Figure 3 .

Modeling procedures. (a) “Voxelwise modeling.” Voxelwise models were fit in individual subjects using passive-listening data. To account for hemodynamic response, a linearized 4-tap FIR filter spanning delayed effects at 2–8 s was used. Models were fit via L2-regularized linear regression. BOLD responses were predicted based on fit voxelwise models on held-out passive-listening data. Prediction scores were taken as the Pearson’s correlation between predicted and measured BOLD responses. For a given subject, speech-selective voxels were taken as the union of voxels significantly predicted by spectral, articulatory, or semantic models (q(FDR) < 10⁻⁵, t-test). (b) “Assessment of attentional modulation.” Passive-listening models for single voxels were tested on cocktail-party data to quantify attentional modulations in selectivity. In a given run, one of the speakers in a 2-speaker story was attended while the other speaker was ignored. Separate response predictions were obtained using the isolated story stimuli for the attended speaker and for the unattended speaker. Since a voxel can represent information from both attended and unattended stimuli, a linear combination of these predicted responses was considered with varying combination weights (w_c in [0 1]). BOLD responses were predicted based on each combination weight separately. Three separate prediction scores were calculated based on only the attended stimulus (w_c = 1), based on only the unattended stimulus (w_c = 0), and based on the optimal combination of the 2 stimuli. A model-specific attention index, () was then computed as the ratio of the difference in prediction scores for attended versus unattended stories to the prediction score for their optimal combination (see Materials and Methods).