Josh McDermott continues MIT's course 9.35, Perception, with a lecture on how the visual system recognizes objects and faces, then turns to texture. He lays out the cues and computations thought to underlie texture perception in human vision and compares them with how convolutional neural networks represent and classify texture. The lecture builds on prior sessions on object recognition, extending the discussion of what distinguishes reliable recognition of faces and objects from other visual tasks. Expect discussion of neural network models as working analogies for biological vision, with McDermott pointing out where the computational models match human perceptual behavior and where they diverge. As part of MIT OpenCourseWare's Spring 2024 offering of 9.35, the lecture assumes some prior exposure to the course's framework for studying perception through both psychophysics and computational modeling.