December 2022
Volume 22, Issue 14
Open Access
Vision Sciences Society Annual Meeting Abstract  |   December 2022
The temporal dynamics of visual crowding and segmentation
Author Affiliations & Notes
  • Michael Herzog
    EPFL
  • Greg Francis
    EPFL
    Purdue
  • Mauro Manassi
    University of Aberdeen
  • Footnotes
    Acknowledgements  GF: Human Brain Project SGA3 (945539), Visiting Scientist Grant from the Swiss National Science Foundation; MM: Carnegie Trust for the Universities of Scotland RIG009850 ; MHH: “Basics of visual processing: from elements to figures” of the SNF
Journal of Vision December 2022, Vol.22, 3641. doi:https://doi.org/10.1167/jov.22.14.3641
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      Michael Herzog, Greg Francis, Mauro Manassi; The temporal dynamics of visual crowding and segmentation. Journal of Vision 2022;22(14):3641. https://doi.org/10.1167/jov.22.14.3641.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Perception of a target strongly deteriorates when flanking elements are presented (crowding). Classically, crowding is explained by pooling mechanisms where target and flanker features are combined, e.g., when neurons in higher visual areas with larger receptive fields pool information from neurons in lower visual areas with smaller receptive fields. Crowding is proposed to occur in the feedforward sweep of information processing. Here, we show that crowding occurs in a highly temporal fashion, requiring substantial processing time. We presented a vertical vernier for 20ms flanked by either 2 vertical lines (one on each side of the vernier) or 2 cuboids (the cuboids contained the lines). For lines and cuboids, strong crowding occurred. When we increased the stimulus duration, crowding remained strong for the 2 lines but gradually decreased for the cuboids reaching nearly unflanked performance from a duration of about 120ms on (uncrowding). These results show that, first, uncrowding cannot be explained by simple pooling models. Second, as we proposed previously, uncrowding occurs when the flankers make up a good Gestalt (cuboids) that segregate from the target vernier. Third, the computation of good Gestalts of the cuboids takes substantial time. What matters for uncrowding is not stimulus duration per se, but processing time. When we presented only the 2 cuboids alone for 20ms, then an ISI of 120ms, and then the cuboids plus the vernier, strong uncrowding occurred. Using the Laminart model, we show how processing evolves dynamically in a recurrent fashion.

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