Connectionist Models Of Social Reasoning And Social Behavior

Although neural network models have had a dramatic impact on the cognitive and brain sciences, social psychology has remained largely unaffected by this intellectual explosion. The first to apply neural network models to social phenomena, this book includes chapters by nearly all of the individuals currently working in this area. Bringing these various approaches together in one place, it allows readers to appreciate the breadth of these approaches, as well as the theoretical commonality of many of these models. The contributors address a number of central issues in social psychology and show how these kinds of models provide insight into many classic issues. Many chapters hint that this approach provides the seeds of a theoretical integration that the field has lacked. Each chapter discusses an explicit connectionist model of a central problem in social psychology. Since many of the contributors either use a standard architecture or provide a computer program, interested readers, with a little work, should be able to implement their own variations of models. Chapters are devoted to the following topics and models: * the learning and application of social categories and stereotypes; * causal reasoning, social explanation, and person perception; * personality and social behavior; * classic dissonance phenomena; and * belief change and the coherence of large scale belief systems.

Computational Social Psychology showcases a new approach to social psychology that enables theorists and researchers to specify social psychological processes in terms of formal rules that can be implemented and tested using the power of high speed computing technology and sophisticated software. This approach allows for previously infeasible investigations of the multi-dimensional nature of human experience as it unfolds in accordance with different temporal patterns on different timescales. In effect, the computational approach represents a rediscovery of the themes and ambitions that launched the field over a century ago. The book brings together social psychologists with varying topical interests who are taking the lead in this redirection of the field. Many present formal models that are implemented in computer simulations to test basic assumptions and investigate the emergence of higher-order properties; others develop models to fit the real-time evolution of people’s inner states, overt behavior, and social interactions. Collectively, the contributions illustrate how the methods and tools of the computational approach can investigate, and transform, the diverse landscape of social psychology.

This comprhnsve yet accssible txt brngs togethr key resrch and theory in a soc cog and applied cog psych to prvide a thorough grndg in these incrsingly poplar areas. Suitble txt for upper-level undergrads and a refrnce for graduate-level readers alike.

Population health is complex and multileveled, encompassing dynamic interactions between cells, societies, and everything in between. Our typical approach to studying population health, however, remains oriented around a reductionist approach to conceptualizing, empirically analyzing, and intervening to improve population health. The trouble is that interventions founded on simplifying a complex world often do not work, sometimes yielding failure or, even worse, harm. The difficult truth is that "silver bullet" health science often fails, and understanding these failures can help us improve our approach to health science, and, ultimately, population health. SYSTEMS SCIENCE AND POPULATION HEALTH employs principles from across a range of sciences to refine the way we understand population health. By augmenting traditional analytic approaches with new tools like machine learning, microsimulation, and social network analysis, population health can be studied as a dynamic and complex system. This allows us to understand population health as a complex whole, offering new insights and perspectives that stand to improve the health of the public. This text offers the first educational and practical guide to this forward-thinking approach. Comprising 17 chapters from the vanguard of population health, epidemiology, computer science, and medicine, this book offers a three-part introduction to the subject: · An intellectual and conceptual history of systems science as it intersects with population health · Concise, introductory overviews of important and emerging methodological tools in systems science, including systems dynamics, agent-based modeling, microsimulation, social network analysis, and machine-learning-all with relevant examples drawn from population health literature · An exploration of future implications for systems science and its applications to our understanding of population health issues For researchers, students, and practitioners, SYSTEMS SCIENCE AND POPULATION HEALTH redefines many of the foundational elements of how we understand population health. It should not be missed.

Connectionist Models of Learning, Development and Evolution comprises a selection of papers presented at the Sixth Neural Computation and Psychology Workshop - the only international workshop devoted to connectionist models of psychological phenomena. With a main theme of neural network modelling in the areas of evolution, learning, and development, the papers are organized into six sections: The neural basis of cognition Development and category learning Implicit learning Social cognition Evolution Semantics Covering artificial intelligence, mathematics, psychology, neurobiology, and philosophy, it will be an invaluable reference work for researchers and students working on connectionist modelling in computer science and psychology, or in any area related to cognitive science.

Many of our thoughts and decisions occur without us being conscious of them taking place; connectionism attempts to reveal the internal hidden dynamics that drive the thoughts and actions of both individuals and groups. Connectionist modeling is a radically innovative approach to theorising in psychology, and more recently in the field of social psychology. The connectionist perspective interprets human cognition as a dynamic and adaptive system that learns from its own direct experiences or through indirect communication from others. Social Connectionism offers an overview of the most recent theoretical developments of connectionist models in social psychology. The volume is divided into four sections, beginning with an introduction and overview of social connectionism. This is followed by chapters on causal attribution, person and group impression formation, and attitudes. Each chapter is followed by simulation exercises that can be carried out using the FIT simulation program; these guided exercises allow the reader to reproduce published results. Social Connectionism will be invaluable to graduate students and researchers primarily in the field of social psychology, but also in cognitive psychology and connectionist modeling.

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Recent years have witnessed the ascendance of a new way to conceptualize and investigate the nature of dynamism at different levels of psychological reality. Areas of inquiry as diverse as cognitive neuroscience, developmental psychology, organizational behavior, and political sociology are being reframed in terms that allow rigorous and precise insight into basic dynamic processes. There are signs that this new approach to dynamics is emerging as a potentially integrative paradigm for personality and social psychology as well. This special issue highlights this new paradigm and illustrates its relevance to a broad spectrum of topics in personality and social psychology.