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Vision is widely understood as an inference problem. However, two contrastingconceptions of the inference process have each been influential in research onbiological vision as well as the engineering of machine vision. The firstemphasizes bottom-up signal flow, describing vision as a largely feedforward,discriminative inference process that filters and transforms the visualinformation to remove irrelevant variation and represent behaviorally relevantinformation in a format suitable for downstream functions of cognition andbehavioral control. In this conception, vision is driven by the sensory data,and perception is direct because the processing proceeds from the data to thelatent variables of interest. The notion of "inference" in this conception isthat of the engineering literature on neural networks, where feedforwardconvolutional neural networks processing images are said to perform inference.The alternative conception is that of vision as an inference process inHelmholtz's sense, where the sensory evidence is evaluated in the context of agenerative model of the causal processes giving rise to it. In this conception,vision inverts a generative model through an interrogation of the evidence in aprocess often thought to involve top-down predictions of sensory data toevaluate the likelihood of alternative hypotheses. The authors includescientists rooted in roughly equal numbers in each of the conceptions andmotivated to overcome what might be a false dichotomy between them and engagethe other perspective in the realm of theory and experiment. The primate brainemploys an unknown algorithm that may combine the advantages of bothconceptions. We explain and clarify the terminology, review the key empiricalevidence, and propose an empirical research program that transcends thedichotomy and sets the stage for revealing the mysterious hybrid algorithm ofprimate vision.

How does the primate brain combine generative and discriminative computations in vision?