How to fit models of recognition memory data using maximum likelihood.

Abstract

The aim of this paper is to provide an introductory tutorial to how to fit different models of recognition memory using maximum likelihood estimation. It is in four main parts. The first part describes how recognition memory data is collected and analysed. The second part introduces four current models that will be fitted to the data. The third part describes in detail how a model is fit using maximum likelihood estimation. The fourth part examines how the fit of a model can be evaluated and the appropriate statistical test applied.

Keywords

• Recognition memory
• maximum likelihood estimation
• signal detection theory
• mixture models
• high threshold models