Chapter
Quantitative Fit and Qualitative Predictions
Production Systems: ACT-R
Neural-Network Architectures: Spaun
Relating Architectures to Data
The Use of Models in Cognitive Neuroscience
Chapter 2: Bayesian Methods in Cognitive Modeling
Advantages of Bayesian Methods
Graphical Model Representation
Alternative Models With Vague Priors
Interpreting and Summarizing the Posterior Distribution
Model Testing Using Prior and Posterior Distributions
Prediction and Generalization
Chapter 3: Model Comparison in Psychology
Foundations of Model Comparison
Model Evaluation Criteria
Generalizability: The Yardstick of Model Comparison
The Importance of Model Complexity
The Practice of Model Comparison
Model Falsifiability, Identifiability, and Equivalence
Methods of Model Comparison
Appendix A - Matlab Code for Illustrated Example
Appendix B - R2JAGS Code for Illustrated Example
Chapter 4: Statistical Inference
What Is Statistical Inference?
Populations and Parameters
Relevance of Stopping Rules
Using Likelihood for Frequentist Inference
Informing the Choice of Prior
Parametric and Nonparametric Inference
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
The Confidence-Rating Method
Characterizing Performance Across Conditions
Forced Choice, Ranking Judgments, and the Area Theorem
A Note on Data Aggregation
Chapter 6: Cultural Consensus Theory
An Example of a CCT Analysis of Response Profile Data
A CCT Analysis of the Mathematics Course Exam Responses
The General Condorcet Model
Some Empirical Studies Using the GCM
CCT Models Where Consensus Truth Is on a Continuum
CCT Models for Continuous Responses
A CCT Model for an Ordinal (Likert) Scale
CCT Models for Other Questionnaire Designs
Allowing a "Don't-Know" Response in the GCM
CCT Models for Determining Consensus Ties in a Network
CCT Models for Ranking and Matching Responses
Statistical Inference for CCT Models
Bayesian Statistical Inference
Incorporating Covariates in Estimation
Software and Graphic User Interfaces (GUIs) for CCT Models
Nonhierarchical Bayesian Software for Fitting CCT Models
Hierarchical Bayesian Software Packages
Hierarchical Condorcet Modeling Toolbox
Hierarchical, Extended Condorcet Model to Capture Uncertainty in Decision Making
CCTpack—An R Package for Hierarchical Bayesian Implementations of Single and Multicultural Versions of CCT Models for Binary, Ordered Categorical, and Continuous Data
Alternative Methods for Applying CCT Methodology and Models
Appendix: Proofs of Observations
Chapter 7: Methods in Psychophysics
Contrast Sensitivity Functions
Visual-Haptic Integration
Context Effects in Brightness Perception
The Psychometric Function I: The Binomial Model
The Psychometric Function II: The Binomial Mixture Model
Violations of the i.i.d. Assumption
The Psychometric Function III: The Beta-Binomial Mixture Model
The Psychometric Function IV: Bayesian Inference
Importance of the Width of the Psychometric Function
Bias and Sensitivity Differences in 2IFC and 2AFC
Multidimensional Psychometric Functions
Chapter 8: The Categorization Experiment: Experimental Design and Data Analysis
Categorization versus Identification
Rule-Based Category-Learning Tasks
Information-Integration Category-Learning Tasks
Unstructured Category-Learning Tasks
Prototype-Distortion Category-Learning Tasks
Real-World Versus Artificial Stimuli
Binary- Versus Continuous-Valued Stimulus Dimensions
Separable Versus Integral Dimensions
Number of Stimulus Dimensions
Constructing the Categories
RB and II Categories: The Randomization Technique
Prototype-Distortion Categories
Supervised Versus Unsupervised Training
Observational Versus Feedback-Based Training
Deterministic Versus Probabilistic Feedback
Forward- Versus Backward-Learning Curves
Procedural-Learning Models
Chapter 9: Response Times and Decision-Making
Overview of Decision-Making Models
Response Time Models as Theory Development
Choices Between More Than Two Options
Nonstationary Decision Processes
Response Times in Cognitive Science and Neuroscience
Examples of Cognitive Neuroscience Linked with RT Models
Response Time Models as Measurement Tools
Chapter 10: The Stop-Signal Paradigm
Independent Horse-Race Model of Response Inhibition
Independent Horse-Race Model: The Basics
Independent Horse-Race Model With Constant SSRT
The Complete Independent Horse-Race Model
Stop-Signal Reaction Times
Estimating Summary Measures of SSRT
Estimating SSRT Variability
Estimating SSRT Distributions
Process Models of Response Inhibition
Describing the Properties of the Go and Stop Process
Describing How Responses Are Inhibited
Testing the Goodness-of-Fit of the Horse-Race Model
The Independence Assumption in Practice
