Ian Charest

The paper introduces an open-source Python toolbox for representational similarity analysis, providing tools to evaluate neural representations and models.

This study identifies neural computations underlying face identification loss in a prosopagnosia patient using EEG, RSA, and deep neural networks, revealing impaired high-level visual and semantic processing.

The paper decodes face recognition abilities from brain activity in super-recognisers and typical recognisers using multivariate pattern analysis and compares them to computational models.

The paper shows that re-expression of CA1 and entorhinal activity patterns predicts temporal context memory over long timescales in humans.

This study uses EEG-fMRI fusion to show that visual object memory retrieval follows a backward spatio-temporal trajectory from conceptual to perceptual brain regions.

The paper investigates five animacy-related object dimensions in brain and behavior using fMRI, EEG, and similarity judgments, finding they explain variance in both brain representations and behavior.

GLMsingle improves single-trial fMRI response estimates using custom HRFs, noise regressors, and ridge regression, enhancing reliability and decoding performance across datasets.

The paper shows that sleep spindles correlate with learning patterns and predict memory consolidation in humans.

The paper introduces a large-scale 7T fMRI dataset of natural scenes to advance cognitive and computational neuroscience research.

The paper identifies hippocampal gamma power as a marker of the switch from perceptual cues to mnemonic representations during memory recall, revealing hippocampal-cortical dynamics.

This study reveals a hierarchical progression of category selectivity in sound processing by analyzing spatiotemporal dynamics using MEG and fMRI.

This paper shows recurrent neural networks outperform feedforward models in visual recognition tasks, enabling flexible speed-accuracy trade-offs and better prediction of human reaction times.

The paper shows that individuals with higher autistic traits rely more on fine image details for natural image recognition.

The paper shows that the medial prefrontal cortex encodes value-weighted schemas, where edge strength is influenced by value, experience, and node centrality.

The paper shows that decreases in alpha/beta power correlate with increased fidelity of stimulus-specific information in the brain across different cognitive tasks.

The paper investigates how category-related visual features influence conscious perception of natural images using the Attentional Blink paradigm and deep neural network activation patterns.

This study investigates the neurocognitive mechanisms of retrieval-mediated learning using fMRI, finding that retrieval increases prefrontal cortex activity and decreases hippocampal activity, supporting a fast consolidation hypothesis.

GLMdenoise improves replicability and classification accuracy in multivariate pattern analysis of fMRI data by reducing noise in GLM estimates.

The paper investigates how alpha and beta oscillatory activity relates to sub-second temporal judgments, finding that higher power correlates with shorter perceived time.

The paper investigates how brain activity links visual stimuli to behaviour, finding that behaviour-related activity emerges rapidly in the ventral visual pathway and cannot be explained by visual features or semantic categories alone.

This paper reviews advances in neuroimaging analysis that move beyond traditional averaging by modeling individual representational idiosyncrasies using multivariate pattern analysis and representational dissimilarity matrices.