Abstract
Decoding syntactic class from EEG during spoken word recognition
The Journal of the Acoustical Society of America, Vol.152(4 Supplement), pp.A59-A60
10/2022
DOI: 10.1121/10.0015543
Abstract
A fundamental issue in spoken language comprehension involves understanding the interaction of linguistic representations across different levels of organization (e.g., phonological, lexical, syntactic, and semantic). In particular, there is debate about when different levels are accessed during spoken word recognition. Under serial processing models, comprehension is sequential. In contrast, under parallel processing models, simultaneous activation of representations at multiple levels can occur. The current study investigates this issue by isolating neural responses to syntactic class distinctions from acoustic and phonological responses. EEG data were collected in an event-related potential (ERP) experiment in which participants (N = 26) listened to words varying in syntactic class (nouns versus adjectives) that were controlled for low-level acoustic differences via cross-splicing. Machine learning techniques were used to decode syntactic class from ERP responses over time. Results showed that syntactic class is decodable approximately 160–190 ms after the average syntactic point of disambiguation in the words, during which listeners are still processing acoustic information. This supports the prediction that different levels of representation have overlapping timecourses. Overall, these results are consistent with a parallel, interactive processing model of spoken word recognition, in which higher-level information—such as syntactic class—is accessed while acoustic analysis is still occurring.
Details
- Title: Subtitle
- Decoding syntactic class from EEG during spoken word recognition
- Creators
- McCall E. Sarrett - Villanova UniversityAlexa S. Gonzalez - Villanova UniversityOlivia Montañez - Villanova UniversityJoseph C. Toscano - Villanova University
- Resource Type
- Abstract
- Publication Details
- The Journal of the Acoustical Society of America, Vol.152(4 Supplement), pp.A59-A60
- DOI
- 10.1121/10.0015543
- ISSN
- 0001-4966
- eISSN
- 1520-8524
- Number of pages
- 2
- Date published
- 10/2022
- Academic Unit
- Psychological and Brain Sciences
- Record Identifier
- 9984631942002771
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