Abstract
We argue that existing learning algorithms are often poorly equipped to solve problems involving a certain type of important and widespread regularity that we call 'type-2 regularity'. The solution in these cases is to trade achieved representation against computational search. We investigate several ways in which such a trade-off may be pusued includig simple incremental learning, modular connectionism, and the developmental hypothesis of 'representational redescription'.
| Original language | English |
|---|---|
| Pages (from-to) | 83-90 |
| Number of pages | 8 |
| Journal | Behavioral and Brain Sciences |
| Volume | 20 |
| Issue number | 1 |
| Publication status | Published - 1997 |
| Externally published | Yes |