Neural Processing of Noise-Vocoded Speech Under Divided Attention: An fMRI-Machine Learning Study.

ABSTRACT: In real-life interaction, we often need to communicate under challenging conditions, such as when speech is acoustically degraded. This issue is compounded by the fact that our attentional resources are often divided when we simultaneously need to engage in other tasks. The interaction between the perception of degraded speech and simultaneously performing additional cognitive tasks is poorly understood. Here, we combined a dual-task paradigm with functional magnetic resonance imaging (fMRI) and machine learning to establish the neural network supporting degraded speech perception under divided attention. We presented 25 human participants with noise-vocoded sentences while they engaged in a concurrent visuomotor recognition task, employing a factorial design that manipulated both speech d

SUBMITTER: Wang H 

PROVIDER: S-EPMC12329574 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature