Learning about an absent cause: Discounting and augmentation of positively and independently related causes

Frank Van Overwalle, Bert Timmermans

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution


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.
Original languageEnglish
Title of host publicationConnectionist Models of Learning, Development and Evolution
Subtitle of host publicationProceedings of the Sixth Neural Computation and Psychology Workshop, Liège, Belgium, 16–18 September 2000
EditorsRobert M French, Jacques P Sougné
Number of pages10
ISBN (Print)978-1-85233-354-6
Publication statusPublished - 2001

Publication series

NamePerspectives in Neural Computing


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