Andrew Heathcote

The paper shows that flexible adaptation of task-positive brain networks predicts individual differences in evidence accumulation efficiency, explaining 36%-39% of the variance.

This study investigates how proactive cues affect stopping and responding in a bimanual task using EMG and computational modeling, finding reduced stopping delay with no effect on stopping speed.

The paper presents a cognitive process model to address context independence violations in the ABCD Study stop-signal task, improving SSRT estimation accuracy.

This paper introduces a new reinforcement learning-error accumulation model (RL-ARD) that better captures response time dynamics in instrumental learning tasks compared to existing models.

This study uses a changing information paradigm and Bayesian methods to show that information processing in decision-making is relative, urgency and evidence accumulation are equally important, and leakage occurs with a 200-250ms time scale.

This study uses a parametric ex-Gaussian model to analyze stop-signal task performance, revealing differences in neural correlates of response inhibition between stop-success and stop-failure trials.

The paper investigates how animacy categorization in the human brain is affected by stimulus degradation, finding asymmetric shifts in neural activation patterns for animate versus inanimate objects.