Capacity and tradeoffs in neural encoding of concurrent speech during Selective and Distributed Attention

Abstract

Speech comprehension is severely compromised when several people talk at once, due to limited perceptual and cognitive resources. Under some circumstances listeners can employ top-down attention to prioritize the processing of task-relevant speech. However, whether the system can effectively represent more than one speech input remains highly debated. Here we studied how task-relevance affects the neural representation of concurrent speakers under two extreme conditions: when only one speaker was task-relevant (Selective Attention), vs. when two speakers were equally relevant (Distributed Attention). Neural activity was measured using magnetoencephalography (MEG) and we analysed the speech-tracking responses to both speakers. Crucially, we explored different hypotheses as to how the brain may have represented the two speech streams, without making a-priori assumptions regarding participants internal allocation of attention. Results indicate that neural tracking of concurrent speech did not fully mirror their instructed task-relevance. When Distributed Attention was required, we observed a tradeoff between the two speakers despite their equal task-relevance, akin to the top-down modulation observed during Selective Attention. This points to the systems inherent limitation to fully process two speech streams, and highlights the complex nature of attention, particularly for continuous speech.