Cognitive science is undergoing a profound transformation through the integration of behavioral research, neural measurements, and computational models of development. Recent advances in neuroscience and computational modeling have made it possible to probe the mechanisms underlying perception, memory, language, and decision-making across multiple scales—ranging from the activity of single neurons to complex behavioral patterns—and across the lifespan, from infancy to older adulthood. Despite significant progress, major questions persist regarding how processes at the molecular, circuit, and system levels give rise to coherent behaviors, and how these phenomena unfold over development.

Integrative research approaches are beginning to illuminate the cross-scale principles that underpin cognition but often lack a common framework or struggle to draw principled, testable connections between biological mechanisms, computational theories, and behavioral phenomena. There is ongoing debate about the best ways to connect observational data with models that can explain and predict cognitive dynamics. Recent studies highlight the potential for interdisciplinary methods—combining cognitive modeling, advanced neural recording and neuroimaging, and data science/machine learning—to generate robust and reproducible findings. Yet there remains a pressing need for work that bridges computational and biological perspectives in ways that advance both theory and application. Moreover, the broader impact of such research on artificial intelligence, clinical interventions, and societal well-being is only beginning to be realized.

This Research Topic aims to advance our understanding of cognition by fostering work that systematically connects behavioral evidence, neural/biological data, and computational models across developmental stages and representational levels. We welcome submissions spanning the lifespan—including infancy, childhood, adolescence, adulthood, and aging—and encourage studies that elucidate developmental trajectories of cognitive functions. We particularly invite contributions that develop or test theories linking neural mechanisms with cognitive processes, support open and reproducible science, and clarify implications for improving artificial intelligence or clinical outcomes.

We encourage both convergent, multi-method investigations and strong single- or dual-modality studies (e.g., behavioral with computational modeling; behavioral with neuroimaging or other biological measures) that provide clear, principled links across levels of analysis. Studies that share data, code, and materials to promote reproducibility are especially welcome.

We welcome empirical, theoretical, methodological, and translational work that integrates across neural, cognitive, and developmental perspectives. Priority will be given to research with implications for theory, AI, and societal impact, and to open, reproducible practices.

Topics of interest include, but are not limited to:

- Mechanisms linking neural activity and biological measures to behavioral outcomes
- Developmental trajectories in cognition and their neural/biological underpinnings (from infancy through aging)
- Computational models that connect biological and behavioral data and generate testable predictions
- Interdisciplinary methods combining neuroimaging/recording, cognitive modeling, and data science
- Reproducibility and open science in multiscale and developmental cognitive research
- Theoretical work with implications for AI and real-world applications
- Translational studies relevant to clinical, educational, or public-health contexts

Article types: Original research, theoretical papers, methodological advances, data/resource reports, reviews, and perspectives.

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Multiscale Cognition: Linking Behavior, Neural Data, and Computational Models of Development