Open Access

Chiara Fioravanti, Christoph Braun, Axel Lindner, Sergio Ruiz, Ranganatha Sitaram, Diljit Singh Kajal

• Adaptive threshold estimation procedures sample close to a subject’s perceptual threshold by dynamically adapting the stimulation based on the subject’s performance. Yet, perceptual thresholds not only depend on the observers’ sensory capabilities but also on any bias in terms of their expectations and response preferences, thus distorting the precision of the threshold estimates. Using the framework of signal detection theory (SDT), independent estimates of both, an observer’s sensitivity and internal processing bias can be delineated from threshold estimates. While this approach is commonly available for estimation procedures engaging the method of constant stimuli (MCS), correction procedures for adaptive methods (AM) are only scarcely applied. In this article, we introduce a new AM that takes individual biases into account, and that allows for a bias-corrected assessment of subjects’ sensitivity. This novel AM is validated with simulations and compared to a typical MCS-procedure, for which the implementation of bias correction has been previously demonstrated. Comparing AM and MCS demonstrates the viability of the presented AM. Besides its feasibility, the results of the simulation reveal both, advantages, and limitations of the proposed AM. The procedure has considerable practical implications, in particular for the design of shaping procedures in sensory training experiments, in which task difficulty has to be constantly adapted to an observer’s performance, to improve training efficiency.