Mixed effects modeling of Morris water maze data revisited: Bayesian censored regression


Young, Clark, Goffus, and Hoane (Learning and Motivation, 40(2), 160–177, 2009) documented significant advantages of linear and nonlinear mixed-effects modeling in the analysis of Morris water maze data. However, they also noted a caution regarding the impact of the common practice of ending a trial when the rat had not reached the platform by a preestablished deadline. The present study revisits their conclusions by considering a new approach that involves multilevel (i.e., mixed effects) censored generalized linear regression using Bayesian analysis. A censored regression explicitly models the censoring created by prematurely ending a trial, and the use of generalized linear regression incorporates the skewed distribution of latency data as well as the nonlinear relationships this can produce. This approach is contrasted with a standard multilevel linear and nonlinear regression using two case studies. The censored generalized linear regression better models the observed relationships, but the linear regression created mixed results and clearly resulted in model misspecification.

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Research reported in this publication was supported by the Cognitive and Neurobiological Approaches to Plasticity (CNAP) Center of Biomedical Research Excellence (COBRE) of the National Institutes of Health under Grant Number P20GM113109.

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Correspondence to Michael E. Young.

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Young, M.E., Hoane, M.R. Mixed effects modeling of Morris water maze data revisited: Bayesian censored regression. Learn Behav (2021). https://doi.org/10.3758/s13420-020-00457-y

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  • Memory
  • Morris water maze
  • Data analysis
  • Censored regression
  • Bayesian analysis