We present a model of similarity-based retrieval which attempts to capture three psychological phenomena: (1) people are extremely good at judging similarity and analogy when given items to compare. (2) Superficial remindings are much more frequent than structural remindings. (3) People sometimes experience and use purely structural analogical remindings. Our model, called MAC/FAC (for "many are called but few are chosen") consists of two stages. The first stage (MAC) uses a computationally cheap, non-structural matcher to filter candidates from a pool of memory items. That is, we redundantly encode structured representations as content vectors, whose dot product yields an estimate of how well the corresponding structural representations will match. The second stage (FAC) uses SME to compute a true structural match between the probe and output from the first stage. MAC/FAC has been fully implemented, and we show that it is capable of modeling patterns of access found in psychological ...

The nature of categories and their acquisition is one of the central open questions in Cognitive Science. We suggest that categories are represented via structured descriptions and formed by a process of progressive abstraction, through successive comparison with incoming exemplars. This paper describes how SEQL (Skorstad, Gentner, & Medin, 1988), a computer model for category learning, which is based on SME (Falkenhainer et al 1986, 1989; Forbus et al 1994) can be used to simulate a recent categorization experiment (Ramscar & Pain, 1996), using a new algorithm, Generalization and Exemplar Learning (GEL). We demonstrate that SEQL produces behavior consistent with human subjects. Introduction Similarity is often viewed as central to categorization. For instance, prototype theories of categorization posit that categorization decisions are made on the basis of the similarity of an entity to the prototypical member of that category (Rosch 1975). However, similarity-based accou...

Similarity-based and rule-based accounts of cognition are often portrayed as opposing accounts. In this paper we suggest that in learning and development, the process of comparison can act as a bridge between similarity-based and rule-based processing. We suggest that comparison involves a process of structural alignment and mapping between two representations. This kind

One of the original motivations for qualitative physics research was the creation of a computational account of mental models. For instance, a key intuition often associated with mental models is that they are runnable, i.e., there is a sense of deriving answers via mental simulation rather than logical reasoning. This paper examines three explanations for runnability, and argues that none of them is sufficient. Instead, a hybrid model combining aspects of all three is proposed, focusing on the integration of ideas from qualitative physics with ideas from analogical processing. Some psychological implications of this hybrid model are discussed.

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Hofstadter and his colleagues have criticized current accounts of analogy, claiming that such accounts do not accurately capture interactions between processes of representation construction and processes of mapping. They suggest instead that analogy should be viewed as a form of high level perception that encompasses both representation building and mapping as indivisible operations within a single model. They argue specifically against SME, our model of analogical matching, on the grounds that it is modular, and offer instead programs such as Mitchell and Hofstadter's Copycat as examples of the high level perception approach. In this paper we argue against this position on two grounds. First, we demonstrate that most of their specific arguments invo lving SME and Copycat are incorrect. Second, we argue that the claim that analogy is high-level perception, while in some ways an attractive metaphor, is too vague to be useful as a technical proposal. We focus on five issues: (1) how perception relates to analogy, (2) how flexibility arises in analogical processing, (3) whether analogy is a domain-general process, (4) how micro-worlds should be used in the study of analogy, and (5) how best to assess the psychological plausibility of a model of analogy. We illustrate our discussion with examples taken from computer models embodying both views.

Reasoning by analogy is a central phenomena in cognition.

Learning spatial prepositions is an important problem in spatial cognition. We describe a model for learning how to classify visual scenes according to what spatial preposition they depict. We use SEQL, an existing model of analogical generalization, to construct relational descriptions from stimuli input as hand-drawn sketches. We show that this model can distinguish between in, on, above, below, and left, after being trained on simple sketches exemplifying each preposition.

Quantities are ubiquitous and an important part of our understanding about the world – we talk of engine horsepower, size, mileage, price of cars; GDP, population, area of countries; wingspan, weight, surface area of birds, and so on. In this paper, we present a sketch of a theory of quantity – cognitively sound representations and principles for generating those representations. We present evidence from psychology, natural language, and ecological constraints to argue for a cognitively plausible representation of quantities. We then propose a general principle of how to make the necessary and relevant distinctions. Structured models of retrieval, similarity, and generalization, and in general models involving symbolic representations, do not handle quantities adequately. That is an artifact of poor representations of quantity, and we believe that the representations proposed here will make these models more quantity-aware. This investigation is at the intersection of qualitative reasoning, cognitive psychology, and linguistics, and builds on existing evidence in these fields to potentially contribute to the understanding of quantities in all the three. 1

ssing. We suggest that some types of similarity are determined automatically. When the cognitive system recognizes similarities, they influence cognitive processing, even when the person does not intend for their processing to be affected by similarities. In order to support this claim, we first outline three different approaches to similarity. Then, we examine how similarity can influence both low-level processes like attention and memory retrieval and higher cognitive processes like analogical reasoning and decision making. Next, we explore a number of examples in which cognitive processing is influenced by the presence of similarities in a stimulus set. Finally, we broaden the discussion to include similarities in more deliberate cognitive processes. Three Approaches to Similarity Representation and Similarity When a person makes a similarity comparison, the result is typically both a judgment of similarity and also some awareness of the commonalities and differences of the pair com