東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Big data in cognitive science /

Jones, Michael N., (1975-)

FindBook

Google Book

Amazon

博客來

Big data in cognitive science /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Big data in cognitive science // edited by Michael N. Jones.
其他作者:	Jones, Michael N.,
出版者:	New York ;Routledge, : c2017,
面頁冊數:	viii, 373 p. :ill. ;23 cm.
標題:	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 /
LDR:04175nam a2200265 a 4500 001 2080372
003 OCoLC
005 20170808021431.0
008 170613s2017 nyua b 001 0 eng d
010 $a 2016021775
020 $a 9781138791930 $q (pbk.) : $c UK39.99
020 $a 1138791938 $q (pbk.)
020 $a 9781138791923 $q (hbk.)
020 $a 113879192X $q (hbk.)
020 $a 9781315413570 $q (ebk.)
020 $a 1315413574 $q (ebk.)
040 $a DLC $b eng
050 0 0 $a BF311 $b .B53135 2017
082 0 0 $a 153.0285 $2 23
245 1 0 $a Big data in cognitive science / $c edited by Michael N. Jones.
260 # $a New York ; $a London : $b Routledge, $c c2017
300 $a viii, 373 p. : $b ill. ; $c 23 cm.
490 1 $a Frontiers of cognitive psychology
504 $a Includes bibliographical references and index.
505 0 # $t Developing cognitive theory by mining large-scale naturalistic data / $r Michael N. Jones -- $t Sequential Bayesianupdating for big data / $r Zita Oravecz, Matt Huentelmen, and Joachim Vandekerckhove -- $t Predicting and improving memory retention : psychological theory matters in the big data era / $r Michael C. Mozer and Robert V. Lindsey -- $t 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.
650 # 0 $a Big data. $3 2045508
650 # 0 $a Cognitive science $x Research $x Data processing. $3 3208712
650 # 0 $a Data mining. $3 562972
650 # 7 $a Datenanalyse $2 gnd. $3 3208713
650 # 7 $a Kognitionswissenschaft $2 gnd. $3 3208714
650 # 7 $a Massendaten $2 gnd. $3 3208715
650 1 2 $a Cognitive Science $x methods. $3 616419
650 2 2 $a Data Mining. $3 919970
650 2 2 $a Datasets as Topic. $3 3208716
700 1 # $a Jones, Michael N., $d 1975- $3 3208710
830 0 $a Frontiers of cognitive psychology. $3 3208711