A "data sharing trust" model for rapid, collaborative science.

Complex datasets provide opportunities for discoveries beyond their initial scope. Effective and rapid data sharing and management practices are crucial to realize this potential; however, they are harder to implement than post-publication access. Here, we introduce the concept of a "data sharing trust" to maximize the value of large datasets.

Online searches to evaluate misinformation can increase its perceived veracity.

Considerable scholarly attention has been paid to understanding belief in online misinformation1,2, with a particular focus on social networks. However, the dominant role of search engines in the information environment remains underexplored, even though the use of online search to evaluate the veracity of information is a central component of media literacy interventions3-5. Although conventional wisdom suggests that searching online when evaluating misinformation would reduce belief in it, there is little empirical evidence to evaluate this claim. Here, across five experiments, we present consistent evidence that online search to evaluate the truthfulness of false news articles actually increases the probability of believing them. To shed light on this relationship, we combine survey data with digital trace data collected using a custom browser extension. We find that the search effect is concentrated among individuals for whom search engines return lower-quality information. Our results indicate that those who search online to evaluate misinformation risk falling into data voids, or informational spaces in which there is corroborating evidence from low-quality sources. We also find consistent evidence that searching online to evaluate news increases belief in true news from low-quality sources, but inconsistent evidence that it increases belief in true news from mainstream sources. Our findings highlight the need for media literacy programmes to ground their recommendations in empirically tested strategies and for search engines to invest in solutions to the challenges identified here.

Introducing new group leaders: Devin Schweppe.

We talk to Devin Schweppe about setting up his group at the University of Washington, studying quantitative-mass-spectrometry-based proteomics (a field that is constantly evolving), overcoming self-doubt, going with your gut when hiring, and how he hopes to build an environment of excellence, inclusion, and trust and always encourage his lab members to keep asking "why?"

Financial incentives for vaccination do not have negative unintended consequences.

Financial incentives to encourage healthy and prosocial behaviours often trigger initial behavioural change1-11, but a large academic literature warns against using them12-16. Critics warn that financial incentives can crowd out prosocial motivations and reduce perceived safety and trust, thereby reducing healthy behaviours when no payments are offered and eroding morals more generally17-24. Here we report findings from a large-scale, pre-registered study in Sweden that causally measures the unintended consequences of offering financial incentives for taking the first dose of a COVID-19 vaccine. We use a unique combination of random exposure to financial incentives, population-wide administrative vaccination records and rich survey data. We find no negative consequences of financial incentives; we can reject even small negative impacts of offering financial incentives on future vaccination uptake, morals, trust and perceived safety. In a complementary study, we find that informing US residents about the existence of state incentive programmes also has no negative consequences. Our findings inform not only the academic debate on financial incentives for behaviour change but also policy-makers who consider using financial incentives to change behaviour.

Historical narratives about the COVID-19 pandemic are motivationally biased.

How people recall the SARS-CoV-2 pandemic is likely to prove crucial in future societal debates on pandemic preparedness and appropriate political action. Beyond simple forgetting, previous research suggests that recall may be distorted by strong motivations and anchoring perceptions on the current situation1-6. Here, using 4 studies across 11 countries (total n = 10,776), we show that recall of perceived risk, trust in institutions and protective behaviours depended strongly on current evaluations. Although both vaccinated and unvaccinated individuals were affected by this bias, people who identified strongly with their vaccination status-whether vaccinated or unvaccinated-tended to exhibit greater and, notably, opposite distortions of recall. Biased recall was not reduced by providing information about common recall errors or small monetary incentives for accurate recall, but was partially reduced by high incentives. Thus, it seems that motivation and identity influence the direction in which the recall of the past is distorted. Biased recall was further related to the evaluation of past political action and future behavioural intent, including adhering to regulations during a future pandemic or punishing politicians and scientists. Together, the findings indicate that historical narratives about the COVID-19 pandemic are motivationally biased, sustain societal polarization and affect preparation for future pandemics. Consequently, future measures must look beyond immediate public-health implications to the longer-term consequences for societal cohesion and trust.

Communicating doctors' consensus persistently increases COVID-19 vaccinations.

