Using crowding to determine whether an object is identified as a whole or by parts
Author(s) -
Denis G. Pelli,
Marialuisa Martelli,
Najib J. Majaj
Publication year - 2004
Publication title -
journal of vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.126
H-Index - 113
ISSN - 1534-7362
DOI - 10.1167/4.8.507
Subject(s) - conjunction (astronomy) , observer (physics) , computer science , object (grammar) , artificial intelligence , perception , property (philosophy) , pattern recognition (psychology) , psychology , epistemology , philosophy , physics , quantum mechanics , astronomy , neuroscience
“Crowding” provides an answer. Classically, crowding is the impairment of recognition of letters or objects in the periphery by neighboring objects. The neighboring objects have no effect if spaced farther than a critical spacing, which defines an isolation field around the target. Bouma (1970) showed that this isolation field has a diameter of roughly half the eccentricity (distance from fixation). Here we examine basic-level identification of familiar objects, including drawings, photos, silhouettes, letters, words, symbols, signs, icons, and logos. We asked the observer, “What is it?” and measured the threshold eccentricity beyond which each object could not be recognized. From these thresholds we computed K = Aobject/Aisolated, the ratio of the area of the object to the area of the isolation field at that eccentricity, Aisolated = (eccentricity/2)2. Three different sets of familiar objects all yield unimodal distributions peaking at K < 1. The demos below indicate that threshold is determined by the size of the isolated part. When K < 1 the whole object is contained inside the isolation field and recognition is “holistic”. When K > 1 recognition requires isolation of a part that is 1/K of the whole and recognition is “by parts”. E27. Using crowding to determine whether an object is identified as a whole or by parts — Isolating to recognize Denis G. Pelli, Marialuisa Martelli, Najib J. Majaj Psychology and Neural Science, New York University Holistic K≤1 By parts K>1
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