People draw subtle distinctions in the normative domain. But it remains unclear exactly what gives rise to such distinctions. On one prominent approach, emotion systems trigger non‐utilitarian judgments. The main alternative, inspired by Chomskyan linguistics, suggests that moral distinctions derive from an innate moral grammar. In this article, we draw on Bayesian learning theory to develop a rational learning account. We argue that the ‘size principle’, which is implicated in word learning, can also explain how children would use scant and equivocal evidence to interpret candidate rules as applying more narrowly than utilitarian rules.
We studied politeness in human–robot interaction based on Lakoff’s politeness theory. In a series of eight studies, we manipulated three different levels of politeness of non-humanoid robots and evaluated their effects. A table-setting task was developed for two different types of robots (a robotic manipulator and a mobile robot). The studies included two different populations (old and young adults) and were conducted in two conditions (video and live). Results revealed that polite robot behavior positively affected users' perceptions of the interaction with the robots and that participants were able to differentiate between the designed politeness levels. Participants reported higher levels of enjoyment, satisfaction, and trust when they interacted with the politest behavior of the robot. A smaller number of young adults trusted the politest behavior of the robot compared to old adults. Enjoyment and trust of the interaction with the robot were higher when study participants were subjected to the live condition compared to video and participants were more satisfied when they interacted with a mobile robot compared to a manipulator.
Physical and cognitive training can maintain and improve older adults’ independence and quality of life. Given the demographic growth of the older adult population and the shortage of caregivers, there is a need for personal trainers for physical and cognitive activities. This study suggests that social robots can satisfy this demand and presents the development of “Gymmy”, a robotic system for the physical and cognitive training of older adults. The system design includes a humanoid mechanical-looking robot to demonstrate exercises, an RGB-Depth (RGB-D) camera to measure performance and a touch screen and speakers to provide instructions and feedback. Experiments with 26 older adults (65–84 years of age) were performed in home environments to examine the effect of users’ characteristics (age, gender, education and attitude toward robots), the addition of cognitive training and the success rate of the acceptability of a robot trainer. The results showed that age, attitude and education influenced the acceptance of the robotic system. The findings highlight the importance of customizing the system to the needs of different users and the role of meaningful feedback. The system was proven to be robust and reliable, demonstrating clear potential to be used as a personal trainer and as a means of motivating older adults.
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