APA
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Kube, T., Kirchner, L., Lemmer, G., & Glombiewski, J. (2021). How the Discrepancy Between Prior Expectations and New Information Influences Expectation Updating in Depression—The Greater, the Better? Clinical Psychological Science : a Journal of the Association for Psychological Science.
Chicago/Turabian
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Kube, T., L. Kirchner, G. Lemmer, and J. Glombiewski. “How the Discrepancy Between Prior Expectations and New Information Influences Expectation Updating in Depression—The Greater, the Better?” Clinical psychological science : a journal of the Association for Psychological Science (2021).
MLA
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Kube, T., et al. “How the Discrepancy Between Prior Expectations and New Information Influences Expectation Updating in Depression—The Greater, the Better?” Clinical Psychological Science : a Journal of the Association for Psychological Science, 2021.
BibTeX Click to copy
@article{t2021a,
title = {How the Discrepancy Between Prior Expectations and New Information Influences Expectation Updating in Depression—The Greater, the Better?},
year = {2021},
journal = {Clinical psychological science : a journal of the Association for Psychological Science},
author = {Kube, T. and Kirchner, L. and Lemmer, G. and Glombiewski, J.}
}
Previous research on expectation updating in relation to psychopathology used to treat expectation-confirming information and expectation-disconfirming information as binary concepts. Here, we varied the extent to which new information deviates from prior expectations and examined its influence on expectation adjustment in both a false-feedback task (Study 1; N = 379) and a social-interaction task (Study 2; N = 292). Unlike traditional learning models, we hypothesized a tipping point in which the discrepancy between expectation and outcome becomes so large that new information is perceived as lacking credibility, thus entailing little updating of expectations. Consistent with the hypothesized tipping point, new information was deemed most valid if it was moderately positive. Moreover, descriptively, expectation update was largest for moderate expectation violations, but this effect was small (Study 2) or even nonsignificant (Study 1). The findings question the assumption of traditional learning models that the larger the prediction error, the larger the update.