Perception and reality: Why a wholly empirical paradigm is needed to understand vision

Dale Purves, Yaniv Morgenstern, William T. Wojtach

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

A central puzzle in vision science is how perceptions that are routinely at odds with physical measurements of real world properties can arise from neural responses that nonetheless lead to effective behaviors. Here we argue that the solution depends on: (1) rejecting the assumption that the goal of vision is to recover, however imperfectly, properties of the world; and (2) replacing it with a paradigm in which perceptions reflect biological utility based on past experience rather than objective features of the environment. Present evidence is consistent with the conclusion that conceiving vision in wholly empirical terms provides a plausible way to understand what we see and why.

Original languageEnglish (US)
Article number156
JournalFrontiers in Systems Neuroscience
Volume9
Issue numberNovember
DOIs
StatePublished - Nov 18 2015

Keywords

  • Bayesian probability
  • Efficient coding
  • Empirical ranking
  • Feature detection
  • Vision
  • Visual perception

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