Paulun Vivian C, Kawabe Takahiro, Nishida Shin'ya, Fleming Roland W
Department of Experimental Psychology, University of Gießen, Germany.
NTT Communication Science Laboratories, Nippon Telegraph and Telephone Corporation, Japan.
Vision Res. 2015 Oct;115(Pt B):163-74. doi: 10.1016/j.visres.2015.01.023. Epub 2015 Feb 9.
Perceiving material properties can be crucial for many tasks-such as determining food edibility, or avoiding getting splashed-yet the visual perception of materials remains poorly understood. Most previous research has focussed on optical characteristics (e.g., gloss, translucency). Here, however, we show that shape also provides powerful visual cues to material properties. When liquids pour, splash or ooze, they organize themselves into characteristic shapes, which are highly diagnostic of the material's properties. Subjects viewed snapshots of simulated liquids of different viscosities, and rated their similarity. Using maximum likelihood difference scaling (Maloney & Yang, 2003), we reconstructed perceptual scales for perceived viscosity as a function of the physical viscosity of the simulated fluids. The resulting psychometric function revealed a distinct sigmoidal shape, distinguishing runny liquids that flow easily from viscous gels that clump up into piles. A parameter-free model based on 20 simple shape statistics predicted the subjects' data surprisingly well. This suggests that when subjects are asked to compare the viscosity of static snapshots of liquids that differ only in terms of viscosity, they rely primarily on relatively simple measures of shape similarity.
感知物质属性对于许多任务至关重要,比如判断食物是否可食用,或者避免被溅到,但人们对物质的视觉感知仍知之甚少。此前的大多数研究都集中在光学特性(如光泽度、半透明度)上。然而,我们在此表明,形状也能为物质属性提供有力的视觉线索。当液体倾倒、飞溅或渗出时,它们会呈现出特定的形状,这些形状能高度诊断出物质的属性。受试者观看了不同粘度模拟液体的快照,并对它们的相似度进行评分。我们使用最大似然差异缩放法(马洛尼和杨,2003年),重建了作为模拟流体物理粘度函数的感知粘度的感知量表。由此产生的心理测量函数呈现出明显的S形,区分出了容易流动的稀液体和聚集成堆的粘性凝胶。一个基于20个简单形状统计量的无参数模型对受试者的数据预测得惊人地准确。这表明,当要求受试者比较仅在粘度方面不同的液体静态快照的粘度时,他们主要依赖相对简单的形状相似度测量方法。