Yael Niv

This study investigates the role of the orbitofrontal cortex in learning state transitions, finding that OFC activity correlates with more optimal predictions rather than simple prediction errors.

The paper shows that pupillary indices correlate with decision-making biases, with low neural gain linked to strong biases and high gain linked to reduced biases but increased indecisiveness.

This study shows that dopamine transients in learning contexts do not create cached value but instead support valueless associative learning.

The paper derives a ventral striatum-specific hemodynamic response function (VS-HRF) to improve statistical power in fMRI analysis of reward prediction error signals.

This paper shows that high reward prediction errors (RPEs) enhance memory encoding and create event boundaries that disrupt sequential memory integration.

This study shows that human hippocampus reactivates sequences of non-spatial task states during wakeful rest, linking offline reactivation to on-task orbitofrontal cortex representations.

This paper identifies bias in RSA methods using fMRI data and introduces BRSA, a Bayesian approach that reduces bias and improves precision in estimating neural representational similarity.

The paper investigates how prediction errors in decision-making tasks are influenced by action execution failures using fMRI and behavioral experiments.

This study shows that rat orbitofrontal cortex activity represents both a value-invariant task structure and expected value during an odor sequence task.

The paper proposes a theory that the orbitofrontal cortex provides state representations for decision-making and reinforcement learning, integrating diverse functions through its connectivity.

This study shows that the orbitofrontal cortex encodes a posterior probability distribution over latent causes using fMRI pattern similarity analyses.