Perspectives on Psychological Science最新文献

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.

As society has come to rely on groups and technology to address many of its most challenging problems, there is a growing need to understand how technology-enabled, distributed, and dynamic collectives can be designed to solve a wide range of problems over time in the face of complex and changing environmental conditions-an ability we define as "collective intelligence." We describe recent research on the Transaction Systems Model of Collective Intelligence (TSM-CI) that integrates literature from diverse areas of psychology to conceptualize the underpinnings of collective intelligence. The TSM-CI articulates the development and mutual adaptation of transactive memory, transactive attention, and transactive reasoning systems that together support the emergence and maintenance of collective intelligence. We also review related research on computational indicators of transactive-system functioning based on collaborative process behaviors that enable agent-based teammates to diagnose and potentially intervene to address developing issues. We conclude by discussing future directions in developing the TSM-CI to support research on developing collective human-machine intelligence and to identify ways to design technology to enhance it.

For over a century, psychology has focused on uncovering mental processes of a single individual. However, humans rarely navigate the world in isolation. The most important determinants of successful development, mental health, and our individual traits and preferences arise from interacting with other individuals. Social interaction underpins who we are, how we think, and how we behave. Here we discuss the key methodological challenges that have limited progress in establishing a robust science of how minds interact and the new tools that are beginning to overcome these challenges. A deep understanding of the human mind requires studying the context within which it originates and exists: social interaction.

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 these global problems 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.

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.

Our beliefs are inextricably shaped through communication with others. Furthermore, even conversation we conduct in pairs may itself be taking place across a wider, connected social network. Our communications, and with that our thoughts, are consequently typically those of individuals in collectives. This has fundamental consequences with respect to how our beliefs are shaped. This article examines the role of dependence on our beliefs and seeks to demonstrate its importance with respect to key phenomena involving collectives that have been taken to indicate irrationality. It is argued that (with the benefit of hindsight) these phenomena no longer seem surprising when one considers the multiple dependencies that govern information acquisition and the evaluation of cognitive agents in their normal (i.e., social) context.

Humans operate in groups that are oftentimes nested in multilayered collectives such as work units within departments and companies, neighborhoods within cities, and regions within nation states. With psychological science mostly focusing on proximate reasons for individuals to join existing groups and how existing groups function, we still poorly understand why groups form ex nihilo, how groups evolve into complex multilayered social structures, and what explains fission-fusion dynamics. Here we address group formation and the evolution of social organization at both the proximate and ultimate level of analysis. Building on models of fitness interdependence and cooperation, we propose that socioecologies can create positive interdependencies among strangers and pave the way for the formation of stable coalitions and groups through reciprocity and reputation-based partner selection. Such groups are marked by in-group bounded, parochial cooperation together with an array of social institutions for managing the commons, allowing groups to scale in size and complexity while avoiding the breakdown of cooperation. Our analysis reveals how distinct group cultures can endogenously emerge from reciprocal cooperation, shows that social identification and group commitment are likely consequences rather than causes of group cooperation, and explains when intergroup relations gravitate toward peaceful coexistence, integration, or conflict.

Humans regularly solve complex problems in cooperative teams. A wide range of mechanisms have been identified that improve the quality of solutions achieved by those teams on reaching consensus. We argue that many of these mechanisms work via increasing the transient diversity of solutions while the group attempts to reach a consensus. These mechanisms can operate at the level of individual psychology (e.g., behavioral inertia), interpersonal communication (e.g., transmission noise), or group structure (e.g., sparse social networks). Transient diversity can be increased by widening the search space of possible solutions or by slowing the diffusion of information and delaying consensus. All of these mechanisms increase the quality of the solution at the cost of increased time to reach it. We review specific mechanisms that facilitate transient diversity and synthesize evidence from both empirical studies and diverse formal models-including multiarmed bandits, NK landscapes, cumulative-innovation models, and evolutionary-transmission models. Apparent exceptions to this principle occur primarily when problems are sufficiently simple that they can be solved by mere trial and error or when the incentives of team members are insufficiently aligned. This work has implications for our understanding of collective intelligence, problem solving, innovation, and cumulative cultural evolution.

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.

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.