One can infer an artist's identity from his or her artworks, but little is known about the neural representation of such elusive categorization. Here, we constructed a ‘neural art appraiser’ based on machine-learning methods that predicted the painter from the functional MRI activity pattern elicited by a painting. We found that Dali's and Picasso's artworks could be accurately classified based on brain activity alone, and that broadly distributed brain activity contributed to the neural prediction. Our approach provides a new means to probe into complex neural processes underlying art experiences.
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aComprehensive Human Sciences, University of Tsukuba, Ibaraki
bAIST Neuroscience Research Institute, Ibaraki
cATR Computational Neuroscience Laboratories, Kyoto, Japan
Correspondence to Dr Yukiyasu Kamitani, PhD, ATR Computational Neuroscience Laboratories, 2-2-2 Hikaridai, Keihanna Science City, Kyoto 619-0288, Japan
Tel: +81 774 95 1212; fax: +81 774 95 1259; e-mail: firstname.lastname@example.org
Hiromi Yamamura and Yasuhito Sawahata contributed equally to this work
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Received 21 August 2009 accepted 15 September 2009