There is increasing interest in how the pupil dynamics of the eye reflect underlying cognitive processes and brain states. Problematic, however, is that pupil changes can be due to non-cognitive factors, for example luminance changes in the environment, accommodation and movement. In this paper we consider how by modeling the response of the pupil in real-world environments we can capture the non-cognitive related changes and remove these to extract a residual signal which is a better index of cognition and performance. (...) Specifically, we utilize sequence measures such as fixation position, duration, saccades, and blink-related information as inputs to a deep recurrent neural network (RNN) model for predicting subsequent pupil diameter. We build and evaluate the model for a task where subjects are watching educational videos and subsequently asked questions based on the content. Compared to commonly-used models for this task, the RNN had the lowest errors rates in predicting subsequent pupil dilation given sequence data. Most importantly was how the model output related to subjects' cognitive performance as assessed by a post-viewing test. Consistent with our hypothesis that the model captures non-cognitive pupil dynamics, we found (1) the model's root-mean square error was less for lower performing subjects than for those having better performance on the post-viewing test, (2) the residuals of the RNN (LSTM) model had the highest correlation with subject post-viewing test scores and (3) the residuals had the highest discriminability (assessed via area under the ROC curve, AUC) for classifying high and low test performers, compared to the true pupil size or the RNN model predictions. This suggests that deep learning sequence models may be good for separating components of pupil responses that are linked to luminance and accommodation from those that are linked to cognition and arousal. (shrink)

As policy innovation is essential for upscaling responsible innovation, understanding its relationship to value change(s) occurring or sought in sociotechnical systems is imperative. In this study, we ask: what are the different types of values in the policy process? And, how does value change influence policy innovation? We propose a disaggregation of values and value change based on a four-stream variant of the multiple streams framework (MSF), a conceptual lens increasingly used for explaining policy innovation in sociotechnical transitions. Specifically, we (...) posit that the values that ‘govern’ problem framing, policy design, political decision making, and technological diffusion can evolve relatively independently, potentially leading to value conflict. We apply this framework to the ongoing case of the market-based economic dispatch of electricity (MBED) policy in the Indian energy transition using content analysis. We find that the MBED scheme—with its emphasis on efficiency (problem), economic principles (policy), low-cost dispatch (technology), and centralization (politics)—attempts value change in each stream. Each instance of value change is, however, widely contested, with the ensuing value conflicts resulting in significant opposition to this policy innovation. We conclude that a disaggregation of values based on the MSF can facilitate an analysis of value change and value conflict in sociotechnical transitions and lay the foundation for systematically studying the relationships among technological change, value change, and policy change. (shrink)

In this paper, we take up the call to further examine structural injustice in health, and racial inequalities in particular. We examine the many facets of racism: structural, interpersonal and institutional as they appeared in the COVID-19 pandemic in the UK, and emphasize the relevance of their systemic character. We suggest that such inequalities were entirely foreseeable, for their causal mechanisms are deeply ingrained in our social structures. It is by recognizing the conventional, un-extraordinary nature of racism within social systems (...) that we can begin to address socially mediated health inequalities. (shrink)

The COVID-19 pandemic and restrictions imposed to stop its advance have affected the entire population. Children living with difficulties or in vulnerable situations prior to the pandemic might have suffered an even greater impact. This present study examines the psychological impact of quarantine on children and adolescents exposed to intimate partner violence against their mothers. Participants were 185 mothers who reported 269 children, as well as 108 children who self-reported. An emotional and behavioral checklist was administered to both mothers and (...) children throughout confinement. Results show mothers observed changes in their children’s psychological state. Children, in turn, reported an increase in different variables. Mothers reported a higher percentage of overall increase for both general and severe symptoms than their children. Differences were found by sex and age. Future research with similar population groups is necessary to establish the support and intervention children require in similar contexts, as well as to clarify possible causes of differences found by age and sex. (shrink)