Representing melodic relationships using network science

Merseal, Hannah M.; Beaty, Roger E.; Kenett, Yoed N.; Lloyd-Cox, James; Manzano, Örjan de; Norgaard, Martin

doi:10.1016/j.cognition.2022.105362

Cited by 6 publications

(2 citation statements)

References 71 publications

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“…This includes both new measurement instruments, but also refined constructs that can be measured. For example, measuring domainspecific creative abilities has become increasingly popular over the last few years, and new measurement instruments for domains like emotions (Weiss et al, 2023), music (Merseal et al, 2023), or scientific creative thinking (Aschauer et al, 2022) were developed and validated. These developments can also be understood as a call for broadening our capabilities of automatically evaluating the creativity of test-takers, as future multivariate studies could benefit from less labor-intensive scoring practices.…”

Section: Complementing Efforts Of Scoring Creativity Automaticallymentioning

confidence: 99%

Automated Scoring of Scientific Creativity in German

Goecke,

DiStefano,

Aschauer

et al. 2024

Preprint

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Automated scoring is a current hot topic in creativity research. However, most research has focused on the English language and popular verbal creative thinking tasks, such as the alternate uses task. Therefore, in this study, we provide a proof-of-concept for automated scoring of a scientific creative thinking task that assesses divergent ideation in experimental tasks in the German language. Participants are required to generate alternative explanations for an empirical observation. This work analyzed a total of 13,423 unique responses. To predict human ratings of originality, we utilized the large language model XLM-RoBERTa. The prediction model was trained on 9400 responses. In a validation dataset of 1341 responses, the correlation between predictions and ratings was strong (r = 0.81; CI-95% [0.79, 0.83]). The correlation remained strong in a held-out test set (n = 2682), with a correlation of r = 0.80; CI-95% [0.79, 0.81]. It is argued that these promising findings represent a strong proof-of-concept for automated scoring of scientific creative thinking in the German language. We encourage researchers to further investigate automated scoring of other scientific creative thinking tasks.

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Section: Complementing Efforts Of Scoring Creativity Automaticallymentioning

confidence: 99%

Automated Scoring of Scientific Creativity in German

Goecke,

DiStefano,

Aschauer

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The present study extends past work by examining how the organization of knowledge relates to creative achievement in the arts and sciences (cf. Groussard et al, 2010; Merseal et al, 2023).…”

mentioning

confidence: 99%

Free association ability distinguishes highly creative artists from scientists: Findings from the Big-C Project.

Merseal¹,

Luchini²,

Kenett³

et al. 2023

Psychology of Aesthetics, Creativity, and the Arts

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Automated Scoring of Scientific Creativity in German

Goecke,

DiStefano,

Aschauer

et al. 2024

Journal of Creative Behavior

View full text Add to dashboard Cite

Automated scoring is a current hot topic in creativity research. However, most research has focused on the English language and popular verbal creative thinking tasks, such as the alternate uses task. Therefore, in this study, we present a large language model approach for automated scoring of a scientific creative thinking task that assesses divergent ideation in experimental tasks in the German language. Participants are required to generate alternative explanations for an empirical observation. This work analyzed a total of 13,423 unique responses. To predict human ratings of originality, we used XLM‐RoBERTa (Cross‐lingual Language Model‐RoBERTa), a large, multilingual model. The prediction model was trained on 9,400 responses. Results showed a strong correlation between model predictions and human ratings in a held‐out test set (n = 2,682; r = 0.80; CI‐95% [0.79, 0.81]). These promising findings underscore the potential of large language models for automated scoring of scientific creative thinking in the German language. We encourage researchers to further investigate automated scoring of other domain‐specific creative thinking tasks.

show abstract

Representing melodic relationships using network science

Cited by 6 publications

References 71 publications

Automated Scoring of Scientific Creativity in German

Automated Scoring of Scientific Creativity in German

Free association ability distinguishes highly creative artists from scientists: Findings from the Big-C Project.

Automated Scoring of Scientific Creativity in German

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