Replication data for: Chunking or predicting – frequency information and reduction in the perception of multi-word sequences
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'This is the data and code from a word-monitoring task, in which participants 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. Accuracy and response times were analysed with mixed-effects generalized additive models (GAMM); the code also includes visualisations of these models. The paper is accepted for publication in Cognitive Linguistics.The experiment was run with OpenSesame (version 3.0.7 for Mac, cf. 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 (R Core Team 2017) for all data analyses, hence the code can best be replicated in R. Abstract:Frequently used lingu' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["chunking","English","entrenchment","frequency information","phonetic reduction","speech perception"]} <|eot_id|>
 Here is the Description of a dataset. Description:'This is the data and code from a word-monitoring task, in which participants 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. Accuracy and response times were analysed with mixed-effects generalized additive models (GAMM); the code also includes visualisations of these models. The paper is accepted for publication in Cognitive Linguistics.The experiment was run with OpenSesame (version 3.0.7 for Mac, cf. 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 (R Core Team 2017) for all data analyses, hence the code can best be replicated in R. Abstract:Frequently used lingu' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
This is the data and code from a word-monitoring task, in which participants 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. Accuracy and response times were analysed with mixed-effects generalized additive models (GAMM); the code also includes visualisations of these models. The paper is accepted for publication in Cognitive Linguistics.The experiment was run with OpenSesame (version 3.0.7 for Mac, cf. 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 (R Core Team 2017) for all data analyses, hence the code can best be replicated in R. Abstract:Frequently used lingu
chunking; English; entrenchment; frequency information; phonetic reduction; speech perception