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Support for the 2010 Annual Meeting of the Society for Mathematical Psychology

Support for the 2010 Annual Meeting of the Society for Mathematical Psychology
支持数学心理学会2010年年会
批准号:
1021089
负责人:
Robin Thomas
金额:
$1.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2010
资助国家:
美国
项目状态:
已结题
起止时间:
2010-09-01 至 2011-08-31

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中文摘要
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英文摘要
This award will provide partial support for the 43rd Annual Meeting of the Society for Mathematical Psychology, to be held in Portland, Oregon on August 7-11, 2010. The Society for Mathematical Psychology Conference encourages the presentation of research in which mathematical, statistical, or simulation methods play a significant role in the development of hypotheses or the interpretation of experimental results in the behavioral, neural, and cognitive sciences. In addition, accepted research papers focus on theoretical developments clearly relating to substantive issues or methodologies of obvious use in psychology, cognitive science, cognitive neuroscience, and related areas and/or experimental results which bear directly on particular mathematical or simulation models of aspects of human behavior. This year's conference themes include: (1) Cognitive models and the 'wisdom of crowds', (2) Contextual reinstatement and episodic memory, (3) New computational approaches to word learning, and (4) Applications of models of response time.Mathematical psychology brings together social scientists, statisticians, mathematicians, and computer scientists to work on problems critical to behavioral scientists. Much transformational research has come from the mathematical psychology community, including mathematical models of brain function, memory, and decision-making, as well as the introduction of new methods for data analysis. While mathematical models improve our understanding of human behavior and provide formal structures for future scientific exploration, new and better methods for data analysis allow us to derive more accurate and nuanced conclusions from behavioral data. The annual meeting of the Society advances discovery and understanding. It promotes training and learning of new models and methods for analyzing behavioral data. It makes possible the broad dissemination of new findings important in all areas of psychology, as well as economics, political science, and sociology.
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