bioRxivNeurosciencePreprint
Voice and linguistic features support early attentional filtering of irrelevant stimuli
Sahil LuthraiD, Eric ParkeriD, Marysia Brown, Gidey W. GezaeiD, Hee So KimiD, Wusheng LiangiD, Abigail L. NoyceiD, Barbara G. Shinn‐CunninghamiD
Abstract
In cocktail party-type environments, listeners must segregate simultaneous acoustic sources and select one for processing. Behavioral studies have established that both low-level voice differences (e.g., pitch) and high-level linguistic differences (e.g., word content) aid these processes. Electroencephalography (EEG) studies have demonstrated that the brain filters voices based on low-level features early in sensory processing, but it is unclear whether linguistic differences also support early filtering. In this EEG study, listeners attended to a lateralized auditory stream of target syllables while ignoring distractor stimuli from the opposite hemifield. In contrast to previous auditory attention studies, target stimuli were always attended and statistically identical across conditions; instead, we manipulated the distractors. Specifically, we manipulated whether targets and distractors were spoken in the same voice or different voices, as well as whether they were composed of the same or different linguistic content. Critically, distractors were temporally offset from targets, enabling an investigation of whether acoustically matched, always attended target stimuli are differentially encoded as a function of surrounding context. Differences in either voice or linguistic features supported target recall: Recall was poor only when both streams were in the same voice and had similar linguistic content. However, voice and linguistic features had independent, additive effects on neural processing. Specifically, target dissimilarity in acoustics and linguistic content both contributed to more robust, larger-magnitude target-evoked EEG responses. Overall, results demonstrate that both linguistic dissimilarity and low-level acoustic dissimilarity improve early attentional filtering of distractors in multi-source acoustic scenes.
- organism: human
- assay: electronic health records
- research focus: cell type annotation
- approach: statistical model
- Citations
- 0
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