Simon P. Kelly

This study investigates whether the centro-parietal positivity (CPP) reflects evidence accumulation or subjective confidence during perceptual decision-making, finding that CPP is an evidence accumulation signal, not confidence.

The paper investigates sequential sampling and integration in perceptual decisions using neural data and models, finding that while accuracy improves with evidence duration, neither integration nor a decision bound can be definitively confirmed.

The paper investigates how two neural signals encode different aspects of belief formation during decision-making in a volatile task, finding that one signal tracks evolving beliefs while the other encodes belief updates.

This paper introduces a leaky evidence accumulation model to explain perceptual confidence and subjective duration in a face detection task under temporal uncertainty.

The paper critiques Frömer et al's analysis of perceptual choice data, arguing their deconvolution method is not suitable for testing bounded accumulation signals.

The paper analyzes visual evoked potentials (VEPs) to determine their anatomical sources in visual cortex using a forward model-informed approach, finding that V1 contributes most to VEP signals with some extrastriate contributions.

The paper uses intracranial EEG to identify motor-independent evidence accumulation dynamics in multiple human brain regions during perceptual decision tasks.

The paper investigates how decision bounds adjust in intermittent target detection tasks and finds that a model incorporating sensory-level criteria better explains both behavior and neural dynamics.

The paper examines how pre- and post-choice neural signals relate to confidence in perceptual decisions, finding that post-choice evidence accumulation continues and is influenced by confidence levels.

The paper constructs a neurally-informed model of decision-making that incorporates prior knowledge of time constraints and stimulus probability, revealing adjustments in neural processes not captured by standard diffusion models.

The paper examines visual gain control in Parkinson's disease patients using steady-state VEP and EEG, finding exaggerated spatial and temporal suppression and increased background noise.

The paper investigates how urgency signals in decision-making affect neural and motor responses across different processing levels.

The paper introduces the Healthy Brain Network (HBN) biobank, a large-scale multimodal dataset for pediatric mental health and learning disorders research, with initial data release.

This study uses eye tracking and EEG to decompose Symbol Search performance into cognitive components, finding that efficient performance depends more on memory encoding than general processing speed.

The paper presents a dataset combining EEG and eye tracking data from 126 individuals to study information processing in the developing brain.