Can two dots form a Gestalt? Measuring emergent features with the capacity coefficient.

Citation data:

Vision research, ISSN: 1878-5646, Vol: 126, Page: 19-33

Publication Year:
2016
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Repository URL:
https://corescholar.libraries.wright.edu/psychology/430
PMID:
25986994
DOI:
10.1016/j.visres.2015.04.019
Author(s):
Hawkins, Robert D.; Houpt, Joseph W.; Eidels, Ami; Townsend, James T.
Publisher(s):
Elsevier BV
Tags:
Medicine; Neuroscience; Perceptual Organization; Visual Perception; Workload Capacity; Psychology; Social and Behavioral Sciences; Perceptual Organization; Visual Perception; Workload Capacity
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article description
While there is widespread agreement among vision researchers on the importance of some local aspects of visual stimuli, such as hue and intensity, there is no general consensus on a full set of basic sources of information used in perceptual tasks or how they are processed. Gestalt theories place particular value on emergent features, which are based on the higher-order relationships among elements of a stimulus rather than local properties. Thus, arbitrating between different accounts of features is an important step in arbitrating between local and Gestalt theories of perception in general. In this paper, we present the capacity coefficient from Systems Factorial Technology (SFT) as a quantitative approach for formalizing and rigorously testing predictions made by local and Gestalt theories of features. As a simple, easily controlled domain for testing this approach, we focus on the local feature of location and the emergent features of Orientation and Proximity in a pair of dots. We introduce a redundant-target change detection task to compare our capacity measure on (1) trials where the configuration of the dots changed along with their location against (2) trials where the amount of local location change was exactly the same, but there was no change in the configuration. Our results, in conjunction with our modeling tools, favor the Gestalt account of emergent features. We conclude by suggesting several candidate information-processing models that incorporate emergent features, which follow from our approach.