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Title of Talk #1
Reconsidering attitudes towards robots
The role of ambivalence Abstract of Talk #1 The Lecture will start out with a brief overview of existing works on attitudes towards robots in general and towards specific subtypes of robots, like service robots or education robots. This review will - at first glance - convey the impression that people ostensibly hold neutral to fairly positive attitudes towards robots. I will argue that this might be a measurement artefact, highlighting that indeed, attitudes towards robots can be characterized by ambivalence, rather than by neutrality. Recent evidence from our lab (Stapels & Eyssel, 2021) will exemplify this idea using novel measures to capture attitudinal ambivalence towards robots. From this follows: The way we measure a construct of interest undoubtedly impacts the resulting outcome. While this sounds trivial in the first place, it implies that a) we might want to reconsider the way we commonly assess attitudes towards social robots, and b) we might want to revisit and reassess existing results in light of the notion of ambivalence in attitudes towards robots.
Title of Talk #2
Diversity, Bias and social robots
The lecture will feature a social psychological perspective on the notion of diversity, with a specific focus on "gender" and social categorization in social robots. That is, I will outline core principles of human social cognition and demonstrate that these principles are likewise used in the context of nonhuman entities. To illustrate, in human-human social cognition, we readily apply fundamental dimensions of social cognition and social categories (e.g., traits like agency and communion or social categories like ethnicity, gender) to form judgments about individuals and groups. A set of empirical experiments will be presented to highlight the impact of design choices on the evaluation and behavior towards social robots. Implications for the notion of diversity in HRI and social robotics will be discussed.
Predictive Processing as a Unified Principle for Cognitive Development
A neuroscientific theory called predictive coding suggests that the human brain works as a predictive machine. That is, the brain tries to minimize prediction errors by updating the internal model and/or by affecting the environment. We have been investigating to what extent the predictive coding theory accounts for human intelligence and whether it provides a unifying principle for the design of robot intelligence. My talk presents computational neural networks we designed to examine how the process of minimizing prediction errors lead to cognitive development in robots. Our experiments demonstrated that both non-social and social cognitive abilities such as goal-directed action, imitation, estimation of others’ intentions, and altruistic behavior emerged as observed in infants. Not only the characteristics of typical development but also those of developmental disorders such as autism spectrum disorder were replicated as a result of aberrant prediction abilities. These results suggest that predictive coding provides a unified computational theory for cognitive development (Nagai, Phil Trans B 2019).