An illustrative guide to expressing cognitive theories using evidence accumulation modelling

Evidence accumulation models (EAMs) explain and predict human choices and response times in a way that maps more directly to cognitive processes than traditional analyses. For example, EAMs can separate the speed-accuracy trade-off from processing capacity. However, little guidance is available regarding how to use EAMs to instantiate cognitive process theories, which often involve complex mappings of parameters to experimental designs. This tutorial illustrates how to embed such theories using the R package EMC2. We show how the effects of cognitive processes can be estimated by mapping EAM parameters to experimental designs using an augmented linear model language. We demonstrate with two examples. The first instantiates a theory of prospective memory. The second instantiates a theory of how humans integrate advice from automated decision aids into their choices. We then show how to combine these two different theories in a unified framework. We conclude by discussing further directions for theory embedding, including non-linear mappings from stimulus values to EAM parameters and the incorporation of trial-by-trial dynamics.