David Pascucci

The paper tests for behavioral rhythms in perceptual averaging tasks and finds task-specific temporal dynamics rather than universal rhythms.

The paper shows that EEG-brain behavior associations are unstable with small samples but become reliable with large cohorts.

The paper identifies three distinct alpha components that jointly determine individual alpha peak frequency, challenging the assumption of a single oscillatory rhythm.

The paper shows that no-response trials alter serial dependence by reducing attractive biases and increasing repulsive biases, with EEG evidence of enhanced neural responses.

The paper shows that predictable visual sequences lead to reduced neural responses and anticipatory biases in orientation representation, mirroring behavioral representational momentum.

The paper uses EEG in the SQM paradigm to identify two distinct neural stages of visual processing: an early unconscious stage and a later conscious stage.

This paper introduces CSET, a method that reconstructs electrical brain source-networks with high spatial resolution using diffusion MRI and EEG data.

This study challenges the idea that perception uses optimal strategies by showing serial dependence is stronger when prior stimuli are more uncertain, suggesting internal observer states influence perception.

The paper investigates whether Feature Distribution Learning (FDL) occurs through passive exposure, finding it happens only when an orientation singleton is present during exposure.

The paper introduces a multimodal neuroimaging dataset containing EEG, MRI, and behavioral data for studying visual evoked potentials and brain connectivity.

The paper introduces a linear adaptive filter that uses structural connectivity priors to improve dynamic and directed functional connectivity estimation in electrophysiological data.

This paper introduces the connectome spectrum as a method to represent brain activity using structural connectivity, showing it provides a more compact and informative representation of fast brain dynamics.

The paper examines how feature-based attention affects brain oscillations and connectivity using fMRI and EEG, revealing distinct roles of beta and gamma rhythms in anticipatory and reactive processing.

This paper introduces a new Kalman filter algorithm, STOK, for modeling dynamic brain networks and shows it outperforms classical methods in tracking time-varying connectivity patterns.

This paper introduces a method using network harmonics to track fast EEG dynamics during a face detection task with subsecond resolution.

The paper shows that human visual perception is shaped by two opposing forces: sensory adaptation repelling from previous stimuli and decisional traces attracting toward previous responses, challenging claims about continuity fields.

This study shows how selective attention modulates cross-frequency coupling in visual cortex by disrupting top-down parietal influences.