Capacity and tradeoffs in neural encoding of concurrent speech during Selective and Distributed Attention Article Swipe
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· 2022
· Open Access
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· DOI: https://doi.org/10.1101/2022.02.08.479628
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 system’s inherent limitation to fully process two speech streams, and highlights the complex nature of attention, particularly for continuous speech.
Related Topics
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2022.02.08.479628
- https://www.biorxiv.org/content/biorxiv/early/2022/02/09/2022.02.08.479628.full.pdf
- OA Status
- green
- Cited By
- 5
- References
- 73
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4210887170
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4210887170Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2022.02.08.479628Digital Object Identifier
- Title
-
Capacity and tradeoffs in neural encoding of concurrent speech during Selective and Distributed AttentionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-02-09Full publication date if available
- Authors
-
Maya Kaufman, Elana Zion GolumbicList of authors in order
- Landing page
-
https://doi.org/10.1101/2022.02.08.479628Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2022/02/09/2022.02.08.479628.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2022/02/09/2022.02.08.479628.full.pdfDirect OA link when available
- Concepts
-
Magnetoencephalography, Task (project management), Relevance (law), Computer science, Encoding (memory), Speech recognition, Comprehension, Perception, Cognitive psychology, Speech perception, Cognition, Psychology, Electroencephalography, Neuroscience, Programming language, Economics, Political science, Management, LawTop concepts (fields/topics) attached by OpenAlex
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5Total citation count in OpenAlex
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-
2025: 1, 2024: 1, 2022: 3Per-year citation counts (last 5 years)
- References (count)
-
73Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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