使用多重痕迹记忆模型整合过度自信的生态模型和误差模型。

Integration of the ecological and error models of overconfidence using a multiple-trace memory model.

作者信息

Dougherty M R

机构信息

Department of Psychology, University of Maryland, College Park 20742-4411, USA.

出版信息

J Exp Psychol Gen. 2001 Dec;130(4):579-99. doi: 10.1037//0096-3445.130.4.579.

DOI:10.1037//0096-3445.130.4.579

PMID:11757870

Abstract

A memory processes account of the calibration of probability judgments was examined. A multiple-trace memory model, Minerva-Decision Making (MDM; M. R. P. Dougherty, C. F. Gettys, & E. E. Ogden, 1999), used to integrate the ecological (Brunswikian) and the error (Thurstonian) models of overconfidence, is described. The model predicts that overconfidence should decrease both as a function of experience and as a function of encoding quality. Both increased experience and improved encoding quality result in lower variance in the output of the model, which in turn leads to improved calibration. Three experiments confirmed these predictions. Implications of MDM's account of overconfidence are discussed.

摘要

我们考察了一个关于概率判断校准的记忆加工解释。文中描述了一种多痕迹记忆模型——密涅瓦决策模型（MDM；M. R. P. 多尔蒂、C. F. 格蒂斯和E. E. 奥格登，1999），该模型用于整合过度自信的生态模型（布伦斯维克模型）和误差模型（瑟斯顿模型）。该模型预测，过度自信应会随着经验和编码质量的变化而降低。经验增加和编码质量提高都会导致模型输出的方差降低，进而带来校准的改善。三项实验证实了这些预测。我们还讨论了MDM对过度自信解释的意义。