title: Modeling psychophysical data at the population-level: the generalized linear mixed model.
abstract: In  psychophysics, researchers usually apply a two-level model for the analysis of the behavior of the single subject and the population. This classical model has two main disadvantages. First, the second level of the analysis discards information on trial repetitions and subject-specific variability. Second, the model does not easily allow assessing the goodness of fit. As an alternative to this classical approach, here we propose the Generalized Linear Mixed Model (GLMM). The GLMM separately estimates the variability of fixed and random effects, it has a higher statistical power, and it allows an easier assessment of the goodness of fit compared with the classical two-level model. GLMMs have been frequently used in many disciplines since the 1990s; however, they have been rarely applied in psychophysics. Furthermore, to our knowledge, the issue of estimating the point-of-subjective-equivalence (PSE) within the GLMM framework has never been addressed. Therefore the article has two purposes: It provides a brief introduction to the usage of the GLMM in psychophysics, and it evaluates two different methods to estimate the PSE and its variability within the GLMM framework. We compare the performance of the GLMM and the classical two-level model on published experimental data and simulated data. We report that the estimated values of the parameters were similar between the two models and Type I errors were below the confidence level in both models. However, the GLMM has a higher statistical power than the two-level model. Moreover, one can easily compare the fit of different GLMMs according to different criteria. In conclusion, we argue that the GLMM can be a useful method in psychophysics.
authors.affiliation: Center of Space BioMedicine, University of Rome "Tor Vergata," Rome, Italy. alessandro.moscatelli@uni-bielefeld.de
authors.forename: Alessandro
authors.initials: A
authors.lastname: Moscatelli
authors.forename: Maura
authors.initials: M
authors.lastname: Mezzetti
authors.forename: Francesco
authors.initials: F
authors.lastname: Lacquaniti
mesh_terms: D006801:Humans; D016014:Linear Models; D009039:Motion Perception / Q000502:physiology*; D010775:Photic Stimulation; D011601:Psychophysics / Q000379:methods*; D013647:Task Performance and Analysis