Alex Clarke

The paper decodes animacy from ECoG data in the human anterior ventral temporal cortex, finding that semantic information is encoded by local populations and interacts with distributed cortical regions.

This study investigates how object-scene congruency affects neural oscillations and top-down connectivity using MEG, finding increased low-frequency activity and top-down connectivity during incongruent object processing.

This paper introduces trial-level RSA (tRSA) to assess neural representation at the trial level, finding it more sensitive and theoretically appropriate than classic RSA.

The paper investigates how real-world contexts modulate neural oscillations using mobile EEG and augmented reality, finding differences in ERPs and theta-band power between congruent and incongruent object environments.

The paper identifies a distributed neural substrate for early speech interpretation using MEG data and Representational Similarity Analysis, showing simultaneous processing of phonology, lexical form, and semantics.

This study investigates how recurrent connectivity in the brain supports visual and semantic object representations during recognition using fMRI and MEG data.

This study investigates how prior scene context modulates semantic object processing using EEG and computational modeling, finding that incongruent objects have prolonged semantic integration.

The paper shows that hippocampal representations modulate visual cortex activity to anticipate expected sensory items during navigation.

This paper combines mobile EEG and augmented reality to study neural responses to faces in real-world environments, finding increased low-frequency EEG activity for inverted faces across multiple tasks.

This study examines how the human hippocampus encodes contextual and goal information during goal-directed navigation, showing context-specific future goal representations and anticipatory activity patterns.

This study compares GWAS and relatedness-based regression for identifying genetic variants associated with brain structure, finding they perform differently based on linkage disequilibrium levels.

The paper investigates how visual information transforms into semantic representations in the ventral visual pathway using computational models and MEG data, finding distinct oscillatory patterns for visual and semantic information.

The paper combines deep visual and semantic attractor networks to model fMRI data from object processing, showing how visual input influences semantic representations in the ventral stream.

This study investigates how neural oscillations change during conditional associative learning using EEG and finds that frontal theta and posterior alpha/beta oscillations are linked to memory formation.