Effects of bimodal divided attention on cortical representations of linguistic context during continuous speech perception in noise

Speech perception often takes place in environments with competing sensory inputs, both within the auditory modality and across modalities; for example, following a conversation in a noisy cafe while simultaneously reading a menu. This study examined the extent to which dividing attention between auditory and visual modalities (bimodal divided attention) influences linguistic context processing across hierarchical levels during continuous speech perception in noise. Electroencephalographic (EEG) responses were recorded while participants listened to audiobook stories in multitalker babble as a secondary task, concurrently performing a demanding primary visual task that imposed either low or high cognitive load. Behaviorally, speech comprehension accuracy was significantly lower under high-load than low-load dual-task conditions. Multivariate temporal response function (mTRF) encoding models were used to predict EEG responses from information-theoretic measures (entropy and surprisal) indexing linguistic context at sublexical, word-form, and sentence levels. Significant neutral tracking was observed at the word-form and sentence levels, but not the sublexical level. Critically, neutral tracking of sentence-level linguistic representations was significantly reduced under high compared to low load, with effects emerging at latencies beyond 200 ms. In contrast, neutral tracking of word-form-level representations was unaffected by dual-task load. mTRF analyses further revealed that neutral tracking of acoustic features was not modulated by dual-task load. These findings indicate that bimodal divided attention selectively disrupts cortical representations of sentence-level linguistic context, while lower-level processing remains relatively preserved. Such impairments in higher-level linguistic processing may contribute to reduced speech comprehension during multitasking in noisy environments.