Big Data in Cognitive Science

Description

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. « Moins Aperçu du livre » Avis des internautes - Rédiger un commentaire Aucun commentaire n'a été trouvé aux emplacements habituels. Livres sur des sujets connexes Computational Modeling of Cognition and Behavior Simon Farrell, Stephan Lewandowsky Proceedings of the 25th Annual Cognitive Science Society Richard Alterman, David Kirsch Arc Hydro: GIS for Water Resources, Volume 1 David R. Maidment Java and JMX Heather Kreger, Ward Harold, Leigh Williamson IP Routing Fundamentals Mark A. Sportack Data Mining Ian H. Witten, Eibe Frank A Guide to SPSS for Analysis of Variance Gustav Levine Data Quality Thomas C. Redman Statistical Pattern Recognition Andrew Webb UML and the Unified Process Liliana Favre XML for Data Architects James Bean Theoretical Models in Biology Glenn Rowe UML for Database Design Eric J. Naiburg, Robert A. Maksimchuck Pages sélectionnées Page Page Page Page Page Page Table des matières Table des matières Table des matières Contributors Sequential Bayesian Updating for Big Data Psychological Tractable Bayesian Teaching Social Structure Relates to Linguistic Information Density Testing the Memory Evaluating the Semantic Spaces Largescale Network Representations of Semantics in the Mental Insights Examining the Simplification Who Aligns and Attention Economies Information Crowding and Language Connecting Preferences to RealWorld How Typists Tune Their Can Big Data Help Us Understand Human Vision? Index Autres éditions - Tout afficher 3 nov. 2016 Aperçu limité 1 nov. 2016 Aucun aperçu 1 nov. 2016 Aucun aperçu Expressions et termes fréquents algorithm alignment analysis attentional control Bayesian behavior Big Data bigram clusters cognitive modeling Cognitive Science collaborative tagging complex Computational Linguistics concepts concreteness corpus correlations dataset decision­making dialogue entropy evaluate example experience Experimental Psychology Figure Flickr fMRI folksonomy function Google hierarchical human Hutchison hypothesis images increases individual differences inference Journal of Experimental knowledge language large­scale Last.fm learning letter frequency likelihood listening marginal likelihood McRae’s features norms mean measures memory cues mental lexicon methods neural n­gram nodes Olivola parameters participants patterns performance posterior distribution predictions priming effects probability processing prospect theory psycholinguistic random Reitter reliability retrieval sampling scenes semantic memory semantic network semantic priming semantic representations sensitivity sequential similar small world social space specific statistics structure syntactic priming tagging target task teaching theory trials trigram typing typists variables vector visual vocabulary voxels WordNet À propos de l'auteur (2016) 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. Informations bibliographiques QR code for Big Data in Cognitive Science Titre Big Data in Cognitive Science Frontiers of Cognitive Psychology Rédacteur Michael N. Jones Éditeur Psychology Press, 2016 ISBN 1315413558, 9781315413556 Longueur 374 pages Exporter la citation Cover......Page 1 Half-title......Page 2 Title page......Page 4 Copyright page......Page 5 Table of contents......Page 6 Contributors......Page 8 1 Developing Cognitive Theory by Mining Large-scale Naturalistic Data......Page 10 What is Big Data?......Page 11 What is Big Data to Cognitive Science?......Page 13 How is Cognitive Research Changing with Big Data?......Page 15 Intertwined Theory and Methods......Page 17 References......Page 18 Introduction......Page 22 Two Schools of Statistical Inference......Page 23 Principles of Bayesian Statistics......Page 25 That Wretched Prior......Page 26 Obtaining the Posterior......Page 27 Sequential Updating with Bayesian Methods......Page 28 Advantages of Sequential Analysisin Big Data Applications......Page 30 MindCrowd......Page 31 Modeling Simple Reaction Time with the LATER Model......Page 32 Study Design......Page 34 Results from the Hierarchical Bayesian LATER Model......Page 36 Combining Cognitive Models......Page 37 Discussion......Page 38 Notes......Page 39 References......Page 40 Introduction......Page 43 Knowledge State......Page 44 Psychological Theories of Long-Term Memory Processes......Page 46 ACT-R......Page 49 MCM......Page 50 Collaborative Filtering......Page 51 Candidate Models......Page 53 Simulation Results......Page 54 Representing Study History......Page 58 Classroom Studies of Personalized Review......Page 60 Discussion......Page 66 Conclusions......Page 67 Appendix: Simulation Methodology for Hybrid Forgetting Model......Page 68 Notes......Page 69 References......Page 70 4 Tractable Bayesian Teaching......Page 74 Complexity in Bayesian Statistics......Page 76 The Metropolis-Hastings Algorithm......Page 77 Recent Advances in Monte Carlo Approximation......Page 78 Teaching Using PM-MCMC......Page 79 Example: Infant-Directed Speech (Infinite Mixtures Models)......Page 80 Learning Phonetic Category Models......Page 81 Teaching DPGMMs......Page 82 Experiments......Page 86 Discussion......Page 88 Sensory Learning of Orientation Distributions......Page 89 Teaching DP-DPGMMs......Page 90 Experiments......Page 91 Discussion......Page 93 Conclusion......Page 94 Notes......Page 96 References......Page 97 Introduction......Page 100 Information and Adaptation......Page 102 Social-Network Structure......Page 103 Linguistic Measures......Page 104 Social Networks......Page 