Decision neuroscience in the age of AI: human-machine learning, agency, and adaptation

Decision neuroscience explores the neural underpinnings of how individuals and groups make choices, integrating perspectives from cognitive neuroscience, psychology, computational modeling, and behavioral economics. The unprecedented rise of artificial intelligence (AI) and machine learning has amplified interest in understanding how humans interact with increasingly autonomous systems. Current research interrogates how agency, control, learning, and adaptation unfold as humans collaborate with or are influenced by AI agents. Recent studies employ neuroimaging and computational techniques to examine neural correlates of trust, prediction error, and shared control in hybrid human-machine environments, revealing both novel insights and new complexities. However, decisive questions remain regarding how traditional models of human decision-making and agency may need to be revisited or reimagined in light of advanced, learning-enabled machines.This Research Topic aims to advance our understanding of how human agency and adaptive learning are negotiated and reshaped within the context of AI-driven systems. It seeks to uncover the neural and computational mechanisms that underlie joint decision-making, the attribution of agency, and the capacity for behavioral adaptation in environments increasingly characterized by human-AI interaction. Key questions include: How do neural representations of self-generated versus externally influenced actions change when collaborating with AI? What are the effects of adaptive AI feedback on learning and strategy adjustment? What ethical and cognitive challenges emerge as AI assumes greater autonomy in decision processes?To gather further insights in the intersection of decision neuroscience, AI, and human adaptation, we welcome articles addressing, but not limited to, the following themes:o Neural representations of agency in human-AI collaborationso Computational modeling of shared decision-making between humans and machineso The impact of explainable AI on trust and learningo Social and ethical implications of shifting agency in hybrid environmentso Behavioral strategies for adaptation to AI-mediated feedbacko Cross-species perspectives on learning and agency with artificial agents Topic editors: Hanliang Fu, Yafeng Niu, Yi Ding Source: https://www.frontiersin.org/research-topics/79374/decision-neuroscience-in-the-age-of-ai-human-machine-learning-agency-and-adaptation