The reluctance of people to get vaccinated represents a fundamental challenge to containing the spread of deadly infectious diseases1,2, including COVID-19. Identifying misperceptions that can fuel vaccine hesitancy and creating effective communication strategies to overcome them are a global public health priority3-5. Medical doctors are a trusted source of advice about vaccinations6, but media reports may create an inaccurate impression that vaccine controversy is prevalent among doctors, even when a broad consensus exists7,8. Here we show that public misperceptions about the views of doctors on the COVID-19 vaccines are widespread, and correcting them increases vaccine uptake. We implement a survey among 9,650 doctors in the Czech Republic and find that 90% of doctors trust the vaccines. Next, we show that 90% of respondents in a nationally representative sample (n = 2,101) underestimate doctors' trust; the most common belief is that only 50% of doctors trust the vaccines. Finally, we integrate randomized provision of information about the true views held by doctors into a longitudinal data collection that regularly monitors vaccination status over 9 months. The treatment recalibrates beliefs and leads to a persistent increase in vaccine uptake. The approach demonstrated in this paper shows how the engagement of professional medical associations, with their unparalleled capacity to elicit individual views of doctors on a large scale, can help to create a cheap, scalable intervention that has lasting positive impacts on health behaviour.

Promoting research quality.

Plenty of awards recognize scientific contributions, but a unique and important one honors those whose efforts significantly enhance the quality and robustness of research. We discuss why this is important to promote trust in science.

Shifting attention to accuracy can reduce misinformation online.

In recent years, there has been a great deal of concern about the proliferation of false and misleading news on social media1-4. Academics and practitioners alike have asked why people share such misinformation, and sought solutions to reduce the sharing of misinformation5-7. Here, we attempt to address both of these questions. First, we find that the veracity of headlines has little effect on sharing intentions, despite having a large effect on judgments of accuracy. This dissociation suggests that sharing does not necessarily indicate belief. Nonetheless, most participants say it is important to share only accurate news. To shed light on this apparent contradiction, we carried out four survey experiments and a field experiment on Twitter; the results show that subtly shifting attention to accuracy increases the quality of news that people subsequently share. Together with additional computational analyses, these findings indicate that people often share misinformation because their attention is focused on factors other than accuracy-and therefore they fail to implement a strongly held preference for accurate sharing. Our results challenge the popular claim that people value partisanship over accuracy8,9, and provide evidence for scalable attention-based interventions that social media platforms could easily implement to counter misinformation online.

A Turing test of whether AI chatbots are behaviorally similar to humans.

We administer a Turing test to AI chatbots. We examine how chatbots behave in a suite of classic behavioral games that are designed to elicit characteristics such as trust, fairness, risk-aversion, cooperation, etc., as well as how they respond to a traditional Big-5 psychological survey that measures personality traits. ChatGPT-4 exhibits behavioral and personality traits that are statistically indistinguishable from a random human from tens of thousands of human subjects from more than 50 countries. Chatbots also modify their behavior based on previous experience and contexts "as if" they were learning from the interactions and change their behavior in response to different framings of the same strategic situation. Their behaviors are often distinct from average and modal human behaviors, in which case they tend to behave on the more altruistic and cooperative end of the distribution. We estimate that they act as if they are maximizing an average of their own and partner's payoffs.

A Scientist's Oath.

Data on the perceptions of scientists suggest a moderate public distrust of scientist's motivations. Bettridge et al. suggest scientist's reluctance to engage the public on controversial ethical issues may be a contributing factor. The authors propose a Scientist's Oath to send a clear message to the public about our ideals.

Election cycles and global religious intolerance.

Mass elections are key mechanisms for collective decision-making. But they are also blamed for creating intergroup enmity, particularly while they are underway; politicians use polarizing campaign strategies, and losing sides feel resentful and marginalized after results are announced. I investigate the impact of election proximity-that is, closeness to elections in time-on social cleavages related to religion, a salient form of group identity worldwide. Integrating data from ∼1.2 million respondents across 25 cross-country survey series, I find no evidence that people interviewed shortly before or after national elections are more likely to express negative attitudes toward religious outgroups than those interviewed at other times. Subgroup analysis reveals little heterogeneity, including by levels of political competition. Generalized social trust, too, is unaffected by election calendars. Elections may not pose as great a risk to social cohesion as is commonly feared.

Abstract cognitive maps of social network structure aid adaptive inference.

