Integrating Converging Evidence in Behavioral Sciences: How to Incite a Scientific Revolution

by Gary L. Brase

Overview

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, in developing more robust and interconnected fields of research, and in potentially paving the way for groundbreaking discoveries.

Product Details

ISBN-13:	9781032882826
Publisher:	Taylor & Francis
Publication date:	09/26/2025
Pages:	180
Product dimensions:	6.88(w) x 9.69(h) x (d)

About the Author

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.

Table of Contents

Preface
Acknowledgements

1. A Quick Guide to Multi-level Converging Lines of Evidence
/Converging lines of Evidence
Multiple Levels of Explanation
The multi-level converging lines of evidence (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 & 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