Abstract
Abstract
There is an abundance of computational models in cognitive neuroscience. A framework for what is desirable in a model, what justifies the introduction of a new one, or what makes one better than another is lacking, however. In this article, we examine key qualities (“virtues”) that are desirable in computational models, and how these are interrelated. To keep the scope of the article manageable, we focus on the field of cognitive control, where we identified six “model virtues”: empirical accuracy, empirical scope, functional analysis, causal detail, biological plausibility, and psychological plausibility. We first illustrate their use in published work on Stroop modeling and then discuss what expert modelers in the field of cognitive control said about them in a series of qualitative interviews. We found that virtues are interrelated and that their value depends on the modeler's goals, in ways that are not typically acknowledged in the literature. We recommend that researchers make the reasons for their modeling choices more explicit in published work. Our work is meant as a first step. Although our focus here is on cognitive control, we hope that our findings will spark discussion of virtues in other fields as well.