Learning to Trust

Trust is full of puzzle and paradox.Trust is both rational and emotional. Trust can go beyond calculative self-interest, but has its limits.People may want to trust, while they may also feel threatened by it.If trust is not in place prior to a relationship, on the basis of institutions, prior experience, or reputation, it has to be built up, in specific relations.For that one needs to learn, in the sense of building empathy, and perhaps a certain degree of identification.In an attempt at a better understanding of the puzzles and processes of trust, this chapter applies the perspective of 'embodied cognition', and insights from mental 'framing' and decision heuristics from social psychology.learning;trust;institutions

Building machines that learn and think about morality

Lake et al. propose three criteria which, they argue, will bring artificial intelligence (AI) systems closer to human cognitive abilities. In this paper, we explore the application of these criteria to a particular domain of human cognition: our capacity for moral reasoning. In doing so, we explore a set of considerations relevant to the development of AI moral decision-making. Our main focus is on the relation between dual-process accounts of moral reasoning and model-free/model-based forms of machine learning. We also discuss how work in embodied and situated cognition could provide a valu- able perspective on future research

The Experience of Regular Exercise Participation for Women Moving into their Middle Years: Its Nature, Meaning and its Benefits

This study added to the limited research on positive aspects of the human condition. It highlighted the perspective that women in western society recognise that there are wider health benefits to be taken from exercise than science suggests. Whilst this study acknowledged the customary fragmentary view, it took a holistic approach to exploring the nature and meaning of regular participation in exercise from the perspective of 41 women aged 30 to 50 years. This qualitative study included the views of regular participants in facility based and non-facility based exercise, along with the views of exercise instructors and the researcher. The study was contextualised within the traditional theories of related disciplines, namely health, women's studies, and exercise science. Also it was founded on the fitness industry's perspective on its service provision and its instructor training. Theory was compared with the experiences of a sector of the female population who, despite all the accepted calls on their time and energy, consistently maintained regular involvement in exercise. The study provided a holistic perspective on the nature, meaning and benefits of regular participation in exercise. Semi-structured interviews and focus groups were utilised in the data gathering process. In each case, the process consisted of a series of questions designed to explore a subjective perception of experience in accordance with the Neuro-Logical Levels process, a model from within the field of Neuro-Linguistic Programming (Dilts, 1990; Dilts, Hallbom and Smith, 1990; O'Connor and Seymour, 1995). This model acknowledges that behaviours and actions, witnessed on a surface level, are driven by internal systems, including personal beliefs and identity structures. It was utilised as an exploratory technique to identify unconscious triggers for behaviour. The use of this process in the interviews facilitated individual exploration of the research topic at increasingly deep levels of awareness. Focus groups demonstrated a consensus on, as well as further individual differences in, the beliefs, attitudes, experiences and feelings of the participants as they arose from the interactive context. The heuristic methodology utilised in the analysis and presentation of the data offered a holistic, person-centred and reflective perspective on the nature, meaning and benefits of exercise (Moustakas, 1990). Individual and exemplary portraits depicted the experience and personal meaning of exercise as it emerged from the data. Composite depictions conveyed the nature of exercise participation from the perspectives of participants and instructors. The researcher's involvement in the complete study facilitated the emergence of a creative synthesis of the essence of exercise. Exercise provided emotional and spiritual gains that extended beyond the traditional lifestyle benefits. Individuals indicated a range of 'special' qualities in exercise, along with benefits to the mind. They noted unique personal benefits and enhanced interpersonal relationships in all spheres of life. Regular participation in exercise greatly enhanced the lives of those involved and contributed to an individual and collective evolutionary process. Effective exercise delivery consisted of interactional and motivational elements beyond the scientific and mechanistic topics traditionally recognised in instructor training programmes and was founded on empathy, facilitation, passion, love and positive energy

Affective Man-Machine Interface: Unveiling human emotions through biosignals

As is known for centuries, humans exhibit an electrical profile. This profile is altered through various psychological and physiological processes, which can be measured through biosignals; e.g., electromyography (EMG) and electrodermal activity (EDA). These biosignals can reveal our emotions and, as such, can serve as an advanced man-machine interface (MMI) for empathic consumer products. However, such a MMI requires the correct classification of biosignals to emotion classes. This chapter starts with an introduction on biosignals for emotion detection. Next, a state-of-the-art review is presented on automatic emotion classification. Moreover, guidelines are presented for affective MMI. Subsequently, a research is presented that explores the use of EDA and three facial EMG signals to determine neutral, positive, negative, and mixed emotions, using recordings of 21 people. A range of techniques is tested, which resulted in a generic framework for automated emotion classification with up to 61.31% correct classification of the four emotion classes, without the need of personal profiles. Among various other directives for future research, the results emphasize the need for parallel processing of multiple biosignals