Simulation-Based Evidence Accumulation Modeling for Single- and Multi-Response Tasks: The eam Package

Evidence accumulation models are widely used to model choices and reaction times incognitive tasks. Despite their success in binary and multi-alternative decision-making, theirapplication to sequential multi-response tasks, such as free recall and verbal fluency, hasremained limited. The primary challenge in modeling multiple responses within a singletrial lies in the absence of tractable likelihood functions. To address this limitation, weintroduce simulation-based inference into the evidence accumulation framework anddevelop an R package that integrates simulation engines for five representative models (thediffusion decision model, leaky competing accumulator model, linear ballistic accumulatormodel, racing diffusion model, and Lévy flight model), extending them to themulti-response contexts. Across three simulation studies, the package demonstrated robustparameter recovery across representative models, their multi-response extensions, andcovariate-dependent variants. An empirical application to an aging-related free recalldataset further demonstrated the package’s ability to uncover computational mechanismsunderlying age-related differences in memory retrieval. Taken together, this packageprovides a foundation for building customized evidence accumulation models and expandsthe range of cognitive processes that can be studied within the evidence accumulationframework.