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Social media users face a tension between presenting themselves in an idealized or authentic way. Here, we explore how prioritizing one over the other impacts users’ well-being. We estimate the degree of self-idealized vs. authentic self-expression as the proximity between a user’s self-reported personality and the automated personality judgements made on the basis Facebook Likes and status updates. Analyzing data of 10,560 Facebook users, we find that individuals who are more authentic in their self-expression also report greater Life Satisfaction. This effect appears consistent across different personality profiles, countering the proposition that individuals with socially desirable personalities benefit from authentic self-expression more than others. We extend this finding in a pre-registered, longitudinal experiment, demonstrating the causal relationship between authentic posting and positive affect and mood on a within-person level. Our findings suggest that the extent to which social media use is related to well-being depends on how individuals use it. It is often tempting for social media users to present themselves in an idealized way. Here, based on analyses of a large set of Facebook profiles together with a longitudinal experiment, the authors find evidence that more authentic self-expression may be psychologically beneficial, as it is related to greater well-being.

Scientific evidence regularly guides policy decisions 1 , with behavioural science increasingly part of this process 2 . In April 2020, an influential paper 3 proposed 19 policy recommendations (‘claims’) detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms ‘physical distancing’ and ‘social distancing’. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization. Evaluation of evidence generated to test 19 proposed policy recommendations and guidance for the future.

The emergence of large language models (LLMs) has made it possible for generative artificial intelligence (AI) to tackle many higher-order cognitive tasks, with critical implications for industry, government, and labor markets. Here, we investigate whether existing, openly-available LLMs can be used to create messages capable of influencing humans’ political attitudes. Across three pre-registered experiments (total N  = 4829), participants who read persuasive messages generated by LLMs showed significantly more attitude change across a range of policies - including polarized policies, like an assault weapons ban, a carbon tax, and a paid parental-leave program - relative to control condition participants who read a neutral message. Overall, LLM-generated messages were similarly effective in influencing policy attitudes as messages crafted by lay humans. Participants’ reported perceptions of the authors of the persuasive messages suggest these effects occurred through somewhat distinct causal pathways. While the persuasiveness of LLM-generated messages was associated with perceptions that the author used more facts, evidence, logical reasoning, and a dispassionate voice, the persuasiveness of human-generated messages was associated with perceptions of the author as unique and original. These results demonstrate that recent developments in AI make it possible to create politically persuasive messages quickly, cheaply, and at massive scale. LLMs can tackle many higher-order tasks, raising the question of its ability to influence people’s political views. Across three preregistered experiments, here the authors show that LLM-generated messages can persuade people on various policy issues.

Open science practices such as posting data or code and pre-registering analyses are increasingly prescribed and debated in the applied sciences, but the actual popularity and lifetime usage of these practices remain unknown. This study provides an assessment of attitudes toward, use of, and perceived norms regarding open science practices from a sample of authors published in top-10 (most-cited) journals and PhD students in top-20 ranked North American departments from four major social science disciplines: economics, political science, psychology, and sociology. We observe largely favorable private attitudes toward widespread lifetime usage (meaning that a researcher has used a particular practice at least once) of open science practices. As of 2020, nearly 90% of scholars had ever used at least one such practice. Support for posting data or code online is higher (88% overall support and nearly at the ceiling in some fields) than support for pre-registration (58% overall). With respect to norms, there is evidence that the scholars in our sample appear to underestimate the use of open science practices in their field. We also document that the reported lifetime prevalence of open science practices increased from 49% in 2010 to 87% a decade later. Open science practices are becoming more common in the social sciences, but there is limited data on their popularity and prevalence. Here, using survey data, the authors provide evidence that levels of adoption are relatively high and underestimated by many in the field.

Anecdotal evidence indicates that people believe that morality is declining 1 , 2 . In a series of studies using both archival and original data ( n  = 12,492,983), we show that people in at least 60 nations around the world believe that morality is declining, that they have believed this for at least 70 years and that they attribute this decline both to the decreasing morality of individuals as they age and to the decreasing morality of successive generations. Next, we show that people’s reports of the morality of their contemporaries have not declined over time, suggesting that the perception of moral decline is an illusion. Finally, we show how a simple mechanism based on two well-established psychological phenomena (biased exposure to information and biased memory for information) can produce an illusion of moral decline, and we report studies that confirm two of its predictions about the circumstances under which the perception of moral decline is attenuated, eliminated or reversed (that is, when respondents are asked about the morality of people they know well or people who lived before the respondent was born). Together, our studies show that the perception of moral decline is pervasive, perdurable, unfounded and easily produced. This illusion has implications for research on the misallocation of scarce resources 3 , the underuse of social support 4 and social influence 5 . We show that the perception of moral decline is pervasive, perdurable, unfounded and easily produced, and suggest that this illusion has implications for research on the misallocation of scarce resources, the underuse of social support and social influence.

