New Content From Perspectives on Psychological Science

Social Psychological Perspectives on Political Polarization: Insights and Implications for Climate Change 
Jennifer Cole, Ash Gillis, Sander Van Der Linden, Mark Cohen, and Michael Vandenbergh  

Political polarization is a barrier to enacting policy solutions to global issues. Social psychology has a rich history of studying polarization, and there is an important opportunity to define and refine its contributions to the present political realities. We do so in the context of one of the most pressing modern issues: climate change. We synthesize the literature on political polarization and its applications to climate change, and we propose lines of further research and intervention design. We focus on polarization in the United States, examining other countries when literature was available. The polarization literature emphasizes two types of mechanisms of political polarization: (1) individual-level psychological processes related to political ideology and (2) group-level psychological processes related to partisan identification. Interventions that address group-level processes can be more effective than those that address individual-level processes. Accordingly, we emphasize the promise of interventions leveraging superordinate identities, correcting misperceived norms, and having trusted leaders communicate about climate change. Behavioral interventions like these that are grounded in scientific research are one of our most promising tools to achieve the behavioral wedge that we need to address climate change and to make progress on other policy issues. 

Past, Present, and Future of Human Chemical Communication
Helene Loos, Benoist Schaal, Bettina Pause, Monique Smeets, Camille Ferdenzi, S. Roberts, Jasper de Groot, Katrin Lübke, Ilona Croy, Jessica Freiherr, Moustafa Bensafi, Thomas Hummel, and Jan Havlíček  

Although chemical signaling is an essential mode of communication in most vertebrates, it has long been viewed as having negligible effects in humans. However, a growing body of evidence shows that the sense of smell affects human behavior in social contexts ranging from affiliation and parenting to disease avoidance and social threat. This article aims to (a) introduce research on human chemical communication in the historical context of the behavioral sciences; (b) provide a balanced overview of recent advances that describe individual differences in the emission of semiochemicals and the neural mechanisms underpinning their perception, that together demonstrate communicative function; and (c) propose directions for future research toward unraveling the molecular principles involved and understanding the variability in the generation, transmission, and reception of chemical signals in increasingly ecologically valid conditions. Achieving these goals will enable us to address some important societal challenges but are within reach only with the aid of genuinely interdisciplinary approaches. 

Between-Level Incongruences in Human Positivity
Shi Yu  

Humans now understand the world as multilevel in nature. For example, societies emerge from individuals, and general experiences of life consist of specific aspects and momentary episodes. A critical feature of multilevel phenomena is between-level incongruences. Applied to human positivity, this means that positive higher-level units are not simply composed of positive lower-level units and that what is good for lower-level units may not be good for higher-level units (and vice versa). For example, killjoys may improve societal well-being, personal achievement may require giving up on certain goals, and a happy life may not arise from simply happy moments. In this article, I provide examples (organized by the positive outcome of well-being and performance and by the social, structural, and temporal forms of multilevel phenomena) to show that such between-level incongruences are ubiquitous. Next, I analyze a few mechanisms that may govern the diverse instantiations of between-level incongruences in positivity. Finally, I discuss implications of this perspective, such as why positivity claims should always qualify their level of analysis; how psychological science may benefit from a multilevel, dynamical, and computational perspective; and how to improve human positivity in light of between-level incongruences. 

People Think That Social Media Platforms Do (but Should Not) Amplify Divisive Content
Steve Rathje, Claire Robertson, William Brady, and Jay Van Bavel  

Recent studies have documented the type of content that is most likely to spread widely, or go “viral,” on social media, yet little is known about people’s perceptions of what goes viral or what should go viral. This is critical to understand because there is widespread debate about how to improve or regulate social media algorithms. We recruited a sample of participants that is nationally representative of the U.S. population (according to age, gender, and race/ethnicity) and surveyed them about their perceptions of social media virality (n = 511). In line with prior research, people believe that divisive content, moral outrage, negative content, high-arousal content, and misinformation are all likely to go viral online. However, they reported that this type of content should not go viral on social media. Instead, people reported that many forms of positive content—such as accurate content, nuanced content, and educational content—are not likely to go viral even though they think this content should go viral. These perceptions were shared among most participants and were only weakly related to political orientation, social media usage, and demographic variables. In sum, there is broad consensus around the type of content people think social media platforms should and should not amplify, which can help inform solutions for improving social media. 

