A Secret of Hypnosis: A Dynamic Rubber Hand Illusion

Presenting a suggestion of heaviness to a person in a hypnotic trance (e.g., "your arm is getting heavier and heavier") tends to result in a corresponding change in the person's body position (e.g., the arm lowers). This phenomenon may be a result of activation of the mirror neuron system, which leads the subject to anticipate actual weight on the arm. The mirror system underlies people's ability to sense, in the absence of actual sensory input, experiences of other people. Perhaps this system allows the same anticipatory experience regarding non-human objects. In this study, we showed participants a picture of a rubber hand holding what appeared to be a lightweight rubber ball. In reality, the ball was weighted with sand. We instructed participants to move their arms to a horizontal position and hold them immobile. Those participants who knew the actual weight of the ball tended to raise their arms above the horizontal, perhaps in response to their expectation of the need to resist the weight of the ball. This illusional phenomenon might be similar to that induced by the hypnotic suggestion of heaviness. That is, the body's response may reflect activity in the mirror system, which anticipates greater weight

The Multidimensional Perfectionism Cognitions Inventory–English (MPCI-E): Reliability, validity, and relationships with positive and negative affect

The Multidimensional Perfectionism Cognitions Inventory (MPCI; Kobori & Tanno, 2004) is a promising new instrument developed in Japan to assess perfectionism cognitions regarding personal standards, pursuit of perfection, and concern over mistakes. The present study examined reliability and validity of the English version of the MPCI, the MPCI-E (Kobori, 2006), in a sample of 371 native English speakers. A confirmatory factor analysis confirmed the MPCI-E’s three-factorial oblique structure. Moreover, correlations with measures of dispositional perfectionism and past-week positive and negative affect provided first evidence of the MPCI-E’s convergent and differential validity. Finally, hierarchical multiple regressions indicated that the MPCI-E showed incremental validity in explaining variance in positive and negative affect above variance explained by dispositional perfectionism. Overall, the findings provide first evidence for the reliability and validity of the MPCI-E as a multidimensional measure of perfectionism cognitions that has the potential to further our understanding of positive and negative cognitions in perfectionism

A Proposal From The Montpellier World Health Organization Collaborating Centre For Better Management And Prevention Of Anaphylaxis

International audienceSince the first description of anaphylaxis in 1902, its clinical importance as an emergency condition has been recognized worldwide. Anaphylaxis is a severe, potentially life-threatening systemic hypersensitivity reaction characterized by rapid onset and the potential to endanger life through respiratory or circulatory compromise. It is usually, although not always, associated with skin and mucosal changes. Although the academic/scientific communities have advocated to promote greater awareness and protocols for the management of anaphylaxis based on best evidence, there are few efforts documenting feedback as to the success of these efforts. In this article, we review the key unmet needs related to the diagnosis and management of anaphylaxis, and propose a public health initiative for prevention measures and a timetable action plan that intends to strengthen the collaboration among health professionals and especially primary care physicians dealing with anaphylaxis, which can encourage enhanced quality of care of patients with anaphylaxis. More than calling for a harmonized action for the best management of anaphylaxis to prevent undue morbidity and mortality, the Montpellier World Health Organization Collaborating Centre here proposes an action plan as a baseline for a global initiative against anaphylaxis. We strongly believe that these collaborative efforts are a strong public health and societal priority that is consistent with the overarching goals of providing optimal care of allergic patients and best practices of allergology

Reasoning with Uncertainty in Deep Learning for Safer Medical Image Computing

Deep learning is now ubiquitous in the research field of medical image computing. As such technologies progress towards clinical translation, the question of safety becomes critical. Once deployed, machine learning systems unavoidably face situations where the correct decision or prediction is ambiguous. However, the current methods disproportionately rely on deterministic algorithms, lacking a mechanism to represent and manipulate uncertainty. In safety-critical applications such as medical imaging, reasoning under uncertainty is crucial for developing a reliable decision making system. Probabilistic machine learning provides a natural framework to quantify the degree of uncertainty over different variables of interest, be it the prediction, the model parameters and structures, or the underlying data (images and labels). Probability distributions are used to represent all the uncertain unobserved quantities in a model and how they relate to the data, and probability theory is used as a language to compute and manipulate these distributions. In this thesis, we explore probabilistic modelling as a framework to integrate uncertainty information into deep learning models, and demonstrate its utility in various high-dimensional medical imaging applications. In the process, we make several fundamental enhancements to current methods. We categorise our contributions into three groups according to the types of uncertainties being modelled: (i) predictive; (ii) structural and (iii) human uncertainty. Firstly, we discuss the importance of quantifying predictive uncertainty and understanding its sources for developing a risk-averse and transparent medical image enhancement application. We demonstrate how a measure of predictive uncertainty can be used as a proxy for the predictive accuracy in the absence of ground-truths. Furthermore, assuming the structure of the model is flexible enough for the task, we introduce a way to decompose the predictive uncertainty into its orthogonal sources i.e. aleatoric and parameter uncertainty. We show the potential utility of such decoupling in providing a quantitative “explanations” into the model performance. Secondly, we introduce our recent attempts at learning model structures directly from data. One work proposes a method based on variational inference to learn a posterior distribution over connectivity structures within a neural network architecture for multi-task learning, and share some preliminary results in the MR-only radiotherapy planning application. Another work explores how the training algorithm of decision trees could be extended to grow the architecture of a neural network to adapt to the given availability of data and the complexity of the task. Lastly, we develop methods to model the “measurement noise” (e.g., biases and skill levels) of human annotators, and integrate this information into the learning process of the neural network classifier. In particular, we show that explicitly modelling the uncertainty involved in the annotation process not only leads to an improvement in robustness to label noise, but also yields useful insights into the patterns of errors that characterise individual experts

Servant Leadership: What Makes It an Effective Leadership Model.

Servant leadership (SL), a universal, ethical leadership style, consistently produces high performance and employee engagement. For the last two decades, lack of business ethics in decision making by senior leaders has resulted in many negative outcomes, such as the WorldCom scandal. The purpose of this descriptive phenomenological study was to identify and report the lived experiences of senior leaders in relation to decision making in SL organizations in the southwestern United States. The study\u27s theoretical/conceptual foundations encompassed Maslow\u27s motivation theories, decision theory, spirituality, spiritual intelligence, Cicero\u27s virtue theory of ethics, and Greenleaf\u27s SL. Data collection involved the use of semistructured interviews with a purposive sample of 18 participants who were senior leaders of SL organizations. Data analysis employed Giorgi\u27s method whereby phenomenological reduction revealed meaning units, and psychological reduction reached descriptive psychological structures of experiences by hand coding and integrative data analysis software. Findings confirmed senior leaders\u27 ethical decision making in SL organizations. Recommendations include addressing ethical decision making in team leadership at the board and operational levels and examining the interrelation of CEO ethical leadership and firm performance. Conclusions reached confirm a prevailing structure of experiences as collaborative, interdependent, egalitarian teamwork, a family metaphor. Application of the findings of this study may result in positive social change by fostering a more ethical, kinder capitalism in everyday life and in building community with more servant leaders and SL organizations