NIH R01 · 2024
Value networks and hippocampal non-local representations
SUMMARY / ABSTRACT This proposal aims to reveal critical neural mechanisms for intelligent reward-driven learning and decision-making. It is well established that animals, including humans, use internal models of the world to guide their behavior. Model-based computations are especially vital in complex environments, where animals often need to plan a sequence of choices leading to later rewards. Furthermore, after receiving a reward animals update their reward predictions (“values”) – both for earlier choices they made, and for alternative ways of reaching that reward. These adaptive behaviors rely on combining simulations (of potential paths) with evaluations (of whether they are…
From the public funding record at NIH RePORTER. Describes the funded project, not the reviews below.