Abstract
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.
Highlights
Researchers have tried to unravel the mechanics of the human visual system—a system that can successfully identify complex, naturalistic objects and materials across an unimaginably wide range of images
We focussed on the perception of liquids—a challenging class of materials due to their extreme mutability and diverse behaviours
We find that the represented features are highly influenced by the size of the networks’ parameter space, while prediction performance remains practically unchanged. This implies that some caution is required in making direct inferences between neural network models and the human visual system
Summary
Researchers have tried to unravel the mechanics of the human visual system—a system that can successfully identify complex, naturalistic objects and materials across an unimaginably wide range of images. Recent advances in artificial neural networks hold some promise for developing detailed, image-computable process models of sophisticated visual inferences, such as object recognition in arbitrary photographs [7,8,9,10]. Artificial neural networks provide an experimental platform for simulating complex visual abilities, and carefully probing the role of specific objective functions, training sets and network architectures that yield human-like performance. Having developed a model that mimics human behaviour, the response properties of all units in the network can be measured with arbitrary precision over arbitrary conditions, like an idealised form of in vivo systems neuroscience performed on a model system rather than real tissue
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