Reliance on Small Samples, the Wavy Recency Effect, and Similarity-Based Learning
Reliance on Small Samples, the Wavy Recency Effect, and Similarity-Based Learning
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DOI：
10.1037/a0039413
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发表时间：
2015-10-01
影响因子：
5.4
通讯作者：
Erev, Ido
中科院分区：
文献类型：
--
作者：
Plonsky, Ori;Teodorescu, Kinneret;Erev, Ido
许多行为现象，包括对罕见事件的低估和概率匹配，都可能是依赖于小样本经验的倾向的产物。为什么要使用小样本，哪些经验可能包括在这些样本中？以前的研究表明，对最近经历的认知有效依赖可能非常有效。我们探索了一个非常不同的和更高的认知要求的过程，解释倾向于依赖于小样本：环境污染的开发。我们研究的第一部分表明，在广泛的动态二元选择环境中，只关注当前任务之前的相同结果序列的经验比关注最近的经验更有效。我们研究的第二部分考察了这些基于序列的规则的心理意义。它表明，这些易于处理的规则再现众所周知的迹象的敏感性序列和预测的非平凡的波浪近因效应的罕见事件。对已发表数据的分析支持这种波动近因预测，但也表明比这些基于序列的规则预测的更波动的影响。这种模式，以及在经验和概率学习任务的基本决策中记录的主要行为现象，可以用基于相似性的模型来捕捉，假设人们在大多数时间内遵循结果的序列，但有时会对趋势做出反应。最后，我们总结了基于相似性学习的理论笔记。
Many behavioral phenomena, including underweighting of rare events and probability matching, can be the product of a tendency to rely on small samples of experiences. Why would small samples be used, and which experiences are likely to be included in these samples? Previous studies suggest that a cognitively efficient reliance on the most recent experiences can be very effective. We explore a very different and more cognitively demanding process explaining the tendency to rely on small samples: exploitation of environmental regularities. The first part of our study shows that across wide classes of dynamic binary choice environments, focusing only on experiences that followed the same sequence of outcomes preceding the current task is more effective than focusing on the most recent experiences. The second part of our study examines the psychological significance of these sequence-based rules. It shows that these tractable rules reproduce well-known indications of sensitivity to sequences and predict a nontrivial wavy recency effect of rare events. Analysis of published data supports this wavy recency prediction, but suggests an even wavier effect than these sequence-based rules predict. This pattern, and the main behavioral phenomena documented in basic decisions from experience and probability learning tasks, can be captured with a similarity-based model assuming that people follow sequences of outcomes most of the time but sometimes respond to trends. We conclude with theoretical notes on similarity-based learning.