PNAS nexus最新文献

Affective polarization, or animosity toward opposing political groups, is a fundamentally intergroup phenomenon. Yet, prevailing explanations of it and interventions against it have overlooked the power of ingroup norm perception. To illustrate this power, we begin with evidence from 3 studies which reveal that partisans' perception of their ingroup's norm of negative attitudes toward the outgroup is exaggerated and uniquely predicts their own polarization-related attitudes. Specifically, our original data show that in predicting affective polarization (i.e. how one feels about one's partisan outgroup), the variance explained by ingroup norm perception is 8.4 times the variance explained by outgroup meta-perception. Our reanalysis of existing data shows that in predicting support for partisan violence (i.e. how strongly one endorses and is willing to engage in partisan violence), ingroup norm perception explains 52% of the variance, whereas outgroup meta-perception explains 0%. Our pilot experiment shows that correcting ingroup norm perception can reduce affective polarization. We elucidate the theoretical underpinnings of the unique psychological power of ingroup norm perception and related ingroup processes. Building on these empirical and theoretical analyses, we propose approaches to designing and evaluating interventions that leverage ingroup norm perception to curb affective polarization. We specify critical boundary conditions that deserve prioritized attention in future intervention research. In sum, scientists and practitioners cannot afford to ignore the power of ingroup norm perception in explaining and curbing affective polarization.

Evidence-based and human-centric design of digital media platforms could reduce many of the problems of misinformation, polarization, and misaligned incentives that plague both society and individual organizations. With these sorts of design changes, it may become possible to build deliberative digital media that are useful both for discussions of contentious issues and for achieving successful collective action. In this Perspective paper, we discuss several issues in which current-day social science indicates the origin of these problems and suggests methods for improvement. Finally, we analyze a popular deliberative democracy platform to illustrate how social science might enable design of next-generation digital media suitable for democratic deliberation, and in which generative artificial intelligence might be useful.

Descriptive social norms interventions, where a behavior is promoted by learning that others engage in that behavior, are a cornerstone of behavior change research and practice. Here, we examine the effect of learning about the behavior of outgroup members in a hyper-polarized context: mask-wearing during the COVID-19 pandemic. Contrary to prior findings, we find a descriptive social norm "backfire": Across three experiments, Biden supporters increased their mask-wearing intentions after being informed that most Trump supporters never wore masks. We also provide evidence consistent with a mechanism whereby this effect was driven by changes in perceptions about how negatively ingroup members view nonmask wearing. Finally, in a fourth study, Biden supporters show the traditional descriptive norms effect (rather than a backfire) from the same treatment when in a nonpolarized context: dishonesty in a coin-flipping task. These findings help to clarify why descriptive social norm interventions promote behavior change, and underscore the importance of social norms in motivating prosocial behaviors. They also suggest an update to current best practices in the design of descriptive norm interventions: in polarized contexts, it can be beneficial to publicize antisocial behavior of outgroup members.

US partisans view each other with increasing negativity. While many attribute the growth of such affective polarization to nationally cross-cutting forces, such as ideological partisan sorting or access to partisan media, others emphasize the effects of contextual and institutional forces. For the first time, we introduce and explore data sufficiently granular to fully map the extent of partisan animosity across the US states. With a massive, nationally representative survey we find that, counter to expectations, variation in affective polarization across states is relatively small, and is instead largely a function of individual-level attitudinal (but not demographic) characteristics. While elections pit regions of the country against others, our results suggest affective polarization is a national, not regional, problem, requiring national interventions.

In polarized societies, divided subgroups of people have different perspectives on a range of topics. Aiming to reduce polarization, authorities may use debunking to lend support to one perspective over another. Debunking by authorities gives all observers shared information, which could reduce disagreement. In practice, however, debunking may have no effect or could even contribute to further polarization of beliefs. We developed a cognitively inspired model of observers' rational inferences from an authority's debunking. After observing each debunking attempt, simulated observers simultaneously update their beliefs about the perspective underlying the debunked claims and about the authority's motives, using an intuitive causal model of the authority's decision-making process. We varied the observers' prior beliefs and uncertainty systematically. Simulations generated a range of outcomes, from belief convergence (less common) to persistent divergence (more common). In many simulations, observers who initially held shared beliefs about the authority later acquired polarized beliefs about the authority's biases and commitment to truth. These polarized beliefs constrained the authority's influence on new topics, making it possible for belief polarization to spread. We discuss the implications of the model with respect to beliefs about elections.

Manufacturing workers face prolonged strenuous physical activities, impacting both financial aspects and their health due to work-related fatigue. Continuously monitoring physical fatigue and providing meaningful feedback is crucial to mitigating human and monetary losses in manufacturing workplaces. This study introduces a novel application of multimodal wearable sensors and machine learning techniques to quantify physical fatigue and tackle the challenges of real-time monitoring on the factory floor. Unlike past studies that view fatigue as a dichotomous variable, our central formulation revolves around the ability to predict multilevel fatigue, providing a more nuanced understanding of the subject's physical state. Our multimodal sensing framework is designed for continuous monitoring of vital signs, including heart rate, heart rate variability, skin temperature, and more, as well as locomotive signs by employing inertial motion units strategically placed at six locations on the upper body. This comprehensive sensor placement allows us to capture detailed data from both the torso and arms, surpassing the capabilities of single-point data collection methods. We developed an innovative asymmetric loss function for our machine learning model, which enhances prediction accuracy for numerical fatigue levels and supports real-time inference. We collected data on 43 subjects following an authentic manufacturing protocol and logged their self-reported fatigue. Based on the analysis, we provide insights into our multilevel fatigue monitoring system and discuss results from an in-the-wild evaluation of actual operators on the factory floor. This study demonstrates our system's practical applicability and contributes a valuable open-access database for future research.

