Big Data in Cognitive Science

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advanced cognitive data analysis methods
Antisaccade Performance
Attention
Bayes Factor
behavioral data mining
Big Data
Big Data Applied
Big Data Approach
Big Data Methods
Big Data Tools
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cognitive neuroscience
Cognitive Science
Complex Network Measure
De Deyne
Decision making
decision making processes
Denser
Education
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Gini Coefficient
Information Theoretic Measures
Language
language change dynamics
Learning
Letter Frequency
Longe SOA
Memory
memory retention modeling
Mental Lexicon
Mesoscopic Level
Methodology
Perception
Semantic Information
semantic network analysis
Semantic Networks
Semantic Priming
Semantic Priming Effect
Short SOA
Social cognition
SRT Task
Syntactic Priming
Thinking and reasoning
Trigram Frequencies
Utilizing Big Data

Product details

  • ISBN 9781138791923
  • Weight: 657g
  • Dimensions: 152 x 229mm
  • Publication Date: 01 Dec 2016
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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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.


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.