Aquitaine, UMR 5287 CNRS) A Alain R. Marchand (University of Bordeaux, Institut de Neurosciences Cognitives et Intégratives d‘Aquitaine, UMR 5287 CNRS) J Jérémie Naudé (University of Montpellier, Institut de Génomique Fonctionnelle, UMR 5203 CNRS, U 1191 INSERM) E Etienne Coutureau (University of Bordeaux, Institut de Neurosciences Cognitives et Intégratives d‘Aquitaine, UMR 5287 CNRS)

Abstract

Adaptive decision-making in dynamic environments requires flexible adjustment of learning speed to balance stability and flexibility. When outcomes are highly stochastic, learners must avoid over-interpreting noise and update more slowly, whereas in volatile environments where contingencies change frequently, learning should accelerate to rapidly incorporate new evidence. Theories propose that internal estimates of uncertainty tune learning rates through neuromodulation-dependent mechanisms. Here, we investigated how noradrenergic inputs from the locus coeruleus (LC) to the orbitofrontal cortex (OFC) support adaptive learning under uncertainty. We show that, in a probabilistic reversal learning task performed across different levels of stochasticity, rats exhibited behavior best explained by an adaptive reinforcement-learning model in which learning rates dynamically adjusted according to model-estimated stochasticity and volatility, outperforming standard fixed-rate models. Noradrenaline release in the OFC closely tracked trial-by-trial, model-derived volatility estimates around contingency changes. Disrupting LC→OFC noradrenergic inputs reproduced the model-predicted impairment in adaptive learning-rate adjustment associated with model-estimated volatility. Together, these findings identify OFC noradrenergic signaling as a key circuit mechanism supporting learning-rate adjustment in response to internal estimates of volatility during adaptive decision-making under uncertainty.

Article Details

Volume / Issue Vol. 123, Issue 29
Published July 21, 2026
ISSN 0027-8424
Publisher National Academy of Sciences

Authors (6)

H

Hadrien Plat

University of Bordeaux, Institut de Neurosciences Cognitives et Intégratives d‘Aquitaine, UMR 5287 CNRS

C

Coline Chevallier

University of Montpellier, Institut de Génomique Fonctionnelle, UMR 5203 CNRS, U 1191 INSERM

A

Alessandro Piccin

University of Bordeaux, Institut de Neurosciences Cognitives et Intégratives d‘Aquitaine, UMR 5287 CNRS

A

Alain R. Marchand

University of Bordeaux, Institut de Neurosciences Cognitives et Intégratives d‘Aquitaine, UMR 5287 CNRS

J

Jérémie Naudé

University of Montpellier, Institut de Génomique Fonctionnelle, UMR 5203 CNRS, U 1191 INSERM

E

Etienne Coutureau

University of Bordeaux, Institut de Neurosciences Cognitives et Intégratives d‘Aquitaine, UMR 5287 CNRS