Humans can effortlessly identify target auditory objects during natural listening and shift their focus between different targets. Unique allocation of brain resources would be inefficient for semantic search task. Here, we hypothesize that auditory attention shifts tuning of cortical voxels toward target category and that attention expands the representation of target words while compressing the representation of behaviorally irrelevant words across cortex. To test, we designed an fMRI experiment with a semantic search task. Subjects listened to natural stories twice while searching for words that are semantically related to either humans' or places'. Fit voxelwise models for two attention tasks were compared to identify semantic tuning shifts in single voxels. Results indicate that attention shifts semantic tuning of single voxels broadly across cortex and attention warps language representation in favor of target words across cortex. We also introduced a novel feature regularization in voxelwise modeling for a naturalistic movie experiment. Feature regularization simply enforces similar model weights over semantically related stimulus features. We tested the proposed method on an fMRI experiment with naturalistic movies. Results suggest that the proposed method offer improved sensitivity in modeling of single voxels. Moreover, we proposed a novel method to improve the sensitivity of phase-sensitive fatwater separation in balanced steady-state free precession (bSSFP) acquisitions. In bSSFP applications using phased-array coils, reconstructed images suffer a lot from spatial sensitivity variations within individual coils. To improve, we first performed region-growing phase correction in individual coil images, then used a linear combination of phase-corrected images. Tests on SSFP angiograms of the thigh, lower leg, and foot suggest that the proposed method enhances fat{water separation in phased-array acquisitions with improved phase estimates.