New Content From Perspectives on Psychological Science

Stuck on Intergroup Attitudes: The Need to Shift Gears to Change Intergroup Behaviors
Markus Brauer  

Decades of research on how to improve intergroup relations has primarily examined ways to change prejudiced attitudes. However, this focus on negative intergroup attitudes has yielded few effective solutions. Because intergroup relations are shaped by behavior during intergroup interactions, it is necessary to identify constructs that have a strong causal impact on intergroup behavior change. In this article, we will discuss evidence showing that intergroup attitude change is neither a sufficient nor necessary cause for intergroup behavior change. Further, we describe empirical research suggesting that intergroup attitudes are difficult to change and have a limited effect on intergroup behavior. We also distinguish between constructs that primarily affect intergroup attitude change (e.g., counterstereotypical exemplars, evaluative conditioning) and constructs that primarily affect intergroup behavior change (e.g., social norms, self-efficacy). Further, we provide suggestions for future research to advance understanding of the various psychological constructs that influence intergroup behavior change, which will help us develop effective methods to improve intergroup relations. 

Better Accuracy for Better Science…Through Random Conclusions
Clintin Davis-Stober, Jason Dana, David Kellen, Sara McMullin, and Wes Bonifay  

The difficulty of conducting research with human subjects often entails limited sample sizes and small empirical effects. We demonstrate that this problem can yield patterns of results that are practically indistinguishable from flipping a coin to determine the direction of treatment effects. We use this idea of random conclusions to establish a baseline for interpreting effect size estimates, in turn producing more stringent thresholds for hypothesis testing and statistical power calculations. An examination of recent meta-analyses in psychology, neuroscience, and medicine confirms that, even if all considered effects are real, results involving small effects are indeed indistinguishable from random conclusions. 

Social Drivers and Algorithmic Mechanisms on Digital Media
Hannah Metzler and David Garcia  

On digital media, algorithms that process data and recommend content have become ubiquitous. Their fast and barely regulated adoption has raised concerns about their role in well-being both at the individual and collective levels. Algorithmic mechanisms on digital media are powered by social drivers, creating a feedback loop that complicates research to disentangle the role of algorithms and already existing social phenomena. Our brief overview of the current evidence on how algorithms affect well-being, misinformation, and polarization suggests that the role of algorithms in these phenomena is far from straightforward and that substantial further empirical research is needed. Existing evidence suggests that algorithms mostly reinforce existing social drivers, which stresses the importance of reflecting on algorithms in the larger societal context including individualism, populist politics, and climate change. We present concrete ideas and research questions to improve algorithms on digital platforms and to investigate their role in current problems and potential solutions. Finally, we discuss how the current shift from social media to more algorithmically curated media brings both risks and opportunities if algorithms are designed for individual and societal flourishing rather than short-term profit. 

When and Why Do People Accept Public-Policy Interventions? An Integrative Public-Policy-Acceptance Framework
Sonja Grelle and Wilhelm Hofmann

The successful introduction of public policies to prompt behavior change hinges on the degree to which citizens endorse the proposed policies. While there is a large body of research on psychological determinants of policy acceptance, these determinants have not yet been synthesized into an integrative framework that proposes hypotheses about their interplay. In this article, we develop a review-based, integrative public-policy-acceptance framework that introduces the desire for governmental support as a motivational foundation in public-policy acceptance. The framework traces the route from problem awareness to policy acceptance and, ultimately, policy compliance. We propose that this relationship is mediated by a desire for governmental support. We integrate numerous key variables assumed to qualify the relationship between problem awareness and the desire for governmental support, such as control attributions, trust, and value fit, as well as the relationship between the desire for governmental support and policy acceptance, such as perceived policy effectiveness, intrusiveness, and fairness. We exemplify the use of the proposed framework by applying it to climate policies.   

