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This article proposes an adaptive connectionist model that implements an attributional account of cognitive dissonance. The model represents an attitude as the connection between the attitude object and behavioral-affective outcomes. Dissonance arises when circumstantial constraints induce a mismatch between the model's (mental) prediction and discrepant behavior or affect. Reduction of dissonance by attitude change is accomplished through long-lasting changes in the connection weights using the error-correcting delta learning algorithm. The model can explain both the typical effects predicted by dissonance theory as well as some atypical effects (i.e., reinforcement effect), using this principle of weight changes and by giving a prominent role to affective experiences. The model was implemented in a standard feedforward connectionist network. Computer simulations showed an adequate fit with several classical dissonance paradigms (inhibition, initiation, forced compliance, free choice & misattribution), as well as novel studies that underscore the role of affect. A comparison with an earlier constraint satisfaction approach (Shultz & Lepper, 1996) indicates that the feedforward implementation provides a similar fit with these human data, while avoiding a number of shortcomings of this previous model.

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

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.

How is information transmitted in a group? A multi-agent connectionist model is proposed that combines features of standard recurrent models to simulate the process of information uptake, integration and memorization within individual agents, with novel aspects that simulate the communication of beliefs and opinions between agents. A crucial aspect in belief updating based on information from other agents is the trust in the information provided, implemented as the consistency with the receiving agents’ existing beliefs. Trust leads to a selective propagation and thus filtering out of less reliable information, and implements Grice’s (1975) maxims of quality and quantity in communication. By studying these communicative aspects within the framework of standard models of information processing, the unique contribution of communicative mechanisms beyond intra-personal factors was explored in simulations of key phenomena involving persuasive communication and polarization, lexical acquisition, spreading of stereotypes and rumors, and a lack of sharing unique information in group decisions.

This article investigates whether and how discounting and augmentation of dispositional and causal attributions differ between each other. In three experiments, the strength of a causal or dispositional attribution to a target actor (or object) was varied by manipulating the number of observations (i.e., sample size) of an alternative actor (or object). The results of Experiments 1 and 2 indicated that a greater sample size of the alternative actor (or object) resulted in greater discounting or augmentation of the target, and that this effect was alike for causal and dispositional attributions. This effect of sample size on discounting and augmentation cannot be explained by current algebraic attribution models, but is consistent with predictions from a connectionist framework. In Experiment 3, the extraction of information was made more difficult, and the effect of sample size on discounting and augmentation remained robust for causal attributions, whereas it disappeared for dispositional attributions. This failure for dispositional attributions was not predicted by any theoretical model. The discussion focuses on some potential explanations for this unexpected finding.

How is social information transmitted in a group? Several studies in social cognition documented that communication about groups typically tends to bolster stereotypes and shared beliefs about these groups (Brauer, Judd & Jacquelin, 2001;

Two experiments examined whether dispositional attributions are sensitive to the sample size of the evidence indicating a given level of covariation between person and behavior. Participants were given high or low levels of covariation (i.e., consensus and distinctiveness), and the acquisition of dispositional attributions was monitored by requesting dispositional trait ratings at fixed intervals. The results showed that dispositional attributions were sensitive to sample size, and increased given more evidence on high person-behavior covariation while they decreased given more evidence on low person-behavior covariation. Additional analyses suggested that in making dispositional inferences (e.g., about the actor), there was a slight preference for agreement information (e.g., low distinctiveness) over difference information (e.g., low consensus). The effects of sample size are inconsistent with current statistical or probabilistic models of covariation, but are in line with connectionist networks using an error-correcting learning algorithm.

How is social information transmitted in a group? How do groups create new identities and judgments about other groups through communicating their beliefs and opinions among the members of their own group? Several studies in social cognition have documented that communication about groups typically tends to bolster stereotypes and shared beliefs about these groups (Brauer, Judd & Jacquelin, 2001; Klein, Jacobs, Gemoets, Licata & Lambert, 2003; Lyons & Kashima, 2003). This confirmation bias can be seen as an example of a type of collective intentionality. In the present paper, a multiagent connectionist model is proposed that is capable of simulating these stereotype confirmation biases in group communication, as well as the effects of some moderating conditions. The model combines features of standard recurrent models to simulate the process of information uptake, integration and memorization within agents with novel aspects that simulate the communication of beliefs and opinions between agents. By studying these novel communicative aspects within the framework of standard models of information processing, the unique communicative mechanisms underlying the emergence of a confirmation bias in groups beyond intra-personal factors can be

A multi-agent connectionist model is proposed that consists of a collection of individual recurrent networks that communicate with each other, and as such is a network of networks. The individual recurrent networks simulate the process of information uptake, integration and memorization within individual agents, while the communication of beliefs and opinions between agents is propagated along connections between the individual networks. A crucial aspect in belief updating based on information from other agents is the trust in the information provided. In the model, trust is determined by the consistency with the receiving agents ’ existing beliefs, and results in changes of the connections between individual networks, called trust weights. Thus activation spreading and weight change between individual networks is analogous to standard connectionist processes, although trust weights take a specific function. Specifically, they lead to a selective propagation and thus filtering out of less reliable information, and they implement Grice’s (1975) maxims of quality and quantity in communication. The unique contribution of communicative mechanisms beyond intra-personal processing of individual networks was explored in