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 understand basic cognitive principles; but we have to be able to put all those pieces together in a reasonable way. This approach 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 to evaluate theories that would not be possible without such scale. The book has a mission to stimulate cognitive scientists to consider new ways to harness big data with the goal of enhancing our understanding of fundamental cognitive processes. Another important aim of the book is 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, Ph.D.,?is William and Katherine Estes Professor of Psychology, Cognitive Science, and Informatics at Indiana University, Bloomington, and is also 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.