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Big data in cognitive science /
~
Jones, Michael N., (1975-)
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Big data in cognitive science /
Record Type:
Language materials, printed : Monograph/item
Title/Author:
Big data in cognitive science // edited by Michael N. Jones.
other author:
Jones, Michael N.,
Published:
New York ;Routledge, : c2017,
Description:
viii, 373 p. :ill. ;23 cm.
Subject:
Big data. -
ISBN:
9781138791930
Big data in cognitive science /
Big data in cognitive science /
edited by Michael N. Jones. - New York ;Routledge,c2017 - viii, 373 p. :ill. ;23 cm. - Frontiers of cognitive psychology. - Frontiers of cognitive psychology..
Includes bibliographical references and index.
Developing cognitive theory by mining large-scale naturalistic data /Michael N. Jones --
"While laboratory research is the backbone of collecting experimental data in cognitive science, a rapidly increasing amount of research is now capitalizing on large-scale and real-world digital data. Each piece of data is a trace of human behavior and offers us a potential clue to understanding basic cognitive principles. However, we have to be able to put the pieces together in a reasonable way, which necessitates both advances in our theoretical models and development of new methodological techniques. The primary goal of this volume is to present cutting-edge examples of mining large-scale and naturalistic data to discover important principles of cognition and evaluate theories that would not be possible without such a scale. This book also has a mission to stimulate cognitive scientists to consider new ways to harness big data in order to enhance our understanding of fundamental cognitive processes. Finally, this book aims to warn of the potential pitfalls of using, or being over-reliant on, big data and to show how big data can work alongside traditional, rigorously gathered experimental data rather than simply supersede it. In sum, this volume presents cognitive scientists and those in related fields with a detailed, stimulating, and realistic introduction to big data - and to show how it may greatly advance our understanding of the principles of human memory, perception, categorization, decision-making, language, problem-solving, and representation."--Provided by publisher.
ISBN: 9781138791930UK39.99
LCCN: 2016021775Subjects--Topical Terms:
2045508
Big data.
LC Class. No.: BF311 / .B53135 2017
Dewey Class. No.: 153.0285
Big data in cognitive science /
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Sequential Bayesianupdating for big data /
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Zita Oravecz, Matt Huentelmen, and Joachim Vandekerckhove --
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Predicting and improving memory retention : psychological theory matters in the big data era /
$r
Michael C. Mozer and Robert V. Lindsey --
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Tractable Bayesian teaching /
$r
Baxter S. Eaves Jr., April M. Schweinhart, and Patrick Shafto --
$t
Social structure relates to linguisticinformation density /
$r
David W. Vinson and Rick Dale --
$t
Music tagging and listening : testing the memory cue hypothesis in a collaborative tagging system /
$r
Jared Lorince and Peter M. Todd --
$t
Flickr® distributional tagspace : evaluating the semantic spaces emerging from Flickr® tag distributions /
$r
Marianna Bolognesi --
$t
Large-scale network representations of semantics in the mental lexicon /
$r
Simon De Deyne [and four others] --
$t
Individual differences in semantic priming performance : insights from the semantic priming project /
$r
Melvin J. Yap, Keith A. Hutchinson, and Luuan Chin Tan --
$t
Small worlds and big data : examining the simplification assumption in cognitive modeling /
$r
Brendan Johns, Douglas J.K. Mewhort, and Michael N. Jones --
$t
Alignment in web-based dialogue : who aligns, and how automatic is it? Studies in big-data computational psycholinguistics /
$r
David Reitter --
$t
Attention economies, information crowding, and language change /
$r
Thomas T. Hills, James S. Adelman, and Takao Noguchi --
$t
Decision by sampling : connecting preferences to real-world regularities /
$r
Christopher Y. Olivola and Nick Chater --
$t
Crunching big data with fingertips : how typists tune their performance toward the statistics of natural language /
$r
LawrenceP. Behmer Jr. and Matthew J.C. Crump --
$t
Can big data help us understand human vision? /
$r
Michael J. Tarr and Elissa M. Aminoff.
520
#
$a
"While laboratory research is the backbone of collecting experimental data in cognitive science, a rapidly increasing amount of research is now capitalizing on large-scale and real-world digital data. Each piece of data is a trace of human behavior and offers us a potential clue to understanding basic cognitive principles. However, we have to be able to put the pieces together in a reasonable way, which necessitates both advances in our theoretical models and development of new methodological techniques. The primary goal of this volume is to present cutting-edge examples of mining large-scale and naturalistic data to discover important principles of cognition and evaluate theories that would not be possible without such a scale. This book also has a mission to stimulate cognitive scientists to consider new ways to harness big data in order to enhance our understanding of fundamental cognitive processes. Finally, this book aims to warn of the potential pitfalls of using, or being over-reliant on, big data and to show how big data can work alongside traditional, rigorously gathered experimental data rather than simply supersede it. In sum, this volume presents cognitive scientists and those in related fields with a detailed, stimulating, and realistic introduction to big data - and to show how it may greatly advance our understanding of the principles of human memory, perception, categorization, decision-making, language, problem-solving, and representation."--Provided by publisher.
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based on 0 review(s)
ISSUES
壽豐校區(SF Campus)
-
last issue:
1 (2017/08/09)
Details
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ALL
五樓西文書區A-HB(5F Western Language Books)
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1
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W0072260
五樓西文書區A-HB(5F Western Language Books)
01.外借(書)_YB
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BF311 B53135 2017
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