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与人工显示相比,视觉数量感知在真实场景中没有优势。

Visual numerosity perception shows no advantage in real-world scenes compared to artificial displays.

机构信息

University of British Columbia, Canada.

Carnegie Mellon University, United States of America.

出版信息

Cognition. 2023 Jan;230:105291. doi: 10.1016/j.cognition.2022.105291. Epub 2022 Sep 29.

Abstract

While the human visual system is sensitive to numerosity, the mechanisms that allow perception to extract and represent the number of objects in a scene remains unknown. Prominent theoretical approaches posit that numerosity perception emerges from passive experience with visual scenes throughout development, and that unsupervised deep neural network models mirror all characteristic behavioral features observed in participants. Here, we derive and test a novel prediction: if the visual number sense emerges from exposure to real-world scenes, then the closer a stimulus aligns with the natural statistics of the real world, the better number perception should be. But - in contrast to this prediction - we observe no such advantage (and sometimes even a notable impairment) in number perception for natural scenes compared to artificial dot displays in college-aged adults. These findings are not accounted for by the difficulty in object identification, visual clutter, the parsability of objects from the rest of the scene, or increased occlusion. This pattern of results represents a fundamental challenge to recent models of numerosity perception based in experiential learning of statistical regularities, and instead suggests that the visual number sense is attuned to abstract number of objects, independent of their underlying correlation with non-numeric features. We discuss our results in the context of recent proposals that suggest that object complexity and entropy may play a role in number perception.

摘要

虽然人类的视觉系统对数量敏感,但允许感知提取和表示场景中物体数量的机制仍不清楚。有影响力的理论方法假设,数量感知是从整个发展过程中对视觉场景的被动体验中产生的,并且无监督的深度神经网络模型反映了参与者观察到的所有特征行为。在这里,我们得出并检验了一个新的预测:如果视觉数量感是从对现实世界场景的暴露中产生的,那么刺激与现实世界的自然统计数据越吻合,数量感知应该越好。但是——与这一预测相反——我们在大学生中观察到,与人工点显示相比,自然场景在数量感知方面并没有这种优势(有时甚至明显受损)。这些发现不能用物体识别的难度、视觉杂乱、从场景其余部分解析物体的能力或增加的遮挡来解释。这些结果模式对基于统计规律经验学习的数量感知的最新模型构成了根本性挑战,反而表明视觉数量感与物体的抽象数量有关,而与物体与非数字特征的潜在相关性无关。我们在最近的一些建议的背景下讨论我们的结果,这些建议表明物体的复杂性和熵可能在数量感知中起作用。

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