The use of analogy is an important component of human cognition. The type of analogy we produce and communicate depends heavily on a number of factors, such as the setting, the level of domain expertise present, and the speaker's goal or intent. In this observational study, we recorded economics experts during scientific discussion and examined the categorical distance and structural depth of the analogies they produced. We also sought to characterize the purpose of the analogies that were generated. Our results (...) supported previous conclusions about the infrequency of superficial similarity in subject-generated analogs, but also showed that distance and depth characteristics were more evenly balanced than in previous observational studies. This finding was likely due to the nature of the goals of the participants, as well as the broader nature of their expertise. An analysis of analogical purpose indicated that the generation of concrete source examples of more general target concepts was most prevalent. We also noted frequent instances of analogies intended to form visual images of source concepts. Other common purposes for analogies were the addition of colorful speech, inclusion (i.e., subsumption) of a target into a source concept, or differentiation between source and target concepts. We found no association between depth and either of the other _two_ characteristics, but our findings suggest a relationship between purpose and distance; i.e., that visual imagery typically entailed an outside-domain source whereas exemplification was most frequently accomplished using within-domain analogies. Overall, we observed a rich and diverse set of spontaneously produced analogical comparisons. The high degree of expertise within the observed group along with the richly comparative nature of the economics discipline likely contributed to this analogical abundance. (shrink)

Analogy and similarity are central phenomena in human cognition, involved in processes ranging from visual perception to conceptual change. To capture this centrality requires that a model of comparison must be able to integrate with other processes and handle the size and complexity of the representations required by the tasks being modeled. This paper describes extensions to Structure-Mapping Engine since its inception in 1986 that have increased its scope of operation. We first review the basic SME algorithm, describe psychological evidence (...) for SME as a process model, and summarize its role in simulating similarity-based retrieval and generalization. Then we describe five techniques now incorporated into the SME that have enabled it to tackle large-scale modeling tasks: Greedy merging rapidly constructs one or more best interpretations of a match in polynomial time: O); Incremental operation enables mappings to be extended as new information is retrieved or derived about the base or target, to model situations where information in a task is updated over time; Ubiquitous predicates model the varying degrees to which items may suggest alignment; Structural evaluation of analogical inferences models aspects of plausibility judgments; Match filters enable large-scale task models to communicate constraints to SME to influence the mapping process. We illustrate via examples from published studies how these enable it to capture a broader range of psychological phenomena than before. (shrink)

One of the central issues in cognitive science is the nature of human representations. We argue that symbolic representations are essential for capturing human cognitive capabilities. We start by examining some common misconceptions found in discussions of representations and models. Next we examine evidence that symbolic representations are essential for capturing human cognitive capabilities, drawing on the analogy literature. Then we examine fundamental limitations of feature vectors and other distributed representations that, despite their recent successes on various practical problems, suggest (...) that they are insufficient to capture many aspects of human cognition. After that, we describe the implications for cognitive architecture of our view that analogy is central, and we speculate on roles for hybrid approaches. We close with an analogy that might help bridge the gap. (shrink)