**Abstract** In psychophysics and psychometrics, an integral method to the discipline involves charting how a person’s response pattern changes according to a continuum of stimuli. For instance, in hearing science, Visual Analogue Scaling (VAS) tasks are experiments in which listeners hear sounds across a speech continuum and give a numeric rating between 0 and 100 conveying whether the sound they heard was more like word "a" or more like word "b" (i.e., each participant is giving a continuous categorization response (CCR)). By taking all the CCRs across the speech continuum, a parametric curve model can be fit to the data and used to analyze any individual’s response pattern by speech continuum. Standard statistical modeling techniques are not able to accommodate all of the specific requirements needed to analyze these data. Thus, Bayesian hierarchical modeling techniques are employed to accommodate group-level non-linear curves, individual-specific non-linear curves, continuum-level random effects, and a subject-specific variance the other model parameters. In this paper, two Bayesian hierarchical models are constructed to model the data from two VAS task studies. Any nonlinear curve function could be used and we demonstrate the technique using the 4-parameter logistic function. Overall, the models were found to fit particularly well to the data from the studies and results suggest that the magnitude of the slope was what most defined each individual’s response pattern. ***Source code*** for the Bayesian nonlinear model of the VAS data is provided in this OSF repository.