Bayesian decision theory in sensorimotor control

Konrad P. Körding*, Daniel M. Wolpert

*Corresponding author for this work

Research output: Contribution to journalReview article

422 Scopus citations

Abstract

Action selection is a fundamental decision process for us, and depends on the state of both our body and the environment. Because signals in our sensory and motor systems are corrupted by variability or noise, the nervous system needs to estimate these states. To select an optimal action these state estimates need to be combined with knowledge of the potential costs or rewards of different action outcomes. We review recent studies that have investigated the mechanisms used by the nervous system to solve such estimation and decision problems, which show that human behaviour is close to that predicted by Bayesian Decision Theory. This theory defines optimal behaviour in a world characterized by uncertainty, and provides a coherent way of describing sensorimotor processes.

Original languageEnglish (US)
Pages (from-to)319-326
Number of pages8
JournalTrends in Cognitive Sciences
Volume10
Issue number7
DOIs
StatePublished - Jul 1 2006

ASJC Scopus subject areas

  • Neuropsychology and Physiological Psychology
  • Experimental and Cognitive Psychology
  • Cognitive Neuroscience

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