Social Influence and the Brain: Persuasion, Susceptibility to Influence and Retransmission

Social influence is an important topic of research, with a particularly long history in the social sciences. Recently, social influence has also become a topic of interest among neuroscientists. The aim of this review is to highlight current research that has examined neural systems associated with social influence, from the perspective of being influenced as well as influencing others, and highlight studies that link neural mechanisms with real-world behavior change beyond the laboratory. Although many of the studies reviewed focus on localizing brain regions implicated in influence within the lab, we argue that approaches that account for networks of brain regions and that integrate neural data with data beyond the laboratory are likely to be most fruitful in understanding influence

The Value of Sharing Information: A Neural Account of Information Transmission

Humans routinely share information with one another. What drives this behavior? We used neuroimaging to test an account of information selection and sharing that emphasizes inherent reward in self-reflection and connecting with other people. Participants underwent functional MRI while they considered personally reading and sharing New York Times articles. Activity in neural regions involved in positive valuation, self-related processing, and taking the perspective of others was significantly associated with decisions to select and share articles, and scaled with preferences to do so. Activity in all three sets of regions was greater when participants considered sharing articles with other people rather than selecting articles to read themselves. The findings suggest that people may consider value not only to themselves but also to others even when selecting news articles to consume personally. Further, sharing heightens activity in these pathways, in line with our proposal that humans derive value from self-reflection and connecting to others via sharing

A neural model of valuation and information virality

Information sharing is an integral part of human interaction that serves to build social relationships and affects attitudes and behaviors in individuals and large groups. We present a unifying neurocognitive framework of mechanisms underlying information sharing at scale (virality). We argue that expectations regarding self-related and social consequences of sharing (e.g., in the form of potential for self-enhancement or social approval) are integrated into a domain-general value signal that encodes the value of sharing a piece of information. This value signal translates into population-level virality. In two studies (n = 41 and 39 participants), we tested these hypotheses using functional neuroimaging. Neural activity in response to 80 New York Times articles was observed in theory-driven regions of interest associated with value, self, and social cognitions. This activity then was linked to objectively logged population-level data encompassing n = 117,611 internet shares of the articles. In both studies, activity in neural regions associated with self-related and social cognition was indirectly related to population-level sharing through increased neural activation in the brain’s value system. Neural activity further predicted populationlevel outcomes over and above the variance explained by article characteristics and commonly used self-report measures of sharing intentions. This parsimonious framework may help advance theory, improve predictive models, and inform new approaches to effective intervention. More broadly, these data shed light on the core functions of sharing—to express ourselves in positive ways and to strengthen our social bonds

TIMSS 2019. Skalenhandbuch zur Dokumentation der Erhebungsinstrumente und Arbeit mit den Datensätzen

Die Studie TIMSS (Trends in International Mathematics and Science Study) ist eine international vergleichende Schulleistungsuntersuchung, die von der International Association for the Evaluation of Educational Achievement (IEA) – einem unabhängigen, internationalen Zusammenschluss von Forschungseinrichtungen, Wissenschaftler:innen sowie Regierungsstellen – durchgeführt wird. Das Kernanliegen der Studie ist es, langfristige Entwicklungen in den Bildungssystemen der teilnehmenden Staaten und Regionen zu untersuchen. Im Fokus stehen mathematische und naturwissenschaftliche Kompetenzen von Viertklässler:innen. Mit dem vorliegenden Handbuch werden die im Rahmen von TIMSS 2019 in Deutschland eingesetzten Befragungsinstrumente dokumentiert. Das Handbuch umfasst damit die Instrumente, die Teil der internationalen Berichterstattung sind, und nationale Ergänzungen dieser Instrumente, die in Deutschland vorgenommen wurden. Um die Arbeit mit den Datensätzen der Studie zu ermöglichen, werden zudem System-, Organisations- und Linkingvariablen sowie nachträglich generierte Indizes dokumentiert. Deskriptive Statistiken und Skalenkennwerte ermöglichen eine Einschätzung der Verteilungen der eingesetzten Variablen sowie der Datenqualität. Die Dokumentation der Erhebungsinstrumente ist in der Reihenfolge der Administration und nach den befragten Personengruppen gegliedert. Ferner ermöglichen Verzeichnisse und Übersichtstabellen einen inhaltlichen Zugang. (DIPF/Orig.

Neural Signals of Video Advertisement Liking:Insights into Psychological Processes and their Temporal Dynamics

What drives the liking of video advertisements? The authors analyzed neural signals during ad exposure from three functional magnetic resonance imaging (fMRI) data sets (113 participants from two countries watching 85 video ads) with automated meta-analytic decoding (Neurosynth). These brain-based measures of psychological processes—including perception and language (information processing), executive function and memory (cognitive functions), and social cognition and emotion (social-affective response)—predicted subsequent self-report ad liking, with emotion and memory being the earliest predictorsafter the first three seconds. Over the span of ad exposure, while the predictiveness of emotion peaked early and fell, that of social cognition had a peak-and-stable pattern, followed by a late peak of predictiveness in perception and executive function.At the aggregate level, neural signals—especially those associated with social-affective response—improved the prediction of out-of-sample ad liking compared with traditional anatomically based neuroimaging analysis and self-report liking. Finally, earlyonset social-affective response predicted population ad liking in a behavioral replication. Overall, this study helps delineate the psychological mechanisms underlying ad processing and ad liking and proposes a novel neuroscience-based approach for generating psychological insights and improving out-of-sample predictions

Machine Learning Algorithms for Classification of MALDI-TOF MS Spectra from Phylogenetically Closely Related Species Brucella melitensis, Brucella abortus and Brucella suis

