School of Psychology, University of New South Wales, Sydney, NSW 2052, Australia.
Mem Cognit. 2011 Feb;39(2):304-18. doi: 10.3758/s13421-010-0009-4.
In four experiments, we investigated how people make feature predictions about objects whose category membership is uncertain. Artificial visual categories were presented and remained in view while a novel instance with a known feature, but uncertain category membership was presented. All four experiments showed that feature predictions about the test instance were most often based on feature correlations (referred to as feature conjunction reasoning). Experiment 1 showed that feature conjunction reasoning was generally preferred to category-based induction in a feature prediction task. Experiment 2 showed that people used all available exemplars to make feature conjunction predictions. Experiments 3 and 4 showed that the preference for predictions based on feature conjunction persisted even when category-level information was made more salient and inferences involving a larger number of categories were required. Little evidence of reasoning based on the consideration of multiple categories (e.g., Anderson, (Psychological Review, 98:409-429, 1991)) or the single, most probable category (e.g., Murphy & Ross, (Cognitive Psychology, 27:148-193, 1994)) was found.
在四项实验中，我们研究了人们如何对类别归属不确定的物体进行特征预测。呈现人工视觉类别，并在呈现具有已知特征但类别归属不确定的新实例时保持可见。这四个实验都表明，对测试实例的特征预测通常基于特征相关性（称为特征联合推理）。实验 1 表明，在特征预测任务中，特征联合推理通常优先于基于类别推断。实验 2 表明，人们使用所有可用的示例来进行特征联合预测。实验 3 和 4 表明，即使在类别信息变得更加突出并且需要涉及更多类别的推理时，基于特征联合的预测的偏好仍然存在。几乎没有证据表明基于考虑多个类别（例如，Anderson，（心理评论，98:409-429，1991））或单个最可能类别（例如，Murphy & Ross，（认知心理学，27:148-193，1994））进行推理。
