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Yaniv Morgenstern, Daniel J. Kersten; The perceptual dimensions of natural dynamic flow. Journal of Vision 2017;17(12):7. doi: https://doi.org/10.1167/17.12.7.
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© ARVO (1962-2015); The Authors (2016-present)
We measured perceptual judgments of category, material attributes, affordances, and similarity to investigate the perceptual dimensions underlying the visual representation of a broad class of natural dynamic flows (sea waves, smoke, and windblown foliage). The dynamic flows were looped 3-s movies windowed with circular apertures of two sizes to manipulate the level of spatial context. In low levels of spatial context (smaller apertures), human observers' judgments of material attributes and affordances were inaccurate, with estimates biased toward assumptions that the flows resulted from objects that were rigid, “pick-up-able,” and not penetrable. The similarity arrangements showed dynamic flow clusters based partly on material, but dominated by color appearance. In high levels of spatial context (large apertures), observers reliably estimated material categories and their attributes. The similarity arrangements were based primarily on categories related to external, physical causes. Representational similarity analysis suggests that while shallow dimensions like color sometimes account for inferences of physical causes in the low-context condition, shallow dimensions cannot fully account for these inferences in the high-context condition. For the current broad data set of dynamic flows, the perceptual dimensions that best account for the similarity arrangements in the high-context condition are related to the intermolecular bond strength of a material's underlying physical structure. These arrangements are also best related to affordances that underlie common motor activities. Thus, the visual system appears to use an efficient strategy to resolve flow ambiguity; vision will sometimes rely on local, image-based, statistical properties that can support reliable inference of external physical causes, and other times it uses deeper causal knowledge to interpret and use flow information to the extent that it is useful for everyday action decisions.
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