Social navigation-such as anticipating where gossip may spread, or identifying which acquaintances can help land a job-relies on knowing how people are connected within their larger social communities. Problematically, for most social networks, the space of possible relationships is too vast to observe and memorize. Indeed, people's knowledge of these social relations is well known to be biased and error-prone. Here, we reveal that these biased representations reflect a fundamental computation that abstracts over individual relationships to enable principled inferences about unseen relationships. We propose a theory of network representation that explains how people learn inferential cognitive maps of social relations from direct observation, what kinds of knowledge structures emerge as a consequence, and why it can be beneficial to encode systematic biases into social cognitive maps. Leveraging simulations, laboratory experiments, and "field data" from a real-world network, we find that people abstract observations of direct relations (e.g., friends) into inferences of multistep relations (e.g., friends-of-friends). This multistep abstraction mechanism enables people to discover and represent complex social network structure, affording adaptive inferences across a variety of contexts, including friendship, trust, and advice-giving. Moreover, this multistep abstraction mechanism unifies a variety of otherwise puzzling empirical observations about social behavior. Our proposal generalizes the theory of cognitive maps to the fundamental computational problem of social inference, presenting a powerful framework for understanding the workings of a predictive mind operating within a complex social world.

A community science approach to public health and public trust in science.

The coronavirus disease 2019 (COVID-19) pandemic has highlighted the importance of health literacy and trust in pandemic management. Collaborating with the community to prepare for pandemics is incredibly effective in fostering understanding and building trust in public health and scientific research.

Pursuing Safety in Social Connection: A Flexibly Fluid Perspective on Risk Regulation in Relationships.

People are fundamentally motivated to be included in social connections that feel safe, connections where they are consistently cared for and protected, not hurt or exploited. Romantic relationships have long played a crucial role in satisfying this fundamental need. This article reconceptualizes the risk-regulation model to argue that people draw on experiences from inside and outside their romantic relationships to satisfy their fundamental need to feel safe depending on others. We first review the direct relational cues (i.e., a partner's affectionate touch, responsive versus unresponsive behavior, and relative power) and indirect cues (i.e., bodily sensations, collective value in the eyes of others, and living conditions) that signal the current safety of social connection and motivate people to connect to others or protect themselves against them. We then review how people's chronic capacity to trust in others controls their sensitivity and reactivity to the safety cues. The article concludes with future research directions.

Deep models of superficial face judgments.

The diversity of human faces and the contexts in which they appear gives rise to an expansive stimulus space over which people infer psychological traits (e.g., trustworthiness or alertness) and other attributes (e.g., age or adiposity). Machine learning methods, in particular deep neural networks, provide expressive feature representations of face stimuli, but the correspondence between these representations and various human attribute inferences is difficult to determine because the former are high-dimensional vectors produced via black-box optimization algorithms. Here we combine deep generative image models with over 1 million judgments to model inferences of more than 30 attributes over a comprehensive latent face space. The predictive accuracy of our model approaches human interrater reliability, which simulations suggest would not have been possible with fewer faces, fewer judgments, or lower-dimensional feature representations. Our model can be used to predict and manipulate inferences with respect to arbitrary face photographs or to generate synthetic photorealistic face stimuli that evoke impressions tuned along the modeled attributes.

Impression management attenuates the effect of ability on trust in economic exchange.

Are competent actors still trusted when they promote themselves? The answer to this question could have far-reaching implications for understanding trust production in a variety of economic exchange settings in which ability and impression management play vital roles, from succeeding in one's job to excelling in the sales of goods and services. Much social science research assumes an unconditional positive impact of an actor's ability on the trust placed in that actor: in other words, competence breeds trust. In this report, however, we challenge this assumption. Across a series of experiments, we manipulated both the ability and the self-promotion of a trustee and measured the level of trust received. Employing both online laboratory studies (n = 5,606) and a field experiment (n = 101,520), we find that impression management tactics (i.e., self-promotion and intimidation) can substantially backfire, at least for those with high ability. An explanation for this effect is encapsuled in attribution theory, which argues that capable actors are held to higher standards in terms of how kind and honest they are expected to be. Consistent with our social attribution account, mediation analyses show that competence combined with self-promotion decreases the trustee's perceived benevolence and integrity and, in turn, the level of trust placed in that actor.