Cristin-resultat-ID: 347083
Sist endret: 22. november 2010, 00:35
Vitenskapelig artikkel

Computational Models for the Combination of Advice and Individual Learning

  • Guido Biele
  • Jörg Rieskamp og
  • Richard Gonzalez


Cognitive Science
ISSN 0364-0213
e-ISSN 1551-6709
NVI-nivå 1

Om resultatet

Vitenskapelig artikkel
Publiseringsår: 2009
Volum: 33
Hefte: 2
Sider: 206 - 242

Beskrivelse Beskrivelse


Computational Models for the Combination of Advice and Individual Learning


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.


Aktiv cristin-person

Guido Philipp Emmanuel Biele

Bidragsyterens navn vises på dette resultatet som Guido Biele
  • Tilknyttet:
    ved Psykologisk institutt ved Universitetet i Oslo

Jörg Rieskamp

  • Tilknyttet:

Richard Gonzalez

  • Tilknyttet:
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