How the brain visually computes the physical properties of complex natural materials is a major open challenge in visual neuroscience. Here, we focussed on the perception of liquidsa particularly challenging class of materials due to their extreme mutability and diverse behaviours. We present the first image-computable model that can predict average human viscosity judgments from fluid simulation movies as well as individual observers can across a wide range of viewing conditions. We trained artificial neural networks to estimate viscosity from 100,000 20-frame simulations, and find that the models best predict human perception after relatively little traininglong before they have reached optimal performance. This suggests that while human viscosity perception is remarkably good, even better performance is theoretically possible. Probing the networks with 'virtual electrophysiology' reveals many different features the networks use to estimate viscosity. Surprisingly, 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. However, the methods presented here provide a systematic framework for comparing humans to neural networks.
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