On behalf of:

This review proposes that implicit learning processes are the cognitive substrate of social intuition. This hypothesis is supported by (a) the conceptual correspondence between implicit learning and social intuition (nonverbal communication) and (b) a review of relevant neuropsychological (Huntington's and Parkinson's disease), neuroimaging, neurophysiological, and neuroanatomical data. It is concluded that the caudate and putamen, in the basal ganglia, are central components of both intuition and implicit learning, supporting the proposed relationship. Parallel, but distinct, processes of judgment and action are demonstrated at each of the social, cognitive, and neural levels of analysis. Additionally, explicit attempts to learn a sequence can interfere with implicit learning. The possible relevance of the computations of the basal ganglia to emotional appraisal, automatic evaluation, script processing, and decision making are discussed. These "feelings " have an efficiency of operation which it is impossi-ble for thought to match. Even our most highly intellectualized operations depend upon them as a "fringe " by which to guide our inferential movements. They give us our sense of rightness and wrongness, of what to select and emphasize and follow up, and what

In the present research, the authors examined contextual variations in automatic attitudes. Using 2 measures of automatic attitudes, the authors demonstrated that evaluative responses differ qualitatively as perceivers focus on different aspects of a target’s social group membership (e.g., race or gender). Contextual variations in automatic attitudes were obtained when the manipulation involved overt categorization (Experiments 1–3) as well as more subtle contextual cues, such as category distinctiveness (Experiments 4–5). Furthermore, participants were shown to be unable to predict such contextual influences on automatic attitudes (Experiment 3). Taken together, these experiments support the idea of automatic attitudes being continuous, online constructions that are inherently flexible and contextually appropriate, despite being outside conscious control.

Abstract not found

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 presents a neural network simulation of the out-group homogeneity effect (OHE). The model is a feedback network with delta-rule learning that has been previously used to simulate other aspects of stereotype learning, as well as causal learning and reasoning, and human memory. This simulation achieves 2 goals: First, we show that the model could successfully simulate the OHE. We argue that this is due to the error-correcting nature of delta-rule learning. Second, we show that each of 5 aspects of the simulation influences the size of the OHE: (a) the ratio of in-group to out-group size, (b) the overall population size, (c) the learning rate, (d) the decay rate for weights, and (e) increased learning for extreme cases. The psychological relevance of these parameters and ways to study them are presented. Advantages of the model in terms of breadth of coverage for studying social cognitive phenomena are discussed. The tendency to perceive out-group members as relatively similar to one another and in-group members as relatively more heterogeneous or dissimilar is known as the out-group homogeneity effect (OHE). This tendency to perceive out-group members as “all the same ” is argued to increase stereotyping (Judd,

In the traditional view, an attitude is a unified and enduring state of readiness to respond (Eagly and Chaiken 1993). The traditional view assumes a consistently updated (either from ongoing thought processes or salient situational factors) predisposition that serves as a convenient evaluative summary of some object, issue, or person. When confronted by a stimulus (and a need to evaluate it), a stored evaluation comes to mind automatically, guides thought, and helps direct behavior. However, evidence of temporal instability in attitudes, even in response to seemingly insignificant contextual changes, together with increasing reliance on state-based explanations (e.g., altered accessibility of concepts and moods, often as a consequence of priming and framing manipulations), has led to a constructivist challenge to the traditional view of attitudes (Schwarz and Bohner 2001). A second major challenge is posed by a recent conceptualization that questions the bedrock