Environmental neurodevelopment toxicity from the perspective of Bronfenbrenner’s bioecological model: a case study of toxic metals

A growing body of literature reports the need for an integrated approach to study the effects of the physical environment on the neurodevelopment of children. Assessment of the true neurotoxicity of pollutants cannot be performed separately from the ecological and multidimensional contexts in which they act. In this study, from the perspective of the Bronfenbrenner’s bioecological model, a conceptual model was developed that encompasses the social and biological characteristics of children from the gestational period to childhood, considering exposure to toxic metals. First, we present the toxicity of the main metals and some concept notions that we used in our framework, such as social and structural determinants of health, allostatic load, embodiment, and epigenetic concepts. Then, the main aspects of the Bronfenbrenner’s bioecological model, which allow integration of the gene-social relationship in addition to the physical environment, where these metals act, are explained. Finally, we present and discuss the conceptual framework showing how, in real life, biological and social factors may together influence the neurodevelopment of children. Although this model is based on a group of contaminants, it opens new horizons on how environmental sciences, such as neurotoxicology and environmental epidemiology, can articulate with the theoretical models from human sciences to provide a broader approach to study the effects on human neurodevelopment

Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning

At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied machine learning on the multinational data collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N = 51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the pandemic. The results point to several valuable insights. Internalized moral identity provided the most consistent predictive contribution—individuals perceiving moral traits as central to their self-concept reported higher adherence to preventive measures. Similar results were found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, and collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-neglible variability in the explained variance and predictive contributions with respect to macro-level factors such as the pandemic stage or cultural region. Overall, the results underscore the importance of morality-related and contextual factors in understanding adherence to public health recommendations during the pandemic.Peer reviewe

Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning

At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied machine learning on the multinational data collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N = 51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the pandemic. The results point to several valuable insights. Internalized moral identity provided the most consistent predictive contribution—individuals perceiving moral traits as central to their self-concept reported higher adherence to preventive measures. Similar results were found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, and collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-neglible variability in the explained variance and predictive contributions with respect to macro-level factors such as the pandemic stage or cultural region. Overall, the results underscore the importance of morality-related and contextual factors in understanding adherence to public health recommendations during the pandemic

Informe final del proyecto: Aprendiendo Matemática a través de la interacción con pares y máquinas inteligentes

Muchos niños en el mundo presentan problemas en el aprendizaje de la matemática, especialmente aquellos que viven en contextos de vulnerabilidad social. La brecha socioeconómica en el aprendizaje de la matemática surge desde muy temprano y se prolonga a lo largo de la vida. Los factores claves que explican esta brecha son las diferencias en el acceso a las actividades y las interacciones sociales que estimulan la comprensión intuitiva del número y la geometría, así como la motivación para aprender matemáticas. Varias iniciativas han logrado reducir la brecha en el acceso a la información apoyándose en la interacción de los niños con máquinas inteligentes. Nuestro grupo fue pionero en Uruguay en implementar (junto al Plan Ceibal) intervenciones que mostraron mejoras en el aprendizaje de la matemática, especialmente para los niños de contextos más desfavorecidos (Valle Lisboa et al., 2017). Sin embargo, otros estudios han mostrado que los juegos con materiales concretos jugados en grupos de niños que se comunican y cooperan o compiten entre sí pueden potenciar el aprendizaje de los conceptos, la lógica y el lenguaje de las matemáticas en la escuela. Recientemente, el laboratorio de E. Spelke en Harvard implementó un programa de intervención basado en este tipo de juegos que mejoró el aprendizaje de la matemática en niños pre-escolares en India (Dillon et al; 2017). En este proyecto pretendemos combinar estos dos enfoques y evaluar si juntos son capaces de promover un aprendizaje más profundo y efectivo de las matemáticas que cada uno por sí mismo. Al combinar juegos sociales que mejoran la comprensión intuitiva y la motivación por el aprendizaje de la matemática, con la interacción individualizada con máquinas inteligentes que adaptan al nivel de rendimiento de cada niño los problemas que les presentan, esperamos maximizar los beneficios de ambos enfoques en la educación matemática inicial.Agencia Nacional de Investigación e Innovació