In order for artificial intelligent systems to interact naturally with human users, they need to be able to learn from human instructions when actions should be imitated. Human tutoring will typically consist of action demonstrations accompanied by speech. In the following, the characteristics of human tutoring during action demonstration will be examined. A special focus will be put on the distinction between two kinds of motion events: path-oriented actions and manner-oriented actions. Such a distinction is inspired by the literature pertaining (...) to cognitive linguistics, which indicates that the human conceptual system can distinguish these two distinct types of motion. These two kinds of actions are described in language by more path-oriented or more manner-oriented utterances. In path-oriented utterances, the source, trajectory, or goal is emphasized, whereas in manner-oriented utterances the medium, velocity, or means of motion are highlighted. We examined a video corpus of adult–child interactions comprised of three age groups of children—pre-lexical, early lexical, and lexical—and two different tasks, one emphasizing manner more strongly and one emphasizing path more strongly. We analyzed the language and motion of the caregiver and the gazing behavior of the child to highlight the differences between the tutoring and the acquisition of the manner and path concepts. The results suggest that age is an important factor in the development of these action categories. The analysis of this corpus has also been exploited to develop an intelligent robotic behavior—the tutoring spotter system—able to emulate children's behaviors in a tutoring situation, with the aim of evoking in human subjects a natural and effective behavior in teaching to a robot. The findings related to the development of manner and path concepts have been used to implement new effective feedback strategies in the tutoring spotter system, which should provide improvements in human–robot interaction. (shrink)

We propose that Mealey's model is limited in its description of sociopathy because it does not provide an adequate role for the main organ mediating genes and behavior, namely, the brain. Further, on the basis of our research, we question the view of sociopaths as a homogeneous group.

Ever since Darwin people have worried about the sceptical implications of evolution. If our minds are products of evolution like those of other animals, why suppose that the beliefs they produce are true, rather than merely useful? We consider this problem for beliefs in three different domains: religion, morality, and commonsense and scientific claims about matters of empirical fact. We identify replies to evolutionary scepticism that work in some domains but not in others. One reply is that evolution can be (...) expected to design systems that produce true beliefs in some domain. This reply works for commonsense beliefs and can be extended to scientific beliefs. But it does not work for moral or religious beliefs. An alternative reply which has been used defend moral beliefs is that their truth does not consist in their tracking some external state of affairs. Whether or not it is successful in the case of moral beliefs, this reply is less plausible for religious beliefs. So religious beliefs emerge as particularly vulnerable to evolutionary debunking. (shrink)

Background. The COVID-19 pandemic necessitated drastic changes to undergraduate medical training at the University of Botswana (UB). To save the academic year when campus was locked down, the Department of Medical Education conducted a needs assessment to determine the readiness for emergency remote teaching (ERT) of the Faculty of Medicine, UB. Objectives. To report on the findings of needs assessment surveys to assess learner and teaching staff preparedness for fair and just ERT, as defined by philosopher John Rawls. Methods. Needs (...) assessment surveys were conducted using Office 365 Forms distributed via WhatsApp, targeting medical students and teaching staff during the 5 undergraduate years. Data were analysed quantitatively and qualitatively. Results. Ninety-two percent (266/289) of students and 73.5% (62/84) of teaching staff responded. Surveys revealed a high penetration of smartphones among students, but poor internet accessibility and affordability in homes. Some teaching staff also reported internet and device insufficiencies. Only WhatsApp was accessible to students and teaching staff. Conclusions. For equitable access to ERT in the future, the surveys revealed infrastructural improvement needs, including wider, stronger, affordable WiFi coverage within Botswana and enhanced digital infrastructures in educational institutions, with increased support for students. (shrink)

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Healthcare professionals encounter many moral challenges in their daily clinical practice. However, there have been few studies on the subject matter in Tanzania. This study aims to provide an account of moral challenges faced by healthcare professionals in Tanzanian hospitals, their understanding of clinical ethics, and the ethics education they have received. Many participants reported receiving some kind of ethics training through formal education and on-the-job training. Some participants understood ethics in healthcare settings as adherence to established laws, regulations, guidelines, (...) procedures, norms, and rules essential in clinical practice. Analysis of the data identified four themes of moral challenges. These challenges are related to 1) decision-making and communication in clinical practice, 2) scarcity of medical resources and prioritization in clinical practice, 3) withdrawal of curative treatment, and 4) conflicts between professional judgment, religious convictions and adherence to alternative treatments. Based on the findings, we suggest a context-sensitive form of clinical ethics training to prepare healthcare professionals to recognize and address these moral challenges. (shrink)

Fourteen healthy children (13.8±2.2 years, range 10 to 16; M:F=5:9) received 30 Hz intermittent theta burst transcranial magnetic stimulation (iTBS) with a stimulation intensity of 70% of resting motor threshold (RMT) with a total of 300 (iTBS300) pulses. All volunteers were free of neurologic, psychiatric and serious medical illnesses, not taking any neuropsychiatric medications, and did not have any contraindications to Transcranial Magnetic Stimulation. Changes in the mean amplitudes of motor-evoked potentials from baseline following iTBS were expressed as a ratio (...) and assessed from 1 to 10 minutes (BLOCK1) and 1 to 30 minutes (BLOCK2) using repeated-measures analysis of variance. All 14 subjects completed iTBS300 over the dominant primary motor cortex (M1) without any clinically reported adverse events. ITBS300 produced significant M1 facilitation (F5,65=3.165, p=0.01) at BLOCK1 and trend level M1 facilitation at BLOCK2 (F10,129=1.69, p=0.089). Although iTBS300 (stimulation duration of 92 seconds at 70% RMT) delivered over M1 in typically developed children was well-tolerated and produced on average significant facilitatory changes in cortical excitability, the post-iTBS300 neurophysiologic response was variable in our small sample. ITBS300-induced changes may represent a potential neuroplastic biomarker in healthy children and those with neuro-genetic or neuro-psychiatric disorders. However, a larger sample size is needed to address safety and concerns of response variability. (shrink)

Identifying patterns in the world requires noticing not only unusual occurrences, but also unusual absences. We examined how people learn from absences, manipulating the extent to which an absence is expected. People can make two types of inferences from the absence of an event: either the event is possible but has not yet occurred, or the event never occurs. A rational analysis using Bayesian inference predicts that inferences from absent data should depend on how much the absence is expected to (...) occur, with less probable absences being more salient. We tested this prediction in two experiments in which we elicited people's judgments about patterns in the data as a function of absence salience. We found that people were able to decide that absences either were mere coincidences or were indicative of a significant pattern in the data in a manner that was consistent with predictions of a simple Bayesian model. (shrink)

Machine learning algorithms may radically improve our ability to diagnose and treat disease. For moral, legal, and scientific reasons, it is essential that doctors and patients be able to understand and explain the predictions of these models. Scalable, customisable, and ethical solutions can be achieved by working together with relevant stakeholders, including patients, data scientists, and policy makers.