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"

structure. Perceptual classification learning experiments were conducted in which presentation frequencies of individual exemplars were manipulated. The exemplars had varying degrees of similarity to members of the target and contrast categories. Classification accuracy and typicality ratings increased for exemplars presented with high frequency and for members of the target category that were similar to the high-frequency exemplars. Typicality decreased for members of the contrast category that were similar to the high-frequency exemplars. A frequency-sensitive similarity-to-exemplars model provided a good quantitative account of the classification learning and typicality data. The interactive relations among similarity, frequency, and categorization are considered in the General Discussion. Among the most well-established findings in the categorization literature is that categories have "graded structures"

Twelve participants were trained to be experts at identifying a set of `Greebles', novel objects that, like faces, all share a common spatial configuration. Tests comparing expert with novice performance revealed: (1) a surprising mix of generalizability and specificity in expert object recognition processes; and (2) that expertise is a multi-faceted phenomenon, neither adequately described by a single term nor adequately assessed by a single task. Greeble recognition by a simple neural-network model is also evaluated, and the model is found to account surprisingly well for both generalization and individuation using a single set of processes and representations. 1998 Elsevier Science Ltd. All rights reserved. Keywords: Configural encoding; Face recognition; Neural networks; Object categorization; Perceptual expertise 1. Introduction Are the mechanisms used by perceivers as they become increasingly familiar with an object class the same as those used by perceivers when they first en...

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Human episodic memory provides a seemingly unlimited storage for everyday experiences, and a retrieval system that allows us to access the experiences with partial activation of their components. The system is believed to consist of a fast, temporary storage in the hippocampus, and a slow, longterm storage within the neocortex. This paper presents a neural network model of the hippocampal episodic memory inspired by Damasio's idea of Convergence Zones. The model consists of a layer of perceptual feature maps and a binding layer. A perceptual feature pattern is coarse coded in the binding layer, and stored on the weights between layers. A partial activation of the stored features activates the binding pattern, which in turn reactivates the entire stored pattern. For many configurations of the model, a theoretical lower bound for the memory capacity can be derived, and it can be an order of magnitude or higher than the number of all units in the model, and several orders of magnitude higher than the number of binding-layer units. Computational simulations further indicate that the average capacity is an order of magnitude larger than the theoretical lower bound, and making the connectivity between layers sparser causes an even further increase in capacity. Simulations also show that if more descriptive binding patterns are used, the errors tend to be more plausible (patterns are confused with other similar patterns), with a slight cost in capacity. The convergence-zone episodic memory therefore accounts for the immediate storage and associative retrieval capability and large capacity of the hippocampal memory, and shows why the memory encoding areas can be much smaller than the perceptual maps, consist of rather coarse computational units, and be only sparsely connected t...

This paper presents a connectionist model of correlation based categorization by 10month -old infants (Younger, 1985). Simple autoencoder networks were exposed to the same stimuli used to test 10-month-olds. The familiarisation regime was kept as close as possible to that used with the infants. The model's performance matched that of the infants. Both infants and networks used co-variation information (when available) to segregate items into separate categories. The model provides a mechanistic account of category learning with a test session. It demonstrates how categorization arises as the product of an inextricable interaction between the subject (the infant) and the environment (the stimuli). The computational characteristics of both subject and environment must be considered in conjunction to understand the observed behaviors. Mechanisms of Categorization in Infancy The ability to categorize underlies much of cognition. It is a way of reducing the load on memory and oth...

Past researchers studied prototype learning by asking subjects to categorize exemplars constructed from different prototypes. This procedure is less than ideal because learning must be inferred from the percentage of correct categorizations pooled across many trials or subjects or both. An alternative procedure is proposed in which subjects are asked to reproduce their estimate of the prototype on each trial, thereby providing trial-by-trial information about changes in the estimated prototype. This procedure provides straightforward tests of three basic properties implied by several prototype learning models: additivity across exemplars, noninterference among features, and time invariance of serial position effects. An experiment is reported and the results provide reasonably good support for the properties of additivity and noninterference, but clear violations of time invariance were observed. The implications of the results for distributedmemory models and multiple-trace models of prototype learning are discussed. It seems quite easy to produce an image of an ideal circle despite the fact that our experience is based on thousands of and reproduce a single image from a myriad of examples is

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Symbolic): The mental representation itself is a symbolic label similar in conception (though not necessarily similar in structure) to the phonemic transcription labels.

this paper should be send to Herv'e Abdi, Program in Applied Cognition and Neuroscience, The University of Texas at Dallas, Richardson, TX 75083-0688, U.S.A.; or to Herv'e Abdi, Universit'e de Bourgogne, Facult'e des Sciences Gabriel, Boulevard Gabriel, 21004 Dijon Cedex, France. E-mail: herve@utdallas.edu; herve@u-bourgogne.fr Part of this paper has been presented at the 24th Annual Mathematical Psychology Meeting (1991), and the 1992 Mind Meeting on neural networks. We are grateful to Jerry R. Busemeyer, Jean-Claude Falmagne, and an anonymous reviewer for their helpful remarks on a previous version of this paper.