The primary goal of this volume is to present cutting-edge examples of mining large and naturalistic datasets to discover important principles of cognition and to evaluate theories in a way that would not be possible without such scale. It explores techniques that have been underexploited by cognitive psychologists and explains how big data from numerous sources can inform researchers with different research interests and shed further light on how brain, cognition and behavior are interconnected. The book fills a major gap in the literature and has the potential to rapidly advance knowledge…mehr
The primary goal of this volume is to present cutting-edge examples of mining large and naturalistic datasets to discover important principles of cognition and to evaluate theories in a way that would not be possible without such scale. It explores techniques that have been underexploited by cognitive psychologists and explains how big data from numerous sources can inform researchers with different research interests and shed further light on how brain, cognition and behavior are interconnected. The book fills a major gap in the literature and has the potential to rapidly advance knowledge throughout the field. It is essential reading for any cognitive psychology researcher.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Inhaltsangabe
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 .
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 .
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