SSL: A Theory of How People Learn to Select Strategies

Jörg Rieskamp and Philipp E. Otto

in Heuristics

Published in print April 2011 | ISBN: 9780199744282
Published online May 2011 | e-ISBN: 9780199894727 | DOI:
SSL: A Theory of How People Learn to Select Strategies

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The assumption that people possess a repertoire of strategies to solve the inference problems they face has been raised repeatedly. However, a computational model specifying how people select strategies from their repertoire is still lacking. The proposed strategy selection learning (SSL) theory predicts a strategy selection process on the basis of reinforcement learning. The theory assumes that individuals develop subjective expectations for the strategies they have and select strategies proportional to their expectations, which are then updated on the basis of subsequent experience. The learning assumption was supported in four experimental studies. Participants substantially improved their inferences through feedback. In all four studies, the best-performing strategy from the participants' repertoires most accurately predicted the inferences after sufficient learning opportunities. When testing SSL against three models representing extensions of SSL and against an exemplar model assuming a memory-based inference process, the authors found that SSL predicted the inferences most accurately.

Keywords: probabilistic inferences; strategy selection; reinforcement learning; exemplar model; learning theory

Chapter.  13164 words.  Illustrated.

Subjects: Cognitive Psychology

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