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Recurrence Quantification Analysis of Processes and Products of Discourse

A Tutorial in R

Bibliographic Data

ID11274125
AuthorsSebastian Wallot (0000-0002-3626-3940, Max Planck Institute for Empirical Aesthetics, Frankfurt, Germany, corresponding author)
Year2017
Volume54
Issue5-6
Pages382-405
Publication date2017-07-04
Open AccessYes
TypeARTICLE
VenueDiscourse Processes (JOURNAL)
Journal identifiersISSN: 0163-853X • E-ISSN: 1532-6950
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/0163853x.2017.1297921
OpenAlexW2606702190
LanguageEN
Citations received6
References cited44

Processes of naturalistic reading and writing are based on complex linguistic input, stretch-out over time, and rely on an integrated performance of multiple perceptual, cognitive, and motor processes. Hence, naturalistic reading and writing performance is nonstationary and exhibits fluctuations and transitions. However, instead of being just complications for the analysis of such data, they are also informative about cognitive change, fluency, and reading or writing skill. To use and quantify such dynamics, one needs appropriate statistics that capture these aspects. In this article I introduce Recurrence Quantification Analysis (RQA) as a tool to capture such dynamic structure. After a conceptual introduction of the analysis, I present a step-by-step tutorial on how to run RQA using R. Guidance is given with regard to common issues and best practices using this time-series analysis technique. Finally, I review previous results from studies applying RQA to reading and writing and summarize current hypotheses and interpretations of RQA measures in the context of reading and writing

Cognition · Cognitive psychology · Context (archaeology · Fluency · Linguistics · Mathematics education · Natural language processing · Nonlinear system · Perception · Reading (process · Recurrence quantification analysis · Action Observation and Synchronization · Advanced Text Analysis Techniques · Computer Science · Neural Networks and Applications · Psychology · Artificial Intelligence

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Citations per year0.86
Citation span2019 - 2026 (8)
Highly citedNo

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