A roadmap to reverse engineering real-world generalization by combining naturalistic paradigms, deep sampling, and predictive computational models

TL;DR

This paper proposes a comprehensive approach to understanding real-world generalization by integrating naturalistic experiments, extensive data sampling, and predictive computational models to better capture neural mechanisms.

Contribution

It advocates for combining ecological validity, deep sampling, and predictive modeling to advance neuro-cognitive theories of generalization.

Findings

Emphasizes importance of multimodal naturalistic paradigms

Highlights role of deep sampling for model stability

Stresses predictive modeling for statistical rigor

Abstract

Real-world generalization, e.g., deciding to approach a never-seen-before animal, relies on contextual information as well as previous experiences. Such a seemingly easy behavioral choice requires the interplay of multiple neural mechanisms, from integrative encoding to category-based inference, weighted differently according to the circumstances. Here, we argue that a comprehensive theory of the neuro-cognitive substrates of real-world generalization will greatly benefit from empirical research with three key elements. First, the ecological validity provided by multimodal, naturalistic paradigms. Second, the model stability afforded by deep sampling. Finally, the statistical rigor granted by predictive modeling and computational controls.