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使用机器学习来预测对西方视觉艺术的判断,以及内容-表示和形式-感知属性
Blanca T M Spee1,2,3, Helmut Leder1,3, Jan Mikuni1
1Vienna Cognitive Science Hub, University of Vienna, Vienna, Austria.
PloS one
|September 6, 2024
概括
机器学习模型可以根据艺术品属性预测艺术判断,例如创造力和情感影响. 这项研究揭示了影响美学欣赏和艺术评价的关键特征.
科学领域:
- 认知科学 认知科学
- 艺术史 艺术史 艺术史
- 计算美学 计算美学
背景情况:
- 艺术研究探讨了艺术品属性 (颜色,复杂性) 和判断 (美丽,创造力) 之间的联系.
- 从主观评估中区分固有的艺术品特征至关重要.
- 进行了对过去50年艺术研究文献的全面审查.
研究的目的:
- 确定影响艺术判断的关键属性.
- 应用机器学习来根据这些属性预测艺术判断.
- 了解客观艺术品特征与主观美学体验之间的关系.
主要方法:
- 在过去的50年里,对艺术研究的文献评论.
- 使用渐变增强决策树 (GBDT),一种机器学习算法.
- 从78名初学者参与者中收集了54件艺术品的17个属性的评分,以预测13个艺术品的评价.
主要成果:
- GBDT模型成功地预测了13个艺术判断,并且具有显著的准确性.
- 被判断的创造力 (31%的差异解释) 和令人不安/刺激的判断 (32%的差异解释) 是最可预测的.
- 情感表达力,价值,象征性和复杂性是关键的预测属性.
- 内容表示属性比形式感知属性更有影响力.
- 在某些属性和判断之间观察到非线性关系.
结论:
- 机器学习可以根据特定的艺术品属性有效地模拟艺术判断.
- 了解这些属性判断动态可以增强我们对美学体验的了解.
- 发现为艺术欣赏,文化实践和未来艺术科学研究提供了洞察力.
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