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Creative Thinking01:25

Creative Thinking

Creative thinking encompasses innovative and unconventional methods for addressing challenges, often leading to groundbreaking solutions. Instead of focusing solely on enhancing existing systems, such as increasing smartphone battery capacity, creative thinking might inspire advancements like energy-efficient batteries or processors that minimize power consumption. This multidimensional approach underscores the importance of exploring novel pathways to innovation.
Divergent thinking is the...

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AuDrA: An automated drawing assessment platform for evaluating creativity.

John D Patterson1, Baptiste Barbot2,3, James Lloyd-Cox4

  • 1Department of Psychology, Pennsylvania State University, University Park, PA, USA. jpttrsn@psu.edu.

Behavior Research Methods
|November 3, 2023
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Summary

Automated Drawing Assessment (AuDrA) uses machine learning to score visual creativity in drawings, matching human ratings and offering an efficient alternative to manual evaluation.

Keywords:
Automated creativity scoringComputational creativityDivergent thinkingDrawing assessmentVisual creativity

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Area of Science:

  • Psychology
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Visual creativity is crucial for human expression and ideation.
  • Current visual creativity assessment relies heavily on time-consuming human raters.
  • Automated methods for verbal creativity exist, but not for visual creativity.

Purpose of the Study:

  • Introduce AuDrA, an Automated Drawing Assessment platform.
  • Develop a machine learning model to automatically score visual creativity from drawings.
  • Validate AuDrA's performance against human creativity ratings.

Main Methods:

  • Trained an automated drawing assessment (AuDrA) model using line drawings and human creativity ratings.
  • Tested AuDrA's generalizability across different drawing sets, raters, and tasks.
  • Compared AuDrA scores with human ratings and drawing elaboration (ink on page).

Main Results:

  • AuDrA scores showed high correlations with human creativity ratings (mean r = .76) across four datasets.
  • Performance generalized to untrained drawing sets, raters, and tasks.
  • AuDrA's sensitivity exceeded simple measures of drawing complexity.

Conclusions:

  • AuDrA provides a reliable and efficient automated solution for visual creativity assessment.
  • The platform overcomes limitations of traditional human-based rating systems.
  • AuDrA enables researchers to efficiently assess creativity in large drawing datasets.