Atsushi Kikumoto

The paper shows that predicted sensory outcomes are integrated into conjunctive neural representations during goal-directed action selection, influencing reaction times and learning.

The paper shows that zero prediction errors in reinforcement learning lead to unique psychological and neural responses, challenging the assumption that only unexpected outcomes drive belief updates.

The paper presents an information-theoretic framework for understanding cognitive control costs in context-dependent decisions, showing that policy complexity increases with response deadlines and correlates with neural dimensionality.

The paper shows that practice optimizes neural representational geometry to enhance task performance by stabilizing and aligning conjunctive neural states that abstract over non-critical task features.

The paper investigates how transient high-dimensional neural geometry supports stable conjunctive subspaces for context-dependent action selection in humans using EEG decoding.

The paper investigates how conjunctive representations support prioritization and selection of planned actions in working memory, showing that high-priority actions are more strongly represented and selected.

The paper investigates how conjunctive representations influence action stopping and finds that stronger conjunctive representations predict lower stopping success and are suppressed during stopping attempts.

This study shows that conjunctive representations combining rules, sensory, and motor features are essential for action selection and influence response times and priming patterns.

The paper shows humans alternate between model-based and memory-free action selection in competitive games, using model-based strategies after wins and stochastic choices after losses.

This study decodes hierarchical control of sequential behavior from oscillatory EEG activity, showing theta and alpha bands encode basic elements and abstract chunk positions, with higher-level chunk information linked to working memory capacity.

This study decodes EEG signals to reveal how task-set representations dynamically influence stimulus and response processing in humans.