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Title Big data in cognitive science / michael N. Jones
Published Basingstoke : Taylor & Francis Ltd, 2016

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Description 1 online resource (392 pages)
Series Frontiers of Cognitive Psychology
Frontiers of cognitive psychology.
Contents Title Page; Copyright Page; Contents; Contributors; 1 Developing Cognitive Theory by Mining Large-scale Naturalistic Data; What is Big Data?; What is Big Data to Cognitive Science?; How is Cognitive Research Changing with Big Data?; Intertwined Theory and Methods; Acknowledgments; Notes; References; 2 Sequential Bayesian Updating for Big Data; Introduction; Two Schools of Statistical Inference; Principles of Bayesian Statistics; That Wretched Prior; Obtaining the Posterior; Sequential Updating with Bayesian Methods; Advantages of Sequential Analysis in Big Data Applications
Application: MindCrowd-Crowdsourcing in the Service of Understanding Alzheimer's DementiaModeling Simple Reaction Time with the LATER Model; Study Design; Results from the Hierarchical Bayesian LATER Model; Combining Cognitive Models; Discussion; Acknowledgments; Notes; References; 3 Predicting and Improving Memory Retention: Psychological Theory Matters in the Big Data Era; Introduction; Knowledge State; Psychological Theories of Long-Term Memory Processes; ACT-R; MCM; Collaborative Filtering; Integrating Psychological Theory with Big-Data Methods: A Case Study of Forgetting
Candidate ModelsSimulation Results; Generalization to New Material; Integrating Psychological Theory with Big Data Methods: A Case Study of Personalized Review; Representing Study History; Classroom Studies of Personalized Review; Experiment 1; Experiment 2; Discussion; Conclusions; Appendix: Simulation Methodology for Hybrid Forgetting Model; Acknowledgments; Notes; References; 4 Tractable Bayesian Teaching; Complexity in Bayesian Statistics; Importance Sampling; The Metropolis-Hastings Algorithm; Recent Advances in Monte Carlo Approximation; Pseudo-Marginal Markov Chain Monte Carlo
Teaching Using PM-MCMCExample: Infant-Directed Speech (Infinite Mixtures Models); Learning Phonetic Category Models; Teaching DPGMMs; Experiments; Discussion; Example: Natural Scene Categories: Infinite Mixtures of Infinite Mixtures; Sensory Learning of Orientation Distributions; Teaching DP-DPGMMs; Experiments; Discussion; Conclusion; Acknowledgments; Notes; References; 5 Social Structure Relates to Linguistic Information Density; Introduction; What Shapes Language?; Information and Adaptation; Social-Network Structure; Current Study; Method; Linguistic Measures; Social Networks
Network MeasuresAdditional Measures; Broad Expectations and Some Predictions; Results; Complex Measures; Discussion; General Discussion; Acknowledgments; Notes; References; 6 Music Tagging and Listening: Testing the Memory Cue Hypothesis in a Collaborative Tagging System; Introduction; Background; Why People Tag; Connections to Psychological Research on Memory Cues; Problem Formalization and Approach; The Challenge; Dataset; Hypotheses; Analytic Approaches; Time Series Analysis; Information Theoretic Analyses; Next Steps: Causal Analyses; Summary and Conclusions; Notes; References
Summary 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
Notes Print version record
Subject Cognitive science -- Research -- Data processing
Data mining.
Big data.
Data Mining
PSYCHOLOGY -- Reference.
Big data
Data mining
Form Electronic book
Author Jones, Michael N
ISBN 9781315413563
1315413566