Comparing sensitivity estimates from MLDS and forced-choice methods in a slant-from-texture experiment.

Maximum likelihood difference scaling (MLDS) is a method for the estimation of perceptual scales based on the judgment of differences in stimulus appearance (Maloney & Yang, 2003). MLDS has recently also been used to estimate near-threshold discrimination performance (Devinck & Knoblauch, 2012). Using MLDS as a psychophysical method for sensitivity estimation is potentially appealing, because MLDS has been reported to need less data than forced-choice procedures, and particularly naive observers report to prefer suprathreshold comparisons to JND-style threshold tasks. Here we compare two methods, MLDS and two-interval forced-choice (2-IFC), regarding their capability to estimate sensitivity assuming an underlying signal-detection model. We first examined the theoretical equivalence between both methods using simulations. We found that they disagreed in their estimation only when sensitivity was low, or when one of the assumptions on which MLDS is based was violated. Furthermore, we found that the confidence intervals derived from MLDS had a low coverage; i.e., they were too narrow, underestimating the true variability. Subsequently we compared MLDS and 2-IFC empirically using a slant-from-texture task. The amount of agreement between sensitivity estimates from the two methods varied substantially across observers. We discuss possible reasons for the observed disagreements, most notably violations of the MLDS model assumptions. We conclude that in the present example MLDS and 2-IFC could equally be used to estimate sensitivity to differences in slant, with MLDS having the benefit of being more efficient and more pleasant, but having the disadvantage of unsatisfying coverage.