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.
By:
Gary L. Brase (Kansas State University)
Imprint: Routledge
Country of Publication: United Kingdom
Dimensions:
Height: 246mm,
Width: 174mm,
ISBN: 9781032882826
ISBN 10: 1032882824
Pages: 150
Publication Date: 26 September 2025
Audience:
College/higher education
,
A / AS level
,
Further / Higher Education
Format: Hardback
Publisher's Status: Forthcoming
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
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.