Struggling With Change: The Fragile Resilience of Collectives
Frank Schweitzer, Christian Zingg, and Giona Casiraghi  

Collectives form nonequilibrium social structures characterized by volatile dynamics. Individuals join or leave. Social relations change quickly. Therefore, unlike engineered or ecological systems, a resilient reference state cannot be defined. We propose a novel resilience measure combining two dimensions: robustness and adaptivity. We demonstrate how they can be quantified using data from a software-developer collective. Our analysis reveals a resilience life cycle (i.e., stages of increasing resilience are followed by stages of decreasing resilience). We explain the reasons for these observed dynamics and provide a formal model to reproduce them. The resilience life cycle allows distinguishing between short-term resilience, given by a sequence of resilient states, and long-term resilience, which requires collectives to survive through different cycles. 

Social Preferences Toward Humans and Machines: A Systematic Experiment on the Role of Machine Payoffs
Alicia von Schenk, Victor Klockmann, and Nils Köbis 

There is growing interest in the field of cooperative artificial intelligence (AI), that is, settings in which humans and machines cooperate. By now, more than 160 studies from various disciplines have reported on how people cooperate with machines in behavioral experiments. Our systematic review of the experimental instructions reveals that the implementation of the machine payoffs and the information participants receive about them differ drastically across these studies. In an online experiment (N = 1,198), we compare how these different payoff implementations shape people’s revealed social preferences toward machines. When matched with machine partners, people reveal substantially stronger social preferences and reciprocity when they know that a human beneficiary receives the machine payoffs than when they know that no such “human behind the machine” exists. When participants are not informed about machine payoffs, we found weak social preferences toward machines. Comparing survey answers with those from a follow-up study (N = 150), we conclude that people form their beliefs about machine payoffs in a self-serving way. Thus, our results suggest that the extent to which humans cooperate with machines depends on the implementation and information about the machine’s earnings.   

Suspicion About Suspicion Probes: Ways Forward 
Daniel Barrett, Steven Neuberg, and Carol Luce  

Suspicion probes are the traditional tool employed to assess the extent to which participants suspect intentional misdirection or deception within the research context. A primary reason psychologists use deception in research settings is to prevent participants from altering their behavior in light of knowing what is being studied, which could undermine internal validity as well as threaten the generalizability of findings to the real world (i.e., external validity). The present article elucidates a number of challenges with suspicion probes. A definition and framework for conceptualizing the construct of suspicion in research settings are proposed. Following a literature review, an analysis of existing evidence, and new data on the prevalence of using and reporting suspicion probes, we conclude that suspicion is a likely problem in research practice. We provide a decision guide to help researchers navigate the numerous choices involved in addressing potential suspicion and call for a combination of (a) renewed research leading to empirically supported tools and best practices and (b) systemic changes to editorial policies, funding practices, professional standards, and research training that would increase rigor and focus on this aspect of research methodology.

See related Observer article.

The Costs of Polarizing a Pandemic: Antecedents, Consequences, and Lessons
Jay Van Bavel, Clara Pretus, Steve Rathje, Philip Pärnamets, Madalina Vlasceanu, and Eric Knowles  

Polarization has been rising in the United States of America for the past few decades and now poses a significant—and growing—public-health risk. One of the signature features of the American response to the COVID-19 pandemic has been the degree to which perceptions of risk and willingness to follow public-health recommendations have been politically polarized. Although COVID-19 has proven more lethal than any war or public-health crisis in American history, the deadly consequences of the pandemic were exacerbated by polarization. We review research detailing how every phase of the COVID-19 pandemic has been polarized, including judgments of risk, spatial distancing, mask wearing, and vaccination. We describe the role of political ideology, partisan identity, leadership, misinformation, and mass communication in this public-health crisis. We then assess the overall impact of polarization on infections, illness, and mortality during the pandemic; offer a psychological analysis of key policy questions; and identify a set of future research questions for scholars and policy experts. Our analysis suggests that the catastrophic death toll in the United States was largely preventable and due, in large part, to the polarization of the pandemic. Finally, we discuss implications for public policy to help avoid the same deadly mistakes in future public-health crises. 

