自动图形分歧思维评估:一种多模式机器学习方法
Hezhi Zhang1, Hang Dong1, Ying Wang1
1Faculty of Psychology, Beijing Normal University, Beijing 100875, China.
Journal of Intelligence
|April 25, 2025
概括
本研究引入了一种多式联网深度学习模型,以客观地评分基于图像的分歧思维测试. 人工智能模型有效地评估创意维度,如新性和流性,提高效率并减少人类偏见.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 教育心理学教育心理学
背景情况:
- 差异性思维 (DT) 的自动评估对于评估创造力至关重要.
- 基于图像的DT测试提供了丰富的多式联络数据,但也带来了评分挑战.
- 当前的方法往往缺乏客观性和效率在创造力评估.
研究的目的:
- 开发和验证一种多式联网深度学习模型,用于基于图像的分歧思维测试的自动评分.
- 整合视觉和语义特征,以提高创造力评估中的客观性和效率.
- 为了评估模型的表现与人类专家的分数对比.
主要方法:
- 开发了一个多式联机深度学习模型,使用ResNet50用于图像功能和GloVe用于文本嵌入.
- 一个完全连接的神经网络融合了这些功能,训练了平均平方误差 (MSE) 损失和Adam优化.
- 该模型在708个来自中国高中学生的答案上进行了训练和验证.
主要成果:
- 该模型与训练组的人类分数有很强的对齐 (Pearson r = 0.810).
- 对未见数据的验证显示出良好的概括能力 (r = 0.561).
- 在参与者层面的分析中,与人类总得分的相关性为0.602.
结论:
- 多模式深度学习有效地捕捉了不同思维的关键维度 (新性,流性,灵活性).
- 这种方法显著减少了主观性,并简化了创造力的评估过程.
- 这些发现支持使用视觉-文本特征融合来进行强大,具有成本效益的认知评估.
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