Limitations of exemplar models of multi-attribute probabilistic inference.
Limitations of exemplar models of multi-attribute probabilistic inference.
复制标题
多属性概率推理示例模型的局限性。
DOI：
10.1037/0278-7393.33.6.999
复制
发表时间：
2007
期刊：
影响因子：
--
通讯作者：
Bergert,FBryabn
中科院分区：
文献类型：
--
作者：
Nosofsky,RobertM;Bergert,FBryabn
观察者被呈现给沿着二进制值属性变化的对象对，并学习预测每对对象中的哪个成员在连续变化的标准变量上具有更大的值。通过测试属性之间的交互作用预测标准变量的大小的结构，将分类样本模型的预测与经典替代模型进行了对比，其中包括广义版本的“取最好的”模型和加权相加模型。在典型的训练条件下，观察者对属性交互作用表现出很小的敏感性，从而挑战了样本模型的预测。在高度扩展训练的情况下，观察者最终了解了属性交互作用和标准变量之间的关系。然而，对观察者做出配对比较决定的反应时间的分析也对样本模型的预测提出了挑战。相反，似乎大多数观察者将相互作用的属性重新编码为紧急的配置线索。然后，他们根据线索的优先级应用一套分层组织的规则来做出决定。(智力信息数据库记录(C)2016 APA，保留所有权利)
Observers were presented with pairs of objects varying along binary-valued attributes and learned to predict which member of each pair had a greater value on a continuously varying criterion variable. The predictions from exemplar models of categorization were contrasted with classic alternative models, including generalized versions of a" take-the-best" model and a weighted-additive model, by testing structures in which interactions between attributes predicted the magnitude of the criterion variable. Under typical training conditions, observers showed little sensitivity to the attribute interactions, thereby challenging the predictions from the exemplar models. In a condition involving highly extended training, observers eventually learned the relations between the attribute interactions and the criterion variable. However, an analysis of the observers' response times for making their paired-comparison decisions also challenged the exemplar model predictions. Instead, it appeared that most observers recoded the interacting attributes into emergent configural cues. They then applied a set of hierarchically organized rules based on the priority of the cues to make their decisions.(PsycINFO Database Record (c) 2016 APA, all rights reserved)