The Loneliness of the Odd One Out: How Deviations From Social Norms Can Help Explain Loneliness Across Cultures
Luzia Cassis Heu  

Loneliness is an important health risk, which is why it is important to understand what can cause persistent or severe loneliness. Previous research has identified numerous personal or relational risk factors for loneliness. Cultural predictors, however, have been considered less. The new framework of norm deviations and loneliness (NoDeL) proposes that social norms, which are defining features of culture, can help explain loneliness within and across cultural contexts. Specifically, people who deviate from social norms are suggested to be at an increased risk for feeling lonely because they are more likely to experience alienation, inauthenticity, lower self-worth, social rejection, relationship dissatisfaction, and/or unfulfilled relational needs. Given that social norms vary by social, geographical, and temporal context, they can furthermore be considered cultural moderators between individual-level risk factors and loneliness: Personal or relational characteristics, such as shyness or being single, may increase the risk for loneliness particularly if they do not fit social norms in a specific environment. Integrating previous quantitative and qualitative findings, I hence offer a framework (NoDeL) to predict loneliness and cultural differences in risk factors for it. Thus, the NoDeL framework may help prepare culture-sensitive interventions against loneliness. 

Facecraft: Race Reification in Psychological Research With Faces
Joel Martinez  

Faces are socially important surfaces of the body on which various meanings are attached. The widespread physiognomic belief that faces inherently contain socially predictive value is why they make a generative stimulus for perception research. However, critical problems arise in studies that simultaneously investigate faces and race. Researchers studying race and racism inadvertently engage in various research practices that transform faces with specific phenotypes into straightforward representatives of their presumed race category, thereby taking race and its phenotypic associations for granted. I argue that research practices that map race categories onto faces using bioessentialist ideas of racial phenotypes constitute a form of racecraft ideology, the dubious reasoning of which presupposes the reality of race and mystifies the causal relation between race and racism. In considering how to study racism without reifying race in face studies, this article places these practices in context, describes how they reproduce racecraft ideology and impair theoretical inferences, and then suggests counterpractices for minimizing this problem. 

Toward Understanding of the Social Hysteresis: Insights From Agent-Based Modeling
Katarzyna Sznajd-Weron, Arkadiusz Jedrzejewski, and Barbara Kamińska  

Hysteresis has been used to understand various social phenomena, such as political polarization, the persistence of the vaccination-compliance problem, or the delayed response of employees in a firm to wage incentives. The aim of this article is to show the insights that can be gained from using agent-based models (ABMs) to study hysteresis. To build up an intuition about hysteresis, we start with an illustrative example from physics that demonstrates how hysteresis manifests as collective memory. Next, we present examples of hysteresis in psychology and social systems. We then present two simple ABMs of binary decisions—the Ising model and the q-voter model—to explain how hysteresis can be observed in ABMs. Specifically, we show that hysteresis can result from the influence of various external factors present in social systems, such as organizational polices, governmental laws, or mass media campaigns, as well as internal noise associated with random changes in agent decisions. Finally, we clarify the relationship between several closely related concepts such as order–disorder transitions or bifurcation, and we conclude the article with a discussion of the advantages of ABMs. 

Information Avoidance: Past Perspectives and Future Directions
Jeremy Foust and Jennifer Taber  

In the present age of unprecedented access to information, it is important to understand how and why people avoid information. Multiple definitions of “information avoidance” exist, and key aspects of these definitions deserve attention, such as distinguishing information avoidance from (lack of) information seeking, considering the intentionality and temporal nature of information avoidance, and considering the personal relevance of the information. In this review, we provide a cross-disciplinary historical account of theories and empirical research on information avoidance and seeking, drawing from research in multiple fields. We provide a framework of antecedents of information avoidance, categorized into beliefs about the information (e.g., risk perceptions), beliefs about oneself (e.g., coping resources), and social and situational factors (e.g., social norms), noting that constructs across categories overlap and are intertwined. We suggest that research is needed on both positive and negative consequences of information avoidance and on interventions to reduce information avoidance (when appropriate). Research is also needed to better understand temporal dynamics of information avoidance and how it manifests in everyday life. Finally, comprehensive theoretical models are needed that differentiate avoidance from seeking. Research on information avoidance is quickly expanding, and the topic will only grow in importance.   

