Ignore Similarity If You Can: A Computational Exploration of Exemplar Similarity Effects on Rule Application.
Ignore Similarity If You Can: A Computational Exploration of Exemplar Similarity Effects on Rule Application.
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DOI：
10.3389/fpsyg.2017.00424
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发表时间：
2017
影响因子：
3.8
通讯作者：
Hahn U
中科院分区：
文献类型：
--
作者：
Brumby DP;Hahn U
一般认为，在进行范畴化判断时，认知系统会把注意力集中在与做出准确判断相关的刺激特征上。这是混合分类系统的一个关键特征，它有选择地权衡基于样本和基于规则的过程的使用。相比之下，研究表明，即使这样做会导致分类错误，人们也会忍不住注意样本相似性。本文通过一系列的计算机模拟，检验了在ACT-R认知结构(BY)中开发的混合分类模型是否能够解释Hahn等人的观点。数据集。该模型实现了基于样本的随机游走模型作为样本路径，并将其与基于规则的模型RULEX的实现相结合。对模型参数空间的彻底搜索表明，尽管对响应时间的样本相似性效应的存在与分类错误有关，但有可能将这两种衡量标准与任务的非监督版本的观察数据相匹配(即，在该版本中没有提供关于准确性的反馈)。当该模型被应用到任务的监督版本时，出现了困难，在该任务中，对准确性给出了明确的反馈。建模结果表明，随着模型学习避开容易出错的样本路径，取而代之的是准确的规则路径，反馈减弱了样本相似性的影响。与模型相反，Hahn等人。研究发现，即使得到反馈，人们也会继续表现出强大的样本相似性效应。这项工作突出了一项挑战，即理解人们在做出分类决策时如何以及为什么结合规则和范例。
It is generally assumed that when making categorization judgments the cognitive system learns to focus on stimuli features that are relevant for making an accurate judgment. This is a key feature of hybrid categorization systems, which selectively weight the use of exemplar- and rule-based processes. In contrast, have shown that people cannot help but pay attention to exemplar similarity, even when doing so leads to classification errors. This paper tests, through a series of computer simulations, whether a hybrid categorization model developed in the ACT-R cognitive architecture (by) can account for the Hahn et al. dataset. This model implements exemplar-based random walk model as its exemplar route, and combines it with an implementation of rule-based model RULEX. A thorough search of the model’s parameter space showed that while the presence of an exemplar-similarity effect on response times was associated with classification errors it was possible to fit both measures to the observed data for an unsupervised version of the task (i.e., in which no feedback on accuracy was given). Difficulties arose when the model was applied to a supervised version of the task in which explicit feedback on accuracy was given. Modeling results show that the exemplar-similarity effect is diminished by feedback as the model learns to avoid the error-prone exemplar-route, taking instead the accurate rule-route. In contrast to the model, Hahn et al. found that people continue to exhibit robust exemplar-similarity effects even when given feedback. This work highlights a challenge for understanding how and why people combine rules and exemplars when making categorization decisions.