Daeyeol Lee

This study investigates how mice and monkeys generate stochastic choices in competitive games, finding that neural circuits decouple reward signals from future choices to achieve unpredictability.

The paper quantifies age-related changes in temporal and spatial scales of resting-state fMRI signals across the lifespan and links these changes to cortical myelination.

This study investigates whether spatial memory mechanisms are shared across tasks requiring working memory and decision-making in primates, finding distinct roles for DLPFC and LIP.

The paper shows that intrinsic and task-related neural timescales increase hierarchically across mammalian cortices, but thalamic timescales differ from this pattern.

This study shows that mice follow a modified marginal value theorem with Bayesian updating of environment representations when foraging under uncertainty.

This study shows chronic ethanol exposure impairs cognitive flexibility and striatal decision signals in male rats, with specific changes in neural representations of actions and outcomes.

The paper shows spatial and temporal autocorrelation explain network topology measures in rs-fMRI data and how they change with aging and drug treatment.

The paper shows that animals briefly suppress motor output when new sensory evidence becomes available, and neurons in the frontal eye field show a corresponding activity dip.

This study investigates how temporal and reward contexts influence perceptual decision-making by training monkeys and modeling their behavior with generalized drift-diffusion models.

The paper estimates multiple timescales of neural dynamics and their integration of task-relevant signals across cortex, finding increasing timescales from parietal to prefrontal to cingulate cortex and no correlation with signal selectivity.

The paper investigates how typical and autistic adolescents represent and update social knowledge during learning, finding that autistic adolescents rely only on their own preferences while typical adolescents use population preferences and prediction error updating.

The paper shows humans use feature-based learning in dynamic environments and switch to option-based learning when features predict object values accurately.