How much to trust the senses: Likelihood learning

Yoshiyuki Sato*, Konrad P. Kording

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

Our brain often needs to estimate unknown variables from imperfect information. Our knowledge about the statistical distributions of quantities in our environment (called priors) and currently available information from sensory inputs (called likelihood) are the basis of all Bayesian models of perception and action. While we know that priors are learned, most studies of priorlikelihood integration simply assume that subjects know about the likelihood. However, as the quality of sensory inputs change over time, we also need to learn about new likelihoods. Here, we show that human subjects readily learn the distribution of visual cues (likelihood function) in a way that can be predicted by models of statistically optimal learning. Using a likelihood that depended on color context, we found that a learned likelihood generalized to new priors. Thus, we conclude that subjects learn about likelihood.

Original languageEnglish (US)
Article number13
JournalJournal of Vision
Volume14
Issue number13
DOIs
StatePublished - 2014

Keywords

  • Bayesian models
  • Context-dependent learning
  • Likelihood learning
  • Sensorimotor integration

ASJC Scopus subject areas

  • Ophthalmology
  • Sensory Systems

Fingerprint Dive into the research topics of 'How much to trust the senses: Likelihood learning'. Together they form a unique fingerprint.

Cite this