Category Learning in a Transitive Inference Paradigm

The implied order of a ranked set of visual images can be learned by transitive inference, without reliance on stimulus features that explicitly signal their order. Such learning is difficult to explain by associative mechanisms but can be accounted for by cognitive representations and processes such as transitive inference. Our study seeks to determine if those representations may be applied to categories of images without explicit verbal instruction. Specifically, we asked whether participants can (a) infer that images being presented belonged to familiar categories, even when every image presented during every trial is unique, and (b) perform transitive inferences about the ordering of those categories. To address these questions, we compared the performance of humans during a standard TI task, which used the same set of images throughout the session, to performance in a category TI tasks, which drew images from a set of categories. Each of the images used in the category TI task was only presented once, limiting the extent to which stimulus-outcome associations could be learned. Participants were able to learn the order of the categories based on transitive inference. However, participants in the category TI condition did not produce a symbolic distance effect. These findings collectively suggest that differing cognitive processes may underpin serial learning when learning about specific stimuli versus stimulus categories.