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The Resource Big data in cognitive science, edited by Michael N. Jones, (electronic book)

Big data in cognitive science, edited by Michael N. Jones, (electronic book)

Label
Big data in cognitive science
Title
Big data in cognitive science
Statement of responsibility
edited by Michael N. Jones
Contributor
Editor
Subject
Language
eng
Summary
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 groundbreaking volume presents cognitive scientists and those in related fields with an exciting, 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
Member of
Cataloging source
  • StDuBDS
  • StDuBDS
Dewey number
153.028557
Illustrations
illustrations
Index
index present
LC call number
BF311
LC item number
.B53135 2016
Literary form
non fiction
Nature of contents
bibliography
http://library.link/vocab/relatedWorkOrContributorDate
1975-
http://library.link/vocab/relatedWorkOrContributorName
Jones, Michael N.
Series statement
Frontiers of cognitive psychology
http://library.link/vocab/subjectName
  • Cognitive science
  • Data mining
  • Big data
Target audience
specialized
Label
Big data in cognitive science, edited by Michael N. Jones, (electronic book)
Instantiates
Publication
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier MARC source
rdacarrier
Content category
  • text
  • still image
Content type MARC source
  • rdacontent
  • rdacontent
Contents
<ol><b> <li>Developing Cognitive Theory by Mining Large-Scale Naturalistic Data Michael N. Jones</li></b> <p><b> <p> <li>Sequential Bayesian Updating for Big Data</li> <p></b> <p>Zita Oravecz</p> <p>Matt Huentelman</p> <p>Joachim Vandekerckhove</p><b> <p> <p> <li>Predicting and Improving Memory Retention: Psychological Theory Matters in the Big Data Era</li> <p></b> <p>Michael C. Mozer and Robert V. Lindsey </p> <p><b> <p> <li>Tractable Bayesian Teaching</li> <p></b> <p>Baxter S. Eaves Jr., April M. Schweinhart, and Patrick Shafto</p> <p><b> <p> <li>Social Structure Relates to Linguistic Information Density</li> <p></b> <p>David W. Vinson and Rick Dale</p> <p><b> <p> <li>Music Tagging and Listening: Testing the Memory Cue Hypothesis in a Collaborative Tagging System</li> <p></b> <p>Jared Lorince and Peter M. Todd</p> <p><b> <p> <li>Flickrr Distributional Tagspace: Evaluating the Semantic Spaces Emerging from Flickrr Tags Distributions</li> <p></b> <p>Marianna Bolognesi</p> <p><b> <p> <li>Large-Scale Network Representations of Semantics in the Mental Lexicon</li> <p></b> <p>Simon De Deyne</p> <p>Yoed N. Kenett</p> <p>David Anaki</p> <p>Miriam Faust</p> <p>Dan Navarro</p> <p><b> <p> <li>Individual Differences in Semantic Priming Performance: Insights from the Semantic Priming Project </li></b> <p> <p>Melvin J. Yap</p> <p>Keith A. Hutchison</p> <p>Luuan Chin Tan</p> <p><b> <p> <li>Small Worlds and Big Data: Examining the Simplification Assumption in Cognitive Modeling </li> <p></b> <p>Brendan Johns</p> <p>Douglas J. K. Mewhort</p> <p>Michael N. Jones</p> <p><b> <p> <li>Alignment in Web-based Dialogue: Who Aligns, and how Automatic is it? Studies in Big-Data Computational Psycholinguistics </li> <p></b> <p>David Reitter</p> <p><b> <p> <li>Attention Economies, Information Crowding, and Language Change </li> <p></b> <p>Thomas T. Hills</p> <p>James Adelman</p> <p>Takao Noguchi</p> <p><b> <p> <li>Decision by Sampling: Co Connecting Preferences to Real-World Regularities </li> <p></b> <p>Christopher Y. Olivola</p> <p>Nick Chater</p> <p><b> <p> <li>Crunching Big Data with Fingertips: How Typists Tune Their Performance Toward the Statistics of Natural Language</li> <p></b> <p>Lawrence P. Behmer Jr. and Matthew J. C. Crump</p> <p><b> <p> <li>Can Big Data Help Us Understand Human Vision? </li> <p></b></ol> <p>Michael J. Tarr and Elissa M. Aminoff</p>
Control code
