Learning about an Absent Cause: Discounting and Augmentation of Positively and Independently Related Causes

Standard connectionist models of pattern completion like an auto-associator, typically fill in the activation of a missing feature with internal input from nodes that are connected to it. However, associative studies on competition between alternative causes, demonstrate that people do not always complete the activation of a missing feature, but rather actively encode it as missing whenever its presence was highly expected. Dickinson and Burke's revaluation hypothesis [4] predicts that there is always forward competition of a novel cause, but that backward competition of a known cause depends on a consistent (positive) relation with the alternative cause. This hypothesis was confirmed in several experiments. These effects cannot be explained by standard auto-associative networks, but can be accounted for by a modified auto-associative network that is able to recognize absent information as missing and provides it with negative, rather than positive activation from