Kwantes Peter J, Neal Andrew
Defence Research and Development Canada-Toronto, Toronto, ON, Canada.
J Exp Psychol Learn Mem Cogn. 2006 Sep;32(5):1019-30. doi: 10.1037/0278-7393.32.5.1019.
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.
E. L. 德洛什、J. R. 布西迈尔和M. A. 麦克丹尼尔（1997年）发现，当学习连续预测变量（x）和连续标准（y）之间的正线性关系时，受训者在被要求进行外推的项目上往往会低估y。在3个实验中，作者研究了这一现象，发现只有在所谓的较低外推区域，即x的新值介于零和训练区域边缘之间时，低估y的趋势才是可靠的。现有的函数学习模型，如外推关联模型（德洛什等人，1997年）和线性专家群体模型（M. L. 卡利什、S. 莱万多夫斯基和J. 克鲁施克，2004年），无法解释这些结果。作者表明，只需进行微小的修改，这两个模型都可以预测正确的结果模式。
