selection process. The absence of a significant effect for a fixed predictor indicates that it has little effect on the predictive ability of the model. The absence of a significant effect for a predictor that is allowed to vary across the training groups indicates that the best-fitting predictors do not, in fact, differ significantly across training groups. The initial model always included generalization, and relational disparity, as g, r, predictors because our and data strongly suggested that both generalized others’ empirical associative strength and relational disparity play an important role in determining an organism’s behavior in the transposition task. We next established whether and produced the best fit g r when allowed to vary across training groups or when fixed. Finally, we added the fixed familiarity predictor, and if it improved the fit, then we allowed it to vary across conditions to f, find the best-fitting model. Model selection for Experiment 1: Small Circle condition Model 1: Two predictors (r and both vary g), AIC = -304.17 Gradient: (2, 199) = 0.12, = 0.89; no evidence that strength of varies across groups F p g Relational disparity: (2, 199) = 29.31, < 0.0001; significant difference in strength of F p r across groups Model 2: Two predictors, varies, is fixed r g AIC = -307.79 Gradient: (1, 201) = 44.15, < 0.0001; significantly improves explanatory power and F p should be retained Relational disparity: (2, 201) = 38.89, < 0.0001; same as above F p Model 3: Three predictors, varies, and are fixed r g f
selection process. The absence of a significant effect for a fixed predictor indicates that it has little effect on the predictive ability of the model. The absence of a significant effect for a predictor that is allowed to vary across the training groups indicates that the best-fitting predictors do not, in fact, differ significantly across training groups. The initial model always included generalization, g, and relational disparity, r, as predictors because our and others’ empirical data strongly suggested that both generalized associative strength and relational disparity play an important role in determining an organism’s behavior in the transposition task. We next established whether g and r produced the best fit when allowed to vary across training groups or when fixed. Finally, we added the fixed familiarity predictor, f, and if it improved the fit, then we allowed it to vary across conditions to find the best-fitting model. Model selection for Experiment 1: Small Circle condition Model 1: Two predictors (r and g), both vary AIC = -304.17 Gradient: F (2, 199) = 0.12, p = 0.89; no evidence that strength of g varies across groups Relational disparity: F (2, 199) = 29.31, p < 0.0001; significant difference in strength of r across groups Model 2: Two predictors, r varies, g is fixed AIC = -307.79 Gradient: F (1, 201) = 44.15, p < 0.0001; significantly improves explanatory power and should be retained Relational disparity: F (2, 201) = 38.89, p < 0.0001; same as above Model 3: Three predictors, r varies, g and f are fixed