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Visual Diet versus Associative Learning as Mechanisms of Change in Body Size Preferences

Overview of attention for article published in PLOS ONE, November 2012
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Title
Visual Diet versus Associative Learning as Mechanisms of Change in Body Size Preferences
Published in
PLOS ONE, November 2012
DOI 10.1371/journal.pone.0048691
Pubmed ID
Authors

Lynda G. Boothroyd, Martin J. Tovée, Thomas V. Pollet

Abstract

Systematic differences between populations in their preferences for body size may arise as a result of an adaptive 'prepared learning' mechanism, whereby cues to health or status in the local population are internalized and affect body preferences. Alternatively, differences between populations may reflect their 'visual diet' as a cognitive byproduct of mere exposure. Here we test the relative importance of these two explanations for variation in body preferences. Two studies were conducted where female observers were exposed to pictures of high or low BMI women which were either aspirational (healthy, attractive models in high status clothes) or non-aspirational (eating disordered patients in grey leotards), or to combinations thereof, in order to manipulate their body-weight preferences which were tested at baseline and at post-test. Overall, results showed good support for visual diet effects (seeing a string of small or large bodies resulted in a change from pre- to post-test whether the bodies were aspirational or not) and also some support for the associative learning explanation (exposure to aspirational images of overweight women induced a towards preferring larger bodies, even when accompanied by equal exposure to lower weight bodies in the non-aspirational category). Thus, both influences may act in parallel.

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Geographical breakdown

Country Count As %
United Kingdom 1 1%
Czechia 1 1%
Australia 1 1%
Brazil 1 1%
Unknown 83 95%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 18 21%
Student > Master 17 20%
Researcher 12 14%
Student > Ph. D. Student 11 13%
Professor > Associate Professor 7 8%
Other 16 18%
Unknown 6 7%
Readers by discipline Count As %
Psychology 39 45%
Medicine and Dentistry 7 8%
Social Sciences 5 6%
Agricultural and Biological Sciences 4 5%
Sports and Recreations 3 3%
Other 16 18%
Unknown 13 15%