Biocognitive Classification of Antisocial Individuals Without Explanatory Reductionism
Marko Jurjako, Luca Malatesti, and Inti A. Brazil
Jurjako and colleagues review the literature supporting the benefits of using biocognitive classifications of antisocial disorders and psychopathy compared with syndrome-based approaches (based on constellations of symptoms) approaches. They clarify that biocognitive classifications do not need to entail explanatory reductionism—a view that psychological disorders can be exclusively categorized and explained in terms of their biological causes. The authors propose that a specific biocognitive approach integrating data on the genetic, neural, cognitive, and affective systems underlying psychiatric conditions may help to diagnose and treat these antisocial disorders.
The Eight Steps of Data Analysis: A Graphical Framework to Promote Sound Statistical Analysis
Fife proposes a general statistical-analysis strategy designed to ensure that researchers do not make common mistakes or follow poor practices (e.g., multiple comparisons, violations of normality or linearity). This general strategy encompasses eight steps, beginning with stating the theoretical hypothesis and ending with replicating a new data set, and emphasizes the importance of plotting the data to avoid common statistical mistakes. Fife suggests that this strategy can assist in eliminating false positives and negatives and revealing interesting insights about the data. The author provides an applied example of the strategy.
Toward a Refined Mindfulness Model Related to Consciousness and Based on ERP
Charles Verdonk, Marion Trousselard, Frédéric Canini, Francois Vialatte, and Céline Ramdani
Verdonk and colleagues reference event-related potential (ERP) literature to discuss the proposed mechanisms of mindfulness (focusing attention on one’s present experiences), such as self-regulation of attention, improved body awareness, improved emotion regulation, and change in self-perspective. The neural features of mindfulness appear to be associated with attention self-regulation, reduced brain reactivity to emotional stimuli, and improved cognitive control. The researchers propose a unified model of mindfulness positing that mindfulness decreases the threshold of conscious access to information.
The MAD Model of Moral Contagion: The Role of Motivation, Attention, and Design in the Spread of Moralized Content Online
William J. Brady, M. J. Crockett, and Jay J. Van Bavel
Brady and colleagues review evidence for moral contagion (i.e., the spread of moralized content) in social networks and propose the motivation, attention, and design (MAD) model to explain the concept. According to the MAD model, people have group-identity-based motivations to share moral-emotional content that is especially likely to capture attention, and the design of social-media platforms amplifies the tendency to spread such content. The authors explain how this model may help to study civic engagement and activism, political polarization, propaganda and disinformation, and other moralized behaviors in the digital age.
How Do Scientific Views Change? Notes From an Extended Adversarial Collaboration
Nelson Cowan, Clément Belletier, Jason M. Doherty, et al.
Adversarial collaborations—when proponents of competing views work together in a tense but productive joint effort—can contribute to the effective assessment of theories, Cowan and colleagues suggest. The researchers share their experience in adversarial collaborations involving three laboratories in different countries with different views regarding working memory. They suggest that, although adversarial collaboration is unlikely to completely change senior researchers’ theoretical views, it can further the understanding of a topic and others’ views by creating a common corpus of evidence that the theory must account for.
Putting the Nonsocial Into Social Neuroscience: A Role for Domain-General Priority Maps During Social Interactions
Richard Ramsey and Rob Ward
Ramsey and Ward propose that for social neuroscience to develop into a more mature research program, it needs to incorporate processes that affect social and nonsocial information, such as prioritization and selection. They describe and evaluate a hybrid model of information processing, based on neurobiological evidence, that generates predictions and transforms expectations of what types of neural mechanisms may contribute to social-information processing. This model may be useful in typical and atypical populations with clinical disorders that impair social-information processing.