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

Roger E Beaty1, Dan R Johnson2

  • 1Department of Psychology, Pennsylvania State University, 140 Moore Building, University Park, PA, 16802, USA. rebeaty@psu.edu.

Behavior Research Methods
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Summary
This summary is machine-generated.

Automated scoring using semantic distance, a natural language processing method, reliably predicts human creativity ratings. This computational approach overcomes limitations of traditional subjective scoring in creativity research.

Keywords:
AssessmentCreativityDivergent thinkingSemantic distanceWord association

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Area of Science:

  • Psychology
  • Computational Linguistics
  • Artificial Intelligence

Background:

  • Creativity research traditionally relies on human raters to assess idea quality, posing challenges in labor cost and inter-rater reliability.
  • Subjective scoring methods in creativity tasks like the Alternate Uses Task (AUT) face psychometric threats due to rater variability.
  • Automated scoring offers a potential solution to enhance the efficiency and objectivity of creativity assessment.

Purpose of the Study:

  • To evaluate the efficacy of automated scoring using semantic distance for assessing verbal creativity.
  • To compare the performance of various semantic models in predicting human creativity and novelty judgments.
  • To address the limitations of subjective scoring in creativity research through computational methods.

Main Methods:

  • Utilized five top-performing semantic models (e.g., GloVe, continuous bag of words) to compute semantic distance between texts.
  • Assessed semantic models against human creativity ratings from the Alternate Uses Task (AUT) and word association tasks.
  • Developed a latent semantic distance factor from common variance across semantic models.

Main Results:

  • A latent semantic distance factor strongly and reliably predicted human creativity and novelty ratings across multiple tasks.
  • The computational method demonstrated convergent validity by correlating with other creativity measures.
  • An established experimental effect (serial order effect) was replicated using semantic distance, supporting its utility.

Conclusions:

  • Automated scoring via semantic distance provides a reliable and valid method for assessing verbal creativity.
  • Computational approaches can overcome the labor and subjectivity limitations inherent in human scoring of creative outputs.
  • An open platform for computing semantic distance is provided to facilitate further research in automated creativity assessment.