Variants of the Stop-Signal Task
Stopping in Stop-Change and Selective Stop Tasks
Discrete Versus Continuous Tasks
How to Run Stop-Signal Experiments
How to Report Stop-Signal Experiments
How to Interpret Stop-Signal Data
Chapter 11: Uncovering Mental Architecture and Related Mechanisms in Elementary Human Perception, Cognition, and Action
Introduction: Brief History and General Conception
Major Characteristics of Elementary Cognitive Systems and Theorems of Mimicry: The Event Space Basis
Strong Experimental Tests on Response Times I: Event Space Expansion
Strong Experimental Tests on Response Times II: Applications of Selective Influence
Strong Experimental Tests on Response Frequencies
Back to the State Spaces: Tests Based on Partial States of Completion
Manipulating Process Durations of Available Information
Appendix: Applications of Systems Factorial Technology
Age-Related Changes in Perception and Cognition
Identity and Emotion Perception
Individual Differences/Clinical Populations
Learning and Reward Processing
Temporal Order Processing
Visual Search/Visual Attention
Working Memory/Cognitive Load
Chapter 12: Convergent Methods in Memory Research
Introduction: Background on Convergent Methods/Analyses
An Empirical and Simulation Test of Vertical Convergence in Decision Models
Repetition Priming and Its Neural Bases: A Case Illustration of the Search for Convergence
New and/or Underutilized Methodologies in Experimental Memory Research
Memory Content Analysis via Machine/Statistical Learning Algorithms
Monte Carlo Simulation as an Alternative to Model Fitting
Chapter 13: Models and Methods for Reinforcement Learning
Prediction and Control With Immediate Reward
Prediction and Control Over Time
Pavlovian and Instrumental Interactions
Chapter 14: An Overview of Neural Time Series Analyses
Philosophies of EEG Data Analysis
Temporal Filtering of EEG Data
Time-Domain Analyses of EEG Data
Neural Oscillations to Study Neural Mechanisms of Cognition
Frequency-Domain Analyses of EEG Data
Time-Frequency-Domain Analyses of EEG Data
Example of Time and Time-Frequency-Domain Analyses
Spatial Filtering of EEG Data
Source Localization of EEG Data
Linear Multivariate Transformations and Decompositions
Chapter 15: Methods for fMRI Analysis
History and Development of fMRI
What Kinds of Questions can fMRI Answer?
Localization and Brain Mapping
Representation and Processing
Classification and Prediction With fMRI
Summary of fMRI Questions
Relationship Between BOLD and Neural Activity
Nonneural Contributions to the BOLD Signal
Summary of the BOLD Signal
Experimental Design for fMRI
Contrasts and Selecting a Baseline
Beyond Simple Contrasts: Parametric Design
Efficiency, Power, and Sample Size
Voxel-Based Analysis With the General Linear Model
Thresholding and Correction for Multiple Comparisons
Beyond Univariate Analysis of fMRI
Meta-Analysis with fMRI Data
Platforms for Meta-Analysis of fMRI Data
Chapter 16: Neural Recordings at Multiple Scales
History and Fundamentals of Neurophysiology
Population Neuron Recording
EEG and Event-Related Potentials: Relation to Spikes and LFPs
Functional Brain Imaging: Relation of Spiking and LFP
Principles and Acquisition
Relation of BOLD to Spikes and LFP
Chapter 17: Neural Networks and Neurocomputational Modeling
Biophysically Detailed Models
Synaptic Currents, Plasticity, and Networks
Simplified Spiking Neuron Models
Synaptic Currents, Plasticity, and Networks
Abstract Neural Network Models
Abstract Recurrent Neural Networks
Chapter 18: Network Models for Clinical Psychology
Mental Disorders as Complex Dynamical Systems
An Oracle Algorithm to Identify Connections
Predicting Dynamics Over Time
Higher Connectivity, More Problems
The "Open Science" Movement
Major Motivations for Concern
Other Motivations for Concern
Why the Time Is Ripe for Change
Insights From Psychological Science Itself
Practicing Open Science: In General
Practicing Open Science: For Researchers
Make It Intelligible: Organize, Label, Annotate
Make It Discoverable: Share
Preregister Confirmatory Research
Join a Large-Scale Replication Project
Practicing Open Science: For Teachers
Practicing Open Science: For Authors and Reviewers
Choosing Where to Publish (and Share) Research
Objections to Open Science
Acknowledging and Confronting the Problem
Slows Down Scientific Discovery (and Wastes Time)
Produces Inequities in Evaluation
. . .And Wouldn't Help With Some Other Related Concerns
The Future of Open Science
Moving Even Further Forward
Stevens' Handbook of Experimental Psychology and Cognitive Neuroscience, Methodology
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