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Eamon Caddigan, Dirk Walther, Justas Birgiolas, Jonathan Weissman, Diane Beck, Li Fei-Fei; Decoding distributed patterns of activity associated with natural scene categorization. Journal of Vision 2007;7(9):765. doi: 10.1167/7.9.765.
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© ARVO (1962-2015); The Authors (2016-present)
Human observers are able to quickly and efficiently perceive the content of natural scenes (Potter, 1976). Previous studies have examined the time course of this rapid classification (Thorpe et al, 1996) as well as the brain regions activated when subjects categorize natural scenes (Epstein & Higgins, 2006). Using statistical pattern recognition algorithms similar to those employed by Cox and Savoy (2002) to decode the neural states associated with object categories, we asked whether we can identify and discriminate distributed patterns of fMRI activity associated with particular natural scene categories (beaches, mountains, forests, tall buildings, highways, and industrial scenes). fMRI data was aquired while subjects viewed 100 images from each of six categories, in 6 blocks of 10 images each of the same category, organized into 10 runs. A subset of the voxels was selected via traditional univariate statistics comparing stimulus presentation versus blank screen conditions. We tested several pattern recognition algorithms (e.g. Support Vector Machines, Gaussian Naive Bayes, etc.) in a leave-one-run-out procedure on the selected voxels and found that all algorithms predict the natural scene category seen by the subject well above chance. Furthermore, prediction accuracy was still well above chance when retinotopic cortex was excluded from the analysis, suggesting that this multi-voxel analysis does not rely solely on differences in simple visual features or differences in the retinotopic representation of the stimuli.
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