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Part 1: Document Description
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Citation |
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Title: |
Replication data for: Chunking or predicting – frequency information and reduction in the perception of multi-word sequences |
Identification Number: |
doi:10.18710/7TSABU |
Distributor: |
DataverseNO |
Date of Distribution: |
2019-06-06 |
Version: |
1 |
Bibliographic Citation: |
Lorenz, David; Tizón-Couto, David, 2019, "Replication data for: Chunking or predicting – frequency information and reduction in the perception of multi-word sequences", https://doi.org/10.18710/7TSABU, DataverseNO, V1, UNF:6:/bvw/86x+A+n0yewX4UV7A== [fileUNF] |
Citation |
|
Title: |
Replication data for: Chunking or predicting – frequency information and reduction in the perception of multi-word sequences |
Identification Number: |
doi:10.18710/7TSABU |
Authoring Entity: |
Lorenz, David (University of Rostock) |
Tizón-Couto, David (University of Vigo) |
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Producer: |
University of Freiburg |
University of Vigo |
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Date of Production: |
2016 |
Software used in Production: |
OpenSesame |
Software used in Production: |
R |
Grant Number: |
FFI2016-77018-P |
Grant Number: |
IJCI-2015-25843 |
Grant Number: |
ED431C 2017/50 |
Grant Number: |
ED431C 2017/50 |
Distributor: |
DataverseNO |
Distributor: |
The Tromsø Repository of Language and Linguistics (TROLLing) |
Access Authority: |
Lorenz, David |
Depositor: |
Lorenz, David |
Date of Deposit: |
2019-04-18 |
Holdings Information: |
https://doi.org/10.18710/7TSABU |
Study Scope |
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Keywords: |
Arts and Humanities, speech perception, phonetic reduction, chunking, frequency information, entrenchment, English |
Abstract: |
<p>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. </p><p></p> <p>Abstract: Frequently 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.</p> |
Time Period: |
2016-05-09-2016-11-24 |
Date of Collection: |
2016-05-09-2016-11-24 |
Country: |
United States |
Kind of Data: |
experimental data |
Methodology and Processing |
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Sources Statement |
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Data Sources: |
– recorded sentences – Corpus of Contemporary American English https://www.english-corpora.org/coca/ |
Data Access |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Title: |
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 |
Identification Number: |
10.1515/cog-2017-0138 |
Bibliographic Citation: |
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 |
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Notes: |
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Summary Statistics: Min. 0.00483761540957494; StDev 0.2612879241507149; Valid 1367.0; Mean 0.3522021483806115; Max. 0.941176470588235; Variable Format: numeric Notes: UNF:6:XNHx+KqaiGonCyLIUZRFYQ== |
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Summary Statistics: StDev 90.01426603316045; Min. 182.0; Mean 357.55230431602047; Max. 590.0; Valid 1367.0; Variable Format: numeric Notes: UNF:6:vU7D1sxKMVfyZcDA8ewLTg== |
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Summary Statistics: StDev 11.39586481991386; Valid 1367.0; Mean 19.839063643013812; Max. 40.0; Min. 1.0 Variable Format: numeric Notes: UNF:6:jaPUt0moZcXPdVvHaFYSPw== |
f7885 Location: |
Summary Statistics: Mean -0.0431003966329182; Max. 3.92400749359339; Valid 1367.0; Min. -1.7873199026987; StDev 0.812856987843503; Variable Format: numeric Notes: UNF:6:kWKt3zJXCTf2fLYG3vEGdQ== |
f7885 Location: |
Summary Statistics: StDev 416.37677350706474; Valid 1367.0; Max. 2378.60948847802; Min. 0.0274243984067412; Mean 138.70326505776265 Variable Format: numeric Notes: UNF:6:SMIa60B9jO1gezmzs1x97w== |
f7885 Location: |
Variable Format: character Notes: UNF:6:TpUP2BrspxpFOzgGm5HMcg== |
f7885 Location: |
Variable Format: character Notes: UNF:6:oNwcSLyc+la4GgkHCTuoxg== |
f7885 Location: |
Variable Format: character Notes: UNF:6:9EQzzB4PTU93g+4iv/Vtkg== |
f7885 Location: |
Summary Statistics: Max. 1507.84827319944; Valid 1367.0; Mean 260.71678754190566; Min. 1.40778578487938; StDev 319.5108590069988 Variable Format: numeric Notes: UNF:6:Caxst6dXTRCYAbH6Jg5+rA== |
f7885 Location: |
Summary Statistics: Max. 0.362628502318081; StDev 0.07159745516694482; Mean 0.2599364496852037; Valid 1367.0; Min. 0.0277401261977097 Variable Format: numeric Notes: UNF:6:4dqi8C4x/NCyEHkLglfM5A== |
f7885 Location: |
Summary Statistics: Max. 7.77427134738719; Min. -3.59632220900524; Valid 1367.0; Mean 2.1494642484997293; StDev 2.509270804553252; Variable Format: numeric Notes: UNF:6:yDegAKCISknlf48sGGg8hA== |
f7885 Location: |
Summary Statistics: Min. 4.7361984483945; StDev 0.48065475459919094; Valid 1367.0; Mean 6.429213689144078; Max. 7.99867136101578 Variable Format: numeric Notes: UNF:6:Q3GhWbVemRUVQ8tucg9u2g== |
f7885 Location: |
Summary Statistics: Min. -2.85145267700076; StDev 1.0000000000000069; Mean 8.830455670917632E-15; Max. 1.08983732245329; Valid 1367.0 Variable Format: numeric Notes: UNF:6:QfPplBmx23c5QpeHyLeDgQ== |
Label: |
00_ReadMe_ChunkingPredicting.txt |
Text: |
Description of the data sets |
Notes: |
text/plain |
Label: |
ChunkingPredicting_Data.RData |
Text: |
R data frames sall_results, sall_clean, sall_items, sall_target |
Notes: |
application/x-rlang-transport |
Label: |
ChunkingPredicting_GAMM_and_plots.R.txt |
Text: |
mixed-effects generalized additive model (GAMM) for response times, and visualizations of the results |
Notes: |
text/plain |
Label: |
ChunkingPredicting_accuracy-model.R.txt |
Text: |
mixed-effects generalized additive model (GAMM) for accuracy, and visualizations of the results |
Notes: |
text/plain |