title: Predicting binary choices from probability phrase meanings.
abstract: The issues of how individuals decide which of two events is more likely and of how they understand probability phrases both involve judging relative likelihoods. In this study, we investigated whether derived scales representing probability phrase meanings could be used within a choice model to predict independently observed binary choices. If they can, this simultaneously provides support for our model and suggests that the phrase meanings are measured meaningfully. The model assumes that, when deciding which of two events is more likely, judges take a single sample from memory regarding each event and respond accordingly. The model predicts choice probabilities by using the scaled meanings of individually selected probability phrases as proxies for confidence distributions associated with sampling from memory. Predictions are sustained for 34 of 41 participants but, nevertheless, are biased slightly low. Sequential sampling models improve the fit. The results have both theoretical and applied implications.
authors.affiliation: Department of Psychology, University of Maryland, College Park, Maryland 20742, USA. twallsten@psyc.umd.edu
authors.forename: Thomas S
authors.initials: TS
authors.lastname: Wallsten
authors.forename: Yoonhee
authors.initials: Y
authors.lastname: Jang
mesh_terms: D002755:Choice Behavior; D003661:Decision Support Techniques; D006801:Humans; D007600:Judgment; D011337:Probability Learning; D012660:Semantics; D035501:Uncertainty
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