Welcome!
To use the personalized features of this site, please log in or register.
If you have forgotten your username or password, we can help.
|
 |
Using knowledge partitioning to investigate the psychological plausibility of mixtures of experts
| |
|
Using knowledge partitioning to investigate the psychological plausibility of mixtures of experts
Sébastien Hélie1 , Gyslain Giguère2 , Denis Cousineau3 and Robert Proulx2 
| (1) |
Rensselaer Polytechnic Institute, Troy, NY, USA |
| (2) |
Université du Québec À Montréal, Montreal, QC, Canada |
| (3) |
Université de Montréal, Montreal, QC, Canada |
Published online: 22 August 2007
Abstract 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.
Keywords Cognitive model - Function learning - Knowledge partitioning - Mixture-of-experts - Psychology
Fulltext Preview (Small, Large)
 References secured to subscribers.
|
|
|
|
|
|