Finding structure during incremental speech comprehension.
| Autor: | Lyu B; Changping Laboratory, Beijing, China., Marslen-Wilson WD; Centre for Speech, Language and the Brain, Department of Psychology, University of Cambridge, Cambridge, United Kingdom., Fang Y; Centre for Speech, Language and the Brain, Department of Psychology, University of Cambridge, Cambridge, United Kingdom., Tyler LK; Centre for Speech, Language and the Brain, Department of Psychology, University of Cambridge, Cambridge, United Kingdom. |
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| Jazyk: | angličtina |
| Zdroj: | ELife [Elife] 2024 Apr 05; Vol. 12. Date of Electronic Publication: 2024 Apr 05. |
| DOI: | 10.7554/eLife.89311 |
| Abstrakt: | A core aspect of human speech comprehension is the ability to incrementally integrate consecutive words into a structured and coherent interpretation, aligning with the speaker's intended meaning. This rapid process is subject to multidimensional probabilistic constraints, including both linguistic knowledge and non-linguistic information within specific contexts, and it is their interpretative coherence that drives successful comprehension. To study the neural substrates of this process, we extract word-by-word measures of sentential structure from BERT, a deep language model, which effectively approximates the coherent outcomes of the dynamic interplay among various types of constraints. Using representational similarity analysis, we tested BERT parse depths and relevant corpus-based measures against the spatiotemporally resolved brain activity recorded by electro-/magnetoencephalography when participants were listening to the same sentences. Our results provide a detailed picture of the neurobiological processes involved in the incremental construction of structured interpretations. These findings show when and where coherent interpretations emerge through the evaluation and integration of multifaceted constraints in the brain, which engages bilateral brain regions extending beyond the classical fronto-temporal language system. Furthermore, this study provides empirical evidence supporting the use of artificial neural networks as computational models for revealing the neural dynamics underpinning complex cognitive processes in the brain. Competing Interests: BL, WM, YF, LT No competing interests declared (© 2023, Lyu et al.) |
| Databáze: | MEDLINE |
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