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An illustration of the social-interactionist framework proposed in the current article. Psychological phenomena and variables of interest in experimental investigations are shown separately for each of the three phases of the proposed framework: individual level, dyadic level, and network level. In the top row, the blue arrows indicate that participants are exposed to a stimulus (e.g., experienced event, story, hypothetical scenario), the black arrows represent conversations among participants, and the red numbers indicate the sequencing of the conversations in a conversational network. RIF = retrieval-induced forgetting, SE = strengthening effect.
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The formation of collective memories, emotions, and beliefs is a fundamental characteristic of human communities.These emergent outcomes are thought to be the result of a dynamical system of communicative interactions among individuals. But despite recent psychological research on collective phenomena, no programmatic framework to
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... a loss frame is cognitively "stickier" than a gain frame (Ledgerwood & Boydstun, 2014) might impact collective decision making if the majority of individuals in a community show the same bias. Similar conjectures apply to other psychological phenomena that have been widely investigated by cognitive and social psychologists (Kahneman, 2011; see Fig. 3 for an overview of existing research using a social-interactionist ...
Context 2
... a loss frame is cognitively "stickier" than a gain frame (Ledgerwood & Boydstun, 2014) might impact collective decision making if the majority of individuals in a community show the same bias. Similar conjectures apply to other psychological phenomena that have been widely investigated by cognitive and social psychologists (Kahneman, 2011; see Fig. 3 for an overview of existing research using a social-interactionist ...
Citations
... In affective sciences, the development of this idea is also related to the theories of human cognition that stress the socially extended and distributed nature of mental processes (Gallagher, 2013), including emotions (Krueger & Szanto, 2016;Krueger, 2014;Slaby, 2014;Huebner, 2011). Furthermore, research on collective emotion fits into the recent development of integrative research programs in neuroscience and psychology, which span multiple levels of analysis to elucidate the bases of collective cognition (Vlasceanu et al., 2018;Sliwa, 2021). ...
Research on collective emotion spans social sciences, psychology and philosophy. There are detailed case studies and diverse theories of collective emotion. However, experimental evidence regarding the universal characteristics, antecedents and consequences of collective emotion remains sparse. Moreover, current research mainly relies on emotion self-reports, accounting for the subjective experience of collective emotion and ignoring their cognitive and physiological bases. In response to these challenges, we argue for experimental research on collective emotion. We start with an overview of theoretical frameworks to identify a set of three characteristics of collective emotion. Based on research in cognitive and affective sciences, we then examine the corresponding candidate mechanisms. Finally, we highlight outstanding questions, review experimental evidence, and suggest ideas for future experimental research.
... In addition, it is important to note that social interactions not only shape individual memories but also the features of the social network, which, in turn, affect the formation of collective memories within a community. (Hirst et al., 2018;Rajaram & Pereira-Pasarin, 2010;Vlasceanu et al., 2018). For instance, networks characterized by closely interconnected clusters of individuals exhibit a higher likelihood of forming convergent collective memories, whereas networks consisting of sparsely connected clusters tend to show less memory convergence across the members within the community (Coman et al., 2016). ...
Societal structures and theoretical models of memory organization share network-like features, suggesting potential mutual insights into how information spreads and shapes collective memories. Here, we used experimental manipulations of the topological structure in lab-created community networks during a computer-mediated conversational recall task of lists of words from a DRM paradigm to test a central premise from the spreading of activation account in cognitive psychology: the emergence of true and false memories. We hypothesized that social network structure, whether clustered or not, would influence the formation of true and false memories. We found that information exchange promoted true memories in clustered networks by reinforcing the mnemonic convergence of the community members’ memories. Conversely, nonclustered networks lead to a greater number of false memories by increasing widespread cross-activation of nonoverlapping memories, blurring the boundaries between true and false memories. Current findings provide empirical evidence that mnemonic spreading within the social network influenced the emergence of true and false memories and highlight the dynamic interplay between network topology, memory dynamics, and collective knowledge evolution, shedding light on memory processes in both individual and social contexts.
