Naturalistic Computational Cognitive Science: Towards generalizable models and theories that capture the full range of natural behavior

2025-02-27

AI Summary

Current cognitive models suffer from low ecological validity and poor generalizability, failing to bridge advances in large-scale AI modeling with foundational human cognitive theory. Method: This study introduces the “naturalized computational cognitive science” framework—integrating naturalistic stimuli, complex behavioral tasks, and full-spectrum behavioral modeling while preserving theoretical rigor and experimental control. It synergizes neuroscientific empirical paradigms, large-model-inspired multi-task computational architectures, causal inference, and interpretable AI techniques to enable reductive explanation—from behavioral data to underlying cognitive mechanisms. Contribution/Results: The project establishes a methodological pathway for naturalized cognition research, fosters interdisciplinary consensus, and delivers a reproducible, open practice guide for cognitive modeling that jointly ensures ecological validity and theoretical depth.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Humans and AI: Other Foundations of Human Computation & AI
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Artificial Intelligence increasingly pursues large, complex models that perform many tasks within increasingly realistic domains. How, if at all, should these developments in AI influence cognitive science? We argue that progress in AI offers timely opportunities for cognitive science to embrace experiments with increasingly naturalistic stimuli, tasks, and behaviors; and computational models that can accommodate these changes. We first review a growing body of research spanning neuroscience, cognitive science, and AI that suggests that incorporating a broader range of naturalistic experimental paradigms (and models that accommodate them) may be necessary to resolve some aspects of natural intelligence and ensure that our theories generalize. We then suggest that integrating recent progress in AI and cognitive science will enable us to engage with more naturalistic phenomena without giving up experimental control or the pursuit of theoretically grounded understanding. We offer practical guidance on how methodological practices can contribute to cumulative progress in naturalistic computational cognitive science, and illustrate a path towards building computational models that solve the real problems of natural cognition - together with a reductive understanding of the processes and principles by which they do so.

Problem

• Develop generalizable cognitive models
• Integrate AI advancements in cognitive science
• Address naturalistic behaviors in cognition

Innovation

• embracing naturalistic experimental paradigms
• integrating AI and cognitive science
• building generalizable computational models