Reasoning with heuristics and induction: An account based on the CLARION cognitive architecture

Some psychologists have criticized computational cognitive architectures on the basis of model complexity and parameter tweaking. This paper addresses these criticisms by using a well established cognitive architecture, CLARION, and extracting its core theory to explain a wide range of reasoning (and other) data. The resulting model provides principled, almost parameter-free explanations for psychological “laws” of plausible reasoning. This paper concludes with a discussion of the implication of this approach for cognitive science and psychology.