Bayesian Learning Models of Pain: A Call to Action

Learning is fundamentally about action, enabling the successful navigation of a changing and uncertain environment. The experience of pain is central to this process, indicating the need for a change in action so as to mitigate potential threat to bodily integrity. This review considers the application of Bayesian models of learning in pain that inherently accommodate uncertainty and action, which, we shall propose are essential in understanding learning in both acute and persistent cases of pain

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 ethics of digital well-being: a multidisciplinary perspective

This chapter serves as an introduction to the edited collection of the same name, which includes chapters that explore digital well-being from a range of disciplinary perspectives, including philosophy, psychology, economics, health care, and education. The purpose of this introductory chapter is to provide a short primer on the different disciplinary approaches to the study of well-being. To supplement this primer, we also invited key experts from several disciplines—philosophy, psychology, public policy, and health care—to share their thoughts on what they believe are the most important open questions and ethical issues for the multi-disciplinary study of digital well-being. We also introduce and discuss several themes that we believe will be fundamental to the ongoing study of digital well-being: digital gratitude, automated interventions, and sustainable co-well-being

Embodied Decisions and the Predictive Brain

Decision-making has traditionally been modelled as a serial process, consisting of a number of distinct stages. The traditional account assumes that an agent first acquires the necessary perceptual evidence, by constructing a detailed inner repre- sentation of the environment, in order to deliberate over a set of possible options. Next, the agent considers her goals and beliefs, and subsequently commits to the best possible course of action. This process then repeats once the agent has learned from the consequences of her actions and subsequently updated her beliefs. Under this interpretation, the agent’s body is considered merely as a means to report the decision, or to acquire the relevant goods. However, embodied cognition argues that an agent’s body should be understood as a proper part of the decision-making pro- cess. Accepting this principle challenges a number of commonly held beliefs in the cognitive sciences, but may lead to a more unified account of decision-making. This thesis explores an embodied account of decision-making using a recent frame- work known as predictive processing. This framework has been proposed by some as a functional description of neural activity. However, if it is approached from an embodied perspective, it can also offer a novel account of decision-making that ex- tends the scope of our explanatory considerations out beyond the brain and the body. We explore work in the cognitive sciences that supports this view, and argue that decision theory can benefit from adopting an embodied and predictive perspective

The body as laboratory: Prediction-error minimization, embodiment, and representation

In his (2014) paper, Jakob Hohwy outlines a theory of the brain as an organ for prediction-error minimization (PEM), which he claims has the potential to profoundly alter our understanding of mind and cognition. One manner in which our understanding of the mind is altered, according to PEM, stems from the neurocentric conception of the mind that falls out of the framework, which portrays the mind as “inferentially-secluded” from its environment. This in turn leads Hohwy to reject certain theses of embodied cognition. Focusing on this aspect of Hohwy’s argument, we first outline the key components of the PEM framework such as the “evidentiary boundary,” before looking at why this leads Hohwy to reject certain theses of embodied cognition. We will argue that although Hohwy may be correct to reject specific theses of embodied cognition, others are in fact implied by the PEM framework and may contribute to its development. We present the metaphor of the “body as a laboratory” in order to highlight what we believe is a more significant role for the body than Hohwy suggests. In detailing these claims, we will expose some of the challenges that PEM raises for providing an account of representation

The Resistance of Cortical Bone Tissue to Failure under Cyclic Loading is Reduced with Alendronate

Bisphosphonates are the most prescribed preventative treatment for osteoporosis. However, their long-term use has recently been associated with atypical fractures of cortical bone in patients who present with low-energy induced breaks of unclear pathophysiology. The effects of bisphosphonates on the mechanical properties of cortical bone have been exclusively studied under simple, monotonic, quasi-static loading. This study examined the cyclic fatigue properties of bisphosphonate-treated cortical bone at a level in which tissue damage initiates and is accumulated prior to frank fracture in low-energy situations. Physiologically relevant, dynamic, 4-point bending applied to beams (1.5 mm × 0.5 mm × 10 mm) machined from dog rib (n=12/group) demonstrated mechanical failure and micro-architectural features that were dependent on drug dose (3 groups: 0, 0.2, 1.0 mg/kg/day; Alendronate [ALN] for 3 years) with cortical bone tissue elastic modulus (initial cycles of loading) reduced by 21% (p<0.001) and fatigue life (number of cycles to failure) reduced in a stress-life approach by greater than 3-fold with ALN1.0 (p<0.05). While not affecting the number of osteons, ALN treatment reduced other features associated with bone remodeling, such as the size of osteons (−14%, ALN1.0: 10.5±1.8, VEH: 12.2±1.6, ×103 µm2; p<0.01) and the density of osteocyte lacunae (−20%; ALN1.0: 11.4±3.3, VEH: 14.3±3.6, ×102 #/mm2; p<0.05). Furthermore, the osteocyte lacunar density was directly proportional to initial elastic modulus when the groups were pooled (R=0.54, p<0.01). These findings suggest that the structural components normally contributing to healthy cortical bone tissue are altered by high-dose ALN treatment and contribute to reduced mechanical properties under cyclic loading conditions

An Analysis of the Interaction Between Intelligent Software Agents and Human Users

Interactions between an intelligent software agent (ISA) and a human user are ubiquitous in everyday situations such as access to information, entertainment, and purchases. In such interactions, the ISA mediates the user’s access to the content, or controls some other aspect of the user experience, and is not designed to be neutral about outcomes of user choices. Like human users, ISAs are driven by goals, make autonomous decisions, and can learn from experience. Using ideas from bounded rationality (and deploying concepts from artificial intelligence, behavioural economics, control theory, and game theory), we frame these interactions as instances of an ISA whose reward depends on actions performed by the user. Such agents benefit by steering the user’s behaviour towards outcomes that maximise the ISA’s utility, which may or may not be aligned with that of the user. Video games, news recommendation aggregation engines, and fitness trackers can all be instances of this general case. Our analysis facilitates distinguishing various subcases of interaction (i.e. deception, coercion, trading, and nudging), as well as second-order effects that might include the possibility for adaptive interfaces to induce behavioural addiction, and/or change in user belief. We present these types of interaction within a conceptual framework, and review current examples of persuasive technologies and the issues that arise from their use. We argue that the nature of the feedback commonly used by learning agents to update their models and subsequent decisions could steer the behaviour of human users away from what benefits them, and in a direction that can undermine autonomy and cause further disparity between actions and goals as exemplified by addictive and compulsive behaviour. We discuss some of the ethical, social and legal implications of this technology and argue that it can sometimes exploit and reinforce weaknesses in human beings

The ethics of digital well-being: a thematic review

This article presents the first thematic review of the literature on the ethical issues concerning digital well-being. The term ‘digital well-being’ is used to refer to the impact of digital technologies on what it means to live a life that is good for a human being, and review the existing literature on the ethics of digital well-being, with the goal of mapping the current debate and identifying open questions for future research. The review identifies key issues related to four key social domains: healthcare, education, governance and social development, and media and entertainment. It also highlights three broader themes: positive computing, personalised human- computer interaction, and autonomy and self-determination. The review argues that three themes will be central to ongoing discussions and research by showing how they can be used to identify open questions related to the ethics of digital well-being