Vincent B. McGinty

This paper develops a recurrent neural network model that learns to select computations by combining rational meta-reasoning with meta-learning, demonstrating its ability to approximate optimal symbolic models and replicate neural dynamics in macaque and human planning tasks.

The paper investigates the neural code for value in the human orbitofrontal cortex and finds that a probabilistic, non-linear population code represents both value and its uncertainty.

This study compares high-gamma LFPs and spiking activity in the OFC during value-based decisions, finding dissociable value and decision coding properties between the two signals.

This study shows that macaque monkeys exhibit gaze biases in economic decisions similar to humans, supporting a shared mechanism across species.

The paper shows that trial-wise choice variability in primate OFC is explained by variability in decoded value signals from simultaneously recorded neurons.

The paper shows that gaze shifts towards appetitive cues predict reward anticipation in monkeys, but OFC activity does not mediate this relationship.