Li, H, Warrington, KL ORCID: https://orcid.org/0000-0003-3206-8002, Pagán, A, Paterson, KB and Wang, X, 2021. Independent effects of collocation strength and contextual predictability on eye movements in reading. Language Cognition and Neuroscience. ISSN 2327-3798
Preview |
Text
1432368_Warrington.pdf - Post-print Download (327kB) | Preview |
Abstract
Collocations are commonly co-occurring word pairs, such as “black coffee”. Previous research has demonstrated a processing advantage for collocations compared to novel phrases, suggesting that readers are sensitive to the frequency that words co-occur in phrases. However, a further question concerns whether this processing advantage for collocations occurs independently from effects of contextual predictability. We examined this issue in an eye movement experiment using adjective-noun pairs that are strong collocations (e.g., “black coffee”) or weak collocations (e.g., “bitter coffee”), based on co-occurrence statistics. These were presented in sentences where the shared concept they expressed (e.g., coffee) was predictable or unpredictable from the prior sentence context. We observed clear effects of collocation strength, with shorter reading times for strong compared to weak collocations. Moreover, these effects occurred independently of effects of contextual predictability. The findings therefore provide novel evidence that a processing advantage for collocations is not driven by contextual expectations.
Item Type: | Journal article |
---|---|
Publication Title: | Language Cognition and Neuroscience |
Creators: | Li, H., Warrington, K.L., Pagán, A., Paterson, K.B. and Wang, X. |
Publisher: | Taylor & Francis |
Date: | 13 May 2021 |
ISSN: | 2327-3798 |
Identifiers: | Number Type 10.1080/23273798.2021.1922726 DOI 1432368 Other |
Divisions: | Schools > School of Social Sciences |
Record created by: | Laura Ward |
Date Added: | 16 Jul 2021 15:59 |
Last Modified: | 13 May 2022 03:00 |
URI: | https://irep.ntu.ac.uk/id/eprint/43521 |
Actions (login required)
Edit View |
Statistics
Views
Views per month over past year
Downloads
Downloads per month over past year