Articles in 2017

Functional brain-imaging methods provide rich datasets that can be exploited by machine-learning techniques to help assess psychiatric disorders. A recent study uses this approach to identify patients with suicidal thoughts, and to distinguish those who have attempted suicide from those who have not.

Just et al. develop a highly accurate biological classification method for identifying suicidal ideators by applying machine learning to neural representations of death- and life-related concepts.

Mental health technologies, such as apps, clinical texting, social media platforms and web-based tools, have arrived. Channelling these resources to help people with serious mental illnesses, clinicians in need of support, and people in low-and middle-income countries will have the most impact on the global burden of mental illness.

Consistent failure over the past few decades to reduce the high prevalence of stress-related disorders has motivated a search for alternative research strategies. Resilience refers to the phenomenon of many people maintaining mental health despite exposure to psychological or physical adversity. Instead of aiming to understand the pathophysiology of stress-related disorders, resilience research focuses on protective mechanisms that shield people against the development of such disorders and tries to exploit its insights to improve treatment and, in particular, disease prevention. To fully harness the potential of resilience research, a critical appraisal of the current state of the art — in terms of basic concepts and key methods — is needed. We highlight challenges to resilience research and make concrete conceptual and methodological proposals to improve resilience research. Most importantly, we propose to focus research on the dynamic processes of successful adaptation to stressors in prospective longitudinal studies.

A paper in this issue identifies a persistent influence of irrelevant information in social contexts, which results in biased and unfair judgements. These widespread social biases can be insidious as they inadvertently enter research and policy.

In the United States, direct-to-consumer advertisements for medications must disclose each specific side-effect risk. A new study demonstrates a counterintuitive dilution effect: people perceive drug descriptions that include both serious and trivial side effects as less risky than descriptions that only list serious side effects.

Im et al. examine how people process crowd facial expressions as opposed to individual ones, finding significant behavioural and neural differences.

Direct-to-consumer advertising of pharmaceutical drugs requires mention of severe side effects, along with the most frequent. Sivanathan and Kakkar show that this practice dilutes consumers’ judgements of the overall severity of side effects

Women often behave more prosocially than men. Soutschek et al. use pharmacology and neuroimaging to show that the neural reward system appears to be more sensitive to prosocial rewards in women than men, providing a neurobiological account for this gender difference.

A new study shows that brain responses to unfairness during economic decision-making can predict current and future depression indices. Neural response patterns in the amygdala related to inequity tracked indices of depression, particularly for prosocial individuals who tend to be most self-sacrificing.

Cao et al. demonstrate that people systematically rely on social base rates when making judgements about individuals, even when these base rates are statistically irrelevant. The authors show that multiple remedies are required to eliminate this bias of base rate intrusion.

Supporting the Safe Schools Declaration is one way that governments can help protect education and keep students safe during war, argues Susan Raqib.

Haruno et al. combine functional magnetic resonance imaging, an economic game and depression self-reports to show that brain activity in the amygdala and hippocampus induced by inequity can predict present and future depression indices.

Pedroni et al. show that risk preferences vary across behavioural elicitation methods, challenging the view that risk preferences can be consistently captured by a single method.

A quasi-experimental study of the generalized enforcement of low-level violations in New York City finds that proactive policing increases crime. This finding suggests the importance of taking a careful look at aggressive enforcement approaches used by police to reduce crime as they may be causing harm in urban communities.