Proceedings of the Twentieth Annual Conference of the Cognitive Science Society

Proceedings of the Twentieth Annual Conference of the Cognitive Science Society

Author: Morton Ann Gernsbacher

Publisher: Routledge

Published: 2022-05-16

Total Pages: 1305

ISBN-13: 131770844X

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This volume features the complete text of the material presented at the Twentieth Annual Conference of the Cognitive Science Society. As in previous years, the symposium included an interesting mixture of papers on many topics from researchers with diverse backgrounds and different goals, presenting a multifaceted view of cognitive science. This volume contains papers, posters, and summaries of symposia presented at the leading conference that brings cognitive scientists together to discuss issues of theoretical and applied concern. Submitted presentations are represented in these proceedings as "long papers" (those presented as spoken presentations and "full posters" at the conference) and "short papers" (those presented as "abstract posters" by members of the Cognitive Science Society).


Symposia

Symposia

Author: Defense Documentation Center (U.S.)

Publisher:

Published: 1963

Total Pages: 290

ISBN-13:

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Empirical Model Building

Empirical Model Building

Author: James R. Thompson

Publisher: John Wiley & Sons

Published: 2011-11-30

Total Pages: 460

ISBN-13: 1118109627

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Praise for the First Edition "This...novel and highly stimulating book, which emphasizes solving real problems...should be widely read. It will have a positive and lasting effect on the teaching of modeling and statistics in general." - Short Book Reviews This new edition features developments and real-world examples that showcase essential empirical modeling techniques Successful empirical model building is founded on the relationship between data and approximate representations of the real systems that generated that data. As a result, it is essential for researchers who construct these models to possess the special skills and techniques for producing results that are insightful, reliable, and useful. Empirical Model Building: Data, Models, and Reality, Second Edition presents a hands-on approach to the basic principles of empirical model building through a shrewd mixture of differential equations, computer-intensive methods, and data. The book outlines both classical and new approaches and incorporates numerous real-world statistical problems that illustrate modeling approaches that are applicable to a broad range of audiences, including applied statisticians and practicing engineers and scientists. The book continues to review models of growth and decay, systems where competition and interaction add to the complextiy of the model while discussing both classical and non-classical data analysis methods. This Second Edition now features further coverage of momentum based investing practices and resampling techniques, showcasing their importance and expediency in the real world. The author provides applications of empirical modeling, such as computer modeling of the AIDS epidemic to explain why North America has most of the AIDS cases in the First World and data-based strategies that allow individual investors to build their own investment portfolios. Throughout the book, computer-based analysis is emphasized and newly added and updated exercises allow readers to test their comprehension of the presented material. Empirical Model Building, Second Edition is a suitable book for modeling courses at the upper-undergraduate and graduate levels. It is also an excellent reference for applied statisticians and researchers who carry out quantitative modeling in their everyday work.


Experimental and Quasi-Experimental Designs for Research

Experimental and Quasi-Experimental Designs for Research

Author: Donald T. Campbell

Publisher: Ravenio Books

Published: 2015-09-03

Total Pages: 172

ISBN-13:

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We shall examine the validity of 16 experimental designs against 12 common threats to valid inference. By experiment we refer to that portion of research in which variables are manipulated and their effects upon other variables observed. It is well to distinguish the particular role of this chapter. It is not a chapter on experimental design in the Fisher (1925, 1935) tradition, in which an experimenter having complete mastery can schedule treatments and measurements for optimal statistical efficiency, with complexity of design emerging only from that goal of efficiency. Insofar as the designs discussed in the present chapter become complex, it is because of the intransigency of the environment: because, that is, of the experimenter’s lack of complete control.