Article ; Online: Perceptual grouping strategies and texture segmentation: Strategic connections and selection.
2023 Volume 210, Page(s) 108263
Abstract: In a series of articles, Jacob Beck proposed that a variety of texture segmentation phenomena occurs due to emergent features that arise from "links" between elements with appropriate local properties, such as alignment, orientation, and proximity. His ... ...
Abstract | In a series of articles, Jacob Beck proposed that a variety of texture segmentation phenomena occurs due to emergent features that arise from "links" between elements with appropriate local properties, such as alignment, orientation, and proximity. His findings and ideas guided theoretical and computational models, and some of his demonstrations became textbook knowledge about visual perception. We build on this work in two ways. First, we provide a modern replication of a classic texture segmentation study using a much larger sample size. Overall, the replication agrees with Beck's original findings, although there are some quantitative differences. Second, we show how to apply a quantitative model of visual cortex to Beck's experiment and demonstrate that the model can explain many aspects of Beck's findings. Key to the model's success is cognitive control of connections between individual elements (akin to Beck's "links" between elements) and a selection mechanism that makes it easy to identify how well elements within a region connect and how well different regions are disconnected. Overall, the model supports Beck's claim that local properties can facilitate patterns of connections between stimulus elements and that some connection patterns allow an observer to easily distinguish textures. |
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MeSH term(s) | Humans ; Visual Perception ; Vision, Ocular ; Visual Cortex ; Pattern Recognition, Visual |
Language | English |
Publishing date | 2023-06-02 |
Publishing country | England |
Document type | Journal Article |
ZDB-ID | 200427-6 |
ISSN | 1878-5646 ; 0042-6989 |
ISSN (online) | 1878-5646 |
ISSN | 0042-6989 |
DOI | 10.1016/j.visres.2023.108263 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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