title: Why people underestimate y when extrapolating in linear functions.
abstract: E. L. DeLosh, J. R. Busemeyer, and M. A. McDaniel (1997) found that when learning a positive, linear relationship between a continuous predictor (x) and a continuous criterion (y), trainees tend to underestimate y on items that ask the trainee to extrapolate. In 3 experiments, the authors examined the phenomenon and found that the tendency to underestimate y is reliable only in the so-called lower extrapolation region--that is, new values of x that lie between zero and the edge of the training region. Existing models of function learning, such as the extrapolation-association model (DeLosh et al., 1997) and the population of linear experts model (M. L. Kalish, S. Lewandowsky, & J. Kruschke, 2004), cannot account for these results. The authors show that with minor changes, both models can predict the correct pattern of results.
authors.affiliation: Defence Research and Development Canada-Toronto, Toronto, ON, Canada. peter.kwantes@drdc-rddc.gc.ca
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Kwantes
authors.forename: Andrew
authors.initials: A
authors.lastname: Neal
mesh_terms: D006801:Humans; D007600:Judgment / Q000502:physiology*; D007858:Learning / Q000502:physiology*; D016014:Linear Models; D008960:Models, Psychological; D011340:Problem Solving