TABLE III.

Hierarchical GLMM: To reach the appropriate complexity of model, we first modeled correct/incorrect answers as a function of each mouse as a fixed effect (row 1), then added the generalization type (as in Fig. 2) as a fixed effect (row 2), and finally modeled generalization type as a fixed effect nested within each mouse as a random effect (row 3). Since the final model had the best fit, it was used in all reported analyses related to the GLMM.

	DF	χ2	$D{F}_{{\chi }^2}$	Pr(>χ2)
Mouse	2
Mouse + Type	6	2534.46	4	≪ 0.001
Type | Mouse	20	407.22	14	≪ 0.001