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.
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Van Overwalle, F., Timmermans, B. (2001). Learning about an Absent Cause: Discounting and Augmentation of Positively and Independently Related Causes. In: French, R.M., Sougné, J.P. (eds) Connectionist Models of Learning, Development and Evolution. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0281-6_22
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DOI: https://doi.org/10.1007/978-1-4471-0281-6_22
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