title: Perspectives of probabilistic inferences: Reinforcement learning and an adaptive network compared.
abstract: The assumption that people possess a strategy repertoire for inferences has been raised repeatedly. The strategy selection learning theory specifies how people select strategies from this repertoire. The theory assumes that individuals select strategies proportional to their subjective expectations of how well the strategies solve particular problems; such expectations are assumed to be updated by reinforcement learning. The theory is compared with an adaptive network model that assumes people make inferences by integrating information according to a connectionist network. The network's weights are modified by error correction learning. The theories were tested against each other in 2 experimental studies. Study 1 showed that people substantially improved their inferences through feedback, which was appropriately predicted by the strategy selection learning theory. Study 2 examined a dynamic environment in which the strategies' performances changed. In this situation a quick adaptation to the new situation was not observed; rather, individuals got stuck on the strategy they had successfully applied previously. This "inertia effect" was most strongly predicted by the strategy selection learning theory.
authors.affiliation: Max Planck Institute for Human Development, Berlin, Germany. rieskamp@mpib-berlin.mpg.de
authors.forename: Jörg
authors.initials: J
authors.lastname: Rieskamp
mesh_terms: D000328:Adult; D002755:Choice Behavior; D003657:Decision Making; D030141:Feedback, Psychological; D005260:Female; D006801:Humans; D008297:Male; D009042:Motivation; D016571:Neural Networks, Computer; D011337:Probability Learning; D011340:Problem Solving; D012054:Reinforcement, Psychology