Harrison Ritz

The paper investigates how controlled and non-controlled associations are integrated in neural representations to guide adaptive behavior using EEG and behavioral data.

This study compares task-switching dynamics between artificial neural networks and human EEG data using state-space modeling, revealing a shared strategy of converging to a neutral task state.

The paper uses fMRI and a novel multivariate analysis to show orthogonal neural representations of target and distractor processing in cognitive control tasks.

This study evaluates the test-retest reliability of OPM-MEG for whole-brain functional connectivity, showing high group-level repeatability and individual robustness across theta, alpha, and beta frequency ranges.

The paper investigates how cognitive control adjusts target and distractor processing under different task demands, finding distinct mechanisms for enhancing targets and suppressing distractors.

This study uses fMRI to show that cognitive control is critical for processing mildly degraded but perfectly intelligible speech, revealing hidden costs in neural processing.

This paper investigates how reward and punishment differentially influence cognitive control strategies, showing that they affect effort allocation in distinct ways.

The paper investigates rapid changes in neural activity patterns during behavioral shifts, identifying distinct representations in different brain regions that reflect uncertainty and environmental changes.

The paper tests whether a PID controller can model human adaptive behavior in dynamic environments, finding it better describes decision-making than traditional models.