Dual-process models of altruistic choice assume that automatic responses give way to deliberation over time, and are a popular way to conceptualize how people make generous choices and why those choices might change under time pressure. However, these models have led to conflicting interpretations of behaviour and underlying psychological dynamics. Here, we propose that flexible, goal-directed deployment of attention towards information priorities provides a more parsimonious account of altruistic choice dynamics. We demonstrate that time pressure tends to produce early gaze-biases towards a person’s own outcomes, and that individual differences in this bias explain how individuals’ generosity changes under time pressure. Our gaze-informed drift-diffusion model incorporating moment-to-moment eye-gaze further reveals that underlying social preferences both drive attention, and interact with it to shape generosity under time pressure. These findings help explain existing inconsistencies in the field by emphasizing the role of dynamic attention-allocation during altruistic choice. Forcing people to choose quickly often changes pro-social behavior, but it is unclear why. Here, the authors show that under time pressure, people engage in incomplete information searches biased by concern (or lack thereof) for others, explaining effects often attributed to automatic processing.

Standard, well-established cognitive tasks that produce reliable effects in group comparisons also lead to unreliable measurement when assessing individual differences. This reliability paradox has been demonstrated in decision-conflict tasks such as the Simon, Flanker, and Stroop tasks, which measure various aspects of cognitive control. We aim to address this paradox by implementing carefully calibrated versions of the standard tests with an additional manipulation to encourage processing of conflicting information, as well as combinations of standard tasks. Over five experiments, we show that a Flanker task and a combined Simon and Stroop task with the additional manipulation produced reliable estimates of individual differences in under 100 trials per task, which improves on the reliability seen in benchmark Flanker, Simon, and Stroop data. We make these tasks freely available and discuss both theoretical and applied implications regarding how the cognitive testing of individual differences is carried out. Typical cognitive tasks can produce robust experimental effects yet not measure individual differences reliably. Here the authors use hierarchical Bayesian analysis to develop and calibrate tasks that can efficiently achieve good reliability.

In recent years, there has been a great deal of concern about the proliferation of false and misleading news on social media 1 , 2 , 3 – 4 . Academics and practitioners alike have asked why people share such misinformation, and sought solutions to reduce the sharing of misinformation 5 , 6 – 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 accuracy 8 , 9 , and provide evidence for scalable attention-based interventions that social media platforms could easily implement to counter misinformation online. Surveys and a field experiment with Twitter users show that prompting people to think about the accuracy of news sources increases the quality of the news that they share online.

The construct of values is central to many fields in the social sciences and humanities. The last two decades have seen a growing body of psychological research that investigates the content, structure and consequences of personal values in many cultures. Taking a cross-cultural perspective we review, organize and integrate research on personal values, and point to some of the main findings that this research has yielded. Personal values are subjective in nature, and reflect what people think and state about themselves. Consequently, both researchers and laymen sometimes question the usefulness of personal values in influencing action. Yet, self-reported values predict a large variety of attitudes, preferences and overt behaviours. Individuals act in ways that allow them to express their important values and attain the goals underlying them. Thus, understanding personal values means understanding human behaviour. Sagiv et al. review two decades of research into personal values. Although subjective in nature, self-reported values predict a large array of attitudes and preferences. As such, they provide invaluable insight into human behaviour.

The spread of misinformation poses a considerable threat to public health and the successful management of a global pandemic. For example, studies find that exposure to misinformation can undermine vaccination uptake and compliance with public-health guidelines. As research on the science of misinformation is rapidly emerging, this conceptual Review summarizes what we know along three key dimensions of the infodemic: susceptibility, spread, and immunization. Extant research is evaluated on the questions of why (some) people are (more) susceptible to misinformation, how misinformation spreads in online social networks, and which interventions can help to boost psychological immunity to misinformation. Implications for managing the infodemic are discussed. This Review provides an overview of the psychology of misinformation, from susceptibility to spread and interventions to help boost psychological immunity.