Visual Features in the Perception of Liquids.
Visual Features in the Perception of Liquids.
复制标题
DOI：
10.1016/j.cub.2017.12.037
复制
发表时间：
2018-02-05
期刊：
影响因子：
--
通讯作者：
Fleming RW
中科院分区：
文献类型：
--
作者：
van Assen JJR;Barla P;Fleming RW
感知恒定性-识别表面和物体在大的图像变化-仍然是视觉神经科学的一个重要挑战。液体特别具有挑战性，因为它们以复杂，高度可变的方式对外力做出反应，为视觉系统呈现出大量图像。为了达到恒常性，大脑必须进行因果推理，将液体的粘性从外部因素（如重力和物体相互作用）中分离出来，这些因素也会影响液体的行为。在这里，我们测试了视觉系统是否使用“中级”特征来估计粘度，这些特征比其他因素对粘度的响应更大。观察者报告了从水到熔融玻璃的模拟液体的感知粘度，其表现出不同的行为（例如，倾倒、搅拌）。另一组观察者对相同的动画进行了20个中等3D形状和运动特征的评分。应用因素分析的功能评级显示，四个基本因素（分布，不规则性，直线性和动态）的加权组合预测感知粘度非常好，在这个广泛的背景下（R2 = 0.93）。有趣的是，观察者在不知不觉中根据一个跨情境的共同因素对他们的中级判断进行排序：粘度的变化。主成分分析表明，在所有特征中，第一个成分几乎与粘度完全一致（R2 = 0.96）。我们的研究结果表明，视觉系统实现恒常性表示刺激在一个多维的特征空间的基础上互补的，中级功能，成功地集群非常不同的刺激在一起，戏弄类似的刺激分开，使粘度可以很容易地读出。观察者非常善于从视觉上推断流动流体的粘度，他们使用多个中级形状和运动特征来做到这一点。四个因素预测感知粘度恒定性令人惊讶地好。这些特征采用广泛不同的刺激，并通过粘度组织它们。货车阿森等人。使用流动液体的视觉感知来揭示大脑对材料的感官推理背后的计算。通过比较观察者的粘度等级与感知的形状特征，他们展示了大脑如何利用3D形状和运动线索来推断上下文中的粘度，尽管图像发生了戏剧性的变化。
Perceptual constancy—identifying surfaces and objects across large image changes—remains an important challenge for visual neuroscience. Liquids are particularly challenging because they respond to external forces in complex, highly variable ways, presenting an enormous range of images to the visual system. To achieve constancy, the brain must perform a causal inference that disentangles the liquid’s viscosity from external factors—like gravity and object interactions—that also affect the liquid’s behavior. Here, we tested whether the visual system estimates viscosity using “midlevel” features that respond more to viscosity than other factors. Observers reported the perceived viscosity of simulated liquids ranging from water to molten glass exhibiting diverse behaviors (e.g., pouring, stirring). A separate group of observers rated the same animations for 20 midlevel 3D shape and motion features. Applying factor analysis to the feature ratings reveals that a weighted combination of four underlying factors (distribution, irregularity, rectilinearity, and dynamics) predicted perceived viscosity very well across this wide range of contexts (R2 = 0.93). Interestingly, observers unknowingly ordered their midlevel judgments according to the one common factor across contexts: variation in viscosity. Principal component analysis reveals that across the features, the first component lines up almost perfectly with the viscosity (R2 = 0.96). Our findings demonstrate that the visual system achieves constancy by representing stimuli in a multidimensional feature space—based on complementary, midlevel features—which successfully cluster very different stimuli together and tease similar stimuli apart, so that viscosity can be read out easily. Observers are remarkably good at visually inferring the viscosity of flowing fluids They use multiple midlevel shape and motion features to do so Four factors predict perceived viscosity constancy surprisingly well The features take wildly divergent stimuli and organize them by viscosity van Assen et al. use the visual perception of flowing liquids to uncover the computations underlying the brain’s sensory inferences about materials. By comparing observers’ viscosity ratings with perceived shape features, they show how the brain exploits 3D shape and motion cues to infer viscosity across contexts despite dramatic image changes.
登录
查看更多内容
影响因子：
16.6
作者：
Burge, Johannes;Geisler, Wilson S.
通讯作者：
Geisler, Wilson S.
影响因子：
25
作者：
Kim, Juno;Marlow, Phillip J.;Anderson, Barton L.
通讯作者：
Anderson, Barton L.
影响因子：
4.3
作者：
Khaligh-Razavi SM;Kriegeskorte N
通讯作者：
Kriegeskorte N
影响因子：
5.3
作者：
Guclu, Umut;van Gerven, Marcel A. J.
通讯作者：
van Gerven, Marcel A. J.
DOI：
10.1364/josaa.3.000029
发表时间：
1986-01-01
影响因子：
1.9
作者：
MALONEY, LT;WANDELL, BA
通讯作者：
WANDELL, BA