How much to trust the senses: likelihood learning.
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 prior-likelihood 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.
Graduate School of Information Systems, University of Electro-Communications, Japan.
Yoshiyuki
Y
Sato
Departments of Physical Medicine and Rehabilitation, Physiology, and Applied Mathematics, Northwestern University, and Rehabilitation Institute of Chicago, IL, USA.
Konrad P
KP
Kording
D000328:Adult; D001499:Bayes Theorem; D056228:Feedback, Sensory / Q000502:physiology*; D005260:Female; D006801:Humans; D007858:Learning / Q000502:physiology*; D016013:Likelihood Functions; D008297:Male; D009048:Motor Skills / Q000502:physiology; D011597:Psychomotor Performance / Q000502:physiology*; D055815:Young Adult
Bayesian models; context-dependent learning; likelihood learning; sensorimotor integration