Perspectives on Psychological Science最新文献

Collective dynamics play a key role in everyday decision-making. Whether social influence promotes the spread of accurate information and ultimately results in adaptive behavior or leads to false information cascades and maladaptive social contagion strongly depends on the cognitive mechanisms underlying social interactions. Here we argue that cognitive modeling, in tandem with experiments that allow collective dynamics to emerge, can mechanistically link cognitive processes at the individual and collective levels. We illustrate the strength of this cognitive computational approach with two highly successful cognitive models that have been applied to interactive group experiments: evidence-accumulation and reinforcement-learning models. We show how these approaches make it possible to simultaneously study (a) how individual cognition drives social systems, (b) how social systems drive individual cognition, and (c) the dynamic feedback processes between the two layers.

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. Although interactions are a major driver of group emotionality, I review empirical findings that suggest that understanding group emotionality requires a broader view that 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.

Global climate change, the COVID-19 pandemic, and the spread of misinformation on social media are just a handful of highly consequential problems affecting society. We argue that the rough contours of many societal problems can be framed within a "wisdom of crowds" perspective. Such a framing allows researchers to recast complex problems within a simple conceptual framework and leverage known results on crowd wisdom. To this end, we present a simple "toy" model of the strengths and weaknesses of crowd wisdom that easily maps to many societal problems. Our model treats the judgments of individuals as random draws from a distribution intended to represent a heterogeneous population. We use a weighted mean of these individuals to represent the crowd's collective judgment. Using this setup, we show that subgroups have the potential to produce substantively different judgments and we investigate their effect on a crowd's ability to generate accurate judgments about societal problems. We argue that future work on societal problems can benefit from more sophisticated, domain-specific theory and models based on the wisdom of crowds.

Many life-influencing social networks are characterized by considerable informational isolation. People within a community are far more likely to share beliefs than people who are part of different communities. The spread of useful information across communities is impeded by echo chambers (far greater connectivity within than between communities) and filter bubbles (more influence of beliefs by connected neighbors within than between communities). We apply the tools of network analysis to organize our understanding of the spread of beliefs across modularized communities and to predict the effect of individual and group parameters on the dynamics and distribution of beliefs. In our Spread of Beliefs in Modularized Communities (SBMC) framework, a stochastic block model generates social networks with variable degrees of modularity, beliefs have different observable utilities, individuals change their beliefs on the basis of summed or average evidence (or intermediate decision rules), and parameterized stochasticity introduces randomness into decisions. SBMC simulations show surprising patterns; for example, increasing out-group connectivity does not always improve group performance, adding randomness to decisions can promote performance, and decision rules that sum rather than average evidence can improve group performance, as measured by the average utility of beliefs that the agents adopt. Overall, the results suggest that intermediate degrees of belief exploration are beneficial for the spread of useful beliefs in a community, and so parameters that pull in opposite directions on an explore-exploit continuum are usefully paired.

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 opinion formation. 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 article, 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 multiagent 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.

Human societies are complex systems and as such have tipping points. They can rapidly transit from one mode of operation to another and thereby change the way they function as a whole. Such transitions appear as financial or economic crises, rapid swings in collective opinion, political regime shifts, or revolutions. In physics collective transitions are known as phase transitions; for example, water exists in states of liquid, ice, and vapor. A few variables determine which state is realized: temperature, pressure, and volume. For social systems it is less clear what determines collective social states. A better understanding of social tipping points would allow us to tackle some of the big challenges more systematically, such as polarization, loss of social cohesion, fragmentation, or the green transition. The physics concept of universality might be key to understanding some tipping points in human societies and why agent-based models (ABMs) might make sense for identifying the transition points. If universality exists in social systems there is hope that relatively simple ABMs will be sufficient for understanding collective social systems in transition; if it does not exist, highly detailed computational models will be unavoidable. Both are possible. Both need new forms of collaboration between the social and natural sciences, and new types of data will be essential.

How should romantic-relationship quality be approached psychometrically? This is a complicated theoretical and methodological challenge that we begin to address through three studies. In Study 1a, we identified 25 distinct romantic-relationship categories among 754 items from 26 romantic-relationship-quality instruments with a weak Jaccard index (0.38), indicating that the scales' item content was extremely heterogeneous. Study 1b then demonstrated limited structure validity evidence in 43 scale-development-validation articles of 23 of these 26 instruments. Finally, Study 2 surveyed 587 French-speaking participants in a romantic relationship on romantic-relationship quality. Applying a network-based model, we identified four dimensions, three of which are central to relationship quality. The inferences were mostly limited to French-speaking, monogamous, heterosexual women. To resolve challenges detected in the literature, we recommend a multicountry qualitative approach, more diverse sampling, better definitions of romantic-relationship quality, and a dynamic-systems approach to measuring romantic-relationship quality.

How important is the timing of the pretreatment evaluation? If we consider mental health to be a relatively fixed condition, the specific timing (e.g., day, hour) of the evaluation is immaterial and often determined on the basis of technical considerations. Indeed, the fundamental assumption underlying the vast majority of psychotherapy research and practice is that mental health is a state that can be captured in a one-dimensional snapshot. If this fundamental assumption, underlying 80 years of empirical research and practice, is incorrect, it may help explain why for decades psychotherapy failed to rise above the 50% efficacy rate in the treatment of mental-health disorders, especially depression, a heterogeneous disorder and the leading cause of disability worldwide. Based on recent studies suggesting within-individual dynamics, this article proposes that mental health and its underlying therapeutic mechanisms have underlying intrinsic dynamics that manifest across dimensions. Computational psychotherapy is needed to develop individual-specific pretreatment animated profiles of mental health. Such individual-specific animated profiles are expected to improve the ability to select the optimal treatment for each patient, devise adequate treatment plans, and adjust them on the basis of ongoing evaluations of mental-health dynamics, creating a new understanding of therapeutic change as a transition toward a more adaptive animated profile.

Psychological science tends to treat subjective well-being and happiness synonymously. We start from the assumption that subjective well-being is more than being happy to ask the fundamental question: What is the ideal level of happiness? From a cross-cultural perspective, we propose that the idealization of attaining maximum levels of happiness may be especially characteristic of Western, educated, industrial, rich, and democratic (WEIRD) societies but less so for others. Searching for an explanation for why "happiness maximization" might have emerged in these societies, we turn to studies linking cultures to their eco-environmental habitat. We discuss the premise that WEIRD cultures emerged in an exceptionally benign ecological habitat (i.e., faced relatively light existential pressures compared with other regions). We review the influence of the Gulf Stream on the Northwestern European climate as a source of these comparatively benign geographical conditions. We propose that the ecological conditions in which WEIRD societies emerged afforded them a basis to endorse happiness as a value and to idealize attaining its maximum level. To provide a nomological network for happiness maximization, we also studied some of its potential side effects, namely alcohol and drug consumption and abuse and the prevalence of mania. To evaluate our hypothesis, we reanalyze data from two large-scale studies on ideal levels of personal life satisfaction-the most common operationalization of happiness in psychology-involving respondents from 61 countries. We conclude that societies whose members seek to maximize happiness tend to be characterized as WEIRD, and generalizing this across societies can prove problematic if adopted at the ideological and policy level.