AI Era-Informed Innovative Quantitative Research Methods for Social, Behavioral and Cognitive Sciences

The AI revolution has transformed behavioral and cognitive research through unprecedented data volume, velocity, and variety (e.g., neural imaging, real-time digital footprints). However, traditional quantitative methods—designed for smaller, structured datasets—struggle to harness AI’s potential while maintaining theoretical interpretability. Current studies often apply AI as a “black box,” neglecting foundational measurement principles (e.g., validity, reliability) and cognitive theory integration. This disconnect limits scientific rigor and practical utility. A systematic exploration of AI-informed methods—where machine learning, generative AI, and computational modeling are synergized with classical psychometric and cognitive frameworks—is urgently needed to address these gaps and drive the field toward scalable, theory-grounded innovation.

This topic aims to catalyze a paradigm shift in behavioral and cognitive sciences by establishing AI-era quantitative methods as a cohesive research frontier. First, it will showcase novel methodologies (e.g., adaptive measurement algorithms, hybrid neural-symbolic cognitive models, and explainable AI for multi-modal data fusion) that balance technical innovation with psychological theory. Second, it seeks to develop standardized validation protocols to ensure methodological rigor, addressing critiques of “black-box” AI applications. Third, by fostering collaboration between psychologists, data scientists, and cognitive modelers, it will translate methodological advances into concrete outcomes: refining theories of human cognition (e.g., dynamic decision-making models), enhancing measurement precision in clinical/educational settings, and enabling personalized interventions. Ultimately, the topic aspires to position quantitative psychology at the forefront of AI-driven scientific discovery, ensuring methods are both technically robust and theoretically meaningful for understanding human behavior.

This Research Topic welcomes interdisciplinary manuscripts that bridge AI innovation with social, behavioral, and cognitive science rigors. We invite contributions addressing, but not limited to:

1. AI-enhanced measurement tools and models (e.g., adaptive learning/testing systems, multimodal data analysis, automatic item generation and scoring, abnormal psychology and behavior detection, AI-related (e.g., literacy and attitude) questionnaire development);
2. Human-AI interactions (e.g., collective intelligence, human-AI collaborative decision-making and problem-solving, socio-cognitive adaptation to AI feedback)
3. Explainable AI frameworks for modeling dynamic cognitive processes (e.g., attention, decision-making);
4. Validation protocols for AI-driven methods (e.g., reliability benchmarks, theory-aligned interpretability);
5. Implementation of generative AI (e.g., large language model) in experimental design and data analysis.

We encourage original research, methodological innovations, systematic reviews, and registered reports demonstrating concrete integration of AI techniques with psychological theory. Submissions must prioritize reproducibility (e.g., open code/data) and explicitly address limitations of "black-box" approaches. Cross-disciplinary collaborations (psychologists, computational scientists, domain experts) are strongly encouraged.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Registered Report
• Review
• Systematic Review
• Technology and Code

Keywords: AI-Augmented Psychometrics, Dynamic Cognitive Modeling, Human-AI Interaction, Generative AI, Multimodal Data

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.