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快速节俭树的信号检测分析。

A signal-detection analysis of fast-and-frugal trees.

机构信息

School of Social Sciences, Singapore Management University, Singapore, 178903.

出版信息

Psychol Rev. 2011 Apr;118(2):316-38. doi: 10.1037/a0022684.

DOI:10.1037/a0022684
PMID:21381858
Abstract

Models of decision making are distinguished by those that aim for an optimal solution in a world that is precisely specified by a set of assumptions (a so-called "small world") and those that aim for a simple but satisfactory solution in an uncertain world where the assumptions of optimization models may not be met (a so-called "large world"). Few connections have been drawn between these 2 families of models. In this study, the authors show how psychological concepts originating in the classic signal-detection theory (SDT), a small-world approach to decision making, can be used to understand the workings of a class of simple models known as fast-and-frugal trees (FFTs). Results indicate that (a) the setting of the subjective decision criterion in SDT corresponds directly to the choice of exit structure in an FFT; (b) the sensitivity of an FFT (measured in d') is reflected by the order of cues searched and the properties of cues in an FFT, including the mean and variance of cues' individual d's, the intercue correlation, and the number of cues; and (c) compared with the ideal and the optimal sequential sampling models in SDT and a majority model with an information search component, FFTs are extremely frugal (i.e., do not search for much cue information), highly robust, and well adapted to the payoff structure of a task. These findings demonstrate the potential of theory integration in understanding the common underlying psychological structures of apparently disparate theories of cognition.

摘要

决策模型的区别在于那些旨在在一个由一组假设(所谓的“小世界”)精确规定的世界中找到最佳解决方案的模型,以及那些旨在在一个不确定的世界中找到简单但令人满意的解决方案的模型,在这个世界中,优化模型的假设可能无法满足(所谓的“大世界”)。这两种模型类型之间几乎没有联系。在这项研究中,作者展示了经典信号检测理论(SDT)中的心理概念如何能够用于理解一类简单模型的工作原理,这些模型称为快速而节俭的树(FFT)。结果表明:(a)SDT 中的主观决策标准设置直接对应于 FFT 中出口结构的选择;(b)FFT 的敏感性(以 d'衡量)反映了在 FFT 中搜索线索的顺序以及线索的特性,包括线索的个体 d'的均值和方差、线索之间的相关性以及线索的数量;(c)与 SDT 中的理想和最优顺序采样模型以及具有信息搜索组件的多数模型相比,FFT 非常节俭(即,不会搜索太多线索信息),具有高度鲁棒性,并且非常适应任务的收益结构。这些发现表明了理论整合在理解认知的明显不同理论的共同潜在心理结构方面的潜力。

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