Ebook: Big Data in Cognitive Science

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.
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Table des matières
Table des matières
Table des matières
Contributors


Sequential Bayesian Updating for Big Data


Psychological


Tractable Bayesian Teaching


Social Structure Relates to Linguistic Information Density


Testing the Memory


Evaluating the Semantic Spaces


Largescale Network Representations of Semantics in the Mental


Insights


Examining the Simplification


Who Aligns and


Attention Economies Information Crowding and Language


Connecting Preferences to RealWorld


How Typists Tune Their


Can Big Data Help Us Understand Human Vision?


Index



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3 nov. 2016
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Expressions et termes fréquents
algorithm alignment analysis attentional control Bayesian behavior Big Data bigram clusters cognitive modeling Cognitive Science collaborative tagging complex Computational Linguistics concepts concreteness corpus correlations dataset decision­making dialogue entropy evaluate example experience Experimental Psychology Figure Flickr fMRI folksonomy function Google hierarchical human Hutchison hypothesis images increases individual differences inference Journal of Experimental knowledge language large­scale Last.fm learning letter frequency likelihood listening marginal likelihood McRae’s features norms mean measures memory cues mental lexicon methods neural n­gram nodes Olivola parameters participants patterns performance posterior distribution predictions priming effects probability processing prospect theory psycholinguistic random Reitter reliability retrieval sampling scenes semantic memory semantic network semantic priming semantic representations sensitivity sequential similar small world social space specific statistics structure syntactic priming tagging target task teaching theory trials trigram typing typists variables vector visual vocabulary voxels WordNet
À propos de l'auteur (2016)

Michael N. Jones is the William and Katherine Estes Professor of Psychology, Cognitive Science, and Informatics at Indiana University, Bloomington, and the Editor-in-Chief of Behavior Research Methods. His research focuses on large-scale computational models of cognition, and statistical methodology for analyzing massive datasets to understand human behavior.
Informations bibliographiques
QR code for Big Data in Cognitive Science
Titre Big Data in Cognitive Science
Frontiers of Cognitive Psychology
Rédacteur Michael N. Jones
Éditeur Psychology Press, 2016
ISBN 1315413558, 9781315413556
Longueur 374 pages

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