September 2019
Volume 19, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2019
Perceiving Category Set Statistics On-the-fly
Author Affiliations & Notes
  • Shaul Hochstein
    ELSC Brain Research Center & Life Sciences Institute, Hebrew University, Jerusalem
  • Noam Khayat
    ELSC Brain Research Center & Life Sciences Institute, Hebrew University, Jerusalem
  • Marina Pavlovskaya
    Loewenstein Rehabilitation Center & Tel Aviv University
  • Yoram Bonneh
    Bar Ilan University
  • Nachum Soroker
    Loewenstein Rehabilitation Center & Tel Aviv University
  • Stefano Fusi
    Neuroscience Department, Columbia University, New York
Journal of Vision September 2019, Vol.19, 225a. doi:https://doi.org/10.1167/19.10.225a
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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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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

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.

Acknowledgement: Israel Science Foundation (ISF) 
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