Contents
General Introduction to Model-Based Cognitive Neuroscience
1 Introduction
1.1 What Is Model-Based Cognitive Neuroscience?
1.2 Neural Data Constrain the Behavioral Model
1.3 Behavioral Model Predicts Neural Data
1.4 Simultaneous Modeling
2 Prominent Models and Measures in the Field of Model-Based Cognitive Neuroscience
2.1 Types of Behavioral Measures
2.2 Types of Neural Measures
2.3 Types of Cognitive Models
3 Applications in the Field of Model-Based Cognitive Neuroscience
4 Future Directions
5 Open Challenges
References
Linking Models with Brain Measures
1 Introduction
2 Some Functions of Models in Science
3 Levels of Analysis
4 Other Types of Models Useful in Analysing Brain Data
5 General Comparison of Model and Brain Data
6 Cognitive Model as Integral Part of the Data Analysis
7 Individual Differences
8 Models Can Uncover Useful Latent States
9 Comparing Model and Brain Representations
10 Multiple Levels of Representation
11 Conclusions
Questions for Consideration
Further Reading
References
Reinforcement Learning
1 Introduction
2 Reinforcement Learning
2.1 Pavlovian Conditioning
2.1.1 Temporal-Difference Learning
2.2 Instrumental Conditioning
2.2.1 Actor-Critic Model
3 Model-Based fMRI
3.1 Univariate Approach
3.2 Multivariate Analyses
4 Considerations When Linking RL and fMRI Models
4.1 Evaluating Model Quality
4.2 Addressing Model Considerations
5 Bridging Levels of Analyses
5.1 Neural Correlates of Computational Processes
5.2 Leveraging fMRI to Adjudicate Between Models
5.3 Future Directions
6 Exercises
7 Further Reading
References
An Introduction to the Diffusion Model of Decision-Making
1 Historical Origins
2 Diffusion Processes and Random Walks
3 The Standard Diffusion Model
4 Components of Processing
5 Bias and Speed-Accuracy Tradeoff Effects
6 Mathematical Methods for Diffusion Models
7 The Representation of Empirical Data
8 Fitting the Model to Experimental Data
9 Diffusion Models of Continuous Outcome Decisions
10 Conclusion
11 Suggestions for Further Reading
12 Exercises
References
Discovering Cognitive Stages in M/EEG Data to Inform CognitiveModels
1 Introduction
2 Part 1: The Discovery of Processing Stages in M/EEG Data
2.1 The HsMM-MVPA Method
2.2 Discovering Cognitive Processing Stages in Associative Recognition
3 Part 2: A Symbolic Process Model
3.1 The Cognitive Architecture ACT-R
3.2 A Model of Associative Recognition
4 General Discussion
Exercises
Answers
Further Reading
References
Spiking, Salience, and Saccades: Using Cognitive Models to Bridge the Gap Between How'' andWhy''
