An ideal vision model accounts for behavior and neurophysiology in both naturalistic conditions and designed lab experiments. Unlike psychological theories, artificial neural networks (ANNs) actually perform visual tasks and generate testable predictions for arbitrary inputs. These advantages enable ANNs to engage the entire spectrum of the evidence. Failures of particular models drive progress in a vibrant ANN research program of human vision.

Many systems neuroscientists want to understand neurons in terms of mediation; we want to understand how neurons are involved in the causal chain from stimulus to behavior. Unfortunately, most tools are inappropriate for that while our language takes mediation for granted. Here we discuss the contrast between our conceptual drive toward mediation and the difficulty of obtaining meaningful evidence.

Rahnev & Denison (R&D) argue that human behavior is often described as “optimal,” despite many previous findings of suboptimality. We address how the literature handles these concepts and discuss our own findings on suboptimality. Although we agree that the field should embrace the “systematic weirdness of human behavior” (sect. 6, para. 1), this does not detract from the value of the Bayesian approach.