Global and high-level effects in crowding cannot be predicted by either high-dimensional pooling or target cueing

Alban Bornet, Oh-Hyeon Choung, Adrien Doerig, David Whitney, Michael H Herzog, Mauro Manassi

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
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Abstract

In visual crowding, the perception of a target deteriorates in the presence of nearby flankers. Traditionally, target-flanker interactions have been considered as local, mostly deleterious, low-level, and feature specific, occurring when information is pooled along the visual processing hierarchy. Recently, a vast literature of high-level effects in crowding (grouping effects and face-holistic crowding in particular) led to a different understanding of crowding, as a global, complex, and multilevel phenomenon that cannot be captured or explained by simple pooling models. It was recently argued that these high-level effects may still be captured by more sophisticated pooling models, such as the Texture Tiling model (TTM). Unlike simple pooling models, the high-dimensional pooling stage of the TTM preserves rich information about a crowded stimulus and, in principle, this information may be sufficient to drive high-level and global aspects of crowding. In addition, it was proposed that grouping effects in crowding may be explained by post-perceptual target cueing. Here, we extensively tested the predictions of the TTM on the results of six different studies that highlighted high-level effects in crowding. Our results show that the TTM cannot explain any of these high-level effects, and that the behavior of the model is equivalent to a simple pooling model. In addition, we show that grouping effects in crowding cannot be predicted by post-perceptual factors, such as target cueing. Taken together, these results reinforce once more the idea that complex target-flanker interactions determine crowding and that crowding occurs at multiple levels of the visual hierarchy.

Original languageEnglish
Article number10
Pages (from-to)1-25
Number of pages25
JournalJournal of Vision
Volume21
Issue number12
Early online date23 Nov 2021
DOIs
Publication statusPublished - 30 Nov 2021

Bibliographical note

Acknowledgments
The authors thank Ruth Rosenholtz for her detailed comments on this manuscript and for sharing the code of the TTM. We thank both reviewers for their insightful comments. A.B. was supported by the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreements No. 785907 (Human Brain Project SGA2) and No. 945539 (Human Brain Project SGA3). O.H.C. was supported by the Swiss National Science Foundation (SNF) 320030_176153 “Basics of visual processing: from elements to figures.” A.D. was supported by the Swiss National Science Foundation grants No. 176153 “Basics of visual processing: from elements to figures” and No. 191718 “Towards machines that see like us: human eye movements for robust deep recurrent neural networks.” D.W. was supported by the National Institutes of Health grant R01 CA236793.

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