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  4. Cochlea to categories: The spatiotemporal dynamics of semantic auditory representations

Cochlea to categories: The spatiotemporal dynamics of semantic auditory representations

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Cochlea to categories The spatiotemporal dynamics of semantic auditory representations.pdf

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sword-2025-11-12T17:22:30.original.xml (130 B)
Original SWORD entry document
Author(s)
Lowe, Matthew X
•
Mohsenzadeh, Yalda
•
Lahner, Benjamin
•
Charest, Ian
•
Oliva, Aude
•
Teng, Santani
Date Issued
June 21, 2022
Journal
Cognitive Neuropsychology
Publisher
Taylor & Francis
Citation
Lowe, M. X., Mohsenzadeh, Y., Lahner, B., Charest, I., Oliva, A., & Teng, S. (2021). Cochlea to categories: The spatiotemporal dynamics of semantic auditory representations. Cognitive Neuropsychology, 38(7–8), 468–489.
Version
Final published version
Abstract
How does the auditory system categorize natural sounds? Here we apply multimodal neuroimaging to illustrate the progression from acoustic to semantically dominated representations. Combining magnetoencephalographic (MEG) and functional magnetic resonance imaging (fMRI) scans of observers listening to naturalistic sounds, we found superior temporal responses beginning ∼55 ms post-stimulus onset, spreading to extratemporal cortices by ∼100 ms. Early regions were distinguished less by onset/peak latency than by functional properties and overall temporal response profiles. Early acoustically-dominated representations trended systematically toward category dominance over time (after ∼200 ms) and space (beyond primary cortex). Semantic category representation was spatially specific: Vocalizations were preferentially distinguished in frontotemporal voice-selective regions and the fusiform; scenes and objects were distinguished in parahippocampal and medial place areas. Our results are consistent with real-world events coded via an extended auditory processing hierarchy, in which acoustic representations rapidly enter multiple streams specialized by category, including areas typically considered visual cortex.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivatives
https://creativecommons.org/licenses/by-nc-nd/4.0/
Persistent DSpace Link
https://hdl.handle.net/1721.1/163620
DOI of Published Version
https://doi.org/10.1080/02643294.2022.2085085
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