Absence of Differential Protection From Extinction in Human Causal Learning

Elemental models of associative learning typically employ a common prediction-error term. Following a conditioning trial, they predict that the change in the strength of an association between a cue and an outcome is dependent upon how well the outcome was predicted. When multiple cues are present,...

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Veröffentlicht in:Journal of experimental psychology. Animal behavior processes 2024-07, Vol.50 (3), p.161-185
Hauptverfasser: George, David N., Haddon, Josephine E., Griffiths, Oren
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Griffiths, Oren
description Elemental models of associative learning typically employ a common prediction-error term. Following a conditioning trial, they predict that the change in the strength of an association between a cue and an outcome is dependent upon how well the outcome was predicted. When multiple cues are present, they each contribute to that prediction. The same rule applies both to increases in associative strength during excitatory conditioning and the loss of associative strength during extinction. In five experiments using an allergy prediction task, we tested the involvement of a common error term in the extinction of causal learning. Two target cues were each paired with an outcome prior to undergoing extinction in compound either with a second excitatory cue or with a cue that had previously undergone extinction in isolation. At test, there was no difference in the causal ratings of the two target cues. Manipulations designed to bias participants toward elemental processing of cue compounds, to promote the acquisition of inhibitory associations, or to reduce generalization decrement between training and test were each without effect. These results are not consistent with common error term models of associative learning.
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subjects Associative learning
Associative Processes
Causality
Extinction (Learning)
Extinction behavior
Female
Human
Information processing
Male
Prediction Errors
Predictions
title Absence of Differential Protection From Extinction in Human Causal Learning
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