Categorical membership modulates crowding: evidence from characters

Josephine Reuther, Ramakrishna Chakravarthi

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

Abstract

Visual crowding is generally thought to affect recognition mostly or only at the level of feature combination. Calling this assertion into question, recent studies have shown that if a target object and its flankers belong to different categories crowding is weaker than if they belong to the same category. Nevertheless, these results can be explained in terms of featural differences between categories. The current study tests if category- level (i.e., high-level) interference in crowding occurs when featural differences are controlled for. First, replicating previous results, we found lower critical spacing for targets and flankers belonging to different categories. Second, we observed the same, albeit weaker, category-specific effect when objects in both categories had the exact same feature set, suggesting that category-specific effects persist even when featural differences are fully controlled for. Third, we manipulated the semantic content of the flankers while keeping their feature set constant, by using upright or rotated objects, and found that meaning modulated crowding. An exclusively feature-based account of crowding would predict no differences due to such changes in meaning. We conclude that crowding results from not only the well-documented feature-level interactions but also additional interactions at a level where objects are grouped by meaning.
Original languageEnglish
Article number5
Pages (from-to)1-13
Number of pages13
JournalJournal of Vision
Volume14
Issue number6
DOIs
Publication statusPublished - 16 Oct 2014

Bibliographical note

Acknowledgments
We wish to thank Michael H. Herzog for suggesting that changing orientation should eliminate meaning and therefore category membership, which led to Experiment 3.

Keywords

  • crowding
  • object category
  • meaning
  • object recognition
  • feature integration

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