title: From recognition to decisions: extending and testing recognition-based models for multialternative inference.
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.
authors.affiliation: Max Planck Institute for Human Development, Berlin, Germany. marewski@mpib-berlin.mpg.de
authors.forename: Julian N
authors.initials: JN
authors.lastname: Marewski
authors.forename: Wolfgang
authors.initials: W
authors.lastname: Gaissmaier
authors.forename: Lael J
authors.initials: LJ
authors.lastname: Schooler
authors.forename: Daniel G
authors.initials: DG
authors.lastname: Goldstein
authors.forename: Gerd
authors.initials: G
authors.lastname: Gigerenzer
mesh_terms: D000328:Adult; D003463:Cues; D003625:Data Collection; D003657:Decision Making; D005260:Female; D006801:Humans; D007206:Individuality; D008297:Male; D008960:Models, Psychological; D015233:Models, Statistical; D011336:Probability; D021641:Recognition, Psychology