A Computational Model of Trust-, Pupil-, and Motivation Dynamics

Conference Proceeding/Paper | Published | English
Tjøstheim, Trond Arild ; Johansson, Birger ; Balkenius, Christian
Cognitive Science
Cognitive modeling
eSSENCE: The e-Science Collaboration
Research Group:
Cognitive modeling
Autonomous machines are in the near future likely to increasingly interact with humans, and carry out their functions outside controlled settings. Both of these developments increase the requirements of machines to be trustworthy to humans. In this work, we argue that machines may also benefit from being able to explicitly build or withdraw trust with specific humans. The latter is relevant in situations where the integrity of an autonomous system is compromised, or if humans display untrustworthy behaviour towards the system. Examples of systems that could benefit might be delivery robots, maintenance robots, or autonomous taxis. This work contributes by presenting a biologically plausible model of unconditional trust dynamics, which simulates trust building based on familiarity, but which can be modulated by painful and gentle touch. The model displays interactive behaviour by being able to realistically control pupil dynamics, as well as determine approach and avoidance motivation.
1f055c2f-ee38-4f35-b4e5-4eec8b1f8958 | Link: https://lup.lub.lu.se/record/1f055c2f-ee38-4f35-b4e5-4eec8b1f8958 | Statistics

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