Affect Dynamics

Preface
Acknowledgments
Introduction
The Field of Affective Dynamics
Time Is Not the Cause of Affective Dynamics
This Volume
Conclusion
Contents
About the Editors
Part I: Within-Episode Dynamics
Chapter 1: Emotion Duration
1.1 Introduction
1.2 What Is the Definition of Emotion Duration?
1.3 How Long Do Emotions Last?
1.4 What Determines the Duration of an Emotion?
1.4.1 What Happens at the Start Impacts How Long It Takes to Get to the End
1.4.2 Time Itself Does Not Heal All Wounds; What Happens Over Time Matters
1.4.2.1 The Role of Attention
1.4.2.2 The Role of Appraisal Dynamics
1.5 Directions for Future Research
1.6 Concluding Statement
References
Chapter 2: Appraisal Dynamics: A Predictive Mind Process Model Perspective
2.1 Introduction
2.2 The Extended Process Model (EPM) of Emotion Regulation
2.3 The Predictive Mind (PM) Perspective
2.4 A Predictive Mind Process Model Perspective
2.5 Implications for Understanding Temporal Dynamics of Emotion and Emotion Regulation
2.6 Implications for Understanding Individual Differences and Clinical Phenomena
2.7 Concluding Comment
References
Chapter 3: The Neuroscience of Affective Dynamics
3.1 Introduction
3.1.1 Affective Chronometry
3.1.2 Neural, Peripheral, Subjective, and Behavioral Indicators of Emotion
3.1.3 Interim Summary
3.2 Parameter 1: Rise-Time
3.2.1 Critical Circuits
3.2.2 Modulators: What Influences Rise-Time?
3.3 Parameter 2: Intensity
3.3.1 Critical Circuits
3.3.2 Modulators: What Influences intensity?
3.4 Parameter 3: Duration
3.4.1 Critical Circuits
3.4.2 Modulators: What Influences Duration?
3.5 Conclusion
References
Part II: Between-Episode Dynamics
Chapter 4: Emotional Inertia: On the Conservation of Emotional Momentum
4.1 Introduction
4.1.1 Historical Origins of Emotional Inertia
4.2 Quantifying Emotional Inertia
4.2.1 The Multilevel AR(1) Model: Individual Differences in Emotional Inertia
4.2.1.1 Extensions to the Multilevel AR(1) Model
4.2.1.2 Relations Between Emotional Inertia and Other Indices of Affect Dynamics
4.3 Empirical Findings
4.3.1 Depression
4.3.1.1 Inertia of Non-Emotional Processes and Depression
4.3.1.2 Inconsistent Findings Regarding the Depression-Inertia Association
4.3.1.3 Inertia of PA in Relation to Anhedonia
4.3.1.4 Moderators of the Depression-Inertia Association
4.3.1.5 Inertia as a Marker of Depression Vulnerability
4.3.2 Other Forms of Psychopathology
4.3.2.1 Psychosis
4.3.2.2 Borderline Personality Disorder
4.3.2.3 Post-traumatic Stress Disorder
4.3.2.4 Eating Disorders
4.3.3 Personality, Demographics and Other Individual Differences
4.3.3.1 Big Five Personality Traits
4.3.3.2 Age
4.3.3.3 Gender
4.3.3.4 Relationship Factors
4.3.3.5 Emotional Intelligence
4.4 Mechanisms Underlying Emotional Inertia
4.4.1 Genetic Influences
4.4.2 Physiological Processes
4.4.3 Neural Processes
4.4.4 Psychological Processes
4.5 Interventions to Modify Emotional Inertia
4.5.1 Mindfulness
4.5.2 Exercise and Alcohol
4.6 Within-Person Changes in Emotional Inertia
4.6.1 Application of Dynamical Systems Theory
4.6.1.1 Inertia as a Marker of Critical Slowing Down
4.7 Open Questions and Future Directions
4.7.1 Is Emotional Inertia Merely a Surface Phenomenon?
4.7.2 (When) Is Emotional Inertia Maladaptive?
