Convergence and Constraints Revealed in a Qualitative Model Comparison

We contrasted and compared independently developed computational models of human performance in a common dynamic decision-making task. The task, called dynamic stocks and flows, is simple and tractable enough for laboratory experiments yet exhibits many characteristics of macrocognition. A macrocognitive model was developed using a computational instantiation of recognition-primed decision making. A microcognitive model was developed using the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture. Both models followed an instance-based learning paradigm and displayed striking similarities, including their constraints, limitations, and the key breakthrough that enabled satisfactory (though still short of human-like) performance, suggesting the emergence of a general design pattern. On the basis of this comparison we argue that although some substantive differences remain, microcognitive and macrocognitive approaches provide complementary rather than contradictory accounts of human behavior.