Psychometric functions characterize binary sensory decisions along a single stimulus dimension. However, real-life sensory tasks vary along a greater variety of dimensions (e.g. color, contrast and luminance for visual stimuli). Approaches to characterizing high dimensional sensory spaces either require strong parametric assumptions about these additional contextual dimensions, or fail to leverage known properties of classical psychometric curves, such as identifiable thresholds and slopes. We overcome both limitations by introducing a semi-parametric model of sensory discrimination that parameterizes performance along a single intensity dimension via a classical logistic function, but uses Gaussian Processes (GPs) to flexibly model logistic parameters across any number of non-intensity dimensions.The use of GPs additionally enables the use of adaptive sampling, avoiding the need for grid sampling or staircase methods, which are intractable in higher dimensions. We show that this semi-parametric method accurately identifies the true high-dimensional psychometric function in fewer samples than competing approaches and offers behaviorally interpretable parameters.