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

TL;DR

This paper advocates for integrating naturalistic stimuli and AI-driven models into cognitive science to develop generalizable theories that better capture the full range of natural behaviors.

Contribution

It reviews recent advances in AI and neuroscience, proposing a framework for naturalistic computational cognitive science that enhances theory generalization and experimental relevance.

Findings

Naturalistic paradigms elicit distinct neural and cognitive behaviors.

AI models trained on naturalistic data show improved generalization.

Integrating AI progress can advance understanding of natural cognition.

Abstract

How can cognitive science build generalizable theories that span the full scope of natural situations and behaviors? We argue that progress in Artificial Intelligence (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 review cases from cognitive science and neuroscience where naturalistic paradigms elicit distinct behaviors or engage different processes. We then discuss recent progress in AI that shows that learning from naturalistic data yields qualitatively different patterns of behavior and generalization, and examine how these findings impact the conclusions we draw from cognitive modeling, and can help yield new hypotheses for the roots of cognitive and neural phenomena. 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.