106 Network Measures......Page 107 Additional Measures......Page 109 Broad Expectations and Some Predictions......Page 111 Simple Measures......Page 112 Complex Measures......Page 116 Discussion......Page 117 General Discussion......Page 118 Notes......Page 121 References......Page 122 Introduction......Page 126 What is Collaborative Tagging?......Page 128 Why People Tag......Page 129 Connections to Psychological Research on Memory Cues......Page 131 Dataset......Page 134 Hypotheses......Page 136 Analytic Approaches......Page 137 Time Series Analysis......Page 138 Information Theoretic Analyses......Page 141 Next Steps: Causal Analyses......Page 145 Summary and Conclusions......Page 147 Notes......Page 148 References......Page 149 Introduction......Page 153 Related Work: Monomodal and Multimodal Distributional Semantics......Page 154 Flickr Distributional Tagspace......Page 156 The Flickr Environment......Page 157 The Distributional Tagspace......Page 158 Implementing FDT......Page 159 Semantic Spaces and Concept Similarities in FDTand in McRae’s Features Norms......Page 162 Types of Features in Flickr and McRae’s Features Norms......Page 163 Correlation Coefficients Between the Semantic Representationsin FDT and Norms......Page 166 A Comparison with WordNet-Based Similarity Metrics and Discussion......Page 168 Categorization Task in FDT and in McRae’s Feature Norms......Page 169 Cluster Validation and Discussion......Page 173 Conclusions......Page 177 Notes......Page 178 References......Page 179 Introduction......Page 183 Studying the Mental Lexicon......Page 184 Using Association Networks to Represent Lexical Knowledge......Page 187 Representation of Semantic Similarity......Page 188 Spreading Activation......Page 189 Insights at the Macroscopic Level......Page 190 Insights at the Mesoscopic Level......Page 193 Simple Network Centrality Measures to Explain WordProcessing Advantages......Page 198 Discussion......Page 200 Extending the Models to Specific Groups and Individuals......Page 201 Challenges......Page 202 References......Page 205 9 Individual Differences in Semantic Priming Performance: Insights from the......Page 212 Individual Differences in Semantic Priming......Page 215 Is Semantic Priming Reliable?......Page 217 The Present Study......Page 218 Dataset......Page 219 Results......Page 220 Analysis 1: Reliability of Semantic Priming......Page 221 Analysis 2: Individual Differences in Semantic Priming......Page 223 Reliability of Semantic Priming......Page 226 Individual Differences in Semantic Priming......Page 228 Reliability of Isolated Versus Primed Lexical Decision......Page 229 Limitations and Future Directions......Page 230 Notes......Page 231 References......Page 232 10 Small Worlds and Big Data: Examining the Simplification Assumption in......Page 236 Analysis of a Small World......Page 239 Learning a Small World......Page 240 Discussion......Page 243 Training materials......Page 245 Small Worlds......Page 246 Results......Page 247 General Discussion......Page 251 Note......Page 252 References......Page 253 Introduction......Page 255 Integrating Psycholinguistics and Cognitive Modeling......Page 256 Syntactic Priming......Page 258 Characteristics of Syntactic Priming......Page 260 Priming is Evident in Corpus Data......Page 261 How Mechanistic is the Effect?......Page 262 Examining Social Modulation of Alignment......Page 263 Data and Methods......Page 265 Research Questions......Page 266 Results and Discussion......Page 267 Questions and Challenges for Data-Intensive Computational Psycholinguistics......Page 270 Conclusion......Page 272 Notes......Page 273 References......Page 274 12 Attention Economies, Information Crowding, and Language Change......Page 279 Language Change......Page 281 Conceptual Crowding......Page 282 An Illustrative Example: Optimal Conceptual Length......Page 283 Surface Versus Conceptual Complexity......Page 284 Conceptual Efficiency and Concreteness......Page 286 The Rise in Concreteness......Page 287 Semantic Bleaching......Page 288 Reductions in Surface Complexity......Page 291 Word Length......Page 292 Age of Acquisition......Page 294 Discussion of the Absence of Reductionin Surface Complexity......Page 295 Population Density and Concreteness in US States......Page 296 Conclusions......Page 298 References......Page 299 Introduction......Page 303 Breaking Free of Utility: Decision by Sampling......Page 305 The Subjective Value of Monetary Gains and Losses......Page 308 The Subjective Value of Human Lives......Page 312 The Weighting of Probabilities......Page 315 The Perception of Time Delays......Page 318 Recap: Using Big Data to Explain Preference Patterns......Page 319 Causality and Coincidence......Page 321 Notes......Page 323 References......Page 325 Introduction......Page 329 The Serial-Order Problem......Page 330 Hierarchical Control and Skilled Typing......Page 332 Developing a Well-Formed Inner Loop......Page 333 More Than One Way to Speed Up a Typist......Page 334 Testing the Predictions in Skilled Typing......Page 339 Using Big Data Tools to Answer the Question......Page 340 Answering Questions with the Data......Page 342 Discriminating Between SRN and Instance Theory Models......Page 346 Relation to Response-Scheduling Operations......Page 347 References......Page 349 Introduction......Page 352 What is Big Data?......Page 355 Why Does Big Data Work?......Page 360 Applications of Big Data to Human Vision......Page 361 Conclusions......Page 367 Notes......Page 369 References......Page 370 Index......Page 373