Theoretically, low correlations between implicit and explicit measures can be due to (a) motivational biases in explicit selfreports, (b) lack of introspective access to implicitly assessed representations, (c) factors influencing the retrieval of information from memory, (d) method-related characteristics of the two measures, or (e) complete independence of the underlying constructs. The present study addressed these questions from a meta-analytic perspective, investigating the correlation between the Implicit Association Test (IAT) and explicit self-report measures. Based on a sample of 126 studies, the mean effect size was.24, with approximately half of the variability across correlations attributable to moderator variables. Correlations systematically increased as a function of (a) increasing spontaneity of self-reports and (b) increasing conceptual correspondence between measures. These results suggest that implicit and explicit measures are generally related but that higher order inferences and lack of conceptual correspondence can reduce the influence of automatic associations on explicit self-reports.

ABSTRACT This article addresses issues in developing cognitive architectures—generic computational models of cognition. Cognitive architectures are believed to be essential in advancing understanding of the mind, and therefore, developing cognitive architectures is an extremely important enterprise in cognitive science. The article proposes a set of essential desiderata for developing cognitive architectures. It then moves on to discuss in detail some of these desiderata and their associated concepts and ideas relevant to developing better cognitive architectures. It argues for the importance of taking into full consideration these desiderata in developing future architectures that are more cognitively and ecologically realistic. A brief and preliminary evaluation of existing cognitive architectures is attempted on the basis of these ideas. 1.

Abstract. Abductive logic programming (ALP) can be used to model reactive, proactive and pre-active thinking in intelligent agents. Reactive thinking assimilates observations of changes in the environment, whereas proactive thinking reduces goals to sub-goals and ultimately to candidate actions. Pre-active thinking generates logical consequences of candidate actions, to help in deciding between the alternatives. These different ways of thinking are compatible with any way of deciding between alternatives, including the use of both decision theory and heuristics. The different forms of thinking can be performed as they are needed, or they can be performed in advance, transforming high-level goals and beliefs into lower-level condition-action rule form, which can be implemented in neural networks. Moreover, the higher-level and lowerlevel representations can operate in tandem, as they do in dual-process models of thinking. In dual process models, intuitive processes form judgements rapidly, sub-consciously and in parallel, while deliberative processes form and monitor judgements slowly, consciously and serially. ALP used in this way can not only provide a framework for constructing artificial agents, but can also be used as a cognitive model of human agents. As a cognitive model, it combines both a descriptive model of how humans actually think with a normative model of humans can think more effectively. 1

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.

A central theme in recent research on attitudes is the distinction between deliberate, “explicit” attitudes and automatic, “implicit” attitudes. The present article provides an integrative review of the available evidence on implicit and explicit attitude change that is guided by a distinction between associative and propositional processes. Whereas associative processes are characterized by mere activation independent of subjective truth or falsity, propositional reasoning is concerned with the validation of evaluations and beliefs. The proposed Associative-Propositional Evaluation Model (APE Model) makes specific assumptions about the mutual interplay of the two processes, implying several mechanisms that lead to symmetric or asymmetric changes in implicit and explicit attitudes. The model integrates a broad range of empirical evidence and implies several new predictions for implicit and explicit attitude change.

This thesis aims to develop a psychologically plausible account of concepts by integrating key insights from philosophy (on the metaphysical basis for concept possession) and psychology (on the mechanisms underlying concept acquisition). I adopt an approach known as informational atomism, developed by Jerry Fodor. Informational atomism is the conjunction of two theses: (i) informational semantics, according to which conceptual content is constituted exhaustively by nomological mind–world relations; and (ii) conceptual atomism, according to which (lexical) concepts have no internal structure. I argue that informational semantics needs to be supplemented by allowing content-constitutive rules of inference (“meaning postulates”). This is because the content of one important class of concepts, the logical terms, is not plausibly informational. And since, it is argued, no principled distinction can be drawn between logical concepts and the rest, the problem that this raises is a general one.

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.

Our understanding of political phenomena, including political attitudes and sophistication, can be enriched by incorporating the theories and tools of cognitive neuroscience— in particular, the cognitive neuroscience of nonconscious habitual cognition (akin to bicycle riding). From this perspective, different types of informational “building blocks” can be construed from which different types of political attitudes may arise. A reflectionreflexion model is presented that describes how these blocks combine to produce a given political attitude as a function of goals, primes, expertise, and inherent conflict in considerations relevant to the attitude. The ways in which neuroimaging methods can be used to test hypotheses of political cognition are reviewed. KEY WORDS: social cognitive neuroscience, automaticity, habit, political sophistication Scholars since Plato and Aristotle have asked themselves many questions about the intriguingly political nature of the human mind. It is unlikely, however, that many have asked themselves whether political thinking is like riding a bicycle. This isn’t altogether surprising, of course, given that casting a vote and pedaling down the road seem like very different behaviors. Beneath this surface of dissimilarity, however, political thinking and bike riding may frequently depend on flexing a common set of mental “muscles ” that support the formation and expression of habits across a variety of domains (Lieberman, 2000). Political thinking and bicycle riding may seem to be very dissimilar behaviors. But in some circumstances, they may both depend on a common set of mental “muscles ” that support the formation and expression of habits across a variety of domains

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