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Purpose: To improve data quality in basic and clinical applications, Bayesian methods have been developed to adaptively assess thresholds on single [1, 2] or multiple psychometric functions (eg, the contrast sensitivity function [3, 4]). To simplify these procedures–reduce model parameters and increase estimation efficiency-the slope of the psychometric function can be fixed [3, 4]. However, a model mismatch occurs when the assumed slope differs from observer’s true slope. What is the impact of this mismatch on the accuracy, precision, and efficiency of adaptive estimation? In this study, we used Monte Carlo simulations to show that, for methods with fixed slopes, the qFC [2] in m-alternative forced choice tasks (m= 2, 4, 8, and 10) and qCSF [3, 4]:(1) there exists a d’performance level at which the estimated threshold is unbiased, and (2) precision and efficiency increase with the observer’s true slope.

Methods: For …