Statistically optimal perception and learning: from behavior to neural representations

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...

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Bibliographic Details
Published in:Trends in cognitive sciences 2010-03, Vol.14 (3), p.119-130
Main Authors: Fiser, József, Berkes, Pietro, Orbán, Gergő, Lengyel, Máté
Format: Article
Language:English
Subjects:
Animals
Biological and medical sciences
Cerebral Cortex
Fundamental and applied biological sciences. Psychology
Human
Humans
Learning
Learning. Memory
Models, Neurological
Models, Statistical
Neurology
Perception
Psychiatry
Psychology. Psychoanalysis. Psychiatry
Psychology. Psychophysiology
Vision
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Summary: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.
ISSN:1364-6613
1879-307X
DOI:10.1016/j.tics.2010.01.003