Using knowledge partitioning to investigate the psychological plausibility of mixtures of experts
Artificial Intelligence Review
Over the years, the presence of knowledge partitioning (KP) in human function learning data has been used to argue that mixture-of-experts models (MOE) constitute a psychologically plausible explanation of human performance, and that the experts used by humans are always linear. These claims recently led to the proposition of the population of linear experts model (POLE). In this paper, variations of the firefighting paradigm developed by Lewandowsky and his colleagues, which initiated research
... about KP, were used to explore the psychological plausibility of MOE in general and POLE in particular. In a first experiment, these statements were tested by modifying the test display of the firefighting paradigm. The results showed that adding irrelevant information to the display resulted in a smaller proportion of partitioning participants. Also, some participants used non-linear experts to partition the stimulus space. This new type of KP was further explored in a second study, which included more training sessions. The results suggest that linear KP disappears with practice and that non-linear partitioning reflects the incapacity to correctly estimate the position of the function's vertex. It is concluded that MOE are adequate psychological models, but that the linearity and ubiquity claims of the POLE model need to be weakened.