4.7.3 Towards a Standard Modelling Approach
4.8 Concluding Remarks
References
Chapter 5: A Close Look at the Role of Time in Affect Dynamics Research
5.1 Introduction
5.2 The Role(s) of Time in Affect Dynamics
5.3 Time-Related Considerations in Affect Dynamics Research
5.3.1 Choosing the Appropriate Time Scale
5.3.1.1 Example Study 1
5.3.1.2 Special Consideration for Lag Lengths
5.3.1.3 Example Study 2
5.3.2 Considering Linear and/or Cyclical Time Effects
5.3.3 Modeling Within-Individual Variability in Affect Dynamics
5.4 Concluding Thoughts
References
Chapter 6: Affect Dynamics and Time Scales: Pictures of Movies
6.1 Introduction
6.2 Real Time
6.3 Daily Experiences
6.3.1 Affect
6.3.2 Transitions
6.4 Conclusions
6.4.1 Dynamic or Variable?
6.4.2 Dynamics and Intensity
6.4.3 Multiple Time Scales
References
Chapter 7: On the Signal-to-Noise Ratio in Real-Life Emotional Time Series
7.1 Introduction
7.2 What Is the Signal-to-Noise Ratio of a Time Series?
7.3 Determinants of the Signal-to-Noise Ratio
7.3.1 Recovering the Latent AR Parameter: Temporal Measurement Resolution
7.3.2 Maximizing the Event-Specific Noise Term: Strong Contextual Stimuli
7.3.3 Reducing Momentary Measurement Noise: Assessing Measurement Error
7.4 Combining Different Strategies to Improve the Signal-to-Noise Ratio
7.4.1 Interdependencies Among Design Strategies
7.4.2 Design Strategy Implementation Constraints
7.5 Conclusion
References
Part III: Between-Person Dynamics
Chapter 8: Emotion Dynamics in Intimate Relationships: The Roles of Interdependence and Perceived Partner Responsiveness
8.1 Introduction
8.2 Why and How Do Partners Impact Each Other’s Emotions?
8.3 Interpersonal Emotion Dynamics: State of the Art
8.3.1 Interpersonal Emotion Dynamics in Relationship Science
8.3.2 Interpersonal Emotion Dynamics in Emotion Science
8.4 The Rise of Research on Interpersonal Emotion Dynamics
8.5 Challenges in Studying Interpersonal Emotion Dynamics
8.6 Underlying Assumptions About Interpersonal Emotion Dynamics and Well-Being
8.7 Introducing Perceived Partner Responsiveness to Interpersonal Emotion Dynamics
8.8 Implications and Future Directions
8.8.1 Widening the Scope of Interpersonal Emotion Dynamics
8.8.2 The Importance of Perceptions
8.8.3 The Need for More Diverse and Clinical Samples
8.8.4 A Focus on Mechanisms
8.9 Conclusion
References
Chapter 9: A Mutualism, Affiliation and Status Seeking (MASS) Framework of Fundamental Affective Dynamics and Their Survival Benefits
