尺寸-体重错觉及超越:感知体重的新模式。

IF 3.6 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
PLoS Computational Biology Pub Date : 2025-09-15 eCollection Date: 2025-09-01 DOI:10.1371/journal.pcbi.1013496
Veronica Pisu, Erich W Graf, Wendy J Adams
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引用次数: 0

摘要

在尺寸-重量错觉(SWI)中,两个相同重量、相同的物质物体中较小的那个被认为更重。事实证明,很难通过传统的贝叶斯模型来解释SWI,它预测了相反的效果:尺寸的预期重量(更小=更轻)应该与感觉重量相结合,这样较小的物体应该在感知上更轻。其他作者提出,根据贝叶斯原理将重量和密度结合起来,或者结合有效编码的贝叶斯模型可以通过“似然排斥”来预测SWI。然而,这些模型仅在典型SWI刺激的狭窄条件下进行了评估。在这里,我们建立了一个感知重量的一般模型对不同的重量和/或密度和/或大小的不同数量的对象。在一项视觉触觉任务中,参与者(N = 30)抓住并举起成对的立方体,并报告他们感受到的重量。我们报告说,即使在非常小的密度差异下,SWI也会发生,否定了错觉需要预期重量和感觉重量之间的巨大差异的想法。在所有的物体对中,感知的重量被一个模型(R2 =.98)很好地描述了,这个模型包括物体的重量和被判断物体的密度的积极影响,但对其他物体的密度的消极影响。至关重要的是,两种密度对感知重量的影响都受到重量差的强烈调节,零/小重量差的影响是大重量差的三倍。因此,只有在典型SWI研究的不寻常条件下,密度才会在很大程度上影响感知体重。与现有模型(与我们更全面的数据集不一致)不同,我们的描述性模型为各种重量和尺寸条件下的成对物体提供了定量、准确和广义的重量感知。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The size-weight illusion and beyond: A new model of perceived weight.

In the size-weight illusion (SWI), the smaller of two same-weight, same apparent material objects is perceived as heavier. The SWI has proved difficult to explain via traditional Bayesian models, which predict the opposite effect: expected weight from size (smaller = lighter) should be integrated with felt weight, such that the smaller object should be perceptually lighter. Other authors have proposed that weight and density are combined according to Bayesian principles, or that Bayesian models incorporating efficient coding can predict the SWI via 'likelihood repulsion'. These models, however, have been evaluated only under the narrow conditions of typical SWI stimuli. Here we establish a general model of perceived weight for pairs of objects that differ in weight and/or density and/or size by varying amounts. In a visuo-haptic task, participants (N = 30) grasped and lifted pairs of cubes, and reported their perceived heaviness. We report that the SWI occurs even at very small density differences, repudiating the idea that the illusion requires a large difference between expected and felt weight. Across all object pairs, perceived weight was well described by a model (R2 = .98) that includes a positive influence of both objects' weights and the judged object's density, but a negative influence of the other object's density. Critically, the influence of both densities on perceived weight is strongly modulated by weight difference, being three times as large for zero/small weight differences than for large differences. Thus, it is only under the unusual conditions of typical SWI studies that density affects perceived weight to a substantial extent. Unlike existing models, that are inconsistent with our more comprehensive dataset, our descriptive model provides a quantitative, accurate and generalised account of weight perception for pairs of objects across various weight and size conditions.

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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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