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Computational models for the combination of advice and individual learning

Biele, G. and Rieskamp, J. and Gonzalez, R.. (2009) Computational models for the combination of advice and individual learning. Cognitive science, Vol. 33, Nr. 2. pp. 206-242.

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Official URL: http://edoc.unibas.ch/dok/A5261552

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Abstract

Decision making often takes place in social environments where other actors influence individuals' decisions. The present article examines how advice affects individual learning. Five social learning models combining advice and individual learning-four based on reinforcement learning and one on Bayesian learning-and one individual learning model are tested against each other. In two experiments, some participants received good or bad advice prior to a repeated multioption choice task. Receivers of advice adhered to the advice, so that good advice improved performance. The social learning models described the observed learning processes better than the individual learning model. Of the models tested, the best social learning model assumes that outcomes from recommended options are more positively evaluated than outcomes from nonrecommended options. This model correctly predicted that receivers first adhere to advice, then explore other options, and finally return to the recommended option. The model also predicted accurately that good advice has a stronger impact on learning than bad advice. One-time advice can have a long-lasting influence on learning by changing the subjective evaluation of outcomes of recommended options.
Faculties and Departments:07 Faculty of Psychology > Departement Psychologie > Forschungsbereich Sozial-, Wirtschafts- und Entscheidungspsychologie > Economic Psychology (Rieskamp)
UniBasel Contributors:Rieskamp, Jörg
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:Elsevier
ISSN:0364-0213
Note:Publication type according to Uni Basel Research Database: Journal article
Identification Number:
Last Modified:14 Sep 2012 06:50
Deposited On:22 Mar 2012 13:47

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