Neural sources of prediction errors detect unrealistic VR interactions

Abstract

Objective. Neural interfaces hold significant promise to implicitly track user experience. Their application in virtual and augmented reality (VR/AR) simulations is especially favorable as it allows user assessment without breaking the immersive experience. In VR, designing immersion is one key challenge. Subjective questionnaires are the established metrics to assess the effectiveness of immersive VR simulations. However, administering such questionnaires requires breaking the immersive experience they are supposed to assess.Approach. We present a complimentary metric based on a event-related potentials. For the metric to be robust, the neural signal employed must be reliable. Hence, it is beneficial to target the neural signal's cortical origin directly, efficiently separating signal from noise. To test this new complementary metric, we designed a reach-to-tap paradigm in VR to probe electroencephalography (EEG) and movement adaptation to visuo-haptic glitches. Our working hypothesis was, that these glitches, or violations of the predicted action outcome, may indicate a disrupted user experience.Main results. Using prediction error negativity features, we classified VR glitches with 77% accuracy. We localized the EEG sources driving the classification and found midline cingulate EEG sources and a distributed network of parieto-occipital EEG sources to enable the classification success.Significance. Prediction error signatures from these sources reflect violations of user's predictions during interaction with AR/VR, promising a robust and targeted marker for adaptive user interfaces.

Keywords: BCI; EEG; neural interface technology; post-error slowing; prediction error; predictive coding; virtual reality.