Integrating Converging Evidence in Behavioral Sciences

This book 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.

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.

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