Zivot Matthew T, Cohen Andrew L
University of Massachusetts, <location>Amherst, MA, USA</location>
Exp Psychol. 2014;61(4):285-300. doi: 10.1027/1618-3169/a000247.
The goal of the current research is to use the experimental methods and mathematical models of the information integration framework to precisely determine how category and feature information are combined when making an inference. In three experiments, participants were trained on a probabilistic relationship between a category label and the presence of a property and, separately, the relationship between a visual feature and the presence of the property. Participants were then shown the category label alone, the feature alone, or both in combination, and asked to infer the presence or absence of the property. Two information integration models, the fuzzy logical model of perception and the linear integration model, were fit to the data. The modeling results show that participants were non-Bayesian in their combination of the two sources of information, showed diversity in the relative weight placed on category information, and consistently used each source of information to the extent to which it was known.
当前研究的目标是运用信息整合框架的实验方法和数学模型，精确确定在进行推理时类别信息和特征信息是如何结合的。在三个实验中，参与者接受了关于类别标签与属性存在之间概率关系的训练，以及单独关于视觉特征与属性存在之间关系的训练。然后，向参与者单独展示类别标签、单独展示特征或两者结合展示，并要求他们推断属性的存在与否。将两种信息整合模型，即感知模糊逻辑模型和线性整合模型，与数据进行拟合。建模结果表明，参与者在整合这两种信息源时并非遵循贝叶斯方法，在赋予类别信息的相对权重上存在差异，并且在已知每种信息源的程度上持续使用该信息源。
