Replication Data for: Learning to predict - second language perception of reduced multi-word sequences

Resumo

DATASET ABSTRACT This is the data and code from a word-monitoring task, in which advanced learners of English responded to the word 'to' in verb + to-infinitive structures (V-to-Vinf) in English, where 'to' could occur in a full or reduced pronunciation (e.g. "prefer to" [tʊ] or "preferda" [ɾə]). The design of this experiment is replicated from our earlier study with American English native speakers (Lorenz & Tizón-Couto, 2019, see link to paper and dataset below *). We tested the effects of string frequency (V+to) and transitional probability (of 'to' given the V) on the accuracy and speed of recognition of "to" in spoken sentences. These effects were analysed with mixed-effects generalized additive models (GAMM); the code also includes visualisations of these models. The experiment was run with OpenSesame (version 3.2.6 for Mac, see Mathôt et al. 2012). The data include information on frequencies of occurrence of words and bigrams; this was extracted from the Corpus of Contemporary American English (COCA, Davies 2008–). We used R (version 4.3.1, R Core Team 2023) for all data analyses, hence the code can best be replicated in R. *) Lorenz, D. & Tizón-Couto, D. (2019). Chunking or predicting – frequency information and reduction in the perception of multi-word sequences. Cognitive Linguistics, 30(4), 751-784. https://doi.org/10.1515/cog-2017-0138 (the paper); https://doi.org/10.18710/7TSABU (the data)