@inproceedings{b7235ae01aa24355a82ffa4da55e5624,
title = "Learning about an absent cause: Discounting and augmentation of positively and independently related causes",
abstract = "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 related nodes.",
author = "{Van Overwalle}, Frank and Bert Timmermans",
year = "2001",
doi = "10.1007/978-1-4471-0281-6_22",
language = "English",
isbn = "978-1-85233-354-6",
series = "Perspectives in Neural Computing",
publisher = "Springer ",
pages = "219--228",
editor = "French, {Robert M} and Sougn{\'e}, {Jacques P}",
booktitle = "Connectionist Models of Learning, Development and Evolution",
}