人与机器在物体识别方面的性能比较
1School of Psychology, Faculty of Health, University of Plymouth, Plymouth PL4 8AA, UK.
Behavioral sciences (Basel, Switzerland)
|January 28, 2026
概括
深度学习模型显示高准确性,但与现实世界的概括和类似人类的分类斗争. 比较心理学原理显示,模型不能完全复制人类视觉分类细微差别.
科学领域:
- 计算机视觉 计算机视觉
- 认知科学 认知科学
- 人工智能的人工智能
背景情况:
- 深度学习模型声称人类水平的性能,但通常是在人工条件下.
- 现有的研究表明,对与人类分类行为保持一致的模型的结果是不同的.
- 比较心理学为更严格的人类-人工智能比较提供了一个框架.
研究的目的:
- 调查深度学习模型是否达到人类水平的准确性和类似人类的分类.
- 用比较心理学原理,在类似的约束条件下比较模型和人类的表现.
- 探索模型概括,任务适应和定性分类关系.
主要方法:
- 使用ObjectNet数据集子集的三个实验.
- 不同的演示时间,任务复杂性,以及N-way与自由命名分类.
- 分析物体检测/分类分离和个人人类差异.
主要成果:
- 模型在机器优化条件下表现出色,但在没有微调的情况下,在现实世界的概括方面扎.
- 人类的分类在各个任务中保持稳定,而机器的性能在没有适应的情况下会下降.
- 只有多式CoCa模型在人类分类中捕获了定性顺序关系.
结论:
- 当前的深度学习模型接近人类的分类,但不能完全复制它,特别是在复杂的现实世界中.
- 对比心理学原则和对个体差异的考虑对于评估AI至关重要.
- 需要进一步的研究来弥合人工智能准确性和类似人类的分类之间的差距.
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