Trust, Perception, and Cognitive Processes in Human-AI Interaction

Artificial intelligence is now deeply embedded in everyday life, from conversational agents and recommendation systems to autonomous vehicles and collaborative robots. With the rapid proliferation of generative AI and increasingly autonomous decision-making systems, understanding the psychological mechanisms that shape how people perceive, trust, and interact with AI has become a pressing interdisciplinary challenge. Yet existing research remains fragmented across application domains (e.g., chatbots, robots, recommender systems), theoretical traditions (e.g., cognitive, social, and computational approaches), and methodological scales (ranging from controlled laboratory experiments to real-world deployment studies). This fragmentation makes it difficult to identify generalizable principles that govern human-AI interaction and to translate psychological insights into actionable design guidance.This Research Topic aims to bring together original empirical, theoretical, and methodological contributions that advance our understanding of trust, perception, and cognitive processes in human-AI interaction. We are particularly interested in work that bridges the gap between technical AI development and the psychological experience of end users. The collection focuses on the cognitive processes through which people perceive, evaluate, and come to trust AI systems, including how trust is formed, calibrated, and repaired, rather than on the broader effects of AI use on cognitive abilities. We especially encourage work that measures trust and reliance using behavioral and neurophysiological methods (e.g., EEG, fMRI, eye-tracking), that examines how system characteristics such as explainability (XAI), anthropomorphic and social cues, and interaction modality shape trust, and that tests these questions in applied settings such as healthcare, education, and autonomous driving. Cognitive processes such as memory, learning, language, and reasoning are in scope where they are studied in relation to trust, perception, or reliance on AI.Topics of interest include, but are not limited to:• Cognitive and affective processes underlying perception of AI-driven systems, including mental models, attribution, cognitive load, and reliance• Trust formation, calibration, and repair in human-AI and human-robot interaction• The role of AI transparency, explainability, and interpretability in shaping user trust and decision-making• Neural and physiological correlates of trust and reliance in AI-mediated tasks • Human-AI collaborative decision-making, automation bias, and the boundaries of appropriate reliance• User perception of anthropomorphic and social cues in AI agents and robots• Individual differences in trust and acceptance of AI, including personality, prior experience, cognitive style, and cultural background• The influence of interaction modality (e.g., voice, visual, embodied, multimodal) on trust and user experience• Ethical and psychological implications of long-term human-AI relationships• Design frameworks and evaluation methods for trustworthy and user-centered AI systems• Studies conducted in diverse real-world contexts, such as healthcare, education, the workplace, and autonomous driving.We welcome a diverse range of article types, including original research, systematic reviews, meta-analyses, brief research reports, and perspective articles. Contributions employing experimental, survey-based, neuroimaging, qualitative, or mixed-methods approaches are all encouraged. We particularly welcome interdisciplinary work that integrates perspectives from cognitive psychology, social psychology, human factors, human-computer interaction, and AI/robotics engineering. Our goal is to foster cross-domain dialogue and build a shared, generalizable evidence base that can inform both the scientific understanding of human-AI interaction and the design of AI systems that people can understand, trust appropriately, and engage with effectively. Topic editors: Dongwook Hwang, Kyudong Park, Dengbo HE Source: https://www.frontiersin.org/research-topics/86678/trust-perception-and-cognitive-processes-in-human-ai-interaction