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Methodology.

In our work at the LIT Robopsychology Lab we base on the following methodical approaches and guidelines:

Quantitative psychology as a foundation

As a rule, our research projects are based on methods and statistical analysis procedures of empirical psychology and quantitative social sciences. Typically, we are interested in cause-and-effect relationships and therefore in causality testing research designs. We conduct empirical surveys both in laboratory settings (advantage of higher controllability) and in the field (advantage of higher external validity).

Fostering interdisciplinarity

Future-oriented and human-centered technology research requires diversity in approaches, competencies and perspectives. In teaching, we are committed to building bridges between technical-scientific and human-social-scientific fields of study. In research, we work together with experts from a wide variety of backgrounds – from robotics and philosophy to game design and art.

Openness for innovative methods

We are open to the exploration of new approaches in teaching and research. In cooperation with the computer sciences, for example, we combine social science survey methods with analysis methods from machine learning and text mining. Particularly in the early stages of research, e.g. within the framework of hypothesis development, we also rely on qualitative methods or participatory approaches from User-Centered Design and Citizen Science.

Relevance for society and practice

The creation of usable knowledge is an important task of university research ("Third Mission"). The results of our research are intended to strengthen social discourse and serve the people who are increasingly getting into contact with robots and AI systems. Practitioners benefit from empirically founded guidelines for technology design, to the development of which we would like to make a contribution with our work.

The LIT Robopsychology Lab investigates how the interaction of different influencing factors affects the individual experiencing of intelligent machines and the interaction with them. User-specific factors (e.g. age, personality factors, technical experience) as well as robot-specific factors (visual form, behavioral and communication design) and contextual factors (area of application, prior information, media reporting) are considered. ©LIT Robopsychology Lab