Visual linguistic statistical learning is traceable through neural entrainment
The human brain can detect statistical regularities in the environment across a wide variety of contexts. The importance of this process is well‐established not just in language acquisition but across different modalities; in addition, several neural correlates of statistical learning have been iden...
Elmentve itt :
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| Dokumentumtípus: | Cikk |
| Megjelent: |
2024
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| Sorozat: | PSYCHOPHYSIOLOGY
61 No. 8 |
| Tárgyszavak: | |
| doi: | 10.1111/psyp.14575 |
| mtmt: | 34766104 |
| Online Access: | http://publicatio.bibl.u-szeged.hu/35250 |
| Tartalmi kivonat: | The human brain can detect statistical regularities in the environment across a wide variety of contexts. The importance of this process is well‐established not just in language acquisition but across different modalities; in addition, several neural correlates of statistical learning have been identified. A current technique for tracking the emergence of regularity learning and localizing its neural background is frequency tagging (FT). FT can detect neural entrainment not only to the frequency of stimulus presentation but also to that of a hidden structure. Auditory learning paradigms with linguistic and nonlinguistic stimuli, along with a visual paradigm using nonlinguistic stimuli, have already been tested with FT. To complete the picture, we conducted an FT experiment using written syllables as stimuli and a hidden triplet structure. Both behavioral and neural entrainment data showed evidence of structure learning. In addition, we localized two electrode clusters related to the process, which spread across the frontal and parieto‐occipital areas, similar to previous findings. Accordingly, we conclude that fast‐paced visual linguistic regularities can be acquired and are traceable through neural entrainment. In comparison with the literature, our findings support the view that statistical learning involves a domain‐general network. |
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| Terjedelem/Fizikai jellemzők: | 13 |
| ISSN: | 0048-5772 |