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Autonomous navigation systems have yet to reach the advanced capabilities of the human brain. Further understanding of the brain, through neuroscience, has the potential to facilitate the improvement of modern artificial intelligence systems. Neuroscience is an emergent field that takes a principled approach for investigating the brain. This thesis weaves three projects together into a narrative that follows the pipeline of the voxelwise modeling framework. Voxelwise modeling is a modern, data science-inspired approach for fMRI brain encoding (and, consequently, decoding in the reverse direction) within naturalistic environments. We detail our efforts to customize a driving simulator, CARLA (Unreal Engine 4), for brain decoding/encoding stimuli. Next, we propose and verify a pipeline for non-linearly transforming stimuli into semantic features. Finally, we explore fitting voxelwise encoding models, with multiple feature spaces, to find cortical representations of timescale selectivity.

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