Torn Between Love and Hate: Mouse Tracking Ambivalent Attitudes Towards Robots - International Journal of Social Robotics
link.springer.com
Robots are a source of evaluative conflict and thus elicit ambivalence. In fact, psychological research has shown across domains that people simultaneously report strong positive and strong negative evaluations about one and the same attitude object. This is defined as ambivalence. In the current research, we extended existing ambivalence research by measuring ambivalence towards various robot-related stimuli using explicit (i.e., self-report) and implicit measures. Concretely, we used a mouse tracking approach to gain insights into the experience and resolution of evaluative conflict elicited by robots. We conducted an extended replication across four experiments with N = 411 overall. This featured a mixed-methods approach and included a single paper meta-analysis. Thereby, we showed that the amount of reported conflicting thoughts and feelings (i.e., objective ambivalence) and self-reported experienced conflict (i.e., subjective ambivalence) were consistently higher towards robot-related stimuli compared to stimuli evoking univalent responses. Further, implicit measures of ambivalence revealed that response times were higher when evaluating robot-related stimuli compared to univalent stimuli, however results concerning behavioral indicators of ambivalence in mouse trajectories were inconsistent. This might indicate that behavioral indicators of ambivalence apparently depend on the respective robot-related stimulus. We could not obtain evidence of systematic information processing as a cognitive indicator of ambivalence, however, qualitative data suggested that participants might focus on especially strong arguments to compensate their experienced conflict. Furthermore, interindividual differences did not seem to substantially influence ambivalence towards robots. Taken together, the current work successfully applied the implicit and explicit measurement of ambivalent attitudes to the domain of social robotics, while at the same time identifying potential boundaries for its application.

Abstract: Robots are a source of evaluative conflict and thus elicit ambivalence. In fact, psychological research has shown across domains that people simultaneously report strong positive and strong negative evaluations about one and the same attitude object. This is defined as ambivalence. In the current research, we extended existing ambivalence research by measuring ambivalence towards various robot-related stimuli using explicit (i.e., self-report) and implicit measures. Concretely, we used a mouse tracking approach to gain insights into the experience and resolution of evaluative conflict elicited by robots. We conducted an extended replication across four experiments with N = 411 overall. This featured a mixed-methods approach and included a single paper meta-analysis. Thereby, we showed that the amount of reported conflicting thoughts and feelings (i.e., objective ambivalence) and self-reported experienced conflict (i.e., subjective ambivalence) were consistently higher towards robot-related stimuli compared to stimuli evoking univalent responses. Further, implicit measures of ambivalence revealed that response times were higher when evaluating robot-related stimuli compared to univalent stimuli, however results concerning behavioral indicators of ambivalence in mouse trajectories were inconsistent. This might indicate that behavioral indicators of ambivalence apparently depend on the respective robot-related stimulus. We could not obtain evidence of systematic information processing as a cognitive indicator of ambivalence, however, qualitative data suggested that participants might focus on especially strong arguments to compensate their experienced conflict. Furthermore, interindividual differences did not seem to substantially influence ambivalence towards robots. Taken together, the current work successfully applied the implicit and explicit measurement of ambivalent attitudes to the domain of social robotics, while at the same time identifying potential boundaries for its application.

Lay summary (by Claude 3 Sonnet):

This research looked at how people have mixed positive and negative feelings (ambivalence) towards robots. Across four experiments with 411 total participants, the researchers used self-report measures and reaction times to assess ambivalence when viewing robot-related images compared to images evoking just positive or negative feelings.

The results showed people consistently reported more conflicting thoughts and feelings of ambivalence towards the robot images versus the one-sided positive or negative images. Their reaction times were also slower for the robot images, indicating ambivalence. However, their mouse cursor movements (which can reveal ambivalence) were inconsistent across the robot images.

Qualitative data suggested people may have focused on especially strong arguments to try to resolve their ambivalent feelings about robots. Individual differences between people did not seem to substantially impact ambivalence levels.

Overall, this work successfully applied methods for measuring ambivalent attitudes specifically to people’s reactions towards robots. It also identified some potential limitations in applying these ambivalence measures to robot-related stimuli.

In plain language, the research confirmed that robots tend to evoke mixed positive and negative feelings in people, using both self-report and implicit reaction time measures. But some of the behavioral indicators of ambivalence did not consistently show this effect for robots.