Statistically optimal perception and learning: from behavior to neural representations

József Fiser, Pietro Berkes, Gergo Orbán, Máté Lengyel

Research output: Contribution to journalReview Articlepeer-review

Abstract (may include machine translation)

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.

Original languageEnglish
Pages (from-to)119-130
Number of pages12
JournalTrends in Cognitive Sciences
Volume14
Issue number3
DOIs
StatePublished - Mar 2010
Externally publishedYes

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