(1) Background: MALDI-TOF mass spectrometry (MS) is the gold standard for microbial fingerprinting, however, for phylogenetically closely related species, the resolution power drops down to the genus level. In this study, we analyzed MALDI-TOF spectra from 44 strains of B. melitensis, B. suis and B. abortus to identify the optimal classification method within popular supervised and unsupervised machine learning (ML) algorithms. (2) Methods: A consensus feature selection strategy was applied to pinpoint from among the 500 MS features those that yielded the best ML model and that may play a role in species differentiation. Unsupervised k-means and hierarchical agglomerative clustering were evaluated using the silhouette coefficient, while the supervised classifiers Random Forest, Support Vector Machine, Neural Network, and Multinomial Logistic Regression were explored in a fine-tuning manner using nested k-fold cross validation (CV) with a feature reduction step between the two CV loops. (3) Results: Sixteen differentially expressed peaks were identified and used to feed ML classifiers. Unsupervised and optimized supervised models displayed excellent predictive performances with 100% accuracy. The suitability of the consensus feature selection strategy for learning system accuracy was shown. (4) Conclusion: A meaningful ML approach is here introduced, to enhance Brucella spp. classification using MALDI-TOF MS data.Peer Reviewe

Effects of self-transcendence on neural responses to persuasive messages and health behavior change

Self-transcendence refers to a shift in mindset from focusing on self-interests to the well-being of others. We offer an integrative neural model of self-transcendence in the context of persuasive messaging by examining the mechanisms of self-transcendence in promoting receptivity to health messages and behavior change. Specifically, we posited that focusing on values and activities that transcend the self can allow people to see that their self-worth is not tied to a specific behavior in question, and in turn become more receptive to subsequent, otherwise threatening health information. To test whether inducing self-transcendent mindsets before message delivery would help overcome defensiveness and increase receptivity, we used two priming tasks, affirmation and compassion, to elicit a transcendent mindset among 220 sedentary adults. As preregistered, those who completed a self-transcendence task before health message exposure, compared with controls, showed greater increases in objectively logged levels of physical activity throughout the following month. In the brain, self-transcendence tasks up-regulated activity in a region of the ventromedial prefrontal cortex, chosen for its role in positive valuation and reward processing. During subsequent health message exposure, self-transcendence priming was associated with increased activity in subregions of the ventromedial prefrontal cortex, implicated in self-related processing and positive valuation, which predicted later decreases in sedentary behavior. The present findings suggest that having a positive self-transcendent mindset can increase behavior change, in part by increasing neural receptivity to health messaging

Influenza A virus replicates productively in primary human kidney cells and induces factors and mechanisms related to regulated cell death and renal pathology observed in virus-infected patients

IntroductionInfluenza A virus (IAV) infection can cause the often-lethal acute respiratory distress syndrome (ARDS) of the lung. Concomitantly, acute kidney injury (AKI) is frequently noticed during IAV infection, correlating with an increased mortality. The aim of this study was to elucidate the interaction of IAV with human kidney cells and, thereby, to assess the mechanisms underlying IAV-mediated AKI.MethodsTo investigate IAV effects on nephron cells we performed infectivity assays with human IAV, as well as with human isolates of either low or highly pathogenic avian IAV. Also, transcriptome and proteome analysis of IAV-infected primary human distal tubular kidney cells (DTC) was performed. Furthermore, the DTC transcriptome was compared to existing transcriptomic data from IAV-infected lung and trachea cells.ResultsWe demonstrate productive replication of all tested IAV strains on primary and immortalized nephron cells. Comparison of our transcriptome and proteome analysis of H1N1-type IAV-infected human primary distal tubular cells (DTC) with existing data from H1N1-type IAV-infected lung and primary trachea cells revealed enrichment of specific factors responsible for regulated cell death in primary DTC, which could be targeted by specific inhibitors.DiscussionIAV not only infects, but also productively replicates on different human nephron cells. Importantly, multi-omics analysis revealed regulated cell death as potential contributing factor for the clinically observed kidney pathology in influenza

Addressing climate change with behavioral science:A global intervention tournament in 63 countries

Effectively reducing climate change requires marked, global behavior change. However, it is unclear which strategies are most likely to motivate people to change their climate beliefs and behaviors. Here, we tested 11 expert-crowdsourced interventions on four climate mitigation outcomes: beliefs, policy support, information sharing intention, and an effortful tree-planting behavioral task. Across 59,440 participants from 63 countries, the interventions' effectiveness was small, largely limited to nonclimate skeptics, and differed across outcomes: Beliefs were strengthened mostly by decreasing psychological distance (by 2.3%), policy support by writing a letter to a future-generation member (2.6%), information sharing by negative emotion induction (12.1%), and no intervention increased the more effortful behavior-several interventions even reduced tree planting. Last, the effects of each intervention differed depending on people's initial climate beliefs. These findings suggest that the impact of behavioral climate interventions varies across audiences and target behaviors.</p

Addressing climate change with behavioral science: a global intervention tournament in 63 countries

Effectively reducing climate change requires marked, global behavior change. However, it is unclear which strategies are most likely to motivate people to change their climate beliefs and behaviors. Here, we tested 11 expert-crowdsourced interventions on four climate mitigation outcomes: beliefs, policy support, information sharing intention, and an effortful tree-planting behavioral task. Across 59,440 participants from 63 countries, the interventions’ effectiveness was small, largely limited to nonclimate skeptics, and differed across outcomes: Beliefs were strengthened mostly by decreasing psychological distance (by 2.3%), policy support by writing a letter to a future-generation member (2.6%), information sharing by negative emotion induction (12.1%), and no intervention increased the more effortful behavior—several interventions even reduced tree planting. Last, the effects of each intervention differed depending on people’s initial climate beliefs. These findings suggest that the impact of behavioral climate interventions varies across audiences and target behaviors