Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
The influence of word co-occurrence frequency on predictive processing in first and second languages: A webcam-based eye-tracking study. |
| Συγγραφείς: |
Hwang, Haerim, Park, Sun Hee |
| Πηγή: |
Applied Psycholinguistics; 2025, Vol. 46, p1-29, 29p |
| Θεματικοί όροι: |
Prediction models, Prompts (Psychology), Phonological awareness, Cognitive processing speed, Logistic regression analysis, Descriptive statistics, Psycholinguistics, Data analysis software, Confidence intervals, Comparative studies |
| Γεωγραφικοί όροι: |
South Korea |
| Περίληψη: |
The questions of whether first language (L1) speakers and second language (L2) learners can both predict what follows based on given linguistic cues and what factors may influence this predictive processing are still underexplored. Prior research has focused on the success or failure of predictions in real-time processing, paying relatively less attention to the speed of prediction. This study addresses these gaps by investigating the role of word co-occurrence frequency and proficiency in L1 and L2 predictive processing, using the Korean classifier system. In a webcam-based visual-world eye-tracking experiment, both L1-Korean speakers and L2-Korean learners showed sound predictive processing, with the frequency of co-occurrence between classifiers and nouns playing a crucial role. Higher co-occurrence frequency expedited predictive processing for L1-Korean speakers and boosted the ability to make online predictions for L2-Korean learners. The study also revealed a proficiency effect, where more advanced L2-Korean learners made predictions regardless of co-occurrence frequency, unlike their less advanced counterparts. Our findings suggest that predictive mechanisms in L1 and L2 operate in a qualitatively similar way. In addition, the use of webcam eye-tracking is expected to create a more inclusive and equitable research environment for (applied) psycholinguistics. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: |
Complementary Index |