Cited by 342Creativity in Education and NeuroscienceTopic ModelingComputational and Text Analysis Methods

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

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Beaty, Roger E., Johnson, Dan R.·2021·Behavior Research Methods

DOI 10.3758/s13428-020-01453-wPMC PMC8062332

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Abstract

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/ ).

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Fig. 1 Confirmatory factor analysis of human creativity ratings and multiplicative semantic distance for two AUT items. N = 171. dt_hum = divergent thinking, human rating; dt_sem = divergent thinking, semantic distance; rope_hum = AUT rope, human rating; box_hum = AUT box, human rating; rope_sem = AUT rope, semantic distance; box_sem = AUT box, semantic distance; r_r1-r_r4 = AUT rope, rater 1-rater 4; ; b_r1-b_r4 = AUT box, rater 1-rater 4; r/b_glov = AUT rope/box, GloVe semantic distance; r/b_tasa = AUT rope/box, TASA semantic distance; r/b_cbw = AUT rope/box, continuous bag of words, Wiki concatenation, semantic distance; r/b_cbs = AUT rope/box, continuous bag of words, ukwac and subtitle corpus, semantic distance; r/b_cbu = AUT rope/box, continuous bag of words, subtitle corpus, semantic distanceCC BY · PubMed Central

Fig. 2 Scatterplot of the correlation between latent semantic distance and human creativity ratings in Study 1. Latent variable values are standardized for visualization. N = 171CC BY · PubMed Central

Fig. 3 Confirmatory factor analysis of human creativity ratings and multiplicative semantic distance for two AUT items. N = 142. dt_hum = divergent thinking, human rating; dt_sem = divergent thinking, semantic distance; rope_hum = AUT rope, human rating; box_hum = AUT box, human rating; rope_sem = AUT rope, semantic distance; box_sem = AUT box, semantic distance; r_r1-r_r3 = AUT rope, rater 1-rater 3; ; b_r1-b_r3 = AUT box, rater 1-rater 3; r/b_glov = AUT rope/box, GloVe semantic distance; r/b_tasa = AUT rope/box, TASA semantic distance; r/b_cbw = AUT rope/box, continuous bag of words, Wiki concatenation, semantic distance; r/b_cbs = AUT rope/box, continuous bag of words, ukwac and subtitle corpus, semantic distance; r/b_cbu = AUT rope/box, continuous bag of words, subtitle corpus, semantic distanceCC BY · PubMed Central

Fig. 4 Scatterplot of the correlation between latent semantic distance and human creativity ratings in Study 2. Latent variable values are standardized for visualization. N = 142CC BY · PubMed Central

Fig. 5 Scatterplots of the correlations between latent semantic distance and novelty, appropriateness, and creativity ratings in Study 4. Latent variable values are standardized for visualization. Sample 1, N = 62; Sample 2, N = 56CC BY · PubMed Central

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Authors: Beaty, R. E., & Johnson, D. R.
Affiliations: Pennsylvania State University (US)
Washington and Lee University (US)
Corresponding author: Beaty, Roger E.
Johnson, Dan R.
ORCID: Beaty, Roger E.
Johnson, Dan R.
Venue: Behavior Research Methods
Publisher: Springer Science and Business Media LLC
Published: 2020-08-31
Volume: 53
Issue: 2
Pages: 757-780
ISSN: 1554-351X (ISSN-L)
1554-3528
DOI: 10.3758/s13428-020-01453-w
PMC: PMC8062332
OpenAlex: W3081924543
Cited by: 341
References: 96
Citations by year: 2026: 57 · 2025: 66 · 2024: 79 · 2023: 58 · 2022: 54 · 2021: 23 · 2020: 4
FWCI: 33.62
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
Locations: Behavior Research Methods (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
PsyArXiv (OSF Preprints) (submittedVersion, cc-by)
PsyArXiv (OSF Preprints) (submittedVersion)
Funders: National Science Foundation (1920653)
Topics: Social Sciences › Psychology › Experimental and Cognitive Psychology › Creativity in Education and Neuroscience
Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Social Sciences › General Social Sciences › Computational and Text Analysis Methods
Keywords: creativity assessment, semantic distance, alternative uses task, word association task, automated grading, semantic relatedness, gloves, CBOW, serial order effects, convergent validity, open platform
MeSH terms: Creativity, Humans, Psychometrics, Semantics, Reproducibility of Results

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

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