Measuring the completeness of race models for perceptual decision-making

Computational models of perceptual decision-making depend heavily on empirical goodness-of-fit measures for model selection. However, it is not possible to improve models' fit to data indefinitely, particularly when the data in question are variable across multiple elicitations. The completeness of a model or a theory assesses the extent to which it can predict observations in comparison with an ideal model. We measure the completeness of contemporary race models on a paradigmatic perceptual decision-making task - random dot motion discrimination - and show that the simple drift diffusion model is already close to complete in describing random dot motion discrimination data, with more complex models being in fact over-fit to datasets. Thus, in this paper, we quantitatively demonstrate limits to the ability of conventional choice fraction and response time data to disambiguate complex models of perceptual decision-making.