Statistically optimal perception and learning: from behavior to neural representations

Trends Cogn Sci. 2010 Mar;14(3):119-30. doi: 10.1016/j.tics.2010.01.003. Epub 2010 Feb 12.


Human perception has recently been characterized as statistical inference based on noisy and ambiguous sensory inputs. Moreover, suitable neural representations of uncertainty have been identified that could underlie such probabilistic computations. In this review, we argue that learning an internal model of the sensory environment is another key aspect of the same statistical inference procedure and thus perception and learning need to be treated jointly. We review evidence for statistically optimal learning in humans and animals, and re-evaluate possible neural representations of uncertainty based on their potential to support statistically optimal learning. We propose that spontaneous activity can have a functional role in such representations leading to a new, sampling-based, framework of how the cortex represents information and uncertainty.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Animals
  • Cerebral Cortex*
  • Humans
  • Learning*
  • Models, Neurological
  • Models, Statistical*
  • Perception*