From recognition to decisions: Extending and testing recognition-based models for multialternative inference View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2010-06

AUTHORS

Julian N. Marewski, Wolfgang Gaissmaier, Lael J. Schooler, Daniel G. Goldstein, Gerd Gigerenzer

ABSTRACT

The recognition heuristic is a noncompensatory strategy for inferring which of two alternatives, one recognized and the other not, scores higher on a criterion. According to it, such inferences are based solely on recognition. We generalize this heuristic to tasks with multiple alternatives, proposing a model of how people identify the consideration sets from which they make their final decisions. In doing so, we address concerns about the heuristic's adequacy as a model of behavior: Past experiments have led several authors to conclude that there is no evidence for a noncompensatory use of recognition but clear evidence that recognition is integrated with other information. Surprisingly, however, in no study was this competing hypothesis--the compensatory integration of recognition--formally specified as a computational model. In four studies, we specify five competing models, conducting eight model comparisons. In these model comparisons, the recognition heuristic emerges as the best predictor of people's inferences. More... »

PAGES

287-309

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.3758/pbr.17.3.287

DOI

http://dx.doi.org/10.3758/pbr.17.3.287

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1015004553

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/20551350


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