UC Davis

Fear, mistrust, and unclear responsibility continue to impede the adoption of autonomous and collaborative technologies. Embodiment, the way people perceive their body and actions, provides a framework for understanding human–machine interaction, with potential to reduce fear, foster trust, and clarify responsibility between users and systems. However, translating embodiment theory into practical evaluation tools for human–machine interaction has proven challenging, as existing metrics often yield conflicting results. This thesis uses psychophysics to quantify embodiment metrics, investigating their relationships during whole limb movements and in prosthetic tasks. Chapter 2 systematically varies visual, auditory, and haptic feedback to examine how four common embodiment metrics covary, revealing inconsistencies with earlier findings and suggesting that these measures may capture distinct constructs. Chapter 3 focuses on the sense of agency, the experience of authoring one’s actions and their outcomes, using a tightly controlled, forced-choice psychophysical paradigm. We test the validity of intentional binding and introduce a novel precision-based index, the temporal discrimination limen. Results show that intentional binding does not consistently reflect agency, whereas the temporal discrimination limen is selectively sensitive to volitional control. We conclude that the temporal discrimination limen is a promising implicit metric for agency and outline future work to validate its relationship with explicit measures and to examine the role of the supplementary motor area in volition and time perception.