Alexandra Woolgar

The paper develops and validates two large-scale stimulus sets for measuring processing demand across semantic and visuospatial domains, matched using a speed-accuracy algorithm.

The paper tracks cortical computations during speech processing using a language model and MEG, finding comprehension sustains alignment in deep layers while acoustic processing remains unchanged without comprehension.

The paper investigates how aperiodic neural activity relates to cognitive demand across the cortical hierarchy using combined fMRI and MEG data.

This study evaluates how trial averaging and resampling affect decoding accuracy in MVPA, finding that modest averaging improves results and provides code for parameter optimization.

The paper investigates how the brain encodes novel task instructions using fMRI and finds that the MDN uses both abstract and high-dimensional coding to balance generalization and expressivity.

HeteroRC improves neural decoding by capturing nonlinear and non-phase-locked dynamics, outperforming linear methods and revealing latent neural representations in EEG data.

This paper uses EEG and a hierarchical classification method to detect attentional lapses by predicting errors in a visual task based on neural decodability.

This study shows how feedback reshapes neural representations in vlPFC through oscillatory dynamics, making them closer to future retrieval states.

The paper demonstrates that plastic attractors can model flexible rule-based selective attention by forming task-specific synaptic connections and reproducing behavioral and neural findings.

The paper introduces a novel TMS-fMRI setup with 22 RF channels that improves whole-brain signal reception and data quality.

This study investigates how spatial and feature attention interact in the frontoparietal cortex to selectively code task-relevant information during a visual discrimination task.

This study uses TMS-EEG to show that parietal alpha stimulation enhances decoding of spatial attention signals but not feature information.

This study investigates the roles of oscillatory and aperiodic alpha activity in visuospatial attention using EEG and TMS-EEG, finding they contribute complementarily to attentional processing.

The paper investigates how frontoparietal brain regions encode visual and auditory rules, finding that they use modality-specific codes rather than generalizable representations.

This study investigates the electrophysiological mechanisms of domain-general cognition using MEG/EEG, finding that both aperiodic and oscillatory components encode task demand across cognitive tasks.

This study uses fMRI and MEG to analyze information coding and exchange in the multiple demand network, revealing distinct encoding patterns and introducing Fusion-RCA for connectivity analysis.

This study separates the neural bases of selective attention and decision-making using a two-stage task and multimodal neuroimaging data, showing attention affects stimulus representations before decision-making begins.

This study compares the temporal-signal-to-noise ratio (tSNR) of three MR head coils used with TMS-fMRI, finding that dedicated surface coils provide high tSNR under TMS but lower elsewhere.

The paper investigates how participants integrate information from past and future task steps, finding that future-relevant distractors influence decisions but do not reliably capture attention.

This study uses fMRI and RSA to show that frontoparietal cortex encodes exemplar-level stimulus information during visual classification, contrary to expectations that such coding would be restricted to posterior regions.

This study investigates the dynamic effect of attention on visual processing using EEG data, showing preferential coding of attended stimuli around 230ms after stimulus onset, independent of target-related processes and temporal expectation.

This paper examines model-based and model-free representational connectivity analysis, highlighting scenarios where they give conflicting results and suggesting ways to address these issues.

The paper compares decoding visual object categories using combined vs. individual EEG features, finding that individual multiscale Wavelet coefficients outperform combined features in prediction and behavioral explanation.

The paper investigates rapid neural reconfiguration during attention shifts using MEG and MVPA, finding sub-trial changes in feature coding only in complex tasks.

This study uses MEG to analyze how neural activity changes during successful and erroneous goal-directed tasks, revealing distinct patterns in perceptual information processing and decision-making.

This study compares how spatial and feature-selective attention affect neural population coding in the brain, showing distinct effects on stimulus representation and enhancement patterns.

This paper identifies optimal timing parameters for delivering TMS during fMRI slice gaps to minimize artifacts, achieving up to ~10 Hz stimulation frequency.

This study uses concurrent TMS-fMRI and MVPA to show that dlPFC causally supports the representation of task-relevant information by decreasing its coding when disrupted.

The paper uses MEG data to show that neural signatures of vigilance decrements can predict behavioural errors before they occur in a monitoring task.

This study shows that perceptual difficulty and familiarity level influence neural information flow direction in face recognition, with feed-forward dominance for familiar faces and top-down flow when recognition is harder.