How Computational Modeling Can Force Theory Building in Psychological Science.

Area of Science:

• Psychological science
• Cognitive science
• Computational modeling

Background:

• Psychology uses diverse methods to study human behavior.
• A key debate in psychological science involves the use of computational modeling in scientific inference.
• Computational modeling is currently undervalued but offers significant potential for advancing the field.

Purpose of the Study:

• To argue for the critical role of computational modeling in psychological science.
• To highlight how computational modeling fosters "open theory" by demanding explicit conceptual analysis and formalization.
• To present scientific inference as a sequential process where modeling can constrain and improve each step.

Main Methods:

• The study presents a conceptual argument for the utility of computational modeling.
• It frames scientific inference as a path function, emphasizing the impact of each step.
• The authors explain the process of formalizing, specifying, and implementing computational models.

Main Results:

• Computational modeling forces rigorous conceptual analysis, leading to "open theory" and more robust scientific inference.
• Modeling constrains the scientific inference process, enhancing explanatory and predictive power beyond traditional experimental practices.
• The study predicts that eschewing computational modeling will perpetuate replicability crises and hinder coherent theory development.

Conclusions:

• Adopting computational modeling is essential for advancing psychological science, improving theory building, and ensuring transparency.
• Formal modeling provides a framework for open and transparent theorizing, mitigating issues like the replicability crisis.
• The benefits of computational modeling are accessible to all researchers, regardless of their current expertise.