Test of a distributional theory of intuitive numerical prediction |
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| Authors: | Barbara Mellers |
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| Affiliation: | 1. Edna Bennett Pierce Prevention Research Center, The Pennsylvania State University, 115 Health & Human Development Building, University Park, PA 16802, USA;2. Department of Human Development and Family Studies, The Pennsylvania State University, 115 Health & Human Development Building, University Park, PA 16802, USA;3. Department of Psychiatry and Behavioural Neurosciences, McMaster University, St. Joseph''s Healthcare Hamilton, West 5th Campus, Administration B3, 100 West 5th Street, Hamilton, Ontario L8N 3K7, Canada;4. Departments of Communication and Psychology, Stanford University, Building 120, Room 110, 450 Jane Stanford Way, Stanford University, Stanford 94305, CA, USA;1. Department of Psychiatry, Neuroscience, Genetics, Icahn School of Medicine at Mount Sinai, New York, NY, USA;2. Department of Psychology, Montclair State University, Montclair, NJ, USA;3. Department of Psychology, University of Dayton, Dayton, OH, USA;4. Department of Psychiatry, Wright State University Boonshoft School of Medicine, Dayton, OH, USA;5. Department of Psychiatry, Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo, Buffalo, NY, USA;1. College of Mechanical & Electrical Engineering, Shaanxi University of Science and Technology, Xi''an 710021, People''s Republic of China;2. Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China;3. Department of Physics, Yunnan University, Kunming 650091, China |
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| Abstract: | This paper investigates models that describe how subjects combine uncertain information to arrive at an intuitive prediction of a criterion. Subjects were trained, with feedback, to predict a numerical criterion from each of three single cues. Then they were asked to predict the criterion, without feedback, either from pairs of cues or from single cues. Their predictions were not consistent with a relative weight averaging model, since pairs of cues did not combine additively. Instead, the effect of a cue was inversely proportional to the standard deviation of the criterion at each level of the cue. Subjects appeared to apply greater weight to cue levels with smaller variance, i.e., those cue levels that were more valid. The data could be described by a distributional theory referred to as the equal probability model. For the present experiment, this model implies that the criterion means associated with the levels of each cue are weighted by the reciprocals of the standard deviations and then averaged. Relations between the equal probability model and other models of impression formation are discussed. |
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