AI Advice

This Article merges one of our most ancient technologies for the promotion of welfare-advice-with some of our most recent-artificial intelligence (Al) and big data. AI is already writing novels, poetry, newspaper articles, and tweets. Big data may soon capture not only our online activities, but also our real-time heart rate, sleep patterns, and even our current mood. This is the first Article to introduce and examine the possibility of AI advice. AI advice offers the potential for exceedingly accurate personalized recommendations. It also reveals important limits within the burgeoning literature on personalized law. The Article first rejects recent attempts to rehabilitate mandatory disclosures by personalizing them. Ironically, the technological progress required to create effective big data disclosures will itself substantially reduce the need for such disclosures. In this future, advice, not disclosure, will be the dominant paradigm. The Article then dissects our everyday practices of advice-giving to unearth a number of powerful features of advice that promote self-efficacy, reduce motivated reasoning, and make it more likely that people will hear and heed good advice. The capacity to bundle these features with exceedingly accurate recommendations makes AI advice a promising alternative to its two main regulatory rivals: mandatory disclosure and nudges

AI Advice

This Article merges one of our most ancient technologies for the promotion of welfare-advice-with some of our most recent-artificial intelligence (Al) and big data. AI is already writing novels, poetry, newspaper articles, and tweets. Big data may soon capture not only our online activities, but also our real-time heart rate, sleep patterns, and even our current mood. This is the first Article to introduce and examine the possibility of AI advice. AI advice offers the potential for exceedingly accurate personalized recommendations. It also reveals important limits within the burgeoning literature on personalized law. The Article first rejects recent attempts to rehabilitate mandatory disclosures by personalizing them. Ironically, the technological progress required to create effective big data disclosures will itself substantially reduce the need for such disclosures. In this future, advice, not disclosure, will be the dominant paradigm. The Article then dissects our everyday practices of advice-giving to unearth a number of powerful features of advice that promote self-efficacy, reduce motivated reasoning, and make it more likely that people will hear and heed good advice. The capacity to bundle these features with exceedingly accurate recommendations makes AI advice a promising alternative to its two main regulatory rivals: mandatory disclosure and nudges

Mind(sets) over machine? The influence of implicit self-theories in human-robot interaction.

Implicit self-theory asserts that an individual’s underlying beliefs about whether self-attributes (e.g., personality and intelligence) are fixed (entity theory) or mutable (incremental theory) causally affect motivation and behavior—with the most profound effects emerging in situations that involve challenges and setbacks. In support of this notion, several lines of research suggest that these beliefs hold some influence over people’s perception and behavior in diverse domains such as education, brand acceptance, and financial decision-making, among others. It is, however, presently unknown whether implicit self-theories exert such influence on people’s experiences of social robots. To address this gap, this research tested, in a series of three studies, the proposition that implicit self-theories represent an important variable, that influences the manner in which one perceives and responds to social robots. Study 1 provided the first evidence that an individual’s implicit self-theory orientation influences their perception of emerging social robots developed for everyday use. In particular, those endorsing more of an entity theory expressed greater robot anxiety than those endorsing more of an incremental theory. This finding held even when controlling for a range of covariate influences. In addition, incremental theorists, compared to entity theorists responded more favorably to social robots in general. Study 2 built on and substantively extended the findings of Study 1 by examining the effects of implicit self-theories on people’s responses to a robot that praised them for ability (i.e., intelligence), or for effort (i.e., hard work), after completing a difficult task. Results revealed that entity theorists evaluated a robot that delivered ability praise as more likable and intelligent than one that delivered effort praise. However, incremental theorists were unaffected by either praise type and rated the robot favorably regardless of the praise it delivered. Study 3, expanded the findings of Studies 1 and 2 to investigate the impact of implicit self-theories on people’s responses to a robot that defeats human beings in a general knowledge quiz game. Results showed that incremental theorists, compared to entity theorists were more likely to indicate an interest in playing against the robot after imagining losing to it. Whereas entity theorists rated such robots as presenting more identity and realistic threats. Together, these studies extend and enrich the Human-Robot Interaction (HRI) literature by establishing implicit self-theories as an important and meaningful variable for which to advance the understanding of HRI today. In so doing, this research attempts to respond to the ever-increasing demand for research on the psychological variables that underlie how people perceive and interact with robots—which, in many ways, has special urgency given the inexorable rise of AI and robotics in the social domain of everyday experience. In consequence, findings may contribute to the design of new or improved social robots that can reflect or shape beliefs, and, hence, build a greater sense of identification and trust with the intended human user

Immersive Participation:Futuring, Training Simulation and Dance and Virtual Reality

