Towards utilizing evidence accumulation models in applied settings – using informative prior distributions to decrease sample size demands

A hurdle preventing Evidence Accumulation Models (EAMs) from wide utilization in applied settings, where individuals cannot (or will not) provide many repeated decisions, is their sample size demands. In this project, we investigated whether Bayesian hierarchical modeling approaches offer a solution: We hypothesized that informative prior distributions decrease these sample size demands to numbers that are obtainable in practice. Through a simulation study and a reanalysis of experimental data, we explored the lower limit on the sample size to still reliably estimate individual participants’ data-generating parameters. In the simulation study, we first compared the effects of various sample sizes and types of prior distributions (uninformative prior; informative and accurate prior; informative but inaccurate prior) on the estimation of parameters for Diffusion Decision Models (DDMs), a class of EAMs. Results revealed that several DDM parameters can be recovered with sample sizes as small as 10, if the prior is correct and informative. However, especially for very small sample sizes, the type of prior distribution was critically important. Subsequently, we assessed the effect of sample size on parameter recovery under more realistic circumstances by reanalyzing data from a driving experiment. We tested how well parameters can be recovered based only on a few observations from a single participant if data of the remaining participants provide informative prior distributions. For most assessed DDM parameters (drift rate, boundary separation, and bias, but not non-decision time), we achieved satisfactory levels of parameter recovery with 20 observations. Additionally, we confirmed that we meaningfully updated the prior distributions towards the ground truth by including these 20 observations. This work opens the door for reliable estimation of decision-making processes under real-life circumstances (e.g., when individuals cannot provide many repeated decisions; or when we are interested in real-time estimation of parameter fluctuations to monitor changes in people’s mental states).