the properties of distributed network models, and support this account by demonstrating that an implemented simulation closely approximates the empirical findings despite the absence of expectancy-based processes and postlexical semantic matching. The results suggest that distributed network models can provide a viable single-mechanism account of lexical processing. Introduction It is well-established that people are faster and more accurate to read a word (e.g., BUTTER) when it is preceded by a related word (e.g., BREAD) compared with when it is preceded by an unrelated word (e.g., DOCTOR; The research was supported by an NIMH FIRST award (MH55628) to the first author and by NIMH Training Grant 5T32MH19102 and NICHD Grant 80258. The computational simulation was run using customized software written within the Xerion simulator (version 3.1) developed by Drew van Camp, Tony Plate, and Geoff Hinton at the Univers

this article. PRINCIPLES UNDERLYING THE CONNECTIONIST APPROACH The connectionist approach instantiates a number of computational principles that are relevant to morphological processing (see Figure 2). We discuss #ve central ones in some detail because they are important for understanding the conditions under which the approach predicts morphological effects in the absence of semantic and/or phonological similarity (for additional background on principles of connectionist modelling, see Chauvin &Rumelhart, 1995; Hertz, Krogh, &Palmer, 1991; McClelland et al., 1986; Rumelhart, Hinton, & Williams, 1986a; Smolensky, Mozer, & Rumelhart, 1996)

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Young infants show unexplained asymmetries in the exclusivity of categories formed on the basis of visually presented stimuli. We describe a connectionist model that shows similar exclusivity asymmetries when categorizing the same stimuli presented to the infants. The asymmetries can be explained in terms of an associative learning mechanism, distributed internal representations, and the statistics of the feature distributions in the stimuli. We use the model to explore the robustness of this asymmetry. The model predicts that the asymmetry will persist when a category is acquired in the presence of mixed category exemplars. A study with 3- to 4-month-olds show that asymmetric exclusivity continues to persist in the presence of a mixed familiarization, thereby corroborating the model's predictions. We suggest that by interpreting asymmetric exclusivity effects as manifestations of interference in an associative memory system, the model can also be extended to account for interference e...

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Much research on judgment and decision making has focussed on the adequacy of classical rationality as a description of human reasoning. But more recently it has been argued that classical rationality should also be rejected even as normative standards for human reasoning. For example, Gigerenzer and Goldstein (1996) and Gigerenzer and Todd (1999a) argue that reasoning involves ‘‘fast and frugal’ ’ algorithms which are not justified by rational norms, but which succeed in the environment. They provide three lines of argument for this view, based on: (A) the importance of the environment; (B) the existence of cognitive limitations; and (C) the fact that an algorithm with no apparent rational basis, Take-the-Best, succeeds in an judgment task (judging which of two cities is the larger, based on lists of features of each city). We reconsider (A)–(C), arguing that standard patterns of explanation in psychology and the social and biological sciences, use rational norms to explain why simple cognitive algorithms can succeed. We also present new computer simulations that compare Take-the-Best with other cognitive models (which use connectionist, exemplarbased, and decision-tree algorithms). Although Take-the-Best still performs well, it does not perform noticeably better than the other models. We conclude that these results provide no strong reason to prefer Take-the-Best over alternative cognitive models.

Major biases and stereotypes in group judgments are reviewed and modeled from a recurrent connectionist perspective. These biases are in the areas of group impression formation (illusory correlation), group differentiation (accentuation), stereotype change (dispersed vs. concentrated distribution of inconsistent information), and group homogeneity. All these phenomena are illustrated with well-known experiments, and simulated with an autoassociative network architecture with linear activation update and delta learning algorithm for adjusting the connection weights. All the biases were successfully reproduced in the simulations. The discussion centers on how the particular simulation specifications compare with other models of group biases and how they may be used to develop novel hypotheses for testing the connectionist modeling approach and, more generally, for improving theorizing in the field of social biases and stereotype change. Petite, attractive, intelligent, WSF, 30, fond of music, theatre, books, travel, seeks warm, affectionate, fun-loving man to share life’s pleasures with view to lasting relationship. Send photograph. Please no

The nature of the general factor of intelligence, or g, is examined. This article begins by observing that the finding of a general factor of intelligence appears to be inconsistent with current findings in neuroscience and cognitive science, where specific connections are argued to be critical for different intellectual abilities and the brain is argued to develop these connections in response to environmental stimuli. However, it is then observed that if people differed in neural plasticity, or the ability to adapt their connections to the environment, then those highly developed in one intellectual ability would be highly developed in other intellectual abilities as well. Simulations are then used to confirm that such a pattern would be obtained. Such a model is also shown to account for many other findings in the field of intelligence that are currently unexplained. A critical period for intellectual development is then emphasized. Perhaps the most well-known concept that scientific psychology has provided to the wider community is that of IQ. Although few people in the general public are familiar with concepts such as latent inhibition and event schemas, IQ is recognized as an attempt to identify and measure differences in that mysterious characteristic

This article discusses a recurrent connectionist network, simulating empirical phenomena usually explained by current dual-process approaches of attitudes, thereby focusing on the processing mechanisms that may underlie both central and peripheral routes of persuasion. Major findings in attitude formation and change involving both processing modes are reviewed and modeled from a connectionist perspective. We use an autoassociative network architecture with a linear activation update and the delta learning algorithm for adjusting the connection weights. The network is applied to well-known experiments involving deliberative attitude formation, as well as the use of heuristics of length, consensus, expertise, and mood. All these empirical phenomena are successfully reproduced in the simulations. Moreover, the proposed model is shown to be consistent with algebraic models of attitude formation (Fishbein & Ajzen, 1975). The discussion centers on how the proposed network model may be used to unite and formalize current ideas and hypotheses on the processes underlying attitude acquisition and how it can be deployed to develop novel hypotheses in the attitude domain.

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