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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.\nThe 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.
\n\n\nAbstract:\nFrequently used linguistic structures become entrenched in memory; this is often assumed to make their consecutive parts more predictable, as well as fuse them into a single unit (chunking). High frequency moreover leads to a propensity for phonetic reduction. We present a word recognition experiment which tests how frequency information (string frequency, transitional probability) interacts with reduction in speech perception. Detection of the element to is tested in V-to-Vinf sequences in English (e.g. need to Vinf), where to can undergo reduction (“needa”). Results show that reduction impedes recognition, but this can be mitigated by the predictability of the item. Recognition generally benefits from surface frequency, while a modest chunking effect is found in delayed responses to reduced forms of high-frequency items. Transitional probability shows a facilitating effect on reduced but not on full forms. Reduced forms also pose more difficulty when the phonological context obscures the onset of to. We conclude that listeners draw on frequency information in a predictive manner to cope with reduction. High-frequency structures are not inevitably perceived as chunks, but depend on cues in the phonetic form – reduction leads to perceptual prominence of the whole over the parts and thus promotes a holistic access.
","citation:dsDescriptionDate":"2019-04-18"},"publication":{"publicationCitation":"Lorenz, David and Tizón-Couto, David. \"Chunking or predicting – frequency information and reduction in the perception of multi-word sequences \" Cognitive Linguistics, vol. 30, no. 4, 2019, pp. 751-784. https://doi.org/10.1515/cog-2017-0138","publicationIDType":"doi","publicationIDNumber":"10.1515/cog-2017-0138","publicationURL":"https://doi.org/10.1515/cog-2017-0138"},"citation:keyword":[{"citation:keywordValue":"speech perception"},{"citation:keywordValue":"phonetic reduction"},{"citation:keywordValue":"chunking"},{"citation:keywordValue":"frequency information"},{"citation:keywordValue":"entrenchment"},{"citation:keywordValue":"English"}],"citation:distributor":{"citation:distributorName":"The Tromsø Repository of Language and Linguistics (TROLLing)","citation:distributorAbbreviation":"TROLLing","citation:distributorURL":"https://trolling.uit.no/"},"software":[{"citation:softwareName":"OpenSesame","citation:softwareVersion":"3.0.7."},{"citation:softwareName":"R"}],"author":[{"citation:authorName":"Lorenz, David","citation:authorAffiliation":"University of Rostock","authorIdentifierScheme":"ORCID","authorIdentifier":"0000-0002-7451-099X"},{"citation:authorName":"Tizón-Couto, David","citation:authorAffiliation":"University of Vigo","authorIdentifierScheme":"ORCID","authorIdentifier":"0000-0003-0788-7954"}],"citation:producer":[{"citation:producerName":"University of Freiburg","citation:producerURL":"https://uni-freiburg.de/en/"},{"citation:producerName":"University of Vigo","citation:producerURL":"https://www.uvigo.gal/en"}],"geospatial:geographicCoverage":{"geospatial:country":"United States"},"citation:datasetContact":{"citation:datasetContactName":"Lorenz, David","citation:datasetContactAffiliation":"University of Rostock","citation:datasetContactEmail":"david.lorenz2@uni-rostock.de"},"timePeriodCovered":{"citation:timePeriodCoveredStart":"2016-05-09","citation:timePeriodCoveredEnd":"2016-11-24"},"citation:depositor":"Lorenz, David","citation:productionDate":"2016","dataSources":"– recorded sentences\n– Corpus of Contemporary American English https://www.english-corpora.org/coca/","subject":"Arts and Humanities","language":"English","citation:productionPlace":["Freiburg","Vigo"],"dateOfDeposit":"2019-04-18","kindOfData":"experimental data","title":"Replication data for: Chunking or predicting – frequency information and reduction in the perception of multi-word sequences","@id":"https://doi.org/10.18710/7TSABU","@type":["ore:Aggregation","schema:Dataset"],"schema:version":"1.3","schema:name":"Replication data for: Chunking or predicting – frequency information and reduction in the perception of multi-word sequences","schema:dateModified":"Thu Sep 28 19:45:52 GMT 2023","schema:datePublished":"2019-06-06","schema:license":"http://creativecommons.org/publicdomain/zero/1.0","dvcore:fileTermsOfAccess":{"dvcore:fileRequestAccess":true},"schema:includedInDataCatalog":"DataverseNO","schema:isPartOf":{"schema:name":"TROLLing","@id":"https://dataverse.no/dataverse/trolling","schema:description":"Sign Up\n Getting started with TROLLing\n\n\n \n\nNote: No datasets will be curated/published from March 22 to April 1, 2024. More info.
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