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Automating creativity assessment with SemDis: An open platform for computing semantic distance

Creativity research requires assessing the quality of ideas and products. In practice, conducting creativity research often involves asking several human raters to judge participants’ responses to creativity tasks, such as judging the novelty of ideas from the alternate uses task (AUT). Although suc...

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Autores principales: Beaty, Roger E., Johnson, Dan R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8062332/
https://www.ncbi.nlm.nih.gov/pubmed/32869137
http://dx.doi.org/10.3758/s13428-020-01453-w
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author Beaty, Roger E.
Johnson, Dan R.
author_facet Beaty, Roger E.
Johnson, Dan R.
author_sort Beaty, Roger E.
collection PubMed
description Creativity research requires assessing the quality of ideas and products. In practice, conducting creativity research often involves asking several human raters to judge participants’ responses to creativity tasks, such as judging the novelty of ideas from the alternate uses task (AUT). Although such subjective scoring methods have proved useful, they have two inherent limitations—labor cost (raters typically code thousands of responses) and subjectivity (raters vary on their perceptions and preferences)—raising classic psychometric threats to reliability and validity. We sought to address the limitations of subjective scoring by capitalizing on recent developments in automated scoring of verbal creativity via semantic distance, a computational method that uses natural language processing to quantify the semantic relatedness of texts. In five studies, we compare the top performing semantic models (e.g., GloVe, continuous bag of words) previously shown to have the highest correspondence to human relatedness judgements. We assessed these semantic models in relation to human creativity ratings from a canonical verbal creativity task (AUT; Studies 1–3) and novelty/creativity ratings from two word association tasks (Studies 4–5). We find that a latent semantic distance factor—comprised of the common variance from five semantic models—reliably and strongly predicts human creativity and novelty ratings across a range of creativity tasks. We also replicate an established experimental effect in the creativity literature (i.e., the serial order effect) and show that semantic distance correlates with other creativity measures, demonstrating convergent validity. We provide an open platform to efficiently compute semantic distance, including tutorials and documentation (https://osf.io/gz4fc/). ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.3758/s13428-020-01453-w) contains supplementary material, which is available to authorized users.
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spelling pubmed-80623322021-05-05 Automating creativity assessment with SemDis: An open platform for computing semantic distance Beaty, Roger E. Johnson, Dan R. Behav Res Methods Article Creativity research requires assessing the quality of ideas and products. In practice, conducting creativity research often involves asking several human raters to judge participants’ responses to creativity tasks, such as judging the novelty of ideas from the alternate uses task (AUT). Although such subjective scoring methods have proved useful, they have two inherent limitations—labor cost (raters typically code thousands of responses) and subjectivity (raters vary on their perceptions and preferences)—raising classic psychometric threats to reliability and validity. We sought to address the limitations of subjective scoring by capitalizing on recent developments in automated scoring of verbal creativity via semantic distance, a computational method that uses natural language processing to quantify the semantic relatedness of texts. In five studies, we compare the top performing semantic models (e.g., GloVe, continuous bag of words) previously shown to have the highest correspondence to human relatedness judgements. We assessed these semantic models in relation to human creativity ratings from a canonical verbal creativity task (AUT; Studies 1–3) and novelty/creativity ratings from two word association tasks (Studies 4–5). We find that a latent semantic distance factor—comprised of the common variance from five semantic models—reliably and strongly predicts human creativity and novelty ratings across a range of creativity tasks. We also replicate an established experimental effect in the creativity literature (i.e., the serial order effect) and show that semantic distance correlates with other creativity measures, demonstrating convergent validity. We provide an open platform to efficiently compute semantic distance, including tutorials and documentation (https://osf.io/gz4fc/). ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.3758/s13428-020-01453-w) contains supplementary material, which is available to authorized users. Springer US 2020-08-31 2021 /pmc/articles/PMC8062332/ /pubmed/32869137 http://dx.doi.org/10.3758/s13428-020-01453-w Text en © The Author(s) 2020 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Beaty, Roger E.
Johnson, Dan R.
Automating creativity assessment with SemDis: An open platform for computing semantic distance
title Automating creativity assessment with SemDis: An open platform for computing semantic distance
title_full Automating creativity assessment with SemDis: An open platform for computing semantic distance
title_fullStr Automating creativity assessment with SemDis: An open platform for computing semantic distance
title_full_unstemmed Automating creativity assessment with SemDis: An open platform for computing semantic distance
title_short Automating creativity assessment with SemDis: An open platform for computing semantic distance
title_sort automating creativity assessment with semdis: an open platform for computing semantic distance
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8062332/
https://www.ncbi.nlm.nih.gov/pubmed/32869137
http://dx.doi.org/10.3758/s13428-020-01453-w
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