Dance knowledge can inform the development of scenario design in immersive digital simulation environments by strengthening a participant’s capacity to learn through the body. This study engages with processes of participatory practice that question how the transmission and transfer of dance knowledge/embodied knowledge in immersive digital environments is activated and applied in new contexts. These questions are relevant in both arts and industry and have the potential to add value and knowledge through crossdisciplinary collaboration and exchange. This thesis consists of three different research projects all focused on observation, participation, and interviews with experts on embodiment in digital simulation. The projects were chosen to provide a range of perspectives across dance, industry and futures studies. Theories of embodied cognition, in particular the notions of the extended body, distributed cognition, enactment and mindfulness, offer critical lenses through which to explore the relationship of embodied integration and participation within immersive digital environments. These areas of inquiry lead to the consideration of how language from the field of computer science can assist in describing somatic experience in digital worlds through a discussion of the emerging concepts of mindfulness, wayfinding, guided movement and digital kinship. These terms serve as an example of how the mutability of language became part of the process as terms applied in disparate disciplines were understood within varying contexts. The analytic tools focus on applying a posthuman view, speculation through a futures ethnography, and a cognitive ethnographical approach to my research project. These approaches allowed me to examine an ecology of practices in order to identify methods and processes that can facilitate the transmission and transfer of embodied knowledge within a community of practice. The ecological components include dance, healthcare, transport, education and human/computer interaction. These fields drove the data collection from a range of sources including academic papers, texts, specialists’ reports, scientific papers, interviews and conversations with experts and artists.The aim of my research is to contribute both a theoretical and a speculative understanding of processes, as well as tools applicable in the transmission of embodied knowledge in virtual dance and arts environments as well as digital simulation across industry. Processes were understood theoretically through established studies in embodied cognition applied to workbased training, reinterpreted through my own movement study. Futures methodologies paved the way for speculative processes and analysis. Tools to choreograph scenario design in immersive digital environments were identified through the recognition of cross purpose language such as mindfulness, wayfinding, guided movement and digital kinship. Put together, the major contribution of this research is a greater understanding of the value of dance knowledge applied to simulation developed through theoretical and transformational processes and creative tools

Women in Sports and Exercise: From Health to Sports Performance

The current book presents the articles included in the Special Issue “Women in Sports and Exercise: From Health to Sports Performance”. Readers will find in this book evidence about the relationships between physical qualities in sports and how women's performance can be optimized using dedicated training intervention. Moreover, information about the impact of the menstrual cycle on athletic performance will be revealed. Attention to physical activity patterns in women will be also disclosed

Novel Methods of Measuring and Visualising Youths’ Physical Activity

Despite the physiological and psychosocial health benefits of youth achieving at least 60 minutes of moderate-to-vigorous physical activity (MVPA) every day, only a small proportion of youth in the UK meet this daily target. While there are several reasons for this failure to achieve the recommended amount of MVPA, recent evidence suggests that many youths lack awareness of their physical activity levels (PAL) and have difficulty interpreting and applying the guidelines to their daily activity. One solution to counteract this problem is to utilise and integrate technology, such as an objective measurement of PAL in combination with personalised feedback, to enhance youth’s awareness and understanding of, and motivation for, physical activity. Whilst accelerometers are the de facto standard in objectively measuring PAL, they have limitations when it comes to assessing non-linear movements, such as turning, that are habitual to youths’ sporadic activity. Study 1, therefore, investigated the energy expenditure of turning in children, finding that the magnitude and frequency of turns completed are important considerations when measuring habitual PAL. Specifically, significant differences in energy expenditure to straight-line walking within speed were established for 2.5 km·hr-1 at 90° turn (~7% increase) and 3.5, 4.5 and 5.5 km·hr-1 for 180° turns (~13%, ~14% and ~30% increase, respectively). Nonetheless, one innovative method that has potential to make physical activity targets more comprehensible and actionable for youths is personalised, 3D-printed feedback that can conceptualise their PAL. Therefore, Study 2 explored youths’ perceptions of, and designs for, 3D-printed visualisations of PAL. The findings revealed that youths understood the concept of visualising physical activity as a 3D object and felt that such feedback could act as a motivational tool to enhance youths PAL. Following youths’ preferences for weekly models represented as abstract and bar-chart designs, two age-specific 3D models were developed to represent MVPA, across a week, with the recommended guideline depicted as a tangible goal. Study 3 sought to validate youths understanding of the age-specific 3D models and intensities of physical activity. Youth were able to correctly interpret the different components of the age-specific 3D models, although showed some misconceptions when defining moderate-intensity activities. Despite this, the age-specific 3D models showed promise to enhance youths understanding of the recommended guideline and associated MVPA intensities. Study 4 subsequently examined the efficacy of the age-specific 3D models within an intervention setting, whereby youth received personal models of their PAL. Over time, the 3D models enhanced youths’ awareness of their PAL and provided a tool to compare their MVPA levels to the recommended guideline. Youths displayed their 3D models in their home environments and utilised the models as a goal-setting strategy to increase their PAL. In conclusion, the nature of the 3D models being a blend of personalised feedback, a reward and a goal-setting tool, may offer a unique strategy for the promotion of PAL and associations to the recommended guideline