Abstract
Statistical learning, the ability to extract regularities from input (e.g., in language), is likely supported by learners' prior expectations about how component units co-occur. In this paper, we investigated how adults' prior experience with sublexical regularities in their native language influences performance on an empirical language learning task. Forty German-speaking adults completed a speech repetition task, in which they repeated eight-syllable sequences from two experimental languages: one containing bisyllabic words comprised of frequently occurring German syllable transitions (naturalistic words), and the other containing words made from unattested syllable transitions (non-naturalistic words). Participants demonstrated learning from both naturalistic and non-naturalistic stimuli. However, learning was superior for the naturalistic sequences, indicating that participants used their existing distributional knowledge of German to extract naturalistic words faster and more accurately than non-naturalistic words. This supports theories of statistical learning as a form of chunking, whereby frequently co-occurring units become entrenched in the long-term memory.
Original language | English |
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Journal | Language Learning |
Early online date | 10 Oct 2022 |
DOIs | |
Publication status | E-pub ahead of print - 10 Oct 2022 |
Keywords
- Statistical learning
- serial recall
- incremental learning
- long-term memory
- entrenchment