Distinguished Lecturers

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Friederike Eyssel

Bielefeld University
Bielefeld, Germany

Friederike Eyssel is Full Professor of Psychology and Head of the research group "Applied Social Psychology and Gender Research" at the Research Center "Cognitive Interaction Technology" (CITEC), Bielefeld University. She earned her Masters degree in Psychology from University of Heidelberg in 2004 and received her PhD in Psychology from Bielefeld University in 2007. Dr. Eyssel has held visiting professorships in social psychology at the University of Münster, the Technical University of Dortmund, the University of Cologne, and the New York University Abu Dhabi. Dr. Eyssel is interested in a variety of research topics ranging from social robotics, social agents, and ambient intelligence to research on attitudes, their measurement and change as well as gender research. Crossing disciplines, Dr. Eyssel has published her research in leading journals in the field of social psychology social robotics. Her work on robot gender has recently been published in Nature (Tannenbaum, Ellis,Eyssel et al., 2019). Friederike Eyssel has attracted third-party funding for various projects at the national and international level, and is currently running several interdisciplinary third-party funded projects on trust, robot design, acceptance, and successful social HRI and behavior change (see, for example, http://www.perseo.eu/, https://navelrobotics.com/en/research-project-viva/ or https://neo-milk.uni-koeln.de/ ).

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.

Yukie Nagai

University of Tokyo
Tokyo, Japan

E: nagai.yukie@mail.u-tokyo.ac.jp

Yukie Nagai is a Project Professor at the International Research Center for Neurointelligence, the University of Tokyo. She received her Ph.D. in Engineering from Osaka University in 2004 and then worked at the National Institute of Information and Communications Technology, Bielefeld University, and Osaka University. Since 2019, she leads Cognitive Developmental Robotics Lab at the University of Tokyo. Her research interests include cognitive developmental robotics, computational neuroscience, and assistive technologies for developmental disorders. She has been investigating the underlying neural mechanisms for social cognitive development by means of computational approaches. Her theory of cognitive development based on predictive coding provides a unified principle for the temporal continuity and individual diversity in development and has been attracting increasing attention in the fields of cognitive science, developmental psychology, and developmental robotics. She was elected to “30 women in robotics you need to know about” in 2019 and “World’s 50 Most Renowned Women in Robotics” in 2020. She serves as the principal investigator of JST CREST “Cognitive Mirroring” and JST CREST “Cognitive Feeling” since 2016 and 2021, respectively.

Talk #1

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).