Neurobehavioral Mechanisms of Learning and Adaptation: Translational Perspectives from Basic to Applied Science

Dear Colleagues,

Understanding the principles that govern learning and adaptation requires a cross-disciplinary approach bridging neuroscience, behavior analysis, and computational modeling. This Special Issue aims to highlight cutting-edge research that advances theoretical and empirical accounts of behavioral processes, spanning from cellular mechanisms and neural circuits to operant models and applied interventions. Emphasis will be placed on work that integrates findings across species and levels of analysis to deepen our understanding of how behavior is acquired, maintained, and modified in dynamic environments. Contributions that draw on the experimental analysis of behavior, behavioral neuroscience, translational models, and computational behavior analysis are particularly encouraged. This Special Issue invites the submission of both original research articles and theoretical contributions that propose, evaluate, or refine models of learning and behavior change.

While neuroscience and behavior analysis have historically pursued different approaches to understanding learning, recent advances in behavioral neuroscience and computational modeling offer a unique opportunity to unify these traditions. Theories of behavioral processes—spanning operant and Pavlovian learning, reinforcement-based models, and systems-level accounts—provide essential frameworks for understanding how behavior is acquired, maintained, and modified across contexts. These theories are increasingly enriched by advances in neurobehavioral research and computational and translational modeling, which enable more precise descriptions of behavior across species, settings, and levels of analysis. This integration supports a more comprehensive understanding of how organisms adapt to changing environments, how behavior is shaped, maintained, and disrupted, and how diverse behavioral phenotypes can be characterized and linked to real-world applications in education, clinical intervention, and behavioral health.

This Special Issue seeks to bridge basic and applied science by highlighting research that advances our understanding of behavioral processes and the mechanisms underlying learning and adaptation. We welcome contributions that integrate theoretical, experimental, and translational approaches, spanning operant and Pavlovian learning, neurobehavioral science, and computational modeling. Of particular interest is work that connects findings from experimental models (e.g., rodents, pigeons, or simulated agents) with clinical or applied contexts, including populations with neurodevelopmental or psychiatric conditions. By bringing together diverse methodological and conceptual perspectives, this Special Issue aims to foster dialogue across disciplines and promote research with both scientific and practical impact.

Recent developments across behavioral science, neuroscience, and computational fields are transforming how we conceptualize learning, adaptation, and behavioral regulation. A growing body of research is using quantitative and computational approaches to model decision-making, predict behavior across changing environments, and identify the principles that generalize across species and levels of analysis. Advances in reinforcement learning, dynamic systems modeling, and artificial intelligence are providing new tools to analyze behavior with greater precision while also fostering integration with emerging neurobiological and clinical findings. These approaches complement longstanding experimental traditions by offering frameworks that can simulate complex behavior, identify mechanisms underlying persistence and change, and inform both basic science and applied interventions. This Special Issue welcomes contributions that leverage these advances—empirical or theoretical—to enhance our understanding of behavioral processes, their underlying neural mechanisms, and their relevance to real-world applications in education, clinical practice, and behavioral health.

Dr. John Michael Falligant
Dr. Rusty Nall
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• behavioral neuroscience
• experimental analysis of behavior
• computational behavior analysis
• quantitative modeling
• reinforcement learning
• translational research
• behavioral pharmacology
• neurobehavioral mechanisms of learning
• persistence and relapse
• behavioral phenotyping