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Stevens' Handbook of Experimental Psychology and Cognitive Neuroscience, Methodology.
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Newark :
John Wiley & Sons, Incorporated,
2018.
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| Edición: | 4th ed. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Title Page; Copyright; Contributors; Contents; Preface; Chapter 1: Computational Modeling in Cognition and Cognitive Neuroscience; Mathematical Models as Cognitive Prosthesis; Models of Choice Reaction Time Tasks; Models of Rehearsal in Short-Term Memory; The Need for Cognitive Prostheses; Classes of Models; Descriptive Models; Theoretical Models; Measurement Models; Translating Data Into Parameters; Summary; Explanatory Models; Explaining Scale Invariance in Memory; Explanatory Necessity Versus Sufficiency; Model Selection and Model Complexity.
- Quantitative Fit and Qualitative PredictionsSummary; Cognitive Architectures; Production Systems: ACT-R; Neural-Network Architectures: Spaun; Relating Architectures to Data; The Use of Models in Cognitive Neuroscience; Conclusion; References; Chapter 2: Bayesian Methods in Cognitive Modeling; Introduction; Advantages of Bayesian Methods; Overview; A Case Study; Experimental Data; Research Questions; Model Development; Graphical Model Representation; Prior Prediction; Alternative Models With Vague Priors; Parameter Inference; Posterior Prediction.
- Interpreting and Summarizing the Posterior DistributionModel Testing Using Prior and Posterior Distributions; Sensitivity Analysis; Latent-Mixture Modeling; Hierarchical Modeling; Finding Invariances; Common-Cause Modeling; Prediction and Generalization; Conclusion; References; Chapter 3: Model Comparison in Psychology; Introduction; Foundations of Model Comparison; Model Evaluation Criteria; Follies of a Good Fit; Generalizability: The Yardstick of Model Comparison; The Importance of Model Complexity; The Practice of Model Comparison; Model Falsifiability, Identifiability, and Equivalence.
- Model EstimationMethods of Model Comparison; Illustrated Example; Conclusion; Appendix A
- Matlab Code for Illustrated Example; Appendix B
- R2JAGS Code for Illustrated Example; References; Chapter 4: Statistical Inference; What Is Statistical Inference?; Populations and Parameters; Frequentist Approaches; Point Estimation; Hypothesis Testing; Relevance of Stopping Rules; The Likelihood Approach; Parameter Estimation; Using Likelihood for Frequentist Inference; The Likelihood Principle; Bayesian Approaches; From Prior to Posterior; Informing the Choice of Prior; Parameter Estimation.
- Hypothesis TestingBroader Considerations; Parametric and Nonparametric Inference; Model Checking; Conclusion; References; Chapter 5: Elementary Signal Detection and Threshold Theory; Thurstone's Law of Comparative Judgment; SDT and the Introduction of a Decision Stage; Receiver Operating Characteristic Functions; Beyond the EVSDT; The Confidence-Rating Method; Characterizing Performance Across Conditions; Forced Choice, Ranking Judgments, and the Area Theorem; Multidimensional SDT; Threshold Theory; A Note on Data Aggregation; Conclusion; References; Chapter 6: Cultural Consensus Theory.