... Although this empirical work has typically focused its analytical eye on one-off conversational interactions, the effects of communication on mnemonic convergence have at times been examined in broader contexts, for instance, across social networks and across time (Coman et al. 2016; Momennejad et al. 2019;Momennejad, 2021;Vlasceanu et al., 2018). As methodological limitations often constrain the extent to which researchers can examine such extended dynamics experimentally, simulations have become a critical tool in the collective memory researcher's arsenal (e.g. ...
... Understanding the long-term dynamics by which communicating networks of humans either converge or fail to converge onto shared cognitive representations remains a primary challenge in the scientific study of collective memory. The topic is complex, in that it requires the integration of insights at multiple levels of analysis, from memory processes in individual cognition, to conversational influences on memory, to the propagation of information across different social network structures and temporal dynamics (Momennejad, 2021;Vlasceanu et al., 2018). Methodological and analytical challenges arise in attempting to capture these complex dynamics in traditional laboratory settings. ...
Through their selective rehearsal, Central Speakers can reshape collective memory in a group of listeners, both by increasing accessibility for mentioned items (shared practice effects) and decreasing relative accessibility for related but unmentioned items (socially shared retrieval induced forgetting, i.e. SSRIF). Subsequent networked communication in the group can further modify these mnemonic influences. Extant empirical work has tended to examine such downstream influences on a Central Speaker’s mnemonic influence following a relatively limited number of interactions – often only two or three conversations. We develop a set of Markov chain simulations to model the long-term dynamics of such conversational remembering across a variety of group types, based on reported empirical data. These models indicate that some previously reported effects will stabilize in the long-term collective memory following repeated rounds of conversation. Notably, both shared practice effects and SSRIF persist into future steady states. However, other projected future states differ from those described so far in the empirical literature, specifically: the amplification of shared practice effects in communicational versus solo remembering non-conversational groups, the relatively transient impact of social (dis)identification with a Central Speaker, and the sensitivity of communicating networks to much smaller mnemonic biases introduced by the Central Speaker than groups of individual rememberers. Together, these simulations contribute insights into the long-term temporal dynamics of collective memory by addressing questions difficult to tackle using extant laboratory methods and provide concrete suggestions for future empirical work.
... However, as is common in many research areas, work on collective cognition has developed in parallel streams in different subfields, resulting in similar words being used to describe substantively different phenomena. For instance, work in cognitive and social psychology has defined collective memory (e.g., Vlasceanu et al., 2018) and shared attention (Shteynberg, 2015) according to whether the cognition of different group members is the same. By contrast, research in organizational psychology on collective attention (Woolley, Chow, et al., 2023) or transactive memory (Ren & Argote, 2011) has evaluated the strength of these collective cognitive systems according to the total capacity of information they can effectively manage, which is enhanced when the content of members' cognition is mostly unique. ...
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.
... When people know that others have seen and approved of particular viewpoints, they are more likely to adopt those viewpoints themselves (Vlasceanu & Coman, 2022). One reason for this is that when stories propagate extensively among individuals, for example, through conversations, the communities converge on the conveyed beliefs and intentions (Vlasceanu et al., 2018). In studies on this process, participants read stories and then individually recalled them, after which they engaged in several rounds of joint recollections as part of conversational social networks. ...
Stories have played a central role in human social and political life for thousands of years. Despite their ubiquity in culture and custom, however, they feature only peripherally in formal government policymaking. Government policy has tended to rely on tools with more predictable responses—incentives, transfers, and prohibitions. We argue that stories can and should feature more centrally in government policymaking. We lay out how stories can make policy more effective, specifying how they complement established policy tools. We provide a working definition of stories’ key characteristics, contrasting them with other forms of communication. We trace the evolution of stories from their ancient origins to their role in mediating the impact of modern technologies on society. We then provide an account of the mechanisms underlying stories’ impacts on their audiences. We conclude by describing three functions of stories— learning, persuasion, and collective action.
