AI- and Algorithmic Choice Architecture: Attentional, Cognitive, and Neurocognitive Mechanisms of Human Decision-Making

Artificial intelligence and algorithmic systems increasingly shape the architecture of human choice. Recommender systems, personalized ranking, AI-generated summaries, targeted content, and algorithmic curation influence not only which options individuals encounter, but also their order, salience, timing, and informational framing. Rather than focusing on AI as a collaborative decision partner, this Research Topic centers on how AI- and algorithm-mediated choice architecture alters the conditions under which human judgments and choices are formed.From a Decision Neuroscience perspective, a central question is how such changes in information environments translate into decision behavior. AI- and algorithmic choice architectures may redirect attention, alter cognitive effort, shape conflict and uncertainty processing, influence subjective valuation, and change the accumulation of evidence leading to a final choice. These dynamic processes cannot always be captured adequately through self-report or final behavioral outcomes alone. Behavioral experiments, eye-tracking, EEG/ERP, fMRI, fNIRS, psychophysiological measures, and computational models provide complementary approaches for examining their temporal, cognitive, and neurophysiological mechanisms. Accordingly, studies centered primarily on collaboration with AI agents, delegation of decisions to AI, trust in AI agents, or adaptation to AI feedback fall outside the primary scope of this Research Topic and are more closely aligned with research on human–machine learning, agency, and adaptation. In contrast, studies of AI-generated or algorithmically structured information fall within the present scope when their primary question concerns how the resulting information environment alters human decision processes.This Research Topic therefore aims to advance a mechanism-based understanding of how AI- and algorithmic choice architectures influence human decision-making. We particularly welcome studies that identify clearly defined attentional, cognitive, decision-theoretic, or neurocognitive processes and connect them to observable judgment and choice. Application domains such as consumer behavior, digital platforms, education, healthcare, tourism, prosocial behavior, and sustainability are welcome when they serve as contexts for testing these mechanisms rather than as independent bases for inclusion. Submissions should identify or test a clearly defined attentional, cognitive, decision-theoretic, or neurocognitive mechanism through which AI- or algorithm-mediated choice architecture influences human judgment or choice. Studies focusing primarily on application outcomes or general technology effects without examining an underlying decision-making mechanism are outside the scope of this Research Topic. Studies may be situated in diverse substantive contexts, provided that these contexts serve to test or reveal the focal decision-making mechanisms rather than constitute an independent basis for inclusion. Potential themes include, but are not limited to:- Attention allocation and salience, including how recommendation, ranking, personalization, and information highlighting alter attention and the weighting of decision cues.- Cognitive effort and information processing, including cognitive load, processing depth, and decision efficiency under AI-generated or algorithmically filtered information.- Conflict monitoring and uncertainty processing, particularly when algorithmically presented information conflicts with prior beliefs, preferences, or initial judgments.- Value construction and preference formation, including how algorithmically structured information environments influence valuation and choice.- Temporal dynamics of decision formation, examined through EEG/ERP, eye-tracking, fMRI, fNIRS, psychophysiology, or multimodal approaches.- Computational modeling of attention and choice, including evidence-accumulation, reinforcement-learning, and computational cognitive models linking altered information environments to human decisions. Topic editors: Wuke Zhang, Zehong Jimmy Cao, Chih-Chao Chung Source: https://www.frontiersin.org/research-topics/85526/ai--and-algorithmic-choice-architecture-attentional-cognitive-and-neurocognitive-mechanisms-of-human-decision-making