Bereby-Meyer Y, Erev I
Technion-Israel Institute of Technology
J Math Psychol. 1998 Jun;42(2/3):266-86. doi: 10.1006/jmps.1998.1214.
One of the main difficulties in the development of descriptive models of learning in repeated choice tasks involves the abstraction of the effect of losses. The present paper explains this difficulty, summarizes its common solutions, and presents an experiment that was designed to compare the descriptive power of the specific quantifications of these solutions proposed in recent research. The experiment utilized a probability learning task. In each of the experiment's 500 trials participants were asked to predict the appearance of one of two colors. The probabilities of appearance of the colors were different but fixed during the entire experiment. The experimental manipulation involved an addition of a constant to the payoffs. The results demonstrate that learning in the loss domain can be faster than learning in the gain domain; adding a constant to the payoff matrix can affect the learning process. These results are consistent with Erev & Roth's (1996) adjustable reference point abstraction of the effect of losses, and violate all other models. Copyright 1998 Academic Press.
在重复选择任务中开发学习描述模型的主要困难之一涉及损失效应的抽象。本文解释了这一困难，总结了其常见解决方案，并呈现了一项实验，该实验旨在比较近期研究中提出的这些解决方案的具体量化的描述能力。该实验采用了概率学习任务。在实验的500次试验中的每一次，参与者都被要求预测两种颜色之一的出现。颜色出现的概率不同，但在整个实验过程中是固定的。实验操作涉及给收益添加一个常数。结果表明，在损失领域的学习可能比在收益领域的学习更快；给收益矩阵添加一个常数会影响学习过程。这些结果与埃雷夫和罗斯（1996年）关于损失效应的可调整参考点抽象一致，并且与所有其他模型相悖。版权所有1998年学术出版社。
