普遍的概括法则对于自然主义的刺激也是如此
Raja Marjieh1, Nori Jacoby2, Joshua C Peterson3
1Department of Psychology, Princeton University.
Journal of experimental psychology. General
|February 22, 2024
概括
谢帕德的通用化定律适用于复杂的,现实世界的刺激. 这项研究证实,在高维的自然图像空间中,感知到的相似性随着距离的增加而减少.
科学领域:
- 认知心理学 认知心理学
- 感知科学 感知科学 感知科学
- 机器学习 机器学习
背景情况:
- 谢帕德的通用化定律认为,感知相似性是刺激距离的形函数.
- 现有的证据依赖于简化,低维的刺激,不反映现实世界的复杂性.
- 配对相似性判断面临着大型刺激集的可扩展性挑战.
研究的目的:
- 在自然主义,高维的背景下测试谢帕德的宇宙定律.
- 为法律提供强有力的证据,使用大规模的人类概括和相似性判断.
- 弥合理论模型和现实世界的感知现象之间的差距.
主要方法:
- 对人类相似性的大量数据集 (214,200个判断) 的分析.
- 收集和分析一个新的数据集 (390,819个判断) 的人类概括.
- 利用三组不同的自然图像来表示高维的刺激.
主要成果:
- 在一个高维环境中发现了支持谢帕德宇宙定律的强有力的证据.
- 作为刺激距离的形函数,感知相似性始终下降.
- 这些发现适用于广泛的数据集和自然主义图像刺激.
结论:
- 谢帕德的通用化定律适用于复杂的自然刺激.
- 该研究验证了法律在一个高维的制度,克服了以前的局限性.
- 这项研究推动了我们在生态上有效的环境中对人类相似感知的理解.
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