Dataset Information

Beauty and the beholder: highly individual taste for abstract, but not real-world images.

ABSTRACT: How individual are visual preferences? For real-world scenes, there is high agreement in observer's preference ratings. This could be driven by visual attributes of the images but also by non-visual associations, since those are common to most individuals. To investigate this, we developed a set of novel abstract, visually diverse images. At the individual observer level both abstract and real-world images yielded robust and consistent visual preferences, and yet abstract images yielded much lower across observer agreement in preferences than did real-world images. This suggests that visual preferences are typically driven by the semantic content of stimuli, and that shared semantic interpretations then lead to shared preferences. Further experiments showed that highly individual preferences can nevertheless emerge also for real-world scenes, in contexts which de-emphasize their semantic associations.

SUBMITTER: Vessel EA

PROVIDER: S-EPMC3662030 | biostudies-literature | 2010 Feb

REPOSITORIES: biostudies-literature

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Beauty and the beholder: highly individual taste for abstract, but not real-world images.

Vessel Edward A EA Rubin Nava N

Journal of vision 20100222 2

How individual are visual preferences? For real-world scenes, there is high agreement in observer's preference ratings. This could be driven by visual attributes of the images but also by non-visual associations, since those are common to most individuals. To investigate this, we developed a set of novel abstract, visually diverse images. At the individual observer level both abstract and real-world images yielded robust and consistent visual preferences, and yet abstract images yielded much low ...[more]

PMID: 20462319

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Project description:Many medical professions require practitioners to perform visual categorizations in domains such as radiology, dermatology, and neurology. However, acquiring visual expertise is tedious and time-consuming and the perceptual strategies mediating visual categorization skills are poorly understood. In this paper, the Ease algorithm was developed to predict an item's categorization difficulty (Ease value) based on the item's perceptual similarity to all within-category items versus between-category items in the dataset. In this study, Ease values were used to construct an easy-to-hard and hard-to-easy training schedule for teaching melanoma diagnosis. Whereas previous visual training studies suggest that an easy-to-hard schedule benefits learning outcomes, no studies to date have demonstrated the easy-to-hard advantage with complex, real-world images. In our study, 237 melanoma and benign images were collected for training and testing purposes. The diagnostic accuracy of images was verified by an expert dermatologist. Based on their Ease values, the items were grouped into easy, medium, and hard categories, each containing an equal number of melanoma and benign lesions. During training, participants categorized images of skin lesions as either benign or melanoma and were given corrective feedback after each trial. In the easy-to-hard training condition, participants learned to categorize all the easy items first, followed by the medium items, and finally the hard items. Participants in the hard-to-easy training condition learned items in the reverse order. Post-training results showed that training in both conditions transferred to the classification of new melanoma and benign images. Participants in the easy-to-hard condition showed modest advantages both in the acquisition and retention of the melanoma diagnosis skills, but neither scheduling condition exhibited a gross advantage. The Ease values of the items predicted categorization accuracy after, but not before training, suggesting that the Ease algorithm is a promising tool for optimizing medical training in visual categorization.

Dataset Information

Beauty and the beholder: highly individual taste for abstract, but not real-world images.

Publications

Beauty and the beholder: highly individual taste for abstract, but not real-world images.

Similar Datasets

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