Work

Inter-trial convergence of neural task states supports cognitive flexibility

Year: 2026

Type: article

Abstract: The ability to switch between tasks is a core component of human intelligence, yet a mechanistic understanding of this capacity has remained elusive. Long-standing debates over how task switching is influenced by preparation for upcoming tasks or interference from previous tasks have been difficult to resolve without quantitative neural predictions. We advance this debate by using state-space modeling to directly compare the latent task dynamics in task-optimized recurrent neural networks and human electroencephalographic recordings. Over the inter-trial interval, both neural networks and brains reset into a neutral task state, an effective dynamic motif that reconciles the roles of preparation and interference in task switching. These findings provide a quantitative account of cognitive flexibility and a promising paradigm for bridging artificial and biological neural networks.

Source: Current Biology

DOI: 10.1016/j.cub.2026.07.028

Authors: Harrison Ritz, Aditi Jha, Nathaniel D. Daw, Jonathan D. Cohen

Institutions: Princeton University, Queen's University, Stanford University

Language: English

Topic: Neural and Behavioral Psychology Studies

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: hybrid

Estimated APC paid: $7,030

Funder: Office of Naval Research