Conversational Silencing of Racism in Psychological Science: Toward Decolonization in Practice
Kevin Durrheim  

This article addresses a paradox between self-perceptions of psychology as a liberal, progressive, anti-racist discipline and profession and the persistent criticisms of racism and calls for decolonization. It builds on the criticisms of epistemic exclusion and white centering, arguing that white supremacy is maintained by “conversational silencing” in which the focus on doing good psychology systematically draws attention away from the realities of racism and the operation of power. The process is illustrated by investigations of disciplinary discourse around non-WEIRD psychology and on stereotyping, racism, and prejudice reduction, which constitute the vanguard of liberal scholarship in the discipline. This progressive scholarship nurtures “white ignorance,” an absence of belief about systemic racism which psychology plays a part in upholding. 

A Network Approach to Investigate the Dynamics of Individual and Collective Beliefs: Advances and Applications of the BENDING Model
Madalina Vlascenau, Ari Dyckovsky, and Alin Coman  

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 inter-dependent 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. 

What Makes Groups Emotional?
Amit Goldenberg  

When people experience emotions in a group, their emotions tend to have stronger intensity and to last longer, why is that? This question has occupied thinkers throughout history and with the use of digital media it is even more pressing today. Historically, attention has mainly focused on processes driven by the way emotions are shared between people via emotional interactions. While interactions are a major driver of group emotionality, I review empirical findings that suggest that understanding group emotionality requires a broader view which integrates two additional processes: how emotions unfold within the social infrastructure in which they are shared, and how these processes are affected by people’s cognition about emotions. I propose to summarize the literature using an infrastructure, cognition, interaction framework that contributes to a broader understanding of group emotionality, which should improve our ability to predict group emotionality and to change these emotions when they are undesired.

Lay Misperceptions of Culture as “Biological” and Suggestions for Reducing Them
Ronda Lo and Joni Sasaki  

Culture is typically studied as socialized and learned. Yet lay intuitions may hold that culture is associated with biology via perceptions of race, presenting a problem for those who study culture: there may be a mismatch between how psychologists study culture and how their research is interpreted by lay audiences. This paper is a call to researchers to recognize this mismatch as a problem and to critically evaluate the way we study culture. We first describe evidence that lay people tend to associate culture with notions of folk biology. Next, we propose three suggestions for researchers: (1) explicitly address whether biological processes are, or are not, relevant for studying culture in their work, (2) consider using multiple methods because different methods for studying culture may come with assumptions about culture as more tied to socialization or biology, and (3) represent all people as cultural by studying multiple forms of culture and by contextualizing all psychological research. Last, we provide an example for how researchers can implement these suggestions to encourage more accurate interpretations of findings. 

How Do Pandemic Policies and Communication Shape Intergroup Outcomes? Initial Findings From the COVID-19 Pandemic and Open Questions for Research and Policy
Chadly Stern and Benjamin Ruisch  

Government policies can be productive tools for protecting citizens while simultaneously forging more egalitarian societies. At the same time, history has shown that well-intentioned governmental actions, such as those meant to quell pandemics (e.g., blood-donation restrictions), can single out members of marginalized groups (e.g., men who have sex with men). How did government actions shape intergroup outcomes during the COVID-19 pandemic? Here, we draw from emerging research to provide informed conjectures regarding whether and how government actions affected stereotypes (e.g., beliefs about gender), prejudice (e.g., anti-Asian bias), and intergroup violence (e.g., hate crimes against Asian individuals) during the COVID-19 pandemic. We discuss research examining the impact of policies intended to curb the spread of the disease, and we consider possible effects of the strategies used to communicate about the virus. Further, we highlight open questions regarding how and why pandemic policies and communication shape intergroup outcomes, propose key directions for future research, and note possible implications for future development of policy and communication strategies. 

Psychological AI: Designing Algorithms Informed by Human Psychology
Gerd Gigerenzer

Psychological artificial intelligence (AI) applies insights from psychology to design computer algorithms. Its core domain is decision-making under uncertainty, that is, ill-defined situations that can change in unexpected ways, rather than well-defined, stable problems such as chess and Go. Psychological theories about heuristic processes under uncertainty can provide possible insights. I provide two illustrations. The first shows how recency—the human tendency to rely on the most recent information and ignore base rates—can be built into a simple algorithm that predicts the flu substantially better than did Google Flu Trends’ big-data algorithms. The second uses a result from memory research—the paradoxical effect that making numbers less precise increases recall—in the design of algorithms that predict recidivism. These case studies provide an existence proof that psychological AI can help design efficient and transparent algorithms. 

