Context mitigates crowding: Peripheral object recognition in real-world images

Maarten W.A. Wijntjes*, Ruth Rosenholtz

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

7 Citations (Scopus)
69 Downloads (Pure)


Object recognition is often conceived of as proceeding by segmenting an object from its surround, then integrating its features. In turn, peripheral vision's sensitivity to clutter, known as visual crowding, has been framed as due to a failure to restrict that integration to features belonging to the object. We hand-segment objects from their background, and find that rather than helping peripheral recognition, this impairs it when compared to viewing the object in its real-world context. Context is in fact so important that it alone (no visible target object) is just as informative, in our experiments, as seeing the object alone. Finally, we find no advantage to separately viewing the context and segmented object. These results, taken together, suggest that we should not think of recognition as ideally operating on pre-segmented objects, nor of crowding as the failure to do so.

Original languageEnglish
Pages (from-to)158-164
Number of pages7
Publication statusPublished - 2018

Bibliographical note

Accepted author manuscript


  • Context
  • Crowding
  • Integration
  • Object recognition
  • Shrink-wrap


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