Listen to related Under the Cortex episode.

The State of Cognitive Control in Language Processing
Tal Ness, Valerie Langlois, Albert Kim, and Jared Novick  

Understanding language requires readers and listeners to cull meaning from fast-unfolding messages that often contain conflicting cues pointing to incompatible ways of interpreting the input (e.g., “The cat was chased by the mouse”). This article reviews mounting evidence from multiple methods demonstrating that cognitive control plays an essential role in resolving conflict during language comprehension. How does cognitive control accomplish this task? Psycholinguistic proposals have conspicuously failed to address this question. We introduce an account in which cognitive control aids language processing when cues conflict by sending top-down biasing signals that strengthen the interpretation supported by the most reliable evidence available. We also provide a computationally plausible model that solves the critical problem of how cognitive control “knows” which way to direct its biasing signal by allowing linguistic knowledge itself to issue crucial guidance. Such a mental architecture can explain a range of experimental findings, including how moment-to-moment shifts in cognitive-control state—its level of activity within a person—directly impact how quickly and successfully language comprehension is achieved. 

Experimental Therapeutics: Opportunities and Challenges Stemming From the National Institute of Mental Health Workshop on Novel Target Discovery and Psychosocial Intervention Development
Nancy Zucker, Gregory Strauss, Joshua Smyth, K. Suzanne Scherf, Melissa Brotman, Rhonda Boyd, Jimmy Choi, Maria Davila, Olusola Ajilore, Faith Gunning, and Julie Schweitzer  

There has been slow progress in the development of interventions that prevent and/or reduce mental-health morbidity and mortality. The National Institute of Mental Health (NIMH) launched an experimental-therapeutics initiative with the goal of accelerating the development of effective interventions. The emphasis is on interventions designed to engage a target mechanism. A target mechanism is a process (e.g., behavioral, neurobiological) proposed to underlie change in a defined clinical endpoint and through change in which an intervention exerts its effect. This article is based on discussions from an NIMH workshop conducted in February 2020 and subsequent conversations among researchers using this approach. We discuss the components of an experimental-therapeutics approach such as clinical-outcome selection, target definition and measurement, intervention design and selection, and implementation of a team-science strategy. We emphasize the important contributions of different constituencies (e.g., patients, caregivers, providers) in deriving hypotheses about novel target mechanisms. We highlight strategies for target-mechanism identification using published and hypothetical examples. We consider the decision-making dilemmas that arise with different patterns of results in purported mechanisms and clinical outcomes. We end with considerations of the practical challenges of this approach and the implications for future directions of this initiative.  

Basic Emotions or Constructed Emotions: Insights From Taking an Evolutionary Perspective
Karlijn van Heijst, Mariska Kret, and Annemie Ploeger  

The ongoing debate between basic emotion theories (BETs) and the theory of constructed emotion (TCE) hampers progress in the field of emotion research. Providing a new perspective, here we aim to bring the theories closer together by dissecting them according to Tinbergen’s four questions to clarify a focus on their evolutionary basis. On the basis of our review of the literature, we conclude that whereas BETs focus on the evolution question of Tinbergen, the TCE is more concerned with the causation of emotion. On the survival value of emotions both theories largely agree: to provide the best reaction in specific situations. Evidence is converging on the evolutionary history of emotions but is still limited for both theories—research within both frameworks focuses heavily on the causation. We conclude that BETs and the TCE explain two different phenomena: emotion and feeling. Therefore, they seem irreconcilable but possibly supplementary for explaining and investigating the evolution of emotion—especially considering their similar answer to the question of survival value. Last, this article further highlights the importance of carefully describing what aspect of emotion is being discussed or studied. Only then can evidence be interpreted to converge toward explaining emotion.  

