Frederick Callaway

This study shows humans allocate working memory during planning based on decision relevance, not salience, using computational models and behavioral experiments.

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.

This study models human eye movements during immersive visual search using a reinforcement learning framework, finding that model predictions align with human gaze behavior.