I am interested in the creation of chunks (aka configural nodes) from smaller chunks and input features (only interested in System 1 cognition).
Unitization studies (e.g. Goldstone (pdf)), suggest that we start with generic features, and slowly combine them into more specific chunks. As we do this unitization, we wouldn't store all of the learned combinations --- a mere 10 input features can be combined in 3.6 million configurations!
Simon suggested generic elements get overwritten by more specific ones at each exposure. but what would you do with the predictions/rules learned about a given chunk? Transfer them to the newly created specific chunk? What if they do not apply at that more specific level?
Nosofsky was suggesting a rule-plus-exception model, where the more generic elements are always stored and their predictions are recorded as general rules; When those predictions are broken, more specific chunks are stored to explain the exceptions to these rules. What does it mean for predictions to be broken? In a probabilistic environment like ours, sometimes a prediction is correct, sometimes it isn't -- updating the weight of the prediction seems much more reasonable than deeming it invalid in favor of an 'exception'.
When do I chunk two co-occurring features? When do I chunk those two with a third? Do I delete the 2-feature chunk in favor of the 3-feature? What do I do with the memories associated with the deleted chunk?