Abstract
Computational models of cognition provide an interface to connect advanced mathematical tools and methods to empirically supported theories of behavior in psychology, cognitive science, and neuroscience. In this article, we consider a computational model of instance-based learning, implemented in the ACT-R cognitive architecture. We propose an approach for obtaining mathematical reformulations of such cognitive models that improve their computational tractability. For the well-established Sugar Factory dynamic decision making task, we conduct a simulation study to analyze central model parameters. We show how mathematical optimization techniques can be applied to efficiently identify optimal parameter values with respect to different optimization goals. Beyond these methodological contributions, our analysis reveals the sensitivity of this particular task with respect to initial settings and yields new insights into how average human performance deviates from potential optimal performance. We conclude by discussing possible extensions of our approach as well as future steps towards applying more powerful derivative-based optimization methods.
Highlights
Modern cognitive architectures, such as ACT-R [1], allow researchers to construct computational models of behavior that adequately reflect the complexity of human cognition while still being fully formalized
We propose an approach for obtaining mathematical reformulations of such cognitive models that improve their computational tractability
The stopping criterion was set to the minimum run time for which there was no improvement of the found maxima observed
Summary
Modern cognitive architectures, such as ACT-R [1], allow researchers to construct computational models of behavior that adequately reflect the complexity of human cognition while still being fully formalized. Cognitive architectures are typically based on empirical behavioral studies and neurophysiological research. Using a cognitive model of decision making, it becomes possible to answer questions such as “how does a typical decision maker behave in a particular situation” or “what can be expected, in the best or worst case, from a decision maker”. Cognitive models usually focus on specific cognitive phenomena, while cognitive architectures are concerned with the general structure of the cognitive system across different tasks. Different types of cognitive architectures based on symbolic, connectionist, or hybrid frameworks exist, such as Soar [2, 3], Leabra [4], Nengo [5], and ACT-R [1].
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