For each monkey, a decision boundary was fit to across-sessions performance before inactivation or without microstimulation (white-filled circles), then the multiplicative (green) or additive scaling (cyan) parameter was systematically varied to compute a subset of hit and false alarm rate pairs accessible to the model; these are plotted in a space with false alarm rate on the abscissa and hit rate on the ordinate. Each green dot is a (false alarm rate, hit rate) pair resulting from multiplicative scaling by a value s M which varies over [0.01, 1.48], with |Δs M | = 0.03 between adjacent points. Each cyan dot is a (F, H) pair from additive scaling by s A : [-1.55, 0.75], |Δs A | = 0.05 For reference, colored text with arrows indicate s M (green) or s A (cyan) for individual points. Red dot (visible in middle two, and partially in right panel) indicates s M = 1 / s A = 0. Filled black circles indicate across-sessions performance during inactivation (left two plots) or with microstimulation (right two plots). For inactivation experiments, the predictions of the multiplicative and additive scaling models differ radically, with the additive scaling model predicting more strongly yoked changes in hit and false alarm rates than were observed in the data (left two plots). But over the range of performace changes observed in microstimulation experiments, the multiplicative and additive models make predictions that are difficult to disambiguate. These results also clarify why allowing the decision boundary to vary does not meaningfully improve the multiplicative scaling model’s performance: predictions with a constant boundary are quite accurate, so there is no need to invoke a change in decision boundary to explain the behavioral effects of perturbing SC activity. To compare models accounting for performance changes during inactivation or microstimulation, we used a 3 factor ANOVA: (1) monkey (1 or 2), (2) model (multiplicative scaling, additive scaling, or multiplicative scaling with variable boundary), and (3) perturbation condition (inactivation or microstimulation). Data were hit and false alarm rate errors (monkey – model) during inactivation or with microstimulation only. There were 185 degrees of freedom, and 172 error degrees of freedom. Only the “model” factor and “model:condition” interaction terms were significant (p << 0.01; model df = 2, F = 11.76, model:condition df = 2, F = 14.76). Post-hoc Tukey-Kramer testing with α = 0.05 showed that the additive model had significantly larger error than the multiplicative models for inactivation data; for microstimulation data, the errors were equivalent. The same post-hoc testing showed that the multiplicative model with variable boundary had statistically indistinguishable errors compared to the multiplicative model with constant boundary.