Department of Psychological and Brain Sciences, Indiana University, Bloomington, United States.
Department of Psychological and Brain Sciences, Indiana University, Bloomington, United States.
Cogn Psychol. 2023 Sep;145:101596. doi: 10.1016/j.cogpsych.2023.101596. Epub 2023 Aug 30.
Categorization and old-new recognition memory are closely linked topics in the cognitive-psychology literature and there have been extensive past efforts at developing unified formal modeling accounts of these fundamental psychological processes. However, the existing formal-modeling literature has almost exclusively used small sets of simplified stimuli and artificial category structures. The present work extends this literature by collecting both categorization and old-new recognition judgments on a large set of high-dimensional stimuli that form real-world category structures: namely, a set of 540 images of rocks belonging to the geologically-defined categories igneous, metamorphic and sedimentary. Participants first engaged in a learning phase in which they classified large sets of training instances into these real-world categories. This was followed by a test phase in which they classified both training and novel transfer items into the learned categories and also judged whether each item was old or new. We attempted to model both the classification and recognition test data at the level of individual items. Ultimately, the categorization data were well fit by both an exemplar and clustering model, but not by a prototype model. Only the exemplar model was able to provide a reasonable first-order account of the old-new recognition data; however, the standard version of the model failed to capture the variability in hit rates within the class of old-training items themselves. An extended hybrid-similarity version of the exemplar model that made allowance for boosts in self-similarity due to matching distinctive features yielded much improved accounts of the old-new recognition data. The study is among the first to test cognitive-process models on their ability to account quantitatively for old-new recognition of real-world, high-dimensional stimuli at the level of individual items.
分类和新旧识别记忆是认知心理学文献中紧密相关的主题，过去已经有大量的努力来开发这些基本心理过程的统一形式化建模解释。然而，现有的形式化建模文献几乎完全使用了简化的小刺激集和人为的类别结构。本研究通过收集大量形成真实世界类别结构的高维刺激的分类和新旧识别判断，扩展了这一文献，这些刺激是一组 540 张属于火成岩、变质岩和沉积岩的岩石图像。参与者首先参与了一个学习阶段，在这个阶段，他们将大量的训练实例分为这些真实世界的类别。接下来是一个测试阶段，他们将训练和新的转移项目分为所学的类别，并判断每个项目是旧的还是新的。我们试图在单个项目的水平上对分类和识别测试数据进行建模。最终，分类数据被示例模型和聚类模型很好地拟合，但不是原型模型。只有示例模型能够对新旧识别数据提供合理的一阶解释；然而，该模型的标准版本未能捕捉到旧训练项目自身内击中率的变化。一个扩展的混合相似性版本的示例模型，由于匹配独特特征而允许自我相似性的提升，对新旧识别数据提供了更好的解释。这项研究是首次在单个项目水平上测试认知过程模型对真实世界高维刺激的新旧识别的定量解释能力的研究之一。
