Decoding the temporal dynamics of spoken word and nonword processing from EEG
The efficiency of spoken word recognition is essential for real-time communication. There is consensus that this efficiency relies on an implicit process of activating multiple word candidates that compete for recognition as the acoustic signal unfolds in real-time. However, few methods capture the...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-10-01
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| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811922005730 |
| _version_ | 1818084766418731008 |
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| author | Bob McMurray McCall E. Sarrett Samantha Chiu Alexis K. Black Alice Wang Rebecca Canale Richard N. Aslin |
| author_facet | Bob McMurray McCall E. Sarrett Samantha Chiu Alexis K. Black Alice Wang Rebecca Canale Richard N. Aslin |
| author_sort | Bob McMurray |
| collection | DOAJ |
| description | The efficiency of spoken word recognition is essential for real-time communication. There is consensus that this efficiency relies on an implicit process of activating multiple word candidates that compete for recognition as the acoustic signal unfolds in real-time. However, few methods capture the neural basis of this dynamic competition on a msec-by-msec basis. This is crucial for understanding the neuroscience of language, and for understanding hearing, language and cognitive disorders in people for whom current behavioral methods are not suitable. We applied machine-learning techniques to standard EEG signals to decode which word was heard on each trial and analyzed the patterns of confusion over time. Results mirrored psycholinguistic findings: Early on, the decoder was equally likely to report the target (e.g., baggage) or a similar sounding competitor (badger), but by around 500 msec, competitors were suppressed. Follow up analyses show that this is robust across EEG systems (gel and saline), with fewer channels, and with fewer trials. Results are robust within individuals and show high reliability. This suggests a powerful and simple paradigm that can assess the neural dynamics of speech decoding, with potential applications for understanding lexical development in a variety of clinical disorders. |
| first_indexed | 2024-12-10T19:59:06Z |
| format | Article |
| id | doaj.art-88b86f6c3d234dd1b6477f0487a7c9fd |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-12-10T19:59:06Z |
| publishDate | 2022-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-88b86f6c3d234dd1b6477f0487a7c9fd2022-12-22T01:35:35ZengElsevierNeuroImage1095-95722022-10-01260119457Decoding the temporal dynamics of spoken word and nonword processing from EEGBob McMurray0McCall E. Sarrett1Samantha Chiu2Alexis K. Black3Alice Wang4Rebecca Canale5Richard N. Aslin6Dept. of Psychological and Brain Sciences, Dept. of Communication Sciences and Disorders, Dept. of Linguistics and Dept. of Otolaryngology, University of Iowa; Corresponding Author: Bob McMurray, 278 PBSB, Dept. of Psychological and Brain Sciences, University of Iowa, Iowa City, IA 52242.Interdisciplinary Graduate Program in Neuroscience, Unviersity of IowaDept. of Psychological and Brain Sciences, University of IowaSchool of Audiology and Speech Sciences, University of British Columbia, Haskins LaboratoriesDept. of Psychology, University of Oregon, Haskins LaboratoriesDept. of Psychological Sciences, University of Connecticut, Haskins LaboratoriesHaskins Laboratories, Department of Psychology and Child Study Center, Yale University, Department of Psychology, University of ConnecticutThe efficiency of spoken word recognition is essential for real-time communication. There is consensus that this efficiency relies on an implicit process of activating multiple word candidates that compete for recognition as the acoustic signal unfolds in real-time. However, few methods capture the neural basis of this dynamic competition on a msec-by-msec basis. This is crucial for understanding the neuroscience of language, and for understanding hearing, language and cognitive disorders in people for whom current behavioral methods are not suitable. We applied machine-learning techniques to standard EEG signals to decode which word was heard on each trial and analyzed the patterns of confusion over time. Results mirrored psycholinguistic findings: Early on, the decoder was equally likely to report the target (e.g., baggage) or a similar sounding competitor (badger), but by around 500 msec, competitors were suppressed. Follow up analyses show that this is robust across EEG systems (gel and saline), with fewer channels, and with fewer trials. Results are robust within individuals and show high reliability. This suggests a powerful and simple paradigm that can assess the neural dynamics of speech decoding, with potential applications for understanding lexical development in a variety of clinical disorders.http://www.sciencedirect.com/science/article/pii/S1053811922005730Spoken Word RecognitionSpeech DecodingEEGMachine Learning |
| spellingShingle | Bob McMurray McCall E. Sarrett Samantha Chiu Alexis K. Black Alice Wang Rebecca Canale Richard N. Aslin Decoding the temporal dynamics of spoken word and nonword processing from EEG NeuroImage Spoken Word Recognition Speech Decoding EEG Machine Learning |
| title | Decoding the temporal dynamics of spoken word and nonword processing from EEG |
| title_full | Decoding the temporal dynamics of spoken word and nonword processing from EEG |
| title_fullStr | Decoding the temporal dynamics of spoken word and nonword processing from EEG |
| title_full_unstemmed | Decoding the temporal dynamics of spoken word and nonword processing from EEG |
| title_short | Decoding the temporal dynamics of spoken word and nonword processing from EEG |
| title_sort | decoding the temporal dynamics of spoken word and nonword processing from eeg |
| topic | Spoken Word Recognition Speech Decoding EEG Machine Learning |
| url | http://www.sciencedirect.com/science/article/pii/S1053811922005730 |
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