Memory of Fictional Information: A Theoretical Framework
Pierre Gander, Kata Szita, Andreas Falck, and Robert Lowe  

Much of the information people encounter in everyday life is not factual; it originates from fictional sources, such as movies, novels, and video games, and from direct experience such as pretense, role-playing, and everyday conversation. Despite the recent increase in research on fiction, there is no theoretical account of how memory of fictional information is related to other types of memory or of which mechanisms allow people to separate fact and fiction in memory. We present a theoretical framework that places memory of fiction in relation to other cognitive phenomena as a distinct construct and argue that it is an essential component for any general theory of human memory. We show how fictionality can be integrated in an existing memory model by extending Rubin’s dimensional conceptual memory model. By this means, our model can account for explicit and implicit memory of fictional information of events, places, characters, and objects. Further, we propose a set of mechanisms involving various degrees of complexity and levels of conscious processing that mostly keep fact and fiction separated but also allow information from fiction to influence real-world attitudes and beliefs: content-based reasoning, source monitoring, and an associative link from the memory to the concept of fiction.  

A Shared Intentionality Account of Uniquely Human Social Bonding
Wouter Wolf and Michael Tomasello  

Many mechanisms of social bonding are common to all primates, but humans seemingly have developed some that are unique to the species. These involve various kinds of interactive experiences—from taking a walk together to having a conversation—whose common feature is the triadic sharing of experience. Current theories of social bonding have no explanation for why humans should have these unique bonding mechanisms. Here we propose a shared intentionality account of uniquely human social bonding. Humans evolved to participate with others in unique forms of cooperative and communicative activities that both depend on and create shared experience. Sharing experience in these activities causes partners to feel closer because it allows them to assess their partner’s cooperative competence and motivation toward them and because the shared representations created during such interactions make subsequent cooperative interactions easier and more effective.  

Building Human-Like Artificial Agents: A General Cognitive Algorithm for Emulating Human Decision-Making in Dynamic Environments
Cleotilde Gonzalez  

One of the early goals of artificial intelligence (AI) was to create algorithms that exhibited behavior indistinguishable from human behavior (i.e., human-like behavior). Today, AI has diverged, often aiming to excel in tasks inspired by human capabilities and outperform humans, rather than replicating human cognition and action. In this paper, I explore the overarching question of whether computational algorithms have achieved this initial goal of AI. I focus on dynamic decision-making, approaching the question from the perspective of computational cognitive science. I present a general cognitive algorithm that intends to emulate human decision-making in dynamic environments, as defined in instance-based learning theory (IBLT). I use the cognitive steps proposed in IBLT to organize and discuss current evidence that supports some of the human-likeness of the decision-making mechanisms. I also highlight the significant gaps in research that are required to improve current models and to create higher fidelity in computational algorithms to represent human decision processes. I conclude with concrete steps toward advancing the construction of algorithms that exhibit human-like behavior with the ultimate goal of supporting human dynamic decision-making. 

Motivated Cognition in Cooperation
Susann Fiedler, Hooman Habibnia, Alina Fahrenwaldt, and Rima Rahal  

Successful cooperation is tightly linked to individuals’ beliefs about their interaction partners, the decision setting, and existing norms, perceptions, and values. This article reviews and integrates findings from judgment and decision-making, social and cognitive psychology, political science, and economics, developing a systematic overview of the mechanisms underlying motivated cognition in cooperation. We elaborate on how theories and concepts related to motivated cognition developed in various disciplines define the concept and describe its functionality. We explain why beliefs play such an essential role in cooperation, how they can be distorted, and how this fosters or harms cooperation. We also highlight how individual differences and situational factors change the propensity to engage in motivated cognition. In the form of a construct map, we provide a visualization of the theoretical and empirical knowledge structure regarding the role of motivated cognition, including its many interdependencies, feedback loops, and moderating influences. We conclude with a brief suggestion for a future research agenda based on this compiled evidence.  

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