Statistical Learning in Vision

József Fiser, Gábor Lengyel

Research output: Contribution to journalReview Articlepeer-review

Abstract (may include machine translation)

Vision and learning have long been considered to be two areas of research linked only distantly. However, recent developments in vision research have changed the conceptual definition of vision from a signal-evaluating process to a goal-oriented interpreting process, and this shift binds learning, together with the resulting internal representations, intimately to vision. In this review, we consider various types of learning (perceptual, statistical, and rule/abstract) associated with vision in the past decades and argue that they represent differently specialized versions of the fundamental learning process, which must be captured in its entirety when applied to complex visual processes. We show why the generalized version of statistical learning can provide the appropriate setup for such a unified treatment of learning in vision, what computational framework best accommodates this kind of statistical learning, and what plausible neural scheme could feasibly implement this framework. Finally, we list the challenges that the field of statistical learning faces in fulfilling the promise of being the right vehicle for advancing our understanding of vision in its entirety.

Original languageEnglish
Pages (from-to)265-290
Number of pages26
JournalAnnual Review of Vision Science
Volume8
DOIs
StatePublished - 15 Sep 2022

Keywords

  • hierarchical Bayesian modeling
  • perceptual learning
  • probabilistic computation
  • rule learning
  • statistical learning
  • structure learning

Fingerprint

Dive into the research topics of 'Statistical Learning in Vision'. Together they form a unique fingerprint.

Cite this