Methods and Data in Motivation and Reward: Rigorous, Reproducible, FAIR

Motivation, reward processing, reinforcement learning, decision-making, and goal-directed behavior remain a central challenge in behavioral neuroscience. These processes arise from complex interactions among neural circuits, neurotransmitter systems, environmental influences, and learned experience, and are fundamental both to adaptive behavior and to neuropsychiatric disorders such as substance use disorders, compulsive behaviors, depression, and stress-related conditions. Progress depends not only on conceptual advances but on rigorous methods and high-quality data able to interrogate these mechanisms across behavioral, neurobiological, and computational levels.Advances in behavioral paradigms, operant and self-administration models, electrophysiology, neurochemical monitoring, neural circuit interrogation, neuroimaging, digital phenotyping, and computational modeling are transforming how motivation and reward are studied, allowing researchers to characterize reward circuitry, reinforcement mechanisms, motivational states, stress–reward interactions, and addiction-related neuroadaptations across species and contexts. The large, multimodal datasets they generate gain scientific value only through rigorous validation, transparent reporting, reproducible workflows, and FAIR (Findable, Accessible, Interoperable, Reusable) practices that enable reuse, benchmarking, and cross-study integration.This Research Topic gathers methodological innovations, experimental resources, analytical frameworks, and FAIR² Data articles that strengthen the foundations of motivation and reward research. Priority goes to methods and datasets that improve the reliability, reproducibility, interpretability, and translational relevance of findings. Spanning behavioral neuroscience, neuropharmacology, addiction research, systems and computational neuroscience, psychiatry, psychology, and data science, the collection aims to accelerate reproducible, clinically relevant discovery into the neural circuits and neurochemical systems that govern motivated behavior.We particularly welcome Methods, Data Report, FAIR² Data , Brief Research Report, Original Research, and Perspective articles that: Introduce behavioral paradigms, including operant and self-administration models, for quantifying reward valuation, motivation, effort allocation, reinforcement learning, impulsivity, compulsivity, and decision-making in animal models or humans. Develop experimental approaches for studying stress–reward interactions, negative reinforcement, relapse vulnerability, and resilience. Present innovations in electrophysiology, neural circuit interrogation, optogenetics, chemogenetics, fiber photometry, voltammetry, microdialysis, or neurochemical sensing for probing reward-related neural activity. Develop and validate neuropharmacological, circuit-based, and systems-level approaches that dissect the molecular, cellular, and network mechanisms of motivation, reward, reinforcement, and addiction. Provide reusable, well-characterized behavioral, neural, neurochemical, pharmacological, imaging, or multimodal datasets aligned with FAIR² principles that serve as community resources. Release computational methods, analytical pipelines, software, and machine-learning workflows for modeling reinforcement learning, reward prediction, value representation, motivational states, and decision-making, with code and parameters shared for reproducibility and benchmarking. Establish validation, benchmarking, and harmonization frameworks that improve measurement reliability, validity, and comparability across laboratories, species, and paradigms. Advance translational methodologies that bridge preclinical and clinical studies of reward processing, addiction, and stress-related disorders. Enable longitudinal, naturalistic, and real-world assessment of motivation and reward through digital phenotyping, wearable technologies, ecological momentary assessment, and remote monitoring. Generate data resources that support cross-species integration, meta-analyses, secondary analyses, and predictive models of reward-related behavior and neuropsychiatric vulnerability.Submissions should demonstrate methodological innovation, rigorous validation, translational relevance, or reusable resources that advance the behavioral, circuit, and neurochemical mechanisms underlying motivation, reward, reinforcement, decision-making, and addiction. Authors are encouraged to make datasets, workflows, software, and methods openly available, supporting cumulative and reproducible research across the community.If you have questions on FAIR² Data article types please get in touch directly with behavioralneuroscience@frontiersin.org Topic editors: Giuseppe Di Giovanni, Marsida Kallupi Source: https://www.frontiersin.org/research-topics/82698/methods-and-data-in-motivation-and-reward-rigorous-reproducible-fair