Moral psychology of nursing robots: Exploring the role of robots in dilemmas of patient autonomy

Artificial intelligences (AIs) are widely used in tasks ranging from transportation to healthcare and military, but it is not yet known how people prefer them to act in ethically difficult situations. In five studies (an anthropological field study, n = 30, and four experiments, total n = 2150), we presented people with vignettes where a human or an advanced robot nurse is ordered by a doctor to forcefully medicate an unwilling patient. Participants were more accepting of a human nurse's than a robot nurse's forceful medication of the patient, and more accepting of (human or robot) nurses who respected patient autonomy rather than those that followed the orders to forcefully medicate (Study 2). The findings were robust against the perceived competence of the robot (Study 3), moral luck (whether the patient lived or died afterwards; Study 4), and command chain effects (Study 5; fully automated supervision or not). Thus, people prefer robots capable of disobeying orders in favour of abstract moral principles like valuing personal autonomy. Our studies fit in a new era in research, where moral psychological phenomena no longer reflect only interactions between people, but between people and autonomous AIs.     

A Big Five personality inventory in two non-Indo-European languages

In this study we report on two successful replications of a five-factor personality inventory in two non-Indo-European languages, Estonian and Finnish, which both belong to the group of Uralic languages. Costa and McCrae's (1985) NEO Personality Inventory was adapted to these two languages. By all relevant psychometric parameters neither developed construct differs from the original construct: the reliabilities of only 11 per cent for the Estonian and 36 per cent for the Finnish subscale were lower than those of the respective NEO-PI scales. The factor structure of both Estonian and Finnish inventories was very close to the five-factor structure of the NEO-PI, accounting for 71.7 per cent and 67.0 per cent of the variance, respectively. In spite of this generally good agreement, some language- or culture-dependent differences were observed. Both Estonian and Finnish women were more extroverted and conscientious than men, compared with their English-speaking counterparts. Also, some differences exist in the need for other people's company and excitement seeking. In the Balto-Fennic culture gregariousness appears to presuppose some emotional stability and openness and excitement seeking is conceptualized more as a tool of rescuing from anxiety, depression, and low self-esteem. This study is considered as a step towards determination of which parts of the most popular instrument for the measurement of the Big Five personality dimensions are truly universal and which parts of it are specific to a particular language and culture.     

Compression as a Universal Principle of Animal Behavior

A key aim in biology and psychology is to identify fundamental principles underpinning the behavior of animals, including humans. Analyses of human language and the behavior of a range of non‐human animal species have provided evidence for a common pattern underlying diverse behavioral phenomena: Words follow Zipf's law of brevity (the tendency of more frequently used words to be shorter), and conformity to this general pattern has been seen in the behavior of a number of other animals. It has been argued that the presence of this law is a sign of efficient coding in the information theoretic sense. However, no strong direct connection has been demonstrated between the law and compression, the information theoretic principle of minimizing the expected length of a code. Here, we show that minimizing the expected code length implies that the length of a word cannot increase as its frequency increases. Furthermore, we show that the mean code length or duration is significantly small in human language, and also in the behavior of other species in all cases where agreement with the law of brevity has been found. We argue that compression is a general principle of animal behavior that reflects selection for efficiency of coding.     

Using Statistical Models of Morphology in the Search for Optimal Units of Representation in the Human Mental Lexicon

Determining optimal units of representing morphologically complex words in the mental lexicon is a central question in psycholinguistics. Here, we utilize advances in computational sciences to study human morphological processing using statistical models of morphology, particularly the unsupervised Morfessor model that works on the principle of optimization. The aim was to see what kind of model structure corresponds best to human word recognition costs for multimorphemic Finnish nouns: a model incorporating units resembling linguistically defined morphemes, a whole‐word model, or a model that seeks for an optimal balance between these two extremes. Our results showed that human word recognition was predicted best by a combination of two models: a model that decomposes words at some morpheme boundaries while keeping others unsegmented and a whole‐word model. The results support dual‐route models that assume that both decomposed and full‐form representations are utilized to optimally process complex words within the mental lexicon.