Analogy is a powerful tool for fostering conceptual understanding and transfer in STEM and other fields. Well-constructed analogical comparisons focus attention on the causal-relational structure of STEM concepts, and provide a powerful capability to draw inferences based on a well-understood source domain that can be applied to a novel target…

A key property of relational representations is their "generativity": From partial descriptions of relations between entities, additional inferences can be drawn about other entities. A major theoretical challenge is to demonstrate how the capacity to make generative inferences could arise as a result of learning relations from…

How can humans acquire relational representations that enable analogical inference and other forms of high-level reasoning? Using comparative relations as a model domain, we explore the possibility that bottom-up learning mechanisms applied to objects coded as feature vectors can yield representations of relations sufficient to solve analogy…

Many students graduate from K-12 mathematics programs without flexible, conceptual mathematics knowledge. This article reviews psychological and educational research to propose that refining K-12 classroom instruction such that students draw connections through relational comparisons may enhance their long-term ability to transfer and engage with…

Recent studies (e.g. Dawson et al., 2007) have reported that autistic people perform in the normal range on the Raven Progressive Matrices test, a formal reasoning test that requires integration of relations as well as the ability to infer rules and form high-level abstractions. Here we compared autistic and typically developing children, matched…

A fundamental issue for theories of human induction is to specify constraints on potential inferences. For inferences based on shared category membership, an analogy, and/or a relational schema, it appears that the basic goal of induction is to make accurate and goal-relevant inferences that are sensitive to uncertainty. People can use source…

Computational models of analogy have assumed that the strength of an inductive inference about the target is based directly on similarity of the analogs and in particular on shared higher order relations. In contrast, work in philosophy of science suggests that analogical inference is also guided by causal models of the source and target. In 3…

Theories of analogical reasoning have assumed that a 1-to-1 constraint discourages reasoners from mapping a single element in 1 analog to multiple elements in another. Empirical evidence suggests that reasoners sometimes appear to violate the one-to-one constraint when asked to generate mappings, yet virtually never violate it when asked to…

This review of the study of analogy in modern cognitive science sets the stage for three articles that follow to illustrate some current research and theories on analogy use. Analogy is a powerful cognitive mechanism that people use to make inferences and learn new abstractions. (SLD)

A theory of classical conditioning is presented, which is based on a parallel, rule-based performance system integrated with mechanisms for inductive learning. A major inferential heuristic incorporated into the theory involves "unusualness," which is focused on novel cues. The theory is implemented via computer simulation. (TJH)