Integrating Converging Evidence in Behavioral Sciences

How to Incite a Scientific Revolution
1. Auflage 2025
ISBN: 978-1-032-88282-6
Verlag: Taylor & Francis Ltd

Buch, Englisch, 164 Seiten, Format (B × H): 174 mm x 246 mm

Integrating Converging Evidence in Behavioral Sciences presents a fresh approach to understanding the landscape of scientific research, particularly within the behavioral sciences.

By examining the needs for consistency and coherence across different scientific disciplines, this book offers readers a practical framework for evaluating and advancing their research topics. Through a comprehensive overview of established frameworks such as Marr’s computational framework and Tinbergen’s four questions, the book introduces a novel convergence framework specifically tailored to the behavioral sciences. This approach enables a more integrated view of scientific theories and knowledge, empowers researchers to pinpoint areas of high impact, and helps them to recognize potential revolutions in the field. The book serves a dual purpose: As a rubric for students and early-career researchers to grasp and navigate their research topics, and also as a resource for more advanced researchers seeking to delve into deeper issues and apply the framework across different contexts.

This book is an essential guide for anyone interested in harmonizing scientific perspectives, developing more robust and interconnected fields of research, and potentially paving the way for groundbreaking discoveries.

Zielgruppe

Postgraduate and Undergraduate Advanced

Autoren/Hrsg.

Brase, Gary L.

Fachgebiete

• Sozialwissenschaften Psychologie Psychologie / Allgemeines & Theorie Psychologische Forschungsmethoden
• Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Forschungsmethodik, Wissenschaftliche Ausstattung
• Geisteswissenschaften Philosophie Philosophie: Allgemeines, Methoden

Inhaltsverzeichnis

Preface
Acknowledgements

1. A Quick Guide to Multi-Level Converging Lines of Evidence
Converging lines of Evidence

Multiple Levels of Explanation
The MCL framework.
Intentional Level

Algorithmic Level
Biological Level

Advantages of the framework

Putting the framework to work
Where to go from here
References

2. Historical and Philosophical Background of the MCL Framework
Convergence and Consistency
Converging Lines of Evidence
Schmitt and Pilcher’s Multiple Lines of Evidence
Consistency across Multiple Levels
General Levels of Explanation
Tinbergen’s four questions
Marr’s Levels of Explanation

Prior Unification Frameworks
Unifying psychology

Unifying science
Summary of Historical Background
Philosophical Background and Issues
Truth and the Nature of Reality
Against Realism in Science
Supporting Realism
Consistency of Sciences
Modularity, Consciousness, and Free Will
Conclusion
Inciting scientific revolutions
References

3. Concerns, Digressions, and Extensions of the MCL Framework
Introduction
The Persistence of Inertia
The Parsimony versus Complexity Issue

The Difficulty of Interdisciplinarity
Do I really need evolution in this framework?
How Many Levels of explanation?
Intentional Level
Algorithmic Level
Biological Level
How many lines of evidence? and where?
Can This Framework Provide a Score?
How does this framework really lead to better hypotheses?
Improve traditional hypothesis testing
Move to Bayesian statistics

Move to multiple hypotheses
Does this framework make research more replicable?
Can this framework be manipulated?
Conclusion
References

4. Quick Illustrative Examples of the MCL Framework
Language
Learned Taste Aversion
Terror Management Theory

More Examples
Reasoning about Social Exchanges
Exploration versus exploitation in searching
Sex Differences in Wayfinding
Discussion
References

5. Rationality and Quantitative Reasoning, using the MCL Framework
Rationality
Two Visions of Rationality
Constrained Maximization and Dual-Process Models
The Bayesian Reasoning Crucible (part 1)
Satisficing, Extended Adaptations Views, and Favored formats models
The Bayesian Reasoning Crucible (part 2)
Summary
The Intentional Level
Theoretical Evidence on the nature of quantitative reasoning
Constrained maximization and dual-process models
Extended adaptations and favored formats models
Summary
Phylogenetic Evidence on the nature of quantitative reasoning
Constrained maximization and dual-process models
Extended adaptations and favored formats models
Summary
The Algorithmic Level
Psychological Evidence on the nature of quantitative reasoning
Constrained maximization and dual-process models
Extended adaptations and favored formats models
Summary
Developmental Evidence on the nature of quantitative reasoning
Constrained maximization and dual-process models
Extended adaptations and favored formats models
Summary
Cross-Cultural Evidence on the nature of quantitative reasoning
Constrained maximization and dual-process models
Extended adaptations and favored formats models
Summary
Ancestral Environments Evidence on the nature of quantitative reasoning
Constrained maximization and dual-process models
Extended adaptations and favored formats models
Summary
Medical Evidence on the nature of quantitative reasoning
Constrained maximization and dual-process models
Extended adaptations and favored formats models
Summary
The Biological Level
Physiological Evidence on the nature of quantitative reasoning
Constrained maximization and dual-process models
Extended adaptations and favored formats models
Summary
Genetic Evidence on the nature of quantitative reasoning
Constrained maximization and dual-process models
Extended adaptations and favored formats models
Summary
Conclusion
References

Über Autor(innen)

Gary L. Brase is a professor in the Department of Psychological Sciences at Kansas State University, where he studies complex human decision-making using social, cognitive, and evolutionary theories.