Kevin J. Miller

This paper introduces DataDIVER, an automated method for discovering computational models from data, which reveals novel insights into human and animal learning.

This study identifies the dorsal prefrontal cortex, specifically anterior supplementary motor cortex, as a driver of perseverative behavior in mice during a reward learning task.

This paper uses AI to discover an equation explaining how neuronal tuning smoothness controls population code geometry in visual cortex and head-direction cells.

LLM-driven evolutionary program synthesis (CogFunSearch) discovers interpretable Python cognitive models that predict bandit-task choices of humans, rats and fruit flies better than leading hand-designed models.

The paper introduces a dynamic model to analyze strategy shifts in reward learning, showing a progression from model-based exploration to exploitation and reduced engagement in rats.

The paper combines artificial neural networks with classic cognitive models to create interpretable hybrid models that reveal human learning mechanisms similar to reinforcement learning.

This paper learns cognitive models from behavioral data using disentangled RNNs, recovering interpretable models from synthetic and rat behavioral data.

This study investigates whether value representations in the rodent orbitofrontal cortex drive choice or support learning, finding that they instead signal expected reward information to a learning process elsewhere in the brain.

This study provides causal evidence linking the dorsal hippocampus to planning behavior using a novel task and muscimol inactivation in rodents.

The paper presents a new analysis technique for multi-step decision tasks that reduces confounding issues and provides a theory-neutral view of behavior.