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A central goal of education is to provide students with knowledge they: (a) can successfully apply within the domain of learning and (b) can transfer, as appropriate, to new and different domains. Yet, much research has shown that learners frequently fail to access and use applicable stored knowledge when the circumstances at retrieval differ from those at encoding. Recent laboratory studies have revealed that encoding instances of a concept as members of a relational category considerably attenuates such failures of access. In the present work, we evaluate the efficacy of one such technique (category construction) applied in conjunction with direct instruction in the authentic educational setting of middle-school classrooms. Across two experiments, category construction is evaluated relative to a standard classroom practice of a worksheet with comprehension questions in order to assess how well each promotes mastery and transfer of a target concept. In addition, potential supports to improve learning outcomes are assessed: practice with category construction (Experiment 1) and partial precompletion of the category construction task (Experiment 2). Overall, we find favorable evidence that category construction leads to greater sensitivity to the underlying conceptual structure of targeted concepts and delivers transfer advantages over a worksheet control. Educational Impact and Implications Statement: A key goal of education is not only to support mastery of complex content but also to enable students to access and apply what has been learned to new settings and domains. Results from multiple inclass experiments show that supporting initial learning through category construction can enhance performance relative to a control group on assessments of mastery and transfer. This work highlights a lightweight classroom activity that could be implemented broadly to enhance educational outcomes. [ABSTRACT FROM AUTHOR]

It has become widely accepted that standard connectionist models are unable to show identity-based relational reasoning that requires universal generalization. The purpose of this brief report is to show how one of the simplest forms of such models, feed-forward auto-associative networks, satisfies two of the most well-known challenges: universal generalization of the identity function and the reduplication rule. Given the simplicity of the modeling account provided, along with the clarity of the evidence, these demonstrations invite a shift in this high-profile debate over the nature of cognitive architecture and point to a way to bridge some of the presumed gulf between characteristically symbolic forms of reasoning and connectionist mechanisms., (© 2025 The Author(s). Cognitive Science published by Wiley Periodicals LLC on behalf of Cognitive Science Society (CSS).)

The ability to spontaneously access knowledge of relational concepts acquired in one domain and apply it to a novel domain has traditionally been explored in the analogy literature via the problem-solving paradigm. In the present work, we propose a novel procedure based on categorisation as a complementary approach to assess spontaneous analogical transfer-using one category learning task to enhance learning of the same underlying category structures in another domain. In Experiment 1, we demonstrate larger improvements in classification performance across blocks of training in a target category learning task among participants that underwent a base category learning task relative to a separate group of participants learning the target category structures for the first time, thus providing evidence for spontaneous transfer of the category structures. In Experiment 2, we demonstrate similar evidence of spontaneous transfer for participants that underwent a comparison-based base category learning task under a more rigorous context shift between the base and target category learning tasks. Additional exploratory analyses across both experiments showcase ways in which this paradigm can be used to answer questions regarding the analogical transfer of relational category structures and generate promising paths for future work., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.