How the core theory of CLARION captures human decision-making

Some mainstream psychologists have criticized computational cognitive architectures on the issue of model complexity and parameter tweaking (i.e., the likelihood that cognitive architectures can explain any results and their opposites). This paper tries to address these criticisms by tackling the issue of model complexity in cognitive architectures. Here, we start with a well-established cognitive architecture, CLARION, and extract its core theory to explain a wide range of data. The resulting minimal model was used to provide parameter-free principled explanations for several psychological “laws” of uncertain reasoning and decision-making. This paper is concluded by a discussion of the implication of parameter-free modeling in cognitive science and psychology.