Perspectives on Psychological Science最新文献

We present a conceptual framework of situational moderators of gender/sex effects in negotiation, risk-taking, and leadership-three masculine-stereotypic domains associated with gender/sex gaps in pay and authority. We propose that greater situational ambiguity and higher relevance and salience of gender/sex increase the likelihood of gender/sex-linked behaviors in these domains. We argue that greater ambiguity increases the extent to which actors and audiences must search inwardly (e.g., mental schema, past experience) or outwardly (e.g., social norms) for cues on how to behave or evaluate a situation and thereby widens the door for gender/sex-linked influences. Correspondingly, we propose that gender/sex effects on behavior and evaluations in these domains will be more likely when gender/sex is more relevant and salient to the setting or task. We propose further that these two situational moderators may work jointly or interactively to influence the likelihood of gender/sex effects in negotiation, risk-taking, and leadership. We conclude by discussing applications of our conceptual framework to psychological science and its translation to practice, including directions for future research.

The community-of-knowledge framework explains the extraordinary success of the human species, despite individual members' demonstrably shallow understanding of many topics, by appealing to outsourcing. People follow the cues of members of their community because understanding of phenomena is generally distributed across the group. Typically, communities do possess the relevant knowledge, but it is possible in principle for communities to send cues despite lacking knowledge-a weakness in the system's design. COVID-19 in the United States offered a natural experiment in collective-knowledge development because a novel phenomenon arrived at a moment of intense division in political partisanship. We review evidence from the pandemic showing that the thought leaders of the two partisan groups sent radically different messages about COVID, which were, in turn, reinforced by close community members (family, friends, etc.). We show that although actual understanding of the individual plays a role in a key COVID-mitigation behavior (vaccination), it plays a smaller role than perceived understanding of thought leaders and beliefs about COVID-related behaviors of close community members. We discuss implications for theory and practice when all communities are in the same epistemic circumstance-relying on the testimony of others.

Identifying successful approaches for reducing the belief and spread of online misinformation is of great importance. Social media companies currently rely largely on professional fact-checking as their primary mechanism for identifying falsehoods. However, professional fact-checking has notable limitations regarding coverage and speed. In this article, we summarize research suggesting that the "wisdom of crowds" can be harnessed successfully to help identify misinformation at scale. Despite potential concerns about the abilities of laypeople to assess information quality, recent evidence demonstrates that aggregating judgments of groups of laypeople, or crowds, can effectively identify low-quality news sources and inaccurate news posts: Crowd ratings are strongly correlated with fact-checker ratings across a variety of studies using different designs, stimulus sets, and subject pools. We connect these experimental findings with recent attempts to deploy crowdsourced fact-checking in the field, and we close with recommendations and future directions for translating crowdsourced ratings into effective interventions.

Changing entrenched beliefs to alter people's behavior and increase societal welfare has been at the forefront of behavioral-science research, but with limited success. Here, we propose a new framework of characterizing beliefs as a multidimensional system of interdependent mental representations across three cognitive structures (e.g., beliefs, evidence, and perceived norms) that are dynamically influenced by complex informational landscapes: the BENDING (Beliefs, Evidence, Norms, Dynamic Information Networked Graphs) model. This account of individual and collective beliefs helps explain beliefs' resilience to interventions and suggests that a promising avenue for increasing the effectiveness of misinformation-reduction efforts might involve graph-based representations of communities' belief systems. This framework also opens new avenues for future research with meaningful implications for some of the most critical challenges facing modern society, from the climate crisis to pandemic preparedness.