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• W2589969605 abstract "Spotting Differences: How Qualitative Asymmetries Influence Visual Search Rudolph L. Mappus IV (cmappus@cc.gatech.edu) Ronald W. Ferguson (rwf@cc.gatech.edu) Kenneth Czechowski (kentcz@cc.gatech.edu) College of Computing, Georgia Institute of Technology Atlanta, GA 30332 USA Paul M. Corballis (paul.corballis@psych.gatech.edu) Department of Psychology, Georgia Institute of Technology Atlanta, GA 30332 Abstract While our current understanding of symmetry perception is based on the perception of exact symmetry, there is increasing evidence that humans are sensitive to qualitative symmetry, which is based on a figure’s pattern of similar alignable features rather than its geometric invariance about an axis. Previous research on alignment-based models of symmetry perception found evidence that qualitative differences (which break the pattern of alignment in otherwise symmetric figures) disproportionately improve the overall speed and accuracy of symmetry judgments. In this experiment, we examine whether qualitative differences affect the earliest stage of symmetry detection by examining their effect on visual search. There are two central results. First, qualitative differences reduce fixations in visual search. Participants spend less time and fewer fixations on qualitative differences than other differences. This suggests an early role for alignment in symmetry detection. Second, participants are significantly more accurate at judging symmetry of figures with qualitative differences than other differences. This result replicates Ferguson, Aminoff & Gentner (1996) while generalizing that result to stimuli with different fill characteristics displayed both foveally and parafoveally. Introduction Symmetry is a basic quality of many objects in the visual environment, playing a role in perceptual organization and figure reconstruction (Wagemans, 1995). The form of symmetry we perceive is usually understood to be exact or quantitative symmetry, where (for mirror symmetric figures) quantities such as angle and length are identical on both sides of an axis. Understanding symmetry as exact symmetry has lead to simple but useful models of symmetry detection based on the transformational invariance of a figure. Yet as useful as these models are, they fall short when applied to approximate or qualitative symmetry, which is problematic given that many real-world objects (such as human figures) display approximate symmetry. The MAGI model of regularity detection (Ferguson, 1994, 2001) accounts for qualitative symmetry detection by modeling it as a mapping process that aligns similar qualitative relations and features (such as line intersections and boundary concavities) using a structure mapping process like that used to model similarity and analogical comparison (Gentner, 1983). While MAGI handles exact symmetry like transformational invariance, MAGI also readily detects qualitative symmetry, finding the axis and corresponding parts of near-symmetric figures in a way that appears to approximate human performance. MAGI’s performance on qualitative symmetry leads to a testable psychological prediction: that there are two different classes of asymmetry (Figure 1) caused by two difference types. Qualitative deviations from symmetry, which change the set of qualitative features, may block MAGI’s alignment process, allowing quick classification of the figure as asymmetric. In contrast, quantitative deviations from symmetry, which break exact symmetry but preserve alignable qualitative features, may initially fool the alignment process, requiring additional scrutiny to detect the asymmetry. Thus, humans should judge figures with qualitative differences faster or more accurately than figures with quantitative differences. We can make this prediction more concrete by considering polygons as our stimuli. If we consider the vertices of a non-uniform polygon, each vertex (feature) has a concavity characteristic (being concave, or convex). Corresponding features match if they match in their qualitative concavity and quantitative value. A polygon contains a quantitative difference when two corresponding Figure 1: Polygon demonstrating quantitative differences. qualitative and" @default.
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• W2589969605 date "2005-01-01" @default.
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• W2589969605 title "Spotting Differences: How Qualitative Asymmetries Influence Visual Search" @default.
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