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. Author manuscript; available in PMC: 2013 Dec 20.
Published in final edited form as: Neuron. 2012 Dec 20;76(6):1210–1224. doi: 10.1016/j.neuron.2012.10.014

Figure 1.

Figure 1

Schematic of the experiment and model. Subjects viewed two hours of natural movies while BOLD responses were measured using fMRI. Objects and actions in the movies were labeled using 1364 terms from the WordNet lexicon (Miller, 1995). The hierarchical is a relationships defined by WordNet were used to infer the presence of 341 higher- order categories, providing a total of 1705 distinct category labels. A regularized, linearized finite impulse response regression model was then estimated for each cortical voxel recorded in each subject's brain (Kay et al., 2008; Mitchell et al., 2008; Naselaris et al., 2009; Nishimoto et al., 2011). The resulting category model weights describe how various object and action categories influence BOLD signals recorded in each voxel. Categories with positive weights tend to increase BOLD, while those with negative weights tend to decrease BOLD. The response of a voxel to a particular scene is predicted as the sum of the weights for all categories in that scene.