Trust in automation: A survey of neurophysiological perspectives on measurement and modelling

Trust is a critical factor in effective human–automation interaction, influencing user reliance, acceptance, and system performance. As automated systems become increasingly complex and computationally advanced, real-time trust quantification is essential. This review systematically examines the literature using a PRISMA-guided approach, integrating studies on neural mechanisms, physiological measures, and computational modelling. Relevant studies were collected from multiple databases, screened according to inclusion criteria, and categorised by experimental paradigms, neural modalities, preprocessing pipelines, feature types, and machine/deep learning frameworks. We critically evaluate classical machine learning and deep learning approaches, highlighting consistent neural correlates, including oscillatory activity, connectivity patterns, and frontal and temporoparietal activations. The review also reveals a critical misalignment between psychological theories of trust, how trust is labelled in experiments, and how computational models are trained, raising concerns about the specificity and interpretability of many reported trust markers. Key challenges include limited feature exploration, dataset scarcity, real-time assessment strategies, and underutilisation of multimodal fusion across feature-, decision-, and network-level representations. By synthesising current evidence and identifying key gaps, this work provides a roadmap for developing adaptive, interpretable, and generalisable trust models that support safer and more reliable human–automation systems in complex, real-world scenarios.