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Ana Torralbo, Barry Chai, Eamon Caddigan, Dirk Walther, Diane Beck, Li Fei-Fei; Categorization of good and bad examples of natural scene categories. Journal of Vision 2009;9(8):940. doi: 10.1167/9.8.940.
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© ARVO (1962-2015); The Authors (2016-present)
Despite the vast range of images that we might categorize as an example of a particular natural scene category (e.g., beach), human observers are able to quickly and efficiently categorize even briefly presented images of these scenes. However, within the range of images that we might categorize as a “beach”, for example, some will be more representative of that category than others. We asked whether participants' ability to categorize briefly presented scenes differed depending on whether the images were good or bad examples of the scene. 3000 images from six categories (beaches, city streets, forests, highways, mountains and offices) were first rated by naïve subjects as good or bad examples of those categories. On the basis of these ratings, 50 good and 50 bad images were chosen from each category to be used in a categorization experiment, in which a separate set of participants were asked to categorize the scenes by pressing one of six buttons. The images in this experiment were presented very briefly ([[lt]]100 ms), followed by a perceptual mask. Good and bad examples of all the categories were intermixed randomly. As predicted, participants categorized good examples of a category significantly faster and more accurately than bad examples, suggesting that part of what makes an image a good example of a category can be gleaned in very brief presentations. To further understand the neural basis of this categorization ability, in a follow-up fMRI experiment, we will ask whether a statistical pattern recognition algorithm trained to discriminate the distributed patterns of neural activity associated with our six scene categories might also show a decrement in classification accuracy for good versus bad examples of the category. By comparing classification accuracy within different brain regions (e.g., V1, PPA) we can ask which brain regions are most sensitive to this good/bad distinction.
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