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

  • Frank Van Overwalle
  • Bert Timmermans
Part of the Perspectives in Neural Computing book series (PERSPECT.NEURAL)

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 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.

Keywords

External Input Conditioned Inhibition Recurrent Network Causal Judgment Recurrent Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London 2001

Authors and Affiliations

  • Frank Van Overwalle
  • Bert Timmermans

There are no affiliations available

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