Abstract
The development of computational models to provide explanations of psychological data can be achieved using semi-automated
search techniques, such as genetic programming. One challenge with
these techniques is to control the type of model that is evolved to be
cognitively plausible – a typical problem is that of “bloating”, where
continued evolution generates models of increasing size without improving overall fitness. In this paper we describe a system for representing psychological data, a class of process-based models, and
algorithms for evolving models. We apply this system to the delayed-
match-to-sample task. We show how the challenge of bloating may
be addressed by extending the fitness function to include measures
of cognitive performance.
search techniques, such as genetic programming. One challenge with
these techniques is to control the type of model that is evolved to be
cognitively plausible – a typical problem is that of “bloating”, where
continued evolution generates models of increasing size without improving overall fitness. In this paper we describe a system for representing psychological data, a class of process-based models, and
algorithms for evolving models. We apply this system to the delayed-
match-to-sample task. We show how the challenge of bloating may
be addressed by extending the fitness function to include measures
of cognitive performance.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 50th Anniversary Convention of the AISB, 2014 |
| Publication status | Published - 2014 |