Aysel Sabuncu Brain Research Center (BAM)

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    Gendered actions with a genderless robot: gender attribution to humanoid robots in action
    (Springer Science and Business Media B.V., 2023-01-20) Aşkın, Gaye; Saltık, İmge; Elver Boz, Tuğçe; Urgen, Burcu A.
    The present study aims to investigate how gender stereotypes affect people’s gender attribution to social robots. To this end, we examined whether a robot can be assigned a gender depending on a performed action. The study consists of 3 stages. In the first stage, we determined masculine and feminine actions by a survey conducted with 54 participants. In the second stage, we selected a gender-neutral robot by having 76 participants rate several robot stimuli in the masculine-feminine spectrum. In the third stage, we created short animation videos in which the gender-neutral robot determined in stage two performed the masculine and feminine actions determined in stage one. We then asked 102 participants to evaluate the robot in the videos in the masculine-feminine spectrum. We asked them to rate the videos according to their own view (self-view) and how they thought society would evaluate them (society-view). We also used the Socialization of Gender Norms Scale (SGNS) to identify individual differences in gender attribution to social robots. We found the main effect of action category (feminine vs. masculine) on both self-view reports and society-view reports suggesting that a neutral robot was reported to be feminine if it performed feminine actions and masculine if it performed masculine actions. However, society-view reports were more pronounced than the self-view reports: when the neutral robot performed masculine actions, it was found to be more masculine in the society-view reports than the self-view reports; and when it performs feminine actions, it was found to be more feminine in the society-view reports than the self-view reports. In addition, the SGNS predicted the society-view reports (for feminine actions) but not the self-view reports. In sum, our study suggests that people can attribute gender to social robots depending on the task they perform.