Predicting binary choices from probability phrase meanings |
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Authors: | Thomas S Wallsten Yoonhee Jang |
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Institution: | (1) Department of Psychology, Fordham University, 441 East Fordham Road, Bronx, NY 10458, USA;(2) Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA;(3) College of Information Sciences and Technology, Pennsylvania State University, University Park, PA 16802, USA |
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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. |
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Keywords: | |
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