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Aesthetic Judgment in Calligraphic Tracing: The Dominant Role of Dynamic Features
Qian Yuan1, Guoying Yang1, Ruimin Lyu1,2
1School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214125, China.
Behavioral Sciences (Basel, Switzerland)
|April 26, 2025
Summary
Dynamic features, not static ones, significantly enhance aesthetic judgments in calligraphic tracing. This study highlights the importance of motion cues in appreciating art, offering new insights into aesthetic cognition.
Area of Science:
- Cognitive Psychology
- Art and Aesthetics
- Human-Computer Interaction
Background:
- Traditional aesthetic judgment in visual arts prioritizes static features.
- Emerging research indicates dynamic features also significantly influence aesthetic experiences.
- The specific role of dynamic elements in calligraphic tracing aesthetics remains underexplored.
Purpose of the Study:
- To investigate the dominance of dynamic features over static features in calligraphic tracing aesthetics.
- To determine how different visual representations of calligraphy impact aesthetic judgments.
- To contribute to aesthetic modeling by understanding feature weighting in aesthetic cognition.
Main Methods:
- A custom calligraphy acquisition system was used to record expert and novice Chinese character tracing.
- Calligraphy works were presented in three formats: static results (s), writing videos (f), and trajectory videos (b).
- Participants rated the aesthetic dimensions of the presented stimuli.
Main Results:
- Stimuli incorporating motion cues (writing videos and trajectory videos) received higher aesthetic ratings than static results.
- Static features demonstrated limited predictive power for aesthetic scores in calligraphic tracing.
- High structural similarity was observed in traced results across different writers.
Conclusions:
- Dynamic features play a dominant role in the aesthetic judgment of calligraphic tracing.
- Static features have a weak influence on the aesthetics of calligraphic tracing.
- Aesthetic cognition involves dynamically adjusting feature weighting based on context, offering a novel perspective on aesthetic judgment.
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