9.1 Introduction
9.2 Six Benefits of Group Living
9.2.1 Reproduction and Offspring Survival
9.2.2 Anti-predation and Protection
9.2.3 Sustenance
9.2.4 Social Learning and Information Sharing
9.2.5 Wellbeing and Belonging
9.2.6 Collective Intelligence
9.3 Why a New Theory of Social Motives?
9.4 Three Core Social drives: Mutualism, Affiliation and Status-Seeking (MASS)
9.5 MASS Linked Affective Dynamics
9.6 Mutualism
9.6.1 Collaboration, Cooperation and Trust
9.6.2 Altruistic and Third Party Punishment
9.6.3 Morality and Shared Values
9.7 Affiliation
9.7.1 Assimilation
9.7.2 Belonging
9.7.3 Allegiance and In-Group Favoritism
9.7.4 Selective Bonding
9.8 Status Seeking
9.8.1 Status as Social “Currency”
9.8.2 Status Signaling and Conspicuous Consumption
9.8.3 Value Seeking and Reputation Management
9.9 Competition
9.10 Linking Drives to Group Living Success and Survival
9.11 Concluding Remarks
References
Part IV: Computational Models of Affect
Chapter 10: Computational Models for Affect Dynamics
10.1 Introduction
10.1.1 Why Computational Models?
10.1.2 Characteristics of Affective Time Series
10.2 Discrete-Time Models
10.2.1 Autoregressive Models
10.2.1.1 The Autoregressive Model
10.2.1.2 The Vector Autoregressive Model
10.2.1.3 Network Models
10.2.1.4 Extensions
10.2.2 Reinforcement Learning
10.2.2.1 Computational Model of Happiness
10.2.2.2 Integrated Advantage Model of Mood
10.2.2.3 Limitations
10.3 Continuous-Time Models
10.3.1 Differential Equations
10.3.1.1 Interpretation
10.3.2 Linear models
10.3.2.1 Continuous-Time VAR
10.3.2.2 Damped Linear Oscillator
10.3.2.3 Reservoir Model
10.3.3 Nonlinear Models
10.3.3.1 Catastrophe Theory
10.3.3.2 Affective Ising Model
10.3.3.3 Chaos
10.3.4 Limitations
10.4 Conclusion
10.4.1 Undiscussed Topics
10.4.2 Final Note
Appendix 1: Properties of the VAR
Properties of the AR Model
Properties of the VAR Model
Appendix 2: Autocorrelation of Bivariate VAR
References
Chapter 11: Flexibility and Adaptivity of Emotion Regulation: From Contextual Dynamics to Adaptation and Control
11.1 Introduction
11.2 Dynamics for Contextual Flexibility in Emotion Regulation
11.2.1 Contextual Flexibility in Emotion Regulation
11.2.2 Simulated Scenarios for Contextual Flexibility in Emotion Regulation
11.2.2.1 The Computational Network Model for Contextual Flexibility
11.2.2.2 Four Simulated Example Scenarios Addressed for Contextual Flexibility
11.3 Plasticity in Emotion Regulation
11.3.1 Adapting how to regulate emotions over time
11.3.2 Simulated Scenarios for Plasticity in Emotion Regulation
11.3.2.1 A First-Order Adaptive Network Model for Plasticity in Emotion Regulation
11.3.2.2 A Simulated Example Scenario Addressing Plasticity in Emotion Regulation
11.4 Higher-Order Adaptation in Emotion Regulation
11.4.1 Metaplasticity in Emotion Regulation
11.4.2 Simulated Scenarios for Metaplasticity in Emotion Regulation
11.4.2.1 A Second-Order Adaptive Network Model for Metaplasticity in Emotion Regulation
11.4.2.2 A Simulated Example Scenario for Metaplasticity in Emotion Regulation
11.5 Summary
11.6 Further Reading
Appendix 1: Network-Oriented Modeling
Network-Oriented Modeling Based on Temporal-Causal Networks
Self-Models Representing Network Characteristics by Network States
Appendix 2: Tables
11.7 Appendix 3: Role Matrices
References
Chapter 12: A Dynamic Affective Core to Bind the Contents, Context, and Value of Conscious Experience
12.1 Introduction
12.2 Affective Dynamics as a Phenomenon Resulting from Systems Seeking ‘Optimal Control’
12.2.1 Computational Reinforcement Learning Theory and Dopamine
12.2.2 Reinforcement Learning and Affective Dynamics
12.2.3 Limitations of ‘Dopamine/TD-Reward’: Centric Models
12.2.4 Dopaminergic Response to Novelty and Surprise…
12.2.5 Dopaminergic Responses Regarding Context and Information…
12.2.6 Dopaminergic Responses to Aversive Stimuli…?
12.2.7 Summary and Discussion of Dopamine-Centric Limitations
12.3 Extending Temporal Difference Reinforcement Learning as a Functional Motif That Underlies Affective Dynamics: Valence Partitioned Reinforcement Learning
12.3.1 Classic Reward Versus Valence-Partitioned Temporal Difference Reinforcement Learning
12.4 Subjective Experience and the ‘Dynamic Affective Core’ Hypothesis
12.4.1 The Dynamic Affective Core Hypothesis
12.4.2 Experimental Support for the Dynamic Affective Core Hypothesis
12.4.3 Reward Prediction Errors Modulate Task Specific Dynamic Cores
12.5 Summary and Future Directions
References
Index