1 Introduction
1.1 Dimensions of Constraint
2 A Case Study: SCRI
2.1 Phenomena to Be Explained
2.2 The Model
2.2.1 Motivating Principles
2.2.2 Conceptual Outline
2.2.3 Formal Description
2.3 Applying the Model
2.3.1 Structure of the Data
2.3.2 Parameter Estimation
2.3.3 Model Comparison
2.4 Insights
2.4.1 Quality of Fit
2.4.2 Most Important SCRI Mechanisms Across Neurons
2.4.3 Most Important SCRI Mechanisms for Individual Neurons
2.5 Closing the Loop
3 Discussion
3.1 Turning Points
3.1.1 Designing the Model
3.1.2 Fitting and Comparing Models
3.1.3 History Effects in Neural Spiking
3.1.4 Parameters for Unrecorded Neurons
3.1.5 Joint Modeling
3.2 Prospects
A Exercises
B Recommended Reading
References
Ultrahigh Field Magnetic Resonance Imaging for Model-Based Neuroscience
1 Ultrahigh Field MRI
1.1 How Functional Imaging Changes at UHF
1.2 Analysis of UHF Data
1.3 Alternative Functional Contrast Sources
2 Pushing the Limits for Cerebellum, Subcortex, and Within-Cortex Imaging
2.1 In the Subcortex: The Example of the Subthalamic Nucleus and the Locus Coeruleus
2.2 In the Cerebellum: Highly Folded Lobules and Small Subcortical Nuclei
2.3 In the Cerebral Cortex: Layers and Columns
3 UHF Neuroanatomy with Quantitative MRI
3.1 Common qMRI Models
3.2 qMRI at UHF
4 Structure-Function Relationships for Modeling
References
An Introduction to EEG/MEG for Model-Based CognitiveNeuroscience
1 Introduction
2 What Are We Measuring with EEG/MEG?
3 Practical Considerations and Pre-processing Steps
4 Event-Related Potentials
5 Time-Frequency Modulations
6 Estimating Source Locations
7 Advanced Signal Processing
8 Concluding Remarks
Exercises
Answers
Further Reading
References
Advancements in Joint Modeling of Neural and Behavioral Data
1 Introduction
2 Overview of the Joint Modeling Approach
3 Directed
4 Covariance
4.1 FA NDDM
4.2 Trivariate Modeling
4.3 Gaussian Process Joint Modeling
5 Integrative Modeling
6 Practical Concerns
6.1 Accessibility
6.2 Adaptability
6.3 Computation
6.4 Utility
6.5 Constraint
7 Conclusions
8 Suggested Readings
9 Thought-Provoking Questions
References
Cognitive Models as a Tool to Link Decision Behavior with EEGSignals
1 Introduction
2 A Linking Tutorial: EEG and Accumulator Models of Decision Making
2.1 Established Linking Approaches
2.1.1 Correlation-Based Linking
2.1.2 Regression-Based Linking
2.2 Modern Linking Approaches: Joint Models
3 Linking in Practice: EEG and Reinforcement Learning Models of Decision Making
3.1 Correlations and Qualitative Comparisons
3.2 Reinforcement Learning Model Estimates as Regressors in EEG
3.3 Advanced Approaches
3.4 Summary Caveats
4 Future Directions and Outstanding Questions
5 Further Reading
6 Exercises
References
Toward a Model-Based Cognitive Neuroscience of Working Memory Subprocesses
1 Introduction
1.1 Working Memory
1.2 The Stability-Flexibility Tradeoff
2 Revealing the Subprocesses of WM
2.1 Closing the Gate (Entering Maintenance Mode)
2.2 Opening the Gate (Entering Updating Mode)
2.3 Removing Information from WM
2.4 Substituting Items in WM
2.5 Retrieving, Selecting, and Operating on Multiple Items
3 Toward a Model-Based Cognitive Neuroscience of Working Memory Subprocesses
4 Concluding Remarks
A.1 Exercises
B.1 Further Reading
References
Assessing neurocognitive hypotheses in a likelihood-based model of the free-recall task
1 Introduction
2 Overview of the Context Maintenance and Retrieval (CMR) Model
2.1 Basic Operation of the Model
2.2 Evaluating the Model
3 Assessing a Neurocognitive Linking Hypothesis
4 Simulation Exercises
4.1 Exercise 1: Basic Parameter Recovery
4.2 Exercise 2: Fluctuating Temporal Reinstatement and Synthetic Neural Data
5 Conclusion
6 Further Exercises
References
Cognitive Modeling in Neuroeconomics
1 Introduction
2 Attention and Value-Based Decision-Making
2.1 Sequential Sampling Models
2.2 Eye Tracking: A Window into Attention
2.3 The Attentional Drift Diffusion Model
2.3.1 Extensions of the aDDM
2.4 Outstanding Questions on Attention and Value-Based Decision-Making
3 Decisions in Reinforcement Learning
3.1 Modeling of Response Times During Reinforcement Learning
3.2 Reinforcement Learning Diffusion Decision Models
3.2.1 Extensions of the RLDDM
3.2.2 Clinical Applications
3.2.3 Optimality of Behavior
3.2.4 Methodological Advantages of Combined Learning and Choice Models
4 Open Challenges and a Warning
5 Exercises
6 Further Readings
7 Exercises with Answers
References
Cognitive Control of Choices and Actions
1 Introduction
2 Controlling When to Act
3 Controlling Which Actions to Take
3.1 Delay Discounting
3.2 Controlling Spatial Attention
4 Controlling Which Actions to Withhold
4.1 The Stop-Signal Paradigm in Non-human Primates
4.2 The Stop-Signal Paradigm in Humans
4.3 Problems with Modelling Unobserved βResponsesβ
5 Discussion
A.1 Exercises
B.1 Recommended Reading
References
Index
