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基于经验的估值建模:对阿什比和拉科夫(2014年)的评论。

Modeling valuations from experience: A comment on Ashby and Rakow (2014).

作者信息

Wulff Dirk U, Pachur Thorsten

机构信息

Max Planck Institute for Human Development.

出版信息

J Exp Psychol Learn Mem Cogn. 2016 Jan;42(1):158-66. doi: 10.1037/xlm0000165.

Abstract

What are the cognitive mechanisms underlying subjective valuations formed on the basis of sequential experiences of an option's possible outcomes? Ashby and Rakow (2014) have proposed a sliding window model (SWIM), according to which people's valuations represent the average of a limited sample of recent experiences (the size of which is estimated by the model) formed after sampling has been terminated (i.e., an end-of-sequence process). Ashby and Rakow presented results from which they concluded, on the basis of model-selection criteria, that the SWIM performs well compared with alternative models (e.g., value-updating model, summary model). Further, they reported that the individual window sizes estimated by the SWIM correlated with a measure of working-memory capacity. In a reanalysis of the Ashby and Rakow data, we find no clear evidence in support of any of the models tested, and a slight advantage for the summary model. Further, we demonstrate that individual differences in the window-size estimated by the SWIM can reflect differences in noise. In computer simulations, we examine the more general question of how well the models tested by Ashby and Rakow can actually be discriminated. The results reveal that the models' ability to fit data depends on a complex interplay of noise and the sample size of outcomes on which a valuation response is based. This can critically influence model performance and conclusions regarding the underlying cognitive mechanisms. We discuss the implications of these findings and suggest ways of improving model comparisons in valuations from experience. (PsycINFO Database Record

摘要

基于对一个选项可能结果的连续体验形成主观估值的认知机制是什么?阿什比和拉科夫(2014)提出了一种滑动窗口模型(SWIM),根据该模型,人们的估值代表在采样终止后(即序列结束过程)形成的近期体验的有限样本(其大小由模型估计)的平均值。阿什比和拉科夫展示的结果使他们基于模型选择标准得出结论,即与其他模型(例如,价值更新模型、汇总模型)相比,SWIM表现良好。此外,他们报告称,SWIM估计的个体窗口大小与工作记忆容量的一种测量方法相关。在对阿什比和拉科夫的数据进行重新分析时,我们没有发现明确的证据支持所测试的任何模型,且汇总模型有轻微优势。此外,我们证明,SWIM估计的窗口大小的个体差异可以反映噪声差异。在计算机模拟中,我们研究了一个更普遍的问题,即阿什比和拉科夫所测试的模型实际上能被区分得有多好。结果表明,模型拟合数据的能力取决于噪声与估值反应所基于的结果样本大小之间的复杂相互作用。这可能会严重影响模型性能以及关于潜在认知机制的结论。我们讨论了这些发现的意义,并提出了在基于经验的估值中改进模型比较的方法。(PsycINFO数据库记录

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