What do models of visual perception tell us about visual phenomenology?

Abstract

Computational models of visual processing aim to provide a compact, explanatory account of the complex neural processes that underlie visual perception and behavior. But what, if anything, do current modeling approaches say about how conscious visual experience arises from neural processing? Here, we introduce the reader to four commonly used models for understanding visual computations, neural activity, and behavior: signal detection theory, drift diffusion, probabilistic population codes, and sampling. In an attempt to bridge these modeling approaches with experimental and philosophical work on the neural basis of conscious visual perception, we lay out possible relationships between the components of the models and the contents of phenomenal visual experience. We find no unique relation between model components and phenomenal experience in any model; rather, there are multiple logically possible mappings from models to experience. Going forward, we suggest that there are scientific opportunities to develop models that predict and explain a variety of subjective reports and philosophical opportunities to consider what aspects of phenomenal experience are promising scientific targets.