Elise Rowe

Weakening of subcortical and strengthening of cortical visual pathways across early adolescence

The paper uses longitudinal dMRI data to show that subcortical visual pathways weaken while cortical pathways strengthen during early adolescence, with both linked to faster visual processing.

Testing the Spatiotemporal Predictions of Global Neuronal Workspace Theory and Integrated Information Theory

This study reanalyzes MEG data using dynamic causal modeling to test predictions of global neuronal workspace theory and integrated information theory during face perception, finding support for GNWT but mixed results for IIT.

Computational modelling shows evidence in support of both sensory and frontal theories of consciousness

The paper uses computational modeling to show that both sensory and frontal theories of consciousness can explain EEG data, with a slight preference for frontal theories.

Evidence for adaptive myelination of subcortical shortcuts for visual motion perception in healthy adults

The paper reconstructs subcortical visual pathways in humans and finds that the LGN-MT pathway is more heavily myelinated, suggesting faster signal transmission.

Anxiety attenuates learning advantages conferred by statistical stability and induces loss of volatility-attuning in brain activity

This study examines how anxiety affects learning and neural responses to stable versus volatile environments using fMRI and behavioral tasks.

Detecting (un)seen change: The neural underpinnings of (un)conscious prediction errors

This study investigates how the brain processes visible and invisible prediction errors using EEG and DCM, revealing distinct neural mechanisms for each.

Bayesian Model Selection Maps for group studies using M/EEG data

This paper introduces a Bayesian Model Selection method for group-level M/EEG data analysis using SPM, enabling hypothesis comparison across brain regions and participants.

Bayesian mapping reveals that attention boosts neural responses to predicted and unpredicted stimuli

This study uses Bayesian model comparison to show attention boosts neural responses equally to predicted and unpredicted stimuli, supporting the Opposition model over the Interaction model.