Andrew James Anderson

The paper examines how EEG indices of speech processing features correlate with speech comprehension in noise, finding that both low- and high-level features influence behavior depending on noise levels.

The paper uses fMRI and AI models to show that the medial temporal default mode network encodes visual imagery from autobiographical memories.

The paper uses fMRI to show that imagined social and solitary scenarios activate different patterns of the frontoparietal and default mode networks, revealing a gradient that reflects semantic differences in internal cognitive states.

The paper investigates how object and event concepts are represented in the brain using fMRI data, finding they share a common representational code based on perceptual and emotional features.

The paper models EEG responses to natural speech, showing predictable words are processed faster with earlier and smaller N400-like responses.

This paper uses Whisper to model EEG data during audiobook comprehension, finding it accurately captures speech-to-language transformation with context and attention dependencies.

The paper investigates whether electrophysiological indices of semantic dissimilarity reflect narrative understanding or just word-level semantic changes by manipulating speech scrambling and analyzing EEG data.

The paper examines how older and younger adults process speech using electrophysiological measures, finding that older adults show reduced semantic pre-activation during language comprehension.

The paper uses EEG and computational models to show that semantic dissimilarity in speech correlates with electrophysiological responses during natural speech comprehension.