Journal article:
From recognition to decisions : extending and testing recognition-based models for multialternative inference

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June 2010
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Marewski, Julian N.
Schooler, Lael J.
Goldstein, Daniel G.
Gigerenzer, Gerd
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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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Psychonomic Bulletin & Review ; 17 (2010), 3. - pp. 287-309. - ISSN 1069-9384. - eISSN 1531-5320
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Cite This
ISO 690MAREWSKI, 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.17.3.287
BibTex
@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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