Mapping Research Trends in AI-Driven Neuroscience of Decision-Making, Emotions, and Trust

This bibliometric analysis of 500 publications (2020–2025) maps AI and machine learning applications in neuroscientific research on emotion, trust, and decision-making. The corpus shows strong growth in publication output and increasing methodological diversification, including the growing visibility of sequence-based, graph-based, and transformer-based models. At the same time, the literature remains strongly concentrated on emotion recognition and EEG-based studies, whereas trust and decision-making receive comparatively less empirical coverage in the analyzed corpus. Beyond describing these trends, the study suggests that the current research agenda is influenced by a coupling between dominant modality (EEG), dominant task type (emotion recognition), and dominant model families. The study therefore provides a structured overview of publication dynamics, task categories, data modalities, model families, and thematic clusters relevant to NeuroIS, while also identifying directions for future IS-oriented research on trust-sensitive systems, human–AI interaction, and process-oriented decision support.