W.D. Penny

The paper introduces a Bayesian multivariate linear model to test for interactions in multivariate brain imaging data, offering improved statistical inference compared to standard decoding methods.

This paper introduces a cluster-based inference framework for hippocampal-based category learning, showing it improves categorization performance and explains memory effects.

This paper introduces thermodynamic integration for Bayesian model selection and averaging in dynamic causal models, demonstrating its feasibility with efficient implementation and parallel processing.

This study uses longitudinal MRI data from Huntington's Disease patients to show complex regional variations in cortical atrophy over time, with some areas showing accelerated atrophy after diagnosis.

The paper proposes finite retrospective inference as a bounded rationality approach to improve inference and learning by limiting the number of past states considered.

This paper introduces thermodynamic integration for dynamic causal modeling, showing it provides more robust log evidence estimates than variational Bayes in complex models.

The paper uses fMRI to study human navigation behavior in dynamic environments, finding specific brain activity patterns associated with back-tracking, detours, and shortcuts.

This study examines how human theta and alpha oscillations change during navigation, revealing patterns linked to detours and shortcuts.