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Shaul Hochstein, Noam Khayat, Marina Pavlovskaya, Yoram Bonneh, Nachum Soroker, Stefano Fusi; Perceiving Category Set Statistics On-the-fly. Journal of Vision 2019;19(10):225a. doi: https://doi.org/10.1167/19.10.225a.
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A bombardment of information overloads our sensory, perceptual and cognitive systems, which must integrate new information with memory of past scenes and events. Mechanisms employed to overcome sensory system bottlenecks include selective attention, Gestalt gist perception, categorization, and the recently investigated ensemble encoding of set summary statistics. We explore compensatory cognitive processes focusing on categorization and set ensemble summary statistics that relate objects sharing properties or function. Without encoding individual details of all individuals, (or as a shortcut to representing these details), observers perceive category prototype and boundaries or set mean and range, and perhaps higher order statistics as well. We found that observers perceive set mean and range, automatically, implicitly, and on-the-fly, for each presented set sequence, independently, and we found matching properties for category representation, suggesting a similar computational mechanism underlies the two phenomena. But categorization depends on a lifetime of learning about categories and their prototypes and boundaries. We now developed novel abstract “amoeba” forms, which are complex images similar to categories, but have simple ultrametric structure that observers can categorize on-the-fly (rather than depending on pre-learned categories). We find that, not only do observers learn the amoeba categories on-the-fly, they also build representations of their progenitor (related, but not equivalent, to set “mean” or category prototype), as well as category boundaries (related to set range and inter-category boundaries). These findings put set perception in a new light, related to object, scene and category representation.
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