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人类视觉分组基于区域内和跨区域的时间相关性

Yen-Ju Chen¹, Zitang Sun¹, Shin'ya Nishida¹

¹Graduate School of Informatics, Kyoto University, Kyoto, Japan.

PLoS computational biology

|September 11, 2025

概括

人类的视觉细分依赖于时间相似性结构. 我们的研究结果表明,大脑使用类似于计算机视觉模型的全局计算过程来有效地分组图像元素.

科学领域:

认知神经科学认知神经科学
计算机视觉计算机视觉
人类的视觉感知人类的视觉感知

背景情况:

人类视觉系统使用特征相似性的图像进行细分,比如时间发光率相关性.
计算机视觉模型 (例如,视觉变压器) 使用全球相似性计算进行细分.
全球计算在人类视觉细分中的程度尚未得到充分理解.

研究的目的:

研究时间相似性结构如何影响人类视觉细分.
为了确定人类视觉细分是否采用全球计算过程.

主要方法:

开发了一个基于Vision Transformer的刺激生成算法,以控制区域内和跨区域的相似性.
操纵的光度,颜色和空间相位属性的时间相关性.
通过使用一个时间两个替代的强制选择任务来评估人类细分性能.

主要成果:

分段性能受到内部和交叉关联配置的显著影响.
属性类型没有影响相关性配置的影响.
人类的表现与基于图形的计算模型的预测非常相匹配.

结论:

人类纹理细分受全球时间相似性结构的影响.
人类的视觉细分可以通过整合对相似性的全球计算过程来近似.

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