在人群中观察:先平均,后最大。

IF 3.2 3区 心理学 Q1 PSYCHOLOGY, EXPERIMENTAL
Psychonomic Bulletin & Review Pub Date : 2024-08-01 Epub Date: 2024-02-09 DOI:10.3758/s13423-024-02468-6
Xincheng Lu, Ruijie Jiang, Meng Song, Yiting Wu, Yiran Ge, Nihong Chen
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引用次数: 0

摘要

拥挤是物体识别中的一个基本限制,被认为是由于周边视觉中附近物体的过度整合造成的。为了了解其集合机制,我们在方位拥挤任务中测量了受试者的内部反应分布。与平均模型的预测相反,我们观察到的模式表明,知觉判断是基于在噪声干扰项目中选择最大的反应做出的。一个以第一阶段平均和第二阶段符号最大运算为特征的模型可以预测人类观察者在不同信号强度水平下产生的不同错误。这些研究结果表明,在视觉处理的早期和高级阶段,有不同的规则通过实施线性和非线性组合策略来解决瓶颈问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Seeing in crowds: Averaging first, then max.

Seeing in crowds: Averaging first, then max.

Crowding, a fundamental limit in object recognition, is believed to result from excessive integration of nearby items in peripheral vision. To understand its pooling mechanisms, we measured subjects' internal response distributions in an orientation crowding task. Contrary to the prediction of an averaging model, we observed a pattern suggesting that the perceptual judgement is made based on choosing the largest response across the noise-perturbed items. A model featuring first-stage averaging and second-stage signed-max operation predicts the diverse errors made by human observers under various signal strength levels. These findings suggest that different rules operate to resolve the bottleneck at early and high-level stages of visual processing, implementing a combination of linear and nonlinear pooling strategies.

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来源期刊
CiteScore
6.70
自引率
2.90%
发文量
165
期刊介绍: The journal provides coverage spanning a broad spectrum of topics in all areas of experimental psychology. The journal is primarily dedicated to the publication of theory and review articles and brief reports of outstanding experimental work. Areas of coverage include cognitive psychology broadly construed, including but not limited to action, perception, & attention, language, learning & memory, reasoning & decision making, and social cognition. We welcome submissions that approach these issues from a variety of perspectives such as behavioral measurements, comparative psychology, development, evolutionary psychology, genetics, neuroscience, and quantitative/computational modeling. We particularly encourage integrative research that crosses traditional content and methodological boundaries.
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