Abstract not found

Analogy and similarity are often assumed to be distinct psychological processes. In contrast to this position, the authors suggest that both similarity and analogy involve a process of structural alignment and mapping, that is, that similarity is like analogy. In this article, the authors first describe the structure-mapping process as it has been worked out for analogy. Then, this view is extended to similarity, where it is used to generate new predictions. Finally, the authors explore broader implications of structural alignment for psychological processing.

We describe a computational model of how analogs are retrieved from memory using simultaneous satisfaction of a set of semantic, structural, and pragmatic constraints. The model is based on psychological evidence suggesting that human memory retrieval tends to favor analogs that have several kinds of correspondences with the structure that prompts retrieval: semantic similarity, isomorphism, and pragmatic relevance. We describe ARCS, a program that demonstrates how these constraints can be used to select relevant analogs by forming a network of hypotheses and attempting to satisfy the constraints simultaneously. ARCS has been tested on several data bases that display both its psychological plausibility and computational power.

Two connectionist frameworks, GRAIN (McClelland, 1993) and BSB (Anderson, 1991), and the diffusion model (Ratcliff, 1978) were evaluated using data from a signal detection task. Subjects were asked to choose one of two possible responses to a stimulus and were provided feedback about whether the choice was correct. The dependent variables included response probabilities, reaction times for correct and error responses, and reaction time distributions, and the independent variables were stimulus value, stimulus probability, and lag from an abrupt switch in stimulus probability. The diffusion model accounted for all aspects of the asymptotic data, including error reaction times, which had previously been a problem. The connectionist models accounted for many aspects of the data adequately, but each failed to a greater or lesser degree in important ways except for one model very similar to the diffusion model. The connectionist learning mechanisms were unable to account for initial learning or abrupt changes in stimulus probability. The results provide an advance in the development of the diffusion model and show that the long tradition of reaction time research and theory is a fertile domain for development and testing of connectionist assumptions about how decisions are generated over time.

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

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

There has been significant interest in indirect measures of attitudes like the lmplicit Association Test (IAT), presumably because of the possibility of uncovering implicit prejudices. The authors derived a set of qualitative predictions for people's performance in the IAT on the basis of random walk models. These were supported in 3 experiments comparing clearly positive or negative categories to nonwords. They also provided evidence that participants shift their response criterion when doing the IAT. Because of these criterion shifts, a response panem in the IAT can have multiple causes. Thus, it is not possible to infer a single cause (such as prejudice) from IAT results. A surprising additional result was that nonwords were treated a. though they were evaluated more negatively than obviously negative items like insects, suggesting that low familiarity items may generate the pattern of data previously interpreted as evidence for implicit prejudice. What do you think of flowers? Would you evaluate them pos-itively? If so, what do you think of Larnists? Do you think a Larnist is more negative or more positive than a flower? As you may have realized, a Larnist is not an English word; we made it up. Presumably, you do not have a prestored opinion of Larnists, and

We present interpretation-based processing—a theory of sentence processing that builds a syntactic and a semantic representation for a sentence and assigns an interpretation to the sentence as soon as possible. That interpretation can further participate in comprehension and in lexical processing and is vital for relating the sentence to the prior discourse. Our theory offers a unified account of the processing of literal sentences, metaphoric sentences, and sentences containing semantic illusions. It also explains how text can prime lexical access. We show that word literality is a matter of degree and that the speed and quality of comprehension depend both on how similar words are to their antecedents in the preceding text and how salient the sentence is with respect to the preceding text. Interpretation-based processing also reconciles superficially contradictory findings about the difference in processing times for metaphors and literals. The theory has been implemented in ACT-R [Anderson and Lebiere, The

this paper. In the General Discussion we will consider how a one-process model might accommodate the results as well. However, next we continue with our main purpose, which is to contribute to the exposition of two-process models by analyzing data within this framework, in particular by assessing the recall-toreject account

: Holographic Reduced Representations (HRRs) are a method for encoding nested relational structures in fixed width vector representations. HRRs encode relational structures as vector representations in such a way that the superficial similarity of the vectors reflects both superficial and structural similarity of the relational structures. HRRs also support a number of operations that could be very useful in psychological models of human analogy processing: fast estimation of superficial and structural similarity via a vector dot-product; finding corresponding objects in two structures; and chunking of vector representations. Although similarity assessment and discovery of corresponding objects both theoretically take exponential time to perform fully and accurately, with HRRs one can obtain approximate solutions in constant time. The accuracy of these operations with HRRs mirrors patterns of human performance on analog retrieval and processing tasks. Keywords: neural networks, distributed representations, binding, analogy, analog retrieval, structure, chunking, systematicity 1