Emma Holmes

The paper examines individual differences in top-down attention using MEG and pupillometry, finding that sensory processing and strategic resource allocation shape heterogeneous neural network engagement.

This study shows that dynamic auditory figure-ground analysis better predicts speech-in-noise ability than static analysis, with combined measures outperforming age and hearing sensitivity.

The paper develops a UK version of the Iowa Test of Consonant Perception (B-ITCP) and validates its reliability and validity for speech-in-noise testing.

This study shows older adults without hearing loss can direct spatial selective attention as well as younger adults, but this ability declines with age-related hearing loss.

This paper introduces a generative model of auditory word repetition that simulates how words are produced and recognized, showing it can infer and repeat target words from acoustic signals.

The paper shows that musicianship and melodic predictability increase neural gain in auditory cortex during pitch deviance detection, as revealed by dynamic causal modeling of MEG data.

The paper shows that familiar voices are more robust to competing speech, with neural activity patterns in nonprimary auditory cortex being less degraded by masking.

The paper simulates functional recovery from different lesion types using a generative model, showing that complete disconnection of a system invokes the alternative system, while partial damage has more severe long-term effects.

The paper describes a case of auditory segregation deficit following right hemisphere damage, showing impaired speech-in-noise and music streaming, and links it to non-primary auditory cortex.

The paper investigates the neural mechanisms linking speech-in-noise perception difficulties to fundamental auditory grouping using fMRI, finding shared cortical processes in early auditory areas.

The paper investigates the relationship between auditory working memory precision for frequency and speech-in-noise thresholds, finding a significant correlation and a link to musical training.

This paper introduces active listening as a framework for speech synthesis and recognition, showing how word boundaries are selected to maximise evidence for internal models.