... We chose the categories and names that are most relevant to political polarization, interventions, and existing literature on those topics. Our definition of group-level processes does not include collective emotions or attitudes (Hoover et al., 2021;Karimi-Malekabadi et al., 2021;Vlasceanu et al., 2018), in which groups of individuals have a shared psychological experience, and it also does not include processes resulting from punishment and rewards, termed external processes by Gifford et al. (2011). ...
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 refine and define 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 is available. The polarization literature emphasizes two types of mechanisms: individual-level psychological processes related to political ideology and group-level psychological processes related to partisan identification. We highlight the potential intervention strategies of circumventing solution aversion, leveraging superordinate identities, correcting misperceived norms, and having trusted experts and politicians communicate about climate change. Interventions that address group-level processes can be more effective than those that address individual-level processes. These areas of research and intervention development are particularly important given that behavioral interventions grounded in scientific research are one of our most promising tools to achieve the behavioral wedge we need to address climate change and to make progress on other policy issues.
... decisions made by agents within the modelled environment; and not based upon edits to their rules of interaction according to a formulaic procedure(Vlasceanu, Enz & Coman, 2018.) Thus, an agent-based model can theoretically contain an abundance of real-world input data to substitute for written rules of interaction and produce near to 'real life' simulations without depending on interaction-heavy details to substitute for the scarcity of accurate knowledge regarding the efficacy of those rules or the further complexity of the linked global dynamics(Narzisi et al. 2006, Chu, 2015.This makes agent based simulations particularly flexible for situations which may require a coupling process (figure 2.7). ...
The central concerns of this thesis have been to contribute to modelling science and the understanding of flood event dynamics by coupling hydrological models of inundation to agent-based ones of individual and group response, providing a perspective on the nature of micro to macro system scale interactions that generate disasters or lead to their avoidance. To support this, two bespoke modelling systems were developed to examine the dynamics of such interactions and were applied in two key case studies for the UK and Japan. This supported in the identification of drivers that describe the historical evolution of the landscape, economy, and built environment, to provide the boundary conditions for flood event dynamics through historical land use analysis, modelling, and first order conceptualisation. In doing this, the quantitative endeavours of the modelling exercises are consolidated by a demonstrated engagement with theories and concepts from beyond the traditional boundaries of geographic science that deepen the understanding of human-environment relations by promoting conversations ‘across the divides’ in human and physical geography around the core concepts of materiality, agency, emergence, and sustainability. The results here presented form a dialectic of interdisciplinarity and provide metrics that can be used to support the assessment of future sustainability in dynamic urban environments subject to naturally changing conditions.
... These macro-level emotions are often called collective emotions , and they represent the aggregation of emotions of a certain collective in response to a specific situation as it unfolds over time. Research on collective emotions has received increased attention in the past few years as part of a broader realization that macro psychological processes such as collective memory (Vlasceanu et al., 2018), collective attention (Shteynberg, 2015) and collective intelligence (Woolley et al., 2010) can capture unique aspects of social behavior and therefore deserve specific attention. Thus far, however, growing research on collective emotion has focused on emotion generation, paying almost no attention to whether and to what extent collective emotions can be regulated. ...
