Ideal Observer Theory

D. Kersten, P. Mamassian

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Scopus citations


Ideal observer models are applications of Bayesian statistical decision theory to problems of neural information transduction, transmission, and utilization. A basic motivation is that, because sensory inputs provide noisy or ambiguous information about states of the world, probabilistic methods are required to understand how reliable decisions can be made. Thus, the focus is first on modeling the information for a task, independent of the observer under study, and second on comparisons of that model with a test observer, such as a human or neuron. A key rationale for such comparisons is that the ideal observer can be used to normalize performance for task difficulty. An ideal observer can also provide a starting point for modeling perceptual performance.

Original languageEnglish (US)
Title of host publicationEncyclopedia of Neuroscience
PublisherElsevier Ltd
Number of pages7
ISBN (Print)9780080450469
StatePublished - 2009

Bibliographical note

Copyright 2020 Elsevier B.V., All rights reserved.


  • Bayesian theoryIdeal observerPerceptionPsychophysicsSensationSignal detection theory


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