Distinctions have been proposed between systems of reasoning for centuries. This article distills properties shared by many of these distinctions and characterizes the resulting systems in light of recent findings and theoretical developments. One system is associative because its computations reflect similarity structure and relations of temporal contiguity. The other is "rule based " because it operates on symbolic structures that have logical content and variables and because its computations have the properties that are normally assigned to rules. The systems serve complementary functions and can simultaneously generate different solutions to a reasoning problem. The rule-based system can suppress the associative system but not completely inhibit it. The article reviews evidence in favor of the distinction and its characterization. One of the oldest conundrums in psychology is whether people are best conceived as parallel processors of information who operate along diffuse associative links or as analysts who operate by deliberate and sequential manipulation of internal representations. Are inferences drawn through a network of learned associative pathways or through application of a kind of "psychologic"

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Analogy is often viewed as a partial similarity match between domains. But not all partial similarities qualify as analogy: There must be some selection of which commonalities count. Three experiments tested o particular selection constraint in anological mapping, namely, systemoticity. That is, we tested whether a given predicate is more likely to figure in the interpretation of and prediction from on analogy if the predicate participates in a common system of relations. In Experiment 1, subjects judged two matches to be included in on analogy: an isolated match, and a match embedded in. a larger matching system. Subjects preferred the embedded match. In Experiments 2 and 3, subjects mode analogical predictions about a target domain. Subjects predicted information that followed from a causal system that matched the base domain, rather than information that was equally plausible, but that created an isolated match with the base. Results support Gentner's (1983, 1989) structure. mopping theory in that anological mopping concerns systems and not individual predicates, and that attention to shored systematic structure constrains the selection of information to include in an analogy.

There are at least two kinds of similarity. Relational similarity is correspondence between relations, in contrast with attributional similarity, which is correspondence between attributes. When two words have a high degree of attributional similarity, we call them synonyms. When two pairs of words have a high degree of relational similarity, we say that their relations are analogous. For example, the word pair mason:stone is analogous to the pair carpenter:wood. This paper introduces Latent Relational Analysis (LRA), a method for measuring relational similarity. LRA has potential applications in many areas, including information extraction, word sense disambiguation, and information retrieval. Recently the Vector Space Model (VSM) of information retrieval has been adapted to measuring relational similarity, achieving a score of 47 % on a collection of 374 college-level multiple-choice word analogy questions. In the VSM approach, the relation between a pair of words is characterized by a vector of frequencies of predefined patterns in a large corpus. LRA extends the VSM approach in three ways: (1) the patterns are derived automatically from the corpus, (2) the Singular Value Decomposition (SVD) is used to smooth the frequency data, and (3) automatically generated synonyms are used to explore variations of the word pairs. LRA achieves 56 % on the 374 analogy questions, statistically equivalent to the average human score of 57%. On the related problem of classifying semantic relations, LRA achieves similar gains over the VSM. 1.

this article, we present a model of similarity comparison that makes specific time course predictions, which were tested in three experiments. Before turning to that model, we first outline the need for a consideration of similarity processes

(LRA), a method for measuring semantic similarity. LRA measures similarity in the semantic relations between two pairs of words. When two pairs have a high degree of relational similarity, they are analogous. For example, the pair cat:meow is analogous to the pair dog:bark. There is evidence from cognitive science that relational similarity is fundamental to many cognitive and linguistic tasks (e.g., analogical reasoning). In the Vector Space Model (VSM) approach to measuring relational similarity, the similarity between two pairs is calculated by the cosine of the angle between the vectors that represent the two pairs. The elements in the vectors are based on the frequencies of manually constructed patterns in a large corpus. LRA extends

According to an "external grounding" theory of meaning, a concept's meaning depends on its connection to the external world. By a "conceptual web" account, a concept's meaning depends on its relations to other concepts within the same system. We explore one aspect of meaning, the identification of matching concepts across systems (e.g. people, theories, or cultures). We present a computational algorithm called ABSURDIST (Aligning Between Systems Using Relations Derived Inside Systems for Translation) that uses only within-system similarity relations to find between-system translations. While illustrating the sufficiency of a conceptual web account for translating between systems, simulations of ABSURDIST also indicate powerful synergistic interactions between intrinsic, within-system information and extrinsic information. q 2002 Elsevier Science B.V. All rights reserved.

ABSTRACT Therefore, the inclusion of mechanisms to aid Reuse mechanisms, such as inheritance in an object-oriented programming approach, are useful to professional programmers but fail to support the occasional programming needs of the end-user. Consequently, a surprisingly high percentage of end-users resort to "copy and paste " approaches for reuse instead of making appropriate use of object-oriented techniques. Visual Analogies are a reuse mechanism for endusers who otherwise would have resorted to "copy and paste. " This paper illustrates how visual analogies avoid some of the problems intrinsic to object-oriented programming by eliminating the pitfall of overgeneralization and

This paper presents a logic-based approach to grouping and perceptual organization, called Minimal Model theory, and presents efficient methods for computing interpretations in this framework. Grouping interpretations are first defined as logical structures, built out of atomic qualitative scene descriptors ("regularities") that are derived from considerations of non-accidentalness. These interpretations can then be partially ordered by their degree of regularity or constraint (measured numerically by their logical depth). The Genericity Constraint---the principle that interpretations should minimize coincidences in the observed configuration---dictates that the preferred interpretation will be the minimum in this partial order, i.e. the interpretation with maximum depth. This maximum-depth interpretation, also called the minimal model or minimal interpretation, is in a sense the "simplest" (algebraically minimal) interpretation available of the image configuration. As a side-effect, the "most salient" or most structured part of the scene can be identified, as the maximum-depth subtree of the minimal model. An efficient (O(n )) method for computing the minimal interpretation is presented, along with examples. Computational experiments show that the algorithm performs well under a wide range of parameter settings.

this article should be addressed to Robert L. Goldstone, Psychology Department, Indiana University, Bloomington, Indiana 47405. Electronic mail may be sent via Internet to rgoldsto@indiana.edu. Further information can be found at http://cognitrn.psych.indiana.edu