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.
Biography
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.
The advent of large-scale naturalistic data holds exciting promise for developing and testing cognitive theories. But while the challenge of collecting such data is no longer a major hurdle, analyzing and making sense of it is. This book, with contributions from pioneers in this effort, is a fantastic resource for cognitive scientists.
-Jeffrey L. Elman, Chancellor’s Associates Distinguished Professor of Cognitive Science, University of California, San Diego
