Human category learning has been modeled using exemplar, prototype, and rule-based theories. Rule-based models are the least discussed. This paper presents a rule-based model based on evolutionary computation techniques. Such techniques allow for the combination of concepts, an important aspect of human cognition that has been largely overlooked in previous cognitive modeling research. We also include other human-like characteristic in the model, namely a simplicity bias and instance-based learning. The results suggest that such an algorithm can replicate well-known results in human category learning. We discuss the broader issue of which of the three models of categorization make sense in particular situations.
['Toshihiko Matsuka', 'Jeffrey V. Nickerson']
Modeling Human Hypotheses-Testing Behaviors Using Simulated Evolutionary Processes