When we think of emotion and emotion regulation, we typically think of them as processes occurring at the individual level. Even when emotions are experienced by multiple people who interact with each other, analysis is typically centered around individual-level processes. Recently, however, there is a growing realization that there is unique value in examining emotions not only at the individual, micro-level, but also at the collective, macro-level. These macro-level emotions are often called collective emotions (Goldenberg, Garcia, Halperin, & Gross, 2020), and they represent the aggregation of emotions of a certain collective in response to a specific situation as it unfolds over time. Research on collective emotions has received increased attention in the past few years as part of a broader realization that macro psychological processes such as collective memory (Vlasceanu et al., 2018), collective attention (Shteynberg, 2015) and collective intelligence (Woolley et al., 2010) can capture unique aspects of social behavior and therefore deserve specific attention. Thus far, however, growing research on collective emotion has focused on emotion generation, paying almost no attention to whether and to what extent collective emotions can be regulated. The current chapter represents an attempt to explore the concept of collective emotion regulation. In light of the lack of existing empirical on this topic I have four goals in this paper. First, to define collective emotion regulation. Second, to define the notion of emotion regulation. Third, to review some of the strategies in which collective emotion can be regulated. Fourth, to discuss important future directions for research on collective emotion regulation.
... Cooperation becomes more complex as the group size grows. Understanding how psychological phenomena scales from individuals to dyads to social networks is central to understanding various collective-level behaviors (Vlasceanu, Enz, & Coman, 2018). For example, tracking reputational information is crucial in social network based cooperation and relies heavily on the ability to remember who did what in the past (or what you've heard about people's behavior through gossip). ...
Cooperation occurs at all stages of human life and is necessary for small groups and large-scale societies alike to emerge and thrive. This chapter bridges research in the fields of cognitive neuroscience, neuroeconomics, and social psychology to help understand group cooperation. We present a value-based framework for understanding cooperation, integrating neuroeconomic models of decision-making with psychological and situational variables involved in cooperative behavior, particularly in groups. According to our framework, the ventromedial prefrontal cortex serves as a neural integration hub for value computation during cooperative decisions, receiving inputs from various neuro-cognitive processes such as attention, affect, memory, and learning. We describe factors that directly or indirectly shape the value of cooperation decisions, including cultural contexts and social norms, personal and social identity, and intergroup relations. We also highlight the role of economic, social, and cultural institutions in shaping cooperative behavior. We discuss the implications for future research on cooperation.
... Our work (and the work of our colleagues) indicates that the answer is yes. In one instance, Vlasceanu et al. (2018) advocated that individuals' cognitive capacities, such as encoding and recalling memories, have emergent properties at the community level (i.e., "cognition in social context"). Accordingly, as individuals interact with others in their social networks, their individual memories synchronize to shape societal collective memory formation and vice versa. ...
Titone and Tiv's Systems Theory of Bilingualism aims to “urge cognitive scientists and neuroscientists to better embrace sociolinguistic and sociocultural experiences as part of their theoretical and empirical purview”. No stronger case for such a framework can be found than in studies of developmental aspects of bilingualism. In fact, inclusion of sociocultural variation has been a common feature of many studies of dual language learning for some time, especially in studies of pre-school and early-school-age learners (see Genesee, 2019, for a review). Young dual language learners – neonates, infants and toddlers, exposed to more than one language – begin life as virtual tabula rasa and are capable of learning any language with facility. As a result, their language acquisition is necessarily tied to the sociocultural parameters of the learning environments they are exposed to. Moreover, as Titone and Tiv emphasize, the immediate and ultimate purpose of language learning is communication. Thus, young dual language learners must be attuned to and acquire the sociocultural constraints and norms for communicating effectively in more than one language. While sociocultural variation is important for understanding language acquisition even among monolingual children living in relatively homogeneous monocultural environments, sociocultural variation is inherent and, indeed, enhanced in the learning environments of children acquiring more than one language insofar as each language is intimately linked with different socio-cultural parameters. Sociocultural variation can be important for understanding bilingualism in yet another way. In studies of the neuro-cognitive consequences of bilingualism, it has been reported that there is a more pronounced association between dual language exposure and activation of certain brain areas among adolescent bilinguals from relatively low SES backgrounds than bilinguals from relatively high SES backgrounds (Brito & Noble, 2018). Inclusion of sociocultural variation in research on the effects of bilingualism can reveal a more nuanced view than emerges if such variation is not considered.
