Using a surrogate human digital twin to model embodied perception and cognition for neuroergonomics.

Understanding and predicting human behavior in dynamic, multisensory environments is one of the central challenges in neuroergonomics and human-centered artificial intelligence. Computational neuroscience provides mechanistic theories of perception, attention, and decision-making, but these insights have not yet been fully integrated into real-time digital representations of human physical behavior. Conversely, Human Digital Twins (HDTs) can successfully represent human physical characteristics, especially biomechanics and physiology, but remain limited in capturing the added complexities of embodied perception and cognition. Here, we introduce the concept of a Surrogate Human Digital Twin (S-HDT), emphasizing the necessarily approximate nature of the proposed surrogate model. Drawing upon active inference theory and consideration of evolutionary priors, the S-HDT operates as a generative model, continuously computing hidden states of cognition within a human model and predicting their trajectories under a changing environment. We argue that a S-HDT can serve as a computational approximation of embodied human perception and cognition as they function in modern multisensory environments. We present a proof-of-concept S-HDT simulating aspects of human attention and motivation mechanisms within complex sensory environments. The simulation illustrates the behavior of selected components of our proposed framework rather than providing empirical validation of human cognitive-state prediction. Future work should focus on multimodal human-data validation, and application-specific operationalization. This framework opens new opportunities for investigating neuroergonomic system design, human-centered artificial intelligence, and adaptive human-machine interaction.