An interpolation approach for fitting computationally intensive models

Computational cognitive modeling has been established as a useful methodology for exploring and validating quantitative theories about human cognitive processing and behavior. In some cases, however, complex models can create challenges for parameter exploration and estimation due to extended execution times and limited computing capacity. To address this challenge, some modelers have turned to intelligent search algorithms and/or large-scale computational resources. For an emerging class of models, epitomized by attempts to predict the time course effects of cognitive moderators, even these techniques may not be sufficient. In this paper, we present a new methodology and associated software that allows modelers to instantiate a model proxy that can quickly interpolate predictions of model performance anywhere within a defined parameter space. The software integrates with the R statistics environment and is compatible with many of the fitting algorithms therein. To illustrate the utility of these capabilities, we describe a case study where we are using the methodology in our own research.