Rapid offline integration of learned experiences is shaped by valence and biases future decisions

Authors:
Preprint
Publisher website
Google Scholar
Abstract
Rapid offline reactivation of recently learned experiences support adaptive behaviors in changing environments, but whether positive and negative experiences are differentially reactivated immediately after learning and how this shapes decisions remains unclear. Combining fMRI, multivariate predictive modeling and a probabilistic reward-learning task with learning and transfer stages, we examined how gain- and loss-related experiences are encoded during learning, reactivated during post-learning rest, and influence subsequent choices. We used computational modeling to dissociate gain- and loss-related learned experiences, spanning observable stimulus and outcome information as well as latent expected values and prediction errors. Gain- and loss-related experiences were encoded in dissociable neural expressions and were differentially reactivated during post-learning rest. While gain-related experiences exhibited stronger overall reactivation, loss-related reactivation specifically predicted accurate loss avoidance in the subsequent transfer. Moreover, coordinated engagement of a distributed memory network encompassing the posterior cingulate cortex, middle temporal gyrus and hippocampus increased across post-learning rest periods, consistent with systems-level consolidation supporting adaptive loss avoidance. Together, these findings provide evidence that rapid offline reactivation is valence-sensitive and functionally asymmetric, with negative experiences exerting a disproportionate influence on subsequent avoidance despite weaker reinstatement, a pattern that may have broader relevance for understanding persistent negative thoughts in affective disorders.

This publication has 81 references indexed in Scilit: