2017
DOI: 10.1177/2041669517693023
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Curve Appeal: Exploring Individual Differences in Preference for Curved Versus Angular Objects

Abstract: A preference for smooth curvature, as opposed to angularity, is a well-established finding for lines, two-dimensional shapes, and complex objects, but little is known about individual differences. We used two-dimensional black-and-white shapes—randomly generated irregular polygons, and arrays of circles and hexagons—and measured many individual differences, including artistic expertise, personality, and cognitive style. As expected, people preferred curved over angular stimuli, and people’s degree of curvature… Show more

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Cited by 77 publications
(115 citation statements)
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“…We predicted that when the task is to judge the beauty of spaces, contour would have a greater effect on judgments in experts than nonexperts (H1). This would extend the findings of Cotter et al (2017) to the domain of architecture and design by demonstrating greater sensitivity to contour among experts when judging beauty. In contrast to beauty judgments, however, we did not have a strong a priori prediction regarding how expertise would moderate willingness to enter or exit spaces.…”
Section: Present Studymentioning
confidence: 62%
See 1 more Smart Citation
“…We predicted that when the task is to judge the beauty of spaces, contour would have a greater effect on judgments in experts than nonexperts (H1). This would extend the findings of Cotter et al (2017) to the domain of architecture and design by demonstrating greater sensitivity to contour among experts when judging beauty. In contrast to beauty judgments, however, we did not have a strong a priori prediction regarding how expertise would moderate willingness to enter or exit spaces.…”
Section: Present Studymentioning
confidence: 62%
“…Second, in the present study we opted to use dichotomous scales to collect data on beauty judgments and approach-avoidance decisions. Past research in this area has employed a wide variety of response options, including dichotomous scales (e.g., Vartanian et al, 2013) as well as scales with more gradations (Cotter et al, 2017). Arguably, more fine-grained responses could be collected based on scales that offer more than two options, although the effect that a different scale might have on the pattern of results observed here would have to be examined empirically in the future.…”
Section: Discussionmentioning
confidence: 99%
“…Following the reductionist approach of science, the experiments typically analyze the perception of one isolated stimulus. In experimental aesthetics, for instance, the prototypical study shows one image at a time and observers report how beautiful or pleasing they find it (e.g., Cotter, Silvia, Bertamini, Palumbo, & Vartanian, 2017;Graf & Landwehr, 2017;Marin & Leder, 2016;Nakamura & Kawabata, 2015;Savazzi et al, 2014). In everyday life, however, we enjoy the pleasantness of any kind of object (e.g., the attractiveness of a face, the taste of popcorn, the sound of music) in the context of others (e.g., other faces in a crowd, the movie theater, street noise).…”
Section: Introductionmentioning
confidence: 99%
“…This confirmation is, in itself, a meaningful finding. With very few exceptions (Cotter et al, 2017;Gartus & Leder, 2013Silvia, 2007), the effects of these features on liking have previously been analyzed using ANOVAs or t-tests. We have confirmed that these effects hold when data are analyzed using linear mixed effects models, that is to say, when within-and between-participants variations are accounted for.…”
Section: Discussionmentioning
confidence: 99%
“…First, they have been extensively studied in empirical aesthetics (e.g. : Berlyne, 1971;Bertamini et al, 2016;Cotter et al, 2017;Gartus & Leder, 2013 Puerto et al, 2015;Jacobsen & Höfel, 2002;Leder et al, 2019;Wilson & Chatterjee, 2005). Second, researchers have developed well-tested stimuli sets to study the effects of these features on aesthetic valuation (Bertamini et al, 2016;Jacobsen & Höfel, 2002;Wilson & Chatterjee, 2005).…”
Section: Table 1 Differences Between Eysenck and Corradi And Colleagmentioning
confidence: 99%