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Richard GonzalezRichard Gonzalez

Center Director, Research Center for Group Dynamics, Institute for Social Research
Director, BioSocial Methods Collaborative, RCGD
Amos N Tversky Collegiate Professor, Psychology and Statistics, LSA
Professor of Marketing, Stephen M Ross School of Business
Professor of Integrative Systems and Design, College of Engineering

 

E-mail: Email Richard Gonzalez
Address: Research Center for Group Dynamics
Institute for Social Research
University of Michigan
426 Thompson Street
Ann Arbor, Michigan 48106
Phone: 734-647-6785

Is advice treated the same way as evidence in a learning task?

Mar 7, 2011 | Decision Making, Psychology, Statistics/Methods

This paper we investigate several mathematical models of learning and extend them to include advice from others as part of the learning mechanism. We find that a type of reinforcement learning model does well at accounting for the explore-exploit behavior present in the experimental task, and accounts for the data better than Bayesian models. We designed a second study to tease apart model predictions.

Biele, G., Rieskamp, J., & Gonzalez, R. (2009). Computational models for the combination of advice and individual learning. Cognitive Science, 33, 206-42. doi:10.1111/j.1551- 6709.2009.01010.x PDF

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.