Momchil S. Tomov

The paper develops a model integrating time and reward to explain foraging decisions and identifies frontal cortex ramping dynamics as a mechanism for solving patch foraging problems.

The paper shows humans reuse learned strategies for new tasks using a computational process involving successor features and generalised policy improvement, supported by neural activation patterns.

The paper uses theory-based reinforcement learning to analyze brain data and identifies neural mechanisms underlying theory-based learning in humans.

The paper investigates how causal beliefs influence reinforcement learning by analyzing fMRI data from human participants during a task manipulating causal structure beliefs.

This study investigates human multi-task reinforcement learning by comparing participant behavior to algorithms in a two-step decision-making task with changing rewards.

The paper identifies distinct neural correlates of uncertainty in the brain that underlie different exploration strategies during decision-making.

This paper introduces a Bayesian model of hierarchy discovery that explains how humans organize environments into hierarchical structures for planning, supported by simulations and behavioral experiments.

This study uses fMRI to investigate neural correlates of structure learning and associative learning, showing distinct brain regions and predictive behavioral outcomes.