Natasha L. Matthews

This study uses EEG to analyze perceptual decision-making in ADHD, finding altered neural dynamics in attentional selection, sensory evidence accumulation, and response preparation compared to typically developing children.

The paper develops a common algorithm for confidence judgements across visual, auditory, and audio-visual decisions, showing scaled evidence strength models outperform others and suggest a shared algorithm for confidence.

The paper investigates whether confidence judgments in visual and auditory decisions share common computational processes, finding that scaled evidence strength models best explain the data across modalities.

The paper investigates rapid recalibration of temporal order judgements and finds that response bias, not timing perception, explains contradictory results.

This paper uses EEG and machine learning to classify schizophrenia patients and controls using auditory prediction error responses, achieving 80.48% accuracy and correlating MMN with functioning scores.