This regressor was added in order

to explain away spurious correlation between responses in early visual cortex and some categories. Total motion energy was computed as the mean output of a set of 2,139 motion energy filters (Nishimoto et al., 2011), in which each filter consisted of a quadrature pair of space-time Gabor filters (Adelson and Bergen, 1985; Watson and Ahumada, 1985). The motion energy filters tile the image space with a variety of preferred spacial frequencies, orientations, and temporal frequencies. The total motion energy regressor explained much of the response variance in early visual cortex (mainly V1 and V2). This had the desired effect LY2157299 in vitro of explaining away correlations between responses in early visual cortex and categories that feature full-field motion (e.g., “fire” and “snow”). The total motion energy regressor was used to fit the category model but was

not included in the model predictions. The category model was fit to each voxel individually. A set of linear temporal filters was used to model the slow hemodynamic response inherent in the BOLD signal (Nishimoto et al., 2011). To capture the hemodynamic delay, we used concatenated stimulus vectors that had been delayed by two, three, and four samples (4, 6, and 8 s). For example, one stimulus vector indicates the presence of “wolf” 4 s earlier, another the presence of “wolf” 6 s see more earlier, and a third the presence of “wolf” 8 s earlier. Taking the dot product of Oxymatrine this delayed stimulus with a set of linear weights is functionally equivalent to convolution of the original stimulus vector with a linear temporal kernel that has nonzero entries for 4, 6, and 8 s delays.

For details about the regularized regression procedure, model testing, and correction for noise in the validation set, please see the Supplemental Experimental Procedures. All model fitting and analysis was performed using custom software written in Python, which made heavy use of the NumPy (Oliphant, 2006) and SciPy (Jones et al., 2001) libraries. In the semantic category model used here, each category entails the presence of its superordinate categories in the WordNet hierarchy. For example, “wolf” entails the presence of “canine,” “carnivore,” etc. Because these categories must be present in the stimulus if “wolf” is present, the model weight for “wolf” alone does not accurately reflect the model’s predicted response to a stimulus containing only a “wolf.” Instead, the predicted response to “wolf” is the sum of the weights for “wolf,” “canine,” “carnivore,” etc. Thus, to determine the predicted response of a voxel to a given category, we added together the weights for that category and all categories that it entails. This procedure is equivalent to simulating the response of a voxel to a stimulus labeled only with “wolf.