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.

Developing Cognitive Theory by Mining Large-Scale Naturalistic Data, Michael N. Jones. Sequential Bayesian Updating for Big Data, Zita Oravecz, Matt Huentelman, & Joachim Vandekerckhove. Predicting and Improving Memory Retention: Psychological Theory Matters in the Big Data Era, Michael C. Mozer& Robert V. Lindsey. Tractable Bayesian Teaching, Baxter S. Eaves Jr., April M. Schweinhart, & Patrick Shafto. Social Structure Relates to Linguistic Information Density, David W. Vinson& Rick Dale. Music Tagging and Listening: Testing the Memory Cue Hypothesis in a Collaborative Tagging System, Jared Lorince& Peter M. Todd. Flickr® Distributional Tagspace: Evaluating the Semantic Spaces Emerging from Flickr® Tags Distributions, Marianna Bolognesi. Large-Scale Network Representations of Semantics in the Mental Lexicon, Simon De Deyne, Yoed N. Kenett, David Anaki, Miriam Faust, & Dan Navarro. Individual Differences in Semantic Priming Performance: Insights from the Semantic Priming Project, Melvin J. Yap, Keith A. Hutchison, & Luuan Chin Tan. Small Worlds and Big Data: Examining the Simplification Assumption in Cognitive Modeling, Brendan Johns, Douglas J. K. Mewhort, & Michael N. Jones. Alignment in Web-based Dialogue: Who Aligns, and how Automatic is it? Studies in Big-Data Computational Psycholinguistics, David Reitter. Attention Economies, Information Crowding, and Language Change, Thomas T. Hills, James Adelman, & Takao Noguchi. Dcision by Sampling: Co Connecting Preferences to Real-World Regularities. Christopher Y. Olivola & Nick Chater.Crunching Big Data with Fingertips: How Typists Tune Their Performance Toward the Statistics of Natural Language, Lawrence P. Behmer Jr., & Matthew J. C. Crump. Can Big Data Help Us Understand Human Vision?, Michael J. Tarr & Elissa M. Aminoff.

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.