Exploring the diversity of evolved cognitive models with cluster analysis

Cognitive scientists often represent theories of cognitive behavior in the form of computer programs which simulate and model the performance of humans in experimental settings. Earlier work has demonstrated that evolutionary techniques, specifically genetic programming (GP), can be used to generate a pool of candidate models in the form of executable computer programs. However, previous work has not considered the impact of changes to hyper-parameter values, such as those controlling the behavior and timing of operators or those controlling the operation of the GP process. In this paper, we develop and use a cluster analysis technique based around the Silhouette index to investigate the impact of hyper-parameter changes on the composition of evolved populations of programs. Our metrics support visualizations and enable a user to assess both qualitatively and quantitatively the diversity of candidates from different populations. In this way, a cognitive scientist can analyze the output of the evolutionary system in order to uncover or inspire potentially novel theories of human behavior.