An analysis of the process of analogical thinking predicts that analogies will be noticed on the basis of semantic retrieval cues and that the induction of a general schema from concrete analogs will facilitate analogical transfer. These predictions were tested in experiments in which subjects first read one or more stories illustrating problems and their solutions and then attempted to solve a disparate but analogous transfer problem. The studies in Part I attempted to foster the abstraction of a problem schema from a single story analog by means of summarization instructions, a verbal statement of the underlying principle, or a diagrammatic representation of it. None of these devices achieved a notable degree of sucess. In contrast, the experiments in Part II demonstrated that if two prior analogs were given, subjects often derived a problem schema as an incidental product of describing the similarities of the analogs. The quality of the induced schema was highly predictive of subsequent transfer performance. Furthermore, the verbal statements and diagrams that had failed to facilitate transfer from one analog proved highly beneficial when paired with two. The function of examples in learning was discussed in light of the present study. Analogy pervades thought. When a John Donne proposes that "no man is an island," we feel an intuitive grasp of the interconnectedness of human relations. When a William Harvey compares a biological organ to a water pump, a productive scientific model of blood circulation is created; in addition, the meaning of "pump" may take on a new, more abstract form. When a student is told that the atom resembles a miniature solar system, a complex new concept may take root in the learner's mind. To make the novel seem familiar by relating it to prior knowledge, to make This paper is largely based on a PhD dissertation completed by Gick (Note 1) under the direction of Holyoak, together with additional collaborative experiments and analyses. The work benefitted from the guidance of the members of the dissertation committee: John Jonides, Manfred Kochen, David Krantz, and substitute member John Holland. Patricia Cheng provided incisive criticisms of an early draft; a subsequent draft benefitted from the reviews of Dedre Gentner, Earl Hunt, and an anonymous referee. Terra Albert, Holly Brewer, Tim Carroll, Teresa Frankovich, and Michael Smith ably assisted in testing subjects. Susan Petersen and Jean Schtokal assisted with both subject testing and scoring of data.

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This article describes an integrated theory of analogical access and mapping, instantiated in a computational model called LISA (Learning and Inference with Schemas and Analogies). LISA represents predicates and objects as distributed patterns of activation that are dynamically bound into prepositional structures, thereby achieving both the flexibility of a connectionist system and the structure sensitivity of a symbolic system. The model treats access and mapping as types of guided pattern classification, differing only in that mapping is augmented by a capacity to learn new correspondences. The resulting model simulates a wide range of empirical findings concerning human analogical access and mapping. LISA also has a number of inherent limitations, including capacity limits, that arise in human reasoning and suggests a specific computational account of these limitations. Extensions of this approach also account for analogical inference and schema induction.A fundamental challenge for cognitive science is to understand the architecture that underlies human thinking. Two general properties of thinking jointly present extremely challenging design requirements. First, thinking is structure sensitive. Reasoning, problem solving, and learning (as well as language and vision) depend on a capacity to code and manipulate relational knowledge, with complex structures emerging from the systematic recombination of more primitive elements (Fodor & Pylyshyn, 1988). Second, thinking \& flexible in the way in which knowledge is accessed and used. People apply old knowledge to new situations that are similar but by no means identical, somehow recognizing and exploiting useful partial matches. Both of these properties, structure sensitivity and flexibility, are apparent in the use of analogies (Centner, 1983), schemas (Rumelhart, 1980), and rules (Anderson, 1983).The first steps in analogical thinking are access and mapping. Access is the process of retrieving a familiar source analog (or schema, or rule) from memory given a novel target problem as a cue. Mapping is the process of discovering which elements in the target correspond to which in the source. For example, in the analogy between the atom and the solar system, the sun maps to the nucleus of the atom rather than to the electrons (Centner, 1983). Once a source has been retrieved from memory and mapped onto the target, the former can be used to generate inferences about the latter; jointly, the two can be used to induce a more general schema that captures the essential properties

Over the last quarter century, the dominant tendency in comparative cognitive psychology has been to emphasize the similarities between human and nonhuman minds and to downplay the differences as "one of degree and not of kind" (Darwin 1871). In the present target article, we argue that Darwin was mistaken: the profound biological continuity between human and nonhuman animals masks an equally profound discontinuity between human and nonhuman minds. To wit, there is a significant discontinuity in the degree to which human and nonhuman animals are able to approximate the higher-order, systematic, relational capabilities of a physical symbol system (PSS) (Newell 1980). We show that this symbolic-relational discontinuity pervades nearly every domain of cognition and runs much deeper than even the spectacular scaffolding provided by language or culture alone can explain. We propose a representational-level specification as to where human and nonhuman animals' abilities to approximate a PSS are similar and where they differ. We conclude by suggesting that recent symbolic-connectionist models of cognition shed new light on the mechanisms that underlie the gap between human and nonhuman minds.