Dataset Information

Visual Features in the Perception of Liquids.

ABSTRACT: Perceptual constancy-identifying surfaces and objects across large image changes-remains an important challenge for visual neuroscience [1-8]. 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 [9-11] 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 [12-14] 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.

SUBMITTER: van Assen JJR

PROVIDER: S-EPMC5807092 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Visual Features in the Perception of Liquids.

van Assen Jan Jaap R JJR Barla Pascal P Fleming Roland W RW

Current biology : CB 20180127 3

Perceptual constancy-identifying surfaces and objects across large image changes-remains an important challenge for visual neuroscience [1-8]. 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 [9-11] that disentangles the liquid's viscosity from external factors-like gravity and object interactions-that also affe ...[more]

PMID: 29395924

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Project description:Visually inferring material properties is crucial for many tasks, yet poses significant computational challenges for biological vision. Liquids and gels are particularly challenging due to their extreme variability and complex behaviour. We reasoned that measuring and modelling viscosity perception is a useful case study for identifying general principles of complex visual inferences. In recent years, artificial Deep Neural Networks (DNNs) have yielded breakthroughs in challenging real-world vision tasks. However, to model human vision, the emphasis lies not on best possible performance, but on mimicking the specific pattern of successes and errors humans make. We trained a DNN to estimate the viscosity of liquids using 100.000 simulations depicting liquids with sixteen different viscosities interacting in ten different scenes (stirring, pouring, splashing, etc). We find that a shallow feedforward network trained for only 30 epochs predicts mean observer performance better than most individual observers. This is the first successful image-computable model of human viscosity perception. Further training improved accuracy, but predicted human perception less well. We analysed the network's features using representational similarity analysis (RSA) and a range of image descriptors (e.g. optic flow, colour saturation, GIST). This revealed clusters of units sensitive to specific classes of feature. We also find a distinct population of units that are poorly explained by hand-engineered features, but which are particularly important both for physical viscosity estimation, and for the specific pattern of human responses. The final layers represent many distinct stimulus characteristics-not just viscosity, which the network was trained on. Retraining the fully-connected layer with a reduced number of units achieves practically identical performance, but results in representations focused on viscosity, suggesting that network capacity is a crucial parameter determining whether artificial or biological neural networks use distributed vs. localized representations.

Dataset Information

Visual Features in the Perception of Liquids.

Publications

Visual Features in the Perception of Liquids.

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