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Exploring the Creative Personality: Using Machine Learning to Predict Fluency and Originality in Divergent Thinking.

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Title: Exploring the Creative Personality: Using Machine Learning to Predict Fluency and Originality in Divergent Thinking.
Authors: Dumas, Denis (AUTHOR), Dong, Yixiao (AUTHOR), Kagan, Sofiia (AUTHOR), Campbell, W. Keith (AUTHOR)
Source: Creativity Research Journal. Jan-Mar2026, Vol. 38 Issue 1, p92-101. 10p.
Subjects: Originality, Divergent thinking, Five-factor model of personality, Prediction models, Psychological typologies, Machine learning, Creative ability, Cognitive flexibility
Abstract: In this study, 100 self-reported personality items from the Big Five Aspects Scale, responded to by a sample of 334 undergraduate participants, were used to predict quantity (ideational fluency) and quality (originality) of ideas on a divergent thinking (DT) task. The originality of DT responses was scored through a fine-tuned version of the Generative Pre-trained Transformer (GPT) 3.5 (i.e., Ocsai), and a least absolute shrinkage selection operator (LASSO) machine learning model selected the items that were meaningful predictors of each outcome. Results revealed that the personality profiles of highly fluent and highly original individuals were characterized by a tension between seemingly opposed personality attributes. Both ideational fluency and originality were predicted by a playfully open intellectualism that nonetheless avoided more typical work (i.e. was disorderly and unindustrious). Fluency was additionally predicted by a tension between enthusiasm for social interaction and depressive symptoms associated with withdrawal. Originality was predicted by a socially dominant assertiveness that was tempered by awareness and care for others' feelings (e.g. compassion and politeness) as well as stability (i.e. non-volatility). Taken together, these results demonstrate that the creative personality is likely to be composed of aspects of multiple dimensions of typical personality models like the Big 5, and that the highly fluent and the highly original creative personality is different in important ways. [ABSTRACT FROM AUTHOR]
Copyright of Creativity Research Journal is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Exploring the Creative Personality: Using Machine Learning to Predict Fluency and Originality in Divergent Thinking.
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  Data: <searchLink fieldCode="AR" term="%22Dumas%2C+Denis%22">Dumas, Denis</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dong%2C+Yixiao%22">Dong, Yixiao</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kagan%2C+Sofiia%22">Kagan, Sofiia</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Campbell%2C+W%2E+Keith%22">Campbell, W. Keith</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Creativity+Research+Journal%22">Creativity Research Journal</searchLink>. Jan-Mar2026, Vol. 38 Issue 1, p92-101. 10p.
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  Data: <searchLink fieldCode="DE" term="%22Originality%22">Originality</searchLink><br /><searchLink fieldCode="DE" term="%22Divergent+thinking%22">Divergent thinking</searchLink><br /><searchLink fieldCode="DE" term="%22Five-factor+model+of+personality%22">Five-factor model of personality</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Psychological+typologies%22">Psychological typologies</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Creative+ability%22">Creative ability</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+flexibility%22">Cognitive flexibility</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In this study, 100 self-reported personality items from the Big Five Aspects Scale, responded to by a sample of 334 undergraduate participants, were used to predict quantity (ideational fluency) and quality (originality) of ideas on a divergent thinking (DT) task. The originality of DT responses was scored through a fine-tuned version of the Generative Pre-trained Transformer (GPT) 3.5 (i.e., Ocsai), and a least absolute shrinkage selection operator (LASSO) machine learning model selected the items that were meaningful predictors of each outcome. Results revealed that the personality profiles of highly fluent and highly original individuals were characterized by a tension between seemingly opposed personality attributes. Both ideational fluency and originality were predicted by a playfully open intellectualism that nonetheless avoided more typical work (i.e. was disorderly and unindustrious). Fluency was additionally predicted by a tension between enthusiasm for social interaction and depressive symptoms associated with withdrawal. Originality was predicted by a socially dominant assertiveness that was tempered by awareness and care for others' feelings (e.g. compassion and politeness) as well as stability (i.e. non-volatility). Taken together, these results demonstrate that the creative personality is likely to be composed of aspects of multiple dimensions of typical personality models like the Big 5, and that the highly fluent and the highly original creative personality is different in important ways. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Creativity Research Journal is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/10400419.2024.2371725
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      – Code: eng
        Text: English
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        PageCount: 10
        StartPage: 92
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      – SubjectFull: Originality
        Type: general
      – SubjectFull: Divergent thinking
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      – SubjectFull: Five-factor model of personality
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      – SubjectFull: Prediction models
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      – SubjectFull: Psychological typologies
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      – SubjectFull: Machine learning
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      – SubjectFull: Creative ability
        Type: general
      – SubjectFull: Cognitive flexibility
        Type: general
    Titles:
      – TitleFull: Exploring the Creative Personality: Using Machine Learning to Predict Fluency and Originality in Divergent Thinking.
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            NameFull: Dumas, Denis
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            NameFull: Dong, Yixiao
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              Text: Jan-Mar2026
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              Y: 2026
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