Text-based E-contact: Harnessing cooperative Internet interactions to bridge the social and psychological divide

ABSTRACT

In order to bridge the psychological and physical divide between different groups, researchers have harnessed the positive elements of the Internet to improve intergroup contact. One new and effective Internet strategy is Electronic- or E-contact. Unlike other contact approaches, E-contact is an experimental intergroup intervention that uniquely accommodates Allport’s contact theory and recategorisation processes, to create a structured, cooperative, synchronous and goal-directed online text interaction between members from different groups. E-contact has been found to successfully improve intergroup relations between Catholics and Protestants in Northern Ireland and Muslims and Catholics in Australia; and reduce bias against lesbian women and gay men, people with schizophrenia, Indigenous Australians, and transgender individuals. This paper discusses the unique engineering and advantages of E-contact interventions in comparison to existing contact strategies, identifies the theories that guide E-contact interventions, provides meta-analytic evidence of its effects, and discusses the strengths, limitations and future directions for E-contact research.     

The Opinions of GP's Patients About Suicide,Assisted Suicide,Euthanasia, and Suicide Prevention: An Italian Survey

A survey about opinions on end‐of‐life issues of a population represented by 1,171 people in the waiting room of general practitioners' surgeries was conducted in a province of northern Italy. Most subjects did not consider suicide as a reasonable option even in cases of a serious and incurable disease. Moreover, subjects did not consider euthanasia as a possible option either; however, they did express an opposite attitude when considering euthanasia in a third‐person perspective. People with a personal history of suicidal behavior appear to present as a different population, overall expressing more open attitudes.     

Confirmatory reinforcement learning changes with age during adolescence

Understanding how learning changes during human development has been one of the long-standing objectives of developmental science. Recently, advances in computational biology have demonstrated that humans display a bias when learning to navigate novel environments through rewards and punishments: they learn more from outcomes that confirm their expectations than from outcomes that disconfirm them. Here, we ask whether confirmatory learning is stable across development, or whether it might be attenuated in developmental stages in which exploration is beneficial, such as in adolescence. In a reinforcement learning (RL) task, 77 participants aged 11–32 years (four men, mean age = 16.26) attempted to maximize monetary rewards by repeatedly sampling different pairs of novel options, which varied in their reward/punishment probabilities. Mixed-effect models showed an age-related increase in accuracy as long as learning contingencies remained stable across trials, but less so when they reversed halfway through the trials. Age was also associated with a greater tendency to stay with an option that had just delivered a reward, more than to switch away from an option that had just delivered a punishment. At the computational level, a confirmation model provided increasingly better fit with age. This model showed that age differences are captured by decreases in noise or exploration, rather than in the magnitude of the confirmation bias. These findings provide new insights into how learning changes during development and could help better tailor learning environments to people of different ages.

Research Highlights

• Reinforcement learning shows age-related improvement during adolescence, but more in stable learning environments compared with volatile learning environments.
• People tend to stay with an option after a win more than they shift from an option after a loss, and this asymmetry increases with age during adolescence.
• Computationally, these changes are captured by a developing confirmatory learning style, in which people learn more from outcomes that confirm rather than disconfirm their choices.
• Age-related differences in confirmatory learning are explained by decreases in stochasticity, rather than changes in the magnitude of the confirmation bias.