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TECHNOLOGY AND CODE article

Front. Psychol.
Sec. Psychology of Language
Volume 16 - 2025 | doi: 10.3389/fpsyg.2025.1538196
This article is part of the Research Topic Community Series: Spanish Psycholinguistics - Volume II View all 5 articles

sunflower: an R package for handling multiple response attempts and conducting error analysis in aphasia and related disorders

Provisionally accepted
Ismael Gutiérrez-Cordero Ismael Gutiérrez-Cordero 1,2,3*Javier García-Orza Javier García-Orza 1,2,4
  • 1 Department of Basic Psychology, University of Málaga, Málaga, Andalusia, Spain
  • 2 Numerical Cognition Lab, University of Málaga, Málaga, Spain
  • 3 Cognitive Neurology and Aphasia Unit, Centro de Investigaciones Médico-Sanitarias (CIMES), University of Málaga, Málaga, Spain
  • 4 Instituto de Investigación Biomédica de Málaga (IBIMA), Málaga, Spain

The final, formatted version of the article will be published soon.

    Manual classification of production errors and the allocation of speech/spelling scores are timeconsuming, laborious and error-prone tasks, even when conducted by clinicians and specialized researchers. Here we present sunflower, an R package developed to improve the analysis of language production quality for Spanish data. The package offers various functions, including(1) managing dataframes containing single responses and multiple-attempt responses, (2) conducting formal similarity analyses on words as well as positional accuracy data analyses within words, and (3) the classification of errors by considering lexicality, formal similarity and semantic similarity indexes, which are obtained by means of different algorithms and artificial intelligence techniques such as word2vec. The applications of sunflower, which is the first open-source package of its kind, include assessing whether production quality improves over the course of multiple attempts, and identifying which aspects of an individual's productions are most impacted by their impairments. Other potential applications include the analysis of whether improvements arise in a patient's production quality after a given treatment, distinguishing between cases of apraxia of speech and conduction aphasia, as well as simply using the package to improve and speed up the classification of speech/spelling errors with large datasets through automation.

    Keywords: R package, Speech Therapy, Language assessment, paraphasia classification, Language production

    Received: 02 Dec 2024; Accepted: 14 Jan 2025.

    Copyright: © 2025 Gutiérrez-Cordero and García-Orza. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Ismael Gutiérrez-Cordero, Department of Basic Psychology, University of Málaga, Málaga, 29071, Andalusia, Spain

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.