From recognition to decisions : extending and testing recognition-based models for multialternative inference
From recognition to decisions : extending and testing recognition-based models for multialternative inference
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2010
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Psychonomic Bulletin & Review ; 17 (2010), 3. - pp. 287-309. - ISSN 1069-9384. - eISSN 1531-5320
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.
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150 Psychology
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MAREWSKI, Julian N., Wolfgang GAISSMAIER, Lael J. SCHOOLER, Daniel G. GOLDSTEIN, Gerd GIGERENZER, 2010. From recognition to decisions : extending and testing recognition-based models for multialternative inference. In: Psychonomic Bulletin & Review. 17(3), pp. 287-309. ISSN 1069-9384. eISSN 1531-5320. Available under: doi: 10.3758/PBR.17.3.287BibTex
@article{Marewski2010-06recog-28039, year={2010}, doi={10.3758/PBR.17.3.287}, title={From recognition to decisions : extending and testing recognition-based models for multialternative inference}, number={3}, volume={17}, issn={1069-9384}, journal={Psychonomic Bulletin & Review}, pages={287--309}, author={Marewski, Julian N. and Gaissmaier, Wolfgang and Schooler, Lael J. and Goldstein, Daniel G. and Gigerenzer, Gerd} }
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