mixtur: An R package for designing, analysing, and modelling continuous report visual short-term memory studies

Visual short-term memory (vSTM) is often measured via continuous-report tasks whereby participants are presented with stimuli that vary along a continuous dimension (e.g., colour) with the goal of memorising the stimuli features. At test, participants are probed to recall the feature value of one of the memoranda in a continuous manner (e.g., by clicking on a colour wheel). The angular deviation between the participant response and the true feature value provides an estimate of recall---and hence, vSTM---precision. Two prominent models of performance on such tasks are the two- and three-component mixture models (Bays et al., 2009; Zhang & Luck, 2008). Both models decompose participant responses into probabilistic mixtures of: (1) responses to the true target value based on a noisy memory representation; (2) random guessing when memory fails. In addition, the three-component model proposes (3) responses to a non-target feature value (i.e., binding errors). Here we report the development of mixtur, an open-source package written for the statistical programming language R that facilitates the fitting of the 2- and 3-component mixture models to continuous report data. We also report the results of several simulations conducted to develop recommendations for researchers on trial numbers, set-sizes and memoranda similarity, as well as conducting parameter recovery and model recovery simulations. It is our hope that mixtur will lower the barrier of entry for utilising mixture modelling