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Factoring out non-decision time in choice RT data : Theory and implications

Choice RT experiments are an invaluable tool in psychology and neuroscience. A common assumption is that the total choice response time is the sum of a decision and a non-decision part (time spent on perceptual and motor processes). While the decision part is typically modeled very carefully (commonly with diffusion models), a simple and ad hoc distribution (mostly uniform) is assumed for the non-decision component. Nevertheless, it has been shown that the misspecification of the non-decision time can severely distort the decision model parameter estimates. In this paper, we propose an alternative approach to the estimation of choice RT models that elegantly bypasses the specification of the non-decision time distribution by means of an unconventional convolution of data and decision model distributions (hence called the D*M approach). Once the decision model parameters have been estimated, it is possible to compute a non-parametric estimate of the non-decision time distribution. The technique is tested on simulated data, and is shown to systematically remove traditional estimation bias related to misspecified non-decision time, even for a relatively small number of observations. The shape of the actual underlying non-decision time distribution can also be recovered. Next, the D*M approach is applied to a selection of existing diffusion model application papers. For all of these studies, substantial quantitative differences with the original analyses are found. For one study, these differences radically alter its final conclusions, underlining the importance of our approach. Additionally, we find that strongly right skewed non-decision time distributions are not at all uncommon.