How does the availability of excuses for self-interested behavior impact group favoritism? We report the results of a preregistered experiment, conducted on the eve of the 2022 midterm elections, in which American political partisans made payoff distribution choices for themselves and a partner who was known to be a co-partisan or opposing partisan. Under full information, participants exhibit significant group favoritism. However, when the payoff consequences for one's partner are initially hidden, participants exploit this excuse to act selfishly regardless of who their partner is and ignorance rates are identical for in-group and out-group members. As a result, moral wiggle room has a significantly larger impact on selfish behavior for those interacting with co-partisans than opposing partisans, leading to a reduction in group favoritism.

Undemocratic practices, such as voter suppression and election interference, threaten democracies worldwide. Across four studies (N = 4,350), we find that informational and motivational factors drive Americans' support for such practices. Partisans drastically overestimate how much opponents support undemocratic practices, which decreases people's willingness to defend democracy themselves (S1-S2). One remedy for this dynamic is to inform people about the extent to which their rivals actually support democracy, but in polarized contexts, people are incurious about the true beliefs of outpartisans. To address this, we test a new method for improving democratic attitudes-changing beliefs about cross-party empathy. Empathizing across disagreements can improve connections and boost persuasion. When people learn about these valued consequences of empathic engagement, their curiosity about outpartisans increases (S3), and they choose to learn about opponents' support for democracy, which reduces their own support for undemocratic practices and politicians (S4). Our findings suggest that fostering support for democracy requires not just informational strategies but also motivational ones. The power of our combined approach comes in that-instead of presenting people with information about outpartisans-it induces them to seek out that information themselves. Together, these results highlight how cross-party empathy beliefs can increase people's curiosity about those they disagree with and disrupt processes of political escalation.

The mutualistic interaction specificity between attine ants and antibiotic-producing Actinobacteria has been controversial because Pseudonocardia strains cannot always be isolated from worker cuticles across attine ant species, while other actinobacteria can apparently replace Pseudonocardia and also inhibit growth of Escovopsis mycopathogens. Here we report that across field samples of Panamanian species: (i) Cuticular Pseudonocardia were largely restricted to species in the crown of the attine phylogeny and their appearance likely coincided with the first attines colonizing Central/North America. (ii) The phylogenetically basal attines almost always had cuticular associations with other Actinobacteria than Pseudonocardia. (iii) The sub-cuticular glands nourishing cuticular bacteria appear to be homologous throughout the phylogeny, consistent with an ancient general attine-Actinobacteria association. (iv) The basal attine species investigated always had Pseudonocardia as gut symbionts while Pseudonocardia presence appeared mutually exclusive between cuticular and gut microbiomes. (v) Gut-associated Pseudonocardia were phylogenetically ancestral while cuticular symbionts formed a derived crown group within the Pseudonocardia phylogeny. We further show that laboratory colonies often secondarily acquire cuticular Actinobacteria that they do not associate with in the field, suggesting that many previous studies were uninformative for questions of co-adaptation in the wild. An exhaustive literature survey showed that published studies concur with our present results, provided that they analyzed field colonies and that Actinobacteria were specifically isolated from worker cuticles shortly after field collection. Our results offer several testable hypotheses for a better overall understanding of attine-Pseudonocardia interaction dynamics and putative coevolution throughout the Americas.

Converging lines of preclinical and clinical research indicate that females, in stark contrast to males, display an increased prevalence of chronic pain. Females also demonstrate weaker analgesic efficacy in response to opioid therapies when compared with males. These sex-specific differences may be driven by dimorphic endogenous opioidergic responses. In rodent models, analgesia exhibited in males but not females was reversed by inhibiting endogenous opioidergic reception. In humans, the sex-specific endogenous system(s) supporting the direct attenuation of evoked pain has not been identified. To determine whether opioidergic blockade reverses self-regulated analgesia in males as compared to females, the present study combined two operationally analogous clinical trials (n = 98; 51 females and 47 males). In a double-blinded, counterbalanced study involving healthy (n = 39) and chronic low back pain (n = 59) populations, a high-dose naloxone (μ-, κ-, δ-opioid antagonist) vs. placebo-saline cross-over design (15 mg/kg bolus +0.1 mg/kg/h) tested the hypothesis that endogenous opioids mediate analgesia in males but not females. An 11-point visual analog scale (VAS) (0 = no pain; 10 = worst pain imaginable) evaluated pain ratings in response to noxious heat stimulation (49 °C; calf). After baseline pain testing, participants were randomized to a validated four-session mindfulness meditation or sham mindfulness meditation training intervention. Participants practiced their respective meditation during noxious heat, intravenous high-dose naloxone, and placebo saline, respectively. In males and females, meditation significantly lowered evoked pain during saline infusion. Intravenous naloxone inhibited analgesia in males, but pain relief was well preserved in females. The present findings indicate that endogenous opioids mediate self-regulated analgesia in males but not females and underscore the need to establish sex-specific pain therapeutics.