This study investigates how humans process and attend to robot actions compared to human actions under varying cognitive demands. Using a perceptual load paradigm, we examined whether robots capture attention similarly to humans and how this is influenced by the nature of their actions. Participants performed a letter detection task while being presented with task-irrelevant videos of either human or robot agents performing communicative or noncommunicative actions. Results demonstrated that both humans and robots captured attention through their actions, particularly when these actions were communicative. Under high perceptual load conditions, human distractors caused more interference than robot distractors, suggesting that agent identity becomes particularly important when cognitive resources are limited. These findings provide insights into how humans process robot actions in attentiondemanding situations and may have important implications for designing robot behaviors in operational contexts.

The recent shift towards incorporating implicit measurements into the mind perception studies in social robotics has come along with its promises and challenges. The implicit tasks can go beyond the limited scope of the explicit tasks and increase the robustness of empirical investigations in human-robot interaction (HRI). However, designing valid and reliable implicit tasks requires norming and validating all stimuli to ensure no confounding factors interfere with the experimental manipulations. We conducted a lexical norming study to systematically explore the concepts suitable for an implicit task that measures mind perception induced by social robots. Two-hundred seventy-four participants rated an expanded and strictly selected list of forty mental capacities in two categories: Agency and Experience, and in two levels of capacities: High and Low. We used the partitioning around medoids algorithm as an objective way of revealing the clusters. We discussed the different clustering solutions in light of the previous findings. We consulted on frequency-based natural language processing (NLP) on the answers to the open-ended questions. The NLP analyses verified the significance of clear instructions and the presence of some common conceptualizations across dimensions. We proposed a systematic approach that encourages validation and norming studies, which will further improve the reliability and reproducibility of HRI studies.