generalised logistic linear mixed-effects models fitglme  (MathWorks Inc)

Journal: Translational Vision Science & Technology

Article Title: Comparing Objective Perimetry, Matrix Perimetry, and Regional Retinal Thickness in Mild Diabetic Macular Edema

doi: 10.1167/tvst.10.13.32

Figure Lengend Snippet: Mixed-Effects Logistic Regression Model Summary

Article Snippet: We fitted a generalized linear mixed-effects logistic regression model ( fitglme in MATLAB) to account for the effects of multiple comparisons, eyes, and repeats being nested within participants.

Techniques:

Journal: Nature Communications

Article Title: Retrospective model-based inference guides model-free credit assignment

doi: 10.1038/s41467-019-08662-8

Figure Lengend Snippet: MF and MB contributions to performance. a In showing a contribution of the MF system, we analysed only standard trials that followed a standard trial and that offered for choice the previously chosen object. For clarity, we represent objects by their associated pair of rooms; in the experiment participants saw the object images alone. Places for objects whose identity did not affect the analysis (and were marginalized over) are left empty. b The empirical probability of repeating a choice as a function of the previous-trial Common (“C”), and Unique (“U”) outcomes. The main effect of the Common outcome highlights an MF contribution to bandit choices. c Indeed, a pure MB model failed to predict this effect (our models are described in the computational modelling section), d but a pure MF-action model predicted it. e The full model predicted this effect. f In showing a contribution of the MB system, we analysed only standard trials that followed a standard trial and that excluded the previously chosen object. g The empirical main effect of the Common room on generalization probability. h – j The pure MB, pure MF and the full model, all predicted a positive effect for the Common room. k The empirical coefficients of the trial- n Common room’s outcome (unrewarded vs. rewarded; Rew), reward probability (Prob) and their interaction (Int) when choice generalization is regressed on these variables. The positive coefficient of the Common outcome highlights a MB contribution to bandit choices. l Indeed, a pure MB model predicted a positive coefficient for the Common reward, m whereas a pure MF-action model did not. n The full model predicted a positive coefficient for the Common outcome. Error bars correspond to SEM across-participants calculated separately in each condition ( n = 40). Dotted arrows indicate the main effect of focal interest. *,** and *** denote p < 0.05, p < 0 .01 and p < 0.001, respectively. When no asterisk appears, the effect of interest is non-significant ( p > 0.05). In g – j p -values were calculated based on paired-samples t -tests. In panels b – e , k – n p -values were calculated based on mixed effects logistic regression models. Dots in panels b , g correspond to individual participant results. Images adapted from the stimulus set of Kiani et al. 2007, ref.

Article Snippet: Our model-agnostic analyses were conducted using logistic mixed effect models (implemented with MATLAB’s function “fitglme”) with participants serving as random effects with a free covariance matrix.

Techniques:

Journal: Nature Communications

Article Title: Retrospective model-based inference guides model-free credit assignment

doi: 10.1038/s41467-019-08662-8

Figure Lengend Snippet: MF Learning for ghost-nominated and ghost-rejected objects on uncertainty trials. a The probability of repeating a choice, i.e., select the trial- n ghost-nominated object, as a function of the previous-trial non-informative, “N” (here green), and informative, inference-allowing, “I” (here brown) outcomes (bottom). Only “repetition” standard trials that offered for choice the previously ghost-nominated object alongside the object from the previously non-chosen pair, which shared the previously informative outcome with the ghost-nominated object, were analysed. The main effect of the Informative outcome implies that credit from this outcome was assigned by a MF learner to the ghost-nominated object. b Similar to a , but here, the probability to generalize the choice, i.e., select the ghost-rejected object is shown. Only “switch” standard trials that offered for choice the previously ghost-rejected object alongside an object from the previously unchosen pair (the one that shares an outcome with the ghost-rejected object) were analysed. The main effect of the Informative (brown) outcome implies that credit from this outcome was assigned by MF to the ghost-rejected object. c Similar to a but only “clash” standard trials that offered for choice the previously ghost-nominated and rejected objects, i.e., the previously chosen pair, were analysed. The main effect of the non-Informative outcome (green) implies that credit from this first outcome was assigned by MF mainly to the ghost-nominated object. Each standard trial after an uncertain trial was either a repeat, switch or a clash trial and hence contributed to exactly one of the panels. d Comparing the main effects from the analyses in a and b shows that credit from the second informative, inference-allowing, outcome was assigned by MF mainly to the ghost-nominated object. Error bars correspond to SEM across-participants calculated separately in each condition, n = 40. Dotted arrows indicate the main effect of interest. *,** and *** denote p < 0.05, p < 0.01 and p < 0.001, respectively. In case no asterisks are presented, the effect of interest was not significant. In a – c p -values were calculated based on a mixed effects logistic regression models. In d , p -values were calculated based on a paired-sample t -test. Dots represent individual participant results. Images adapted from the stimulus set of Kiani et al. 2007, ref.

Article Snippet: Our model-agnostic analyses were conducted using logistic mixed effect models (implemented with MATLAB’s function “fitglme”) with participants serving as random effects with a free covariance matrix.

Techniques: