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定量估计模型:基于规则和基于范例的过程比较。

Models of quantitative estimations: rule-based and exemplar-based processes compared.

作者信息

von Helversen Bettina, Rieskamp Jörg

机构信息

Department of Psychology, University of Basel, Basel, Switzerland.

出版信息

J Exp Psychol Learn Mem Cogn. 2009 Jul;35(4):867-89. doi: 10.1037/a0015501.

Abstract

The cognitive processes underlying quantitative estimations vary. Past research has identified task-contingent changes between rule-based and exemplar-based processes (P. Juslin, L. Karlsson, & H. Olsson, 2008). B. von Helversen and J. Rieskamp (2008), however, proposed a simple rule-based model-the mapping model-that outperformed the exemplar model in a task thought to promote exemplar-based processing. This raised questions about the assumptions of rule-based versus exemplar-based models that underlie the notion of task contingency of cognitive processes. Rule-based models, such as the mapping model, assume the abstraction of explicit task knowledge. In contrast, exemplar models should profit if storage and activation of the exemplars is facilitated. Two studies tested the importance of the two models' assumptions. When knowledge about cues existed, the rule-based mapping model predicted quantitative estimations best. In contrast, when knowledge about the cues was difficult to gain, participants' estimations were best described by an exemplar model. The results emphasize the task contingency of cognitive processes.

摘要

数量估计背后的认知过程各不相同。过去的研究已经确定了基于规则和基于范例的过程之间因任务而异的变化(P. 尤斯林、L. 卡尔松和H. 奥尔松,2008年)。然而,B. 冯·赫尔弗森和J. 里斯坎普(2008年)提出了一个简单的基于规则的模型——映射模型——在一个被认为促进基于范例处理的任务中,该模型的表现优于范例模型。这就引发了关于基于规则与基于范例的模型假设的问题,这些假设构成了认知过程任务偶然性概念的基础。基于规则的模型,如映射模型,假设明确的任务知识是可抽象提取的。相比之下,如果范例的存储和激活得到促进,范例模型应该会从中受益。两项研究检验了这两种模型假设的重要性。当存在关于线索的知识时,基于规则的映射模型对数量估计的预测最为准确。相比之下,当难以获取关于线索的知识时,参与者的估计最好用范例模型来描述。研究结果强调了认知过程的任务偶然性。

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