Investigating the fit between phonological feature systems and brain responses to speech using EEG

ABSTRACT

This paper describes a technique to assess the correspondence between patterns of similarity in the brain's response to speech sounds and the patterns of similarity encoded in phonological feature systems, by quantifying the recoverability of phonological features from the neural data using supervised learning. The technique is applied to EEG recordings collected during passive listening to consonant-vowel syllables. Three published phonological feature systems are compared, and are shown to differ in their ability to recover certain speech sound contrasts from the neural data. For the phonological feature system that best reflects patterns of similarity in the neural data, a leave-one-out analysis indicates some consistency across subjects in which features have greatest impact on the fit, but considerable across-subject heterogeneity remains in the rank ordering of features in this regard.

KEYWORDS:

• Phonological features
• EEG
• Neurolinguistics
• Speech perception

Acknowledgments

Special thanks to Mark Hasegawa-Johnson and Ed Lalor who were instrumental in the early development of this technique, and to Nick Foti, Bryan Gick, Alex Gramfort, Kathleen Hall, Eric Larson, Majid Mirbagheri, and Doug Pulleyblank for helpful and stimulating discussions about various aspects of the project along the way.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Daniel R. McCloy http://orcid.org/0000-0002-7572-3241

Adrian K. C. Lee http://orcid.org/0000-0002-7611-0500

Additional information

Funding

This work was supported in part by the National Institute on Deafness and Other Communication Disorders (NIDCD) under grant T32DC005361.