Carsten Allefeld

This paper introduces multivariate Bayesian inversion as an alternative to machine learning for supervised tasks, demonstrating improved interpretability and probabilistic predictions.

The paper shows that visual working memory uses different cortical representations, including categorical, semantic, and spatial formats, across multiple brain regions.

The paper investigates neural correlates of different randomization tasks and finds shared and distinct brain activation patterns across task instructions.

The paper shows that categorical coding of color stimuli occurs in extrastriate visual cortex during working memory, not perception.

This paper introduces ITEM, a method to improve decoding accuracy in fMRI by addressing serial correlations in trial-wise estimates.

MACS provides a toolbox for assessing, comparing, and selecting GLMs in fMRI analysis, improving reproducibility and reducing false positives.

This paper introduces cross-validated Bayesian model averaging (cvBMA) to improve parameter estimates in GLM-based fMRI analysis by combining information from all models using posterior probabilities.