Variable responses to transcranial direct current stimulation protocols across individuals are widely reported, but the reasons behind this variation are unclear. This includes tDCS protocols meant to improve attention. Attentional control is impacted by top-down and bottom-up processes, and this relationship is affected by state characteristics such as anxiety. According to Attentional Control Theory, anxiety biases attention towards bottom-up and stimulus-driven processing. The goal of this study was to explore the extent to which differences in state anxiety and related measures (...) affect visual attention and category learning, both with and without the influence of tDCS. Using discovery learning, participants were trained to classify pictures of European streets into two categories while receiving 30 min of 2.0 mA anodal, cathodal, or sham tDCS over the rVLPFC. The pictures were classifiable according to two separate rules, one stimulus and one hypothesis-driven. The Remote Associates Test, Profile of Mood States, and Attention Networks Task were used to understand the effects of individual differences at baseline on subsequent tDCS-mediated learning. Multinomial logistic regression was fit to predict rule learning based on the baseline measures, with subjects classified according to whether they used the stimulus-driven or hypothesis-driven rule to classify the pictures. The overall model showed a classification accuracy of 74.1%. The type of tDCS stimulation applied, attentional orienting score, and self-reported mood were significant predictors of different categories of rule learning. These results indicate that anxiety can influence the quality of subjects’ attention at the onset of the task and that these attentional differences can influence tDCS-mediated category learning during the rapid assessment of visual scenes. These findings have implications for understanding the complex interactions that give rise to the variability in response to tDCS. (shrink)

We seem to be living through an animist revival.1 The term “new animism” first appeared in 2005, in Graham Harvey’s Animism: Respect for the Living World. Harvey’s work draws heavily on Irving Hall...

Ancient Israel's wisdom literature dealt explicitly with moral education. Applying this literature to modern challenges of business ethics requires reading the texts in the light of existential structures that bond the ancient with the modern world. Such structures could include the temporal categories of present and future along with the challenging angst of managing the future. By providing conflicting positions the ancient wisdom literature provides an attitude of heart for the modern person, especially the modern business person, whose all-absorbing attention (...) to the future may well be based on an underlying existential situation behind much unethical behavior in modern business. (shrink)

Ancient Israel's wisdom literature dealt explicitly with moral education. Applying this literature to modern challenges of business ethics requires reading the texts in the light of existential structures that bond the ancient with the modern world. Such structures could include the temporal categories of present and future along with the challenging angst of managing the future. By providing conflicting positions the ancient wisdom literature provides an attitude of heart for the modern person, especially the modern business person, whose all-absorbing attention (...) to the future may well be based on an underlying existential situation behind much unethical behavior in modern business. (shrink)

Facing an increasing number and variety of issues with social salience, firms must determine how to engage with issues that likely have a significant impact on them. Integrating issues management and salience theories, the authors find that firms engage with socially contested issues—where there is a high degree of societal disagreement—in a different manner from issues that have social consensus, or high agreement. Examining social issue resolutions filed by shareholders from 1997 to 2009, the study finds that socially contested issues, (...) as well as those issues with social consensus, are both likely to result in engagement by the firm. For social issues with consensus, a firm is more likely to opt for a low level of shareholder engagement whereas resolutions regarding contested issues lead to engaging shareholders at a higher level. These findings shed new light on the IM and issue salience literature streams that have suggested firms will react differently to these types of issues, even while they remain largely untested. Finally, firms become less engaged with perennial issues over time. rather than more, providing new guidance to researchers, shareholder activists, and firms alike. To the authors’ knowledge, such fined-grained insight into expected levels of firm engagement with social issue salience has not been put forth previously. (shrink)

Computational visual perception, also known as computer vision, is a field of artificial intelligence that enables computers to process digital images and videos in a similar way as biological vision does. It involves methods to be developed to replicate the capabilities of biological vision. The computer vision’s goal is to surpass the capabilities of biological vision in extracting useful information from visual data. The massive data generated today is one of the driving factors for the tremendous growth of computer vision. (...) This survey incorporates an overview of existing applications of deep learning in computational visual perception. The survey explores various deep learning techniques adapted to solve computer vision problems using deep convolutional neural networks and deep generative adversarial networks. The pitfalls of deep learning and their solutions are briefly discussed. The solutions discussed were dropout and augmentation. The results show that there is a significant improvement in the accuracy using dropout and data augmentation. Deep convolutional neural networks’ applications, namely, image classification, localization and detection, document analysis, and speech recognition, are discussed in detail. In-depth analysis of deep generative adversarial network applications, namely, image-to-image translation, image denoising, face aging, and facial attribute editing, is done. The deep generative adversarial network is unsupervised learning, but adding a certain number of labels in practical applications can improve its generating ability. However, it is challenging to acquire many data labels, but a small number of data labels can be acquired. Therefore, combining semisupervised learning and generative adversarial networks is one of the future directions. This article surveys the recent developments in this direction and provides a critical review of the related significant aspects, investigates the current opportunities and future challenges in all the emerging domains, and discusses the current opportunities in many emerging fields such as handwriting recognition, semantic mapping, webcam-based eye trackers, lumen center detection, query-by-string word, intermittently closed and open lakes and lagoons, and landslides. (shrink)

Background: Dementia care at home often involves decisions in which the caregiver must weigh safety concerns with respect for autonomy. These dilemmas can lead to situations where caregivers provide care against the will of persons living with dementia, referred to as involuntary treatment. To prevent this, insight is needed into how family caregivers of persons living with dementia deal with care situations that can lead to involuntary treatment. Objective: To identify and describe family caregivers’ experiences regarding care decisions for situations (...) that can lead to involuntary treatment use in persons living with dementia at home. Research design: A qualitative descriptive interview design. Data were analysed using the Qualitative Analysis Guide of Leuven. Participants and research context: A total of 10 family caregivers providing care for 13 persons living with dementia participated in in-depth semi-structured interviews. Participants were recruited by registered nurses via purposive sampling. Ethical consideration: The study protocol was approved by the Ethics Committee of the University Hospitals Leuven and the Medical Ethical Test Committee Zuyderland. Findings: Family caregivers experience the decision-making process concerning care dilemmas that can lead to involuntary treatment as complicated, stressful and exhausting. Although they consider safety and autonomy as important values, they struggle with finding the right balance between them. Due to the progressive and unpredictable nature of dementia, they are constantly seeking solutions while they adapt to new situations. Family caregivers feel responsible and experience social pressure for the safety of persons living with dementia. They may be blamed if something adverse happens to the persons living with dementia, which increases an already stressful situation. Their experience is influenced by characteristics of the care triad such as practical and emotional support, knowledge, and previous experiences. Discussion and conclusion: To prevent involuntary treatment, professionals need to proactively inform family caregivers, and they need to support each other in dealing with complex care situations. (shrink)

Rid and Wendler propose the development of a Patient Preference Predictor (PPP), an actuarial model for predicting incapacitated patient’s life-sustaining treatment preferences across a wide range of end-of-life scenarios. An actuarial approach to end-of-life decision making has enormous potential, but transferring the logic of actuarial prediction to end-of-life decision making raises several conceptual complexities and logistical problems that need further consideration. Actuarial models have proven effective in targeted prediction tasks, but no evidence supports their effectiveness in the kind of broad (...) spectrum prediction task that is the proposed goal of the PPP. We argue that a more focused approach, targeting specific medical conditions and generating treatment predictions based on the preferences of individuals with actual disease experience, is both more firmly grounded in past research and is a more prudent initial strategy for exploring the efficacy of actuarial prediction in end-of-life decision making. (shrink)