Hiring in life sciences? Share your open positions with our professional community. Read more Close

Advertisement

Neural recordings of continuous speech reveal robust signatures of prediction in second language learners of English

Created on 11 Nov 2025

Authors

Thorburn, C. A., Karunathilake, I. M. D., Dixon, L. N., Lau, E., Simon, J. Z.

Abstract

When listening to speech in their native language, speakers use prior context to anticipate upcoming phonemes, words, and concepts, integrating information at the sublexical, lexical, and sentence level. While it has been suggested that late second language learners do not predict to the same extent as native listeners, adequately evaluating this claim requires measurement of predictions at these multiple levels of representation simultaneously in natural speech. We recorded magnetoencephalography (MEG) responses from native Mandarin and Sinhala speakers listening to continuous narrative English speech. We used multivariate temporal response function (mTRF) analysis to investigate whether second language listeners demonstrate the same markers of prediction in neural data as native English speakers listening to the same stimuli. We demonstrate that late second language listeners exhibit strikingly similar responses to native speakers in sensitivity to phoneme surprisal and entropy with respect to sublexical, lexical, and sentence-level context. The few small response differences we observed appear most likely to arise from specific properties of the native languages, rather than general differences between native and second-language listening. These results provide evidence that late second-language listeners indeed leverage prediction in similar ways as native listeners in understanding continuous speech. This suggests that multivariate analyses of neural data from naturalistic listening may be vital in carefully evaluating the differences and similarities in speech prediction across populations.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 11 Nov 2025.

Advertisement

Stats

  • Community rating n/a 0 votes
  • Your rating

1-terrible, 9-excellent. How would you rate this preprint? Sign in in to submit your rating.

  • Recommendations n/a n/a positive of 0 vote(s)
  • Views 21
  • Comments 0

Recommended by

  • No recommendations yet.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.

Advertisement