Human Crowds as Social Networks: Collective Dynamics of Consensus and Polarization
Willam H. Warren, J. Benjamin Falandays, Kei Yoshida, Trenton D. Wirth, and Brian A. Free  

A ubiquitous type of collective behavior and decision-making is the coordinated motion of bird flocks, fish schools, and human crowds. Collective decisions to move in the same direction, turn right or left, or split into subgroups arise in a self-organized fashion from local interactions between individuals, without central plans or designated leaders. Strikingly similar phenomena of consensus (collective motion), clustering (subgroup formation), and bipolarization (splitting into extreme groups) are also observed in social networks. As we developed models of crowd dynamics and analyzed crowd networks, we found ourselves going down the same path as models of opinion dynamics in social networks. In this paper, we draw out the parallels between human crowds and social networks. We show that models of crowd dynamics and opinion dynamics have a similar mathematical form and generate analogous phenomena in multi-agent simulations. We suggest that they can be unified by a common collective dynamics, which may be extended to other psychological collectives. Models of collective dynamics thus offer a means to account for collective behavior and collective decisions without appealing to a priori mental structures. 

Shifting the Level of Selection in Science
Leo Tiokhin, Karthik Panchanathan, Paul E. Smaldino, and Daniël Lakens  

Criteria for recognizing and rewarding scientists primarily focus on individual contributions. This creates a conflict between what is best for scientists’ careers and what is best for science. In this paper, we show how the theory of multilevel selection provides conceptual tools for modifying incentives to better align individual and collective interests. A core principle is the need to account for indirect effects by shifting the level at which selection operates, from individuals to the groups in which individuals are embedded. This principle is used in several fields to improve collective outcomes, including animal husbandry, team sports, and professional organizations. Shifting the level of selection has the potential to ameliorate several problems in contemporary science, including accounting for scientists’ diverse contributions to knowledge generation, reducing individual-level competition, and promoting specialization and team science. We discuss the difficulties associated with shifting the level of selection and outline directions for future development in this domain.   

Polarization and the Psychology of Collectives
Simon A. Levin and Elke U. Weber 

Achieving global sustainability in the face of climate change, pandemics, and other global systemic threats will require collective intelligence and collective action beyond what we are currently experiencing. Increasing polarization within nations and populist trends that undercut international cooperation make the problem even harder. Allegiance within groups is often strengthened because of conflict among groups, leading to a form of polarization termed “affective.” Hope for addressing the global problems mentioned above will require recognition of the commonality in threats facing all groups, collective intelligence that integrates relevant inputs from all sources but fights misinformation, and coordinated cooperative collective action. Elinor Ostrom’s notion of polycentric governance, involving centers of decision-making from the local to the global in a complex interacting framework, may provide a possible pathway to achieve these goals. 

The Evolution of Developmental Theories Since Piaget: A Metaview
Philippe Rochat  

History counts and cannot be overlooked. As a case in point, the origins of major theoretical tensions in the field of developmental psychology are traced back to Piaget (1896-1980), who paved the way to major discoveries regarding the origins and development of cognition. His theory framed much of the new ideas on early cognitive development that emerged in the 1970s, in the footsteps of the 1960s’ cognitive revolution. Here, I retrace major conceptual changes since Piaget and provide a meta-view on empirical findings that may have triggered the call for such changes. Nine theoretical views and intuitions are identified, all in strong reaction to some or all of the four cornerstone assumptions of Piaget’s developmental account (i.e., action realism, domain generality, stages, and late representation). As a result, new and more extreme stances are now taken in the nature vs. nurture debate. These stances rest on profoundly different, often clashing theoretical intuitions that keep shaping developmental research since Piaget. 

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