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

Lorenz, David LU orcid and Tizon-Couto, David (2024)
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... (More)
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.
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Abstract (Swedish)

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publication status
published
subject
keywords
Data set, data analysis, response times, Gengeralized Additive Models, word-monitoring task
DOI
10.18710/TE5ZOG
language
English
LU publication?
yes
additional info
This Dataset may be reused according to the CLARIN PUB+BY+NC+LRT license. Information about the license terms are described under the Dataset Terms tab: https://dataverse.no/dataset.xhtml?persistentId=doi:10.18710/TE5ZOG&version=1.0&selectTab=termsTab
id
ccac51fe-d3f0-4a18-a58e-d232ba99ac5d
date added to LUP
2024-06-05 16:48:19
date last changed
2024-06-19 13:00:50
@misc{ccac51fe-d3f0-4a18-a58e-d232ba99ac5d,
  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 &amp; Tizón-Couto, 2019, see link to paper and dataset below *).<br/>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.<br/>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.<br/>}},
  author       = {{Lorenz, David and Tizon-Couto, David}},
  keywords     = {{Data set; data analysis; response times; Gengeralized Additive Models; word-monitoring task}},
  language     = {{eng}},
  month        = {{05}},
  title        = {{Replication Data for : Learning to predict - second language perception of reduced multi-word sequences}},
  url          = {{http://dx.doi.org/10.18710/TE5ZOG}},
  doi          = {{10.18710/TE5ZOG}},
  year         = {{2024}},
}