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Poster Session B: Wednesday, August 13, 1:00 – 4:00 pm, de Brug & E‑Hall

The Role of Neural Replay in Structure Learning and Value Generalization

Fabian Renz¹, Shany Grossman², Nathaniel D. Daw³, Peter Dayan², Christian F. Doeller⁴, Nicolas W. Schuck⁵; ¹Max Planck Schools, ²Max-Planck Institute, ³Princeton University, ⁴Max Planck Institute for Human Cognitive and Brain Sciences, ⁵Universität Hamburg

Presenter: Fabian Renz

Humans can quickly learn and update latent task structures, and use them to guide value-based decision-making. In a functional magnetic resonance imaging study, 52 participants learned a latent graph structure requiring non-local value generalization upon reward reversals. Performance was best explained by a latent cause inference model that captures structure learning and admits value generalization. The functional imaging data offered a possible substrate for this generalization by demonstrating that the hippocampus tracked the underlying task structure and exhibited non-local reactivation of unobserved sequences sharing a reward.

Topic Area: Reward, Value & Social Decision Making

Extended Abstract: Full Text PDF