AH30713413
Extent
374 pages
Form of item
electronic
Governing access note
After 5 minutes Preview, click on &#x32;Request Access&#x33;, fill in a form with your details. If triggered, the book will be loaned and tied to the one user for 1 week, during which time users can read or download as they choose. 4th user request triggers auto-purchase
Isbn
9781138791930
Isbn Type
(pbk) :
Lccn
2016021775
Media category
computer
Media MARC source
rdamedia
Other physical details
illustrations (black and white)
Specific material designation
remote
Label
Big data in cognitive science, edited by Michael N. Jones, (electronic book)
Publication
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier MARC source
rdacarrier
Content category
  • text
  • still image
Content type MARC source
  • rdacontent
  • rdacontent
Contents
<ol><b> <li>Developing Cognitive Theory by Mining Large-Scale Naturalistic Data Michael N. Jones</li></b> <p><b> <p> <li>Sequential Bayesian Updating for Big Data</li> <p></b> <p>Zita Oravecz</p> <p>Matt Huentelman</p> <p>Joachim Vandekerckhove</p><b> <p> <p> <li>Predicting and Improving Memory Retention: Psychological Theory Matters in the Big Data Era</li> <p></b> <p>Michael C. Mozer and Robert V. Lindsey </p> <p><b> <p> <li>Tractable Bayesian Teaching</li> <p></b> <p>Baxter S. Eaves Jr., April M. Schweinhart, and Patrick Shafto</p> <p><b> <p> <li>Social Structure Relates to Linguistic Information Density</li> <p></b> <p>David W. Vinson and Rick Dale</p> <p><b> <p> <li>Music Tagging and Listening: Testing the Memory Cue Hypothesis in a Collaborative Tagging System</li> <p></b> <p>Jared Lorince and Peter M. Todd</p> <p><b> <p> <li>Flickrr Distributional Tagspace: Evaluating the Semantic Spaces Emerging from Flickrr Tags Distributions</li> <p></b> <p>Marianna Bolognesi</p> <p><b> <p> <li>Large-Scale Network Representations of Semantics in the Mental Lexicon</li> <p></b> <p>Simon De Deyne</p> <p>Yoed N. Kenett</p> <p>David Anaki</p> <p>Miriam Faust</p> <p>Dan Navarro</p> <p><b> <p> <li>Individual Differences in Semantic Priming Performance: Insights from the Semantic Priming Project </li></b> <p> <p>Melvin J. Yap</p> <p>Keith A. Hutchison</p> <p>Luuan Chin Tan</p> <p><b> <p> <li>Small Worlds and Big Data: Examining the Simplification Assumption in Cognitive Modeling </li> <p></b> <p>Brendan Johns</p> <p>Douglas J. K. Mewhort</p> <p>Michael N. Jones</p> <p><b> <p> <li>Alignment in Web-based Dialogue: Who Aligns, and how Automatic is it? Studies in Big-Data Computational Psycholinguistics </li> <p></b> <p>David Reitter</p> <p><b> <p> <li>Attention Economies, Information Crowding, and Language Change </li> <p></b> <p>Thomas T. Hills</p> <p>James Adelman</p> <p>Takao Noguchi</p> <p><b> <p> <li>Decision by Sampling: Co Connecting Preferences to Real-World Regularities </li> <p></b> <p>Christopher Y. Olivola</p> <p>Nick Chater</p> <p><b> <p> <li>Crunching Big Data with Fingertips: How Typists Tune Their Performance Toward the Statistics of Natural Language</li> <p></b> <p>Lawrence P. Behmer Jr. and Matthew J. C. Crump</p> <p><b> <p> <li>Can Big Data Help Us Understand Human Vision? </li> <p></b></ol> <p>Michael J. Tarr and Elissa M. Aminoff</p>
Control code
AH30713413
Extent
374 pages
Form of item
electronic
Governing access note
After 5 minutes Preview, click on &#x32;Request Access&#x33;, fill in a form with your details. If triggered, the book will be loaned and tied to the one user for 1 week, during which time users can read or download as they choose. 4th user request triggers auto-purchase
Isbn
9781138791930
Isbn Type
(pbk) :
Lccn
2016021775
Media category
computer
Media MARC source
rdamedia
Other physical details
illustrations (black and white)
Specific material designation
remote

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