Comparison of threshold measurements in laboratory and online studies using a Quest+ algorithm

Empirical validation of QUEST+ in PSE and JND estimations in visual discrimination tasks

One of the most precise methods to establish psychometric functions and estimate threshold and slope parameters is the constant stimuli procedure. The large distribution of predetermined stimulus values presented to observers enables the psychometric functions to be fully developed, but makes this procedure time-consuming. Adaptive procedures enable reliable threshold estimation while reducing the number of trials by concentrating stimulus presentations around observers' supposed threshold. Here, the stimulus value for the next trial depends on observer's responses to the previous trials. One recent improvement of these procedures is to also estimate the slope (related to discrimination sensitivity). The Bayesian QUEST+ procedure (Watson Journal of Vision, 17(3), 10, 2017), a generalization and extension of the QUEST procedure, includes this refinement. Surprisingly, this procedure is barely used. Our goal was to empirically assess its precision to evaluate size, orientation, or temporal perception, in three yes/no discrimination tasks that increase in demands. In 72 adult participants in total, we compared points of subjective equivalence (PSEs) or simultaneity (PSSs) as well as discrimination sensitivity obtained with the QUEST+, constant stimuli, and simple up-down staircase procedures. While PSEs did not differ between procedures, sensitivity estimates obtained with the 64-trials QUEST+ procedure were overestimated (i.e., just-noticeable differences, or JNDs, were underestimated). Overall, agreement between procedures was good, and was at its best for the easiest tasks. This study empirically confirmed that the QUEST+ procedure can be considered as a method of choice to accelerate PSE estimation, while keeping in mind that sensitivity estimation should be handled with caution.

jsQuestPlus: A JavaScript implementation of the QUEST+ method for estimating psychometric function parameters in online experiments

The two Bayesian adaptive psychometric methods named QUEST (Watson & Pelli, 1983) and QUEST+ (Watson, 2017) are widely used to estimate psychometric parameters, especially the threshold, in laboratory-based psychophysical experiments. Considering the increase of online psychophysical experiments in recent years, there is a growing need to have the QUEST and QUEST+ methods available online as well. We developed JavaScript libraries for both, with this article introducing one of them: jsQuestPlus. We offer integrations with online experimental tools such as jsPsych (de Leeuw, 2015), PsychoPy/JS (Peirce et al., 2019), and lab.js (Henninger et al., 2021). We measured the computation time required by jsQuestPlus under four conditions. Our simulations on 37 browser-computer combinations showed that the mean initialization time was 461.08 ms, 95% CI [328.29, 593.87], the mean computation time required to determine the stimulus parameters for the next trial was less than 1 ms, and the mean update time was 79.39 ms, 95% CI [46.22, 112.55] even in extremely demanding conditions. Additionally, psychometric parameters were estimated as accurately as the original QUEST+ method did. We conclude that jsQuestPlus is fast and accurate enough to conduct online psychophysical experiments despite the complexity of the matrix calculations. The latest version of jsQuestPlus can be downloaded freely from https://github.com/kurokida/jsQuestPlus under the MIT license.