Bayesian hierarchical models and prior elicitation for fitting psychometric functions.

ABSTRACT: Our previous articles demonstrated how to analyze psychophysical data from a group of participants using generalized linear mixed models (GLMM) and two-level methods. The aim of this article is to revisit hierarchical models in a Bayesian framework. Bayesian models have been previously discussed for the analysis of psychometric functions although this approach is still seldom applied. The main advantage of using Bayesian models is that if the prior is informative, the uncertainty of the parameters is reduced through the combination of prior knowledge and the experimental data. Here, we evaluate uncertainties between and within participants through posterior distributions. To demonstrate the Bayesian approach, we re-analyzed data from two of our previous studies on the tactile discriminatio

SUBMITTER: Mezzetti M 

PROVIDER: S-EPMC10018033 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature