Experimental evidence for scale-induced category convergence across populations

Interaction structure constrains the emergence of conventions in group communication

Real-world communication frequently requires language producers to address more than one comprehender at once, yet most psycholinguistic research focuses on one-on-one communication. As the audience size grows, interlocutors face new challenges that do not arise in dyads. They must consider multiple perspectives and weigh multiple sources of feedback to build shared understanding. Here, we ask which properties of the group's interaction structure facilitate successful communication. We used a repeated reference game paradigm in which directors instructed between one and five matchers to choose specific targets out of a set of abstract figures. Across 313 games (N = 1,319 participants), we manipulated several key constraints on the group's interaction, including the amount of feedback that matchers could give to directors and the availability of peer interaction between matchers. Across groups of different sizes and interaction constraints, describers produced increasingly efficient utterances and matchers made increasingly accurate selections. Critically, however, we found that smaller groups and groups with less-constrained interaction structures ("thick channels") showed stronger convergence to group-specific conventions than large groups with constrained interaction structures ("thin channels"), which struggled with convention formation. Overall, these results shed light on the core structural factors that enable communication to thrive in larger groups.

Mechanisms of alignment: shared control, social cognition and metacognition

In dialogue, speakers process a great deal of information, take and give the floor to each other, and plan and adjust their contributions on the fly. Despite the level of coordination and control that it requires, dialogue is the easiest way speakers possess to come to similar conceptualizations of the world. In this paper, we show how speakers align with each other by mutually controlling the flow of the dialogue and constantly monitoring their own and their interlocutors' way of representing information. Through examples of conversation, we introduce the notions of shared control, meta-representations of alignment and commentaries on alignment, and show how they support mutual understanding and the collaborative creation of abstract concepts. Indeed, whereas speakers can share similar representations of concrete concepts just by mutually attending to a tangible referent or by recalling it, they are likely to need more negotiation and mutual monitoring to build similar representations of abstract concepts. This article is part of the theme issue 'Concepts in interaction: social engagement and inner experiences'.

On the social nature of abstraction: cognitive implications of interaction and diversity

The human capacity for abstraction is remarkable. We effortlessly form abstract representations from varied experiences, generalizing and flexibly transferring experiences and knowledge between contexts, which can facilitate reasoning, problem solving and learning across many domains. The cognitive process of abstraction, however, is often portrayed and investigated as an individual process. This paper addresses how cognitive processes of abstraction-together with other aspects of human reasoning and problem solving-are fundamentally shaped and modulated by online social interaction. Starting from a general distinction between convergent thinking, divergent thinking and processes of abstraction, we address how social interaction shapes information processing differently depending on cognitive demands, social coordination and task ecologies. In particular, we suggest that processes of abstraction are facilitated by the interactive sharing and integration of varied individual experiences. To this end, we also discuss how the dynamics of group interactions vary as a function of group composition; that is, in terms of the similarity and diversity between the group members. We conclude by outlining the role of cognitive diversity in interactive processes and consider the importance of group diversity in processes of abstraction. This article is part of the theme issue 'Concepts in interaction: social engagement and inner experiences'.

The network science of collective intelligence

In the last few years, breakthroughs in computational and experimental techniques have produced several key discoveries in the science of networks and human collective intelligence. This review presents the latest scientific findings from two key fields of research: collective problem-solving and the wisdom of the crowd. I demonstrate the core theoretical tensions separating these research traditions and show how recent findings offer a new synthesis for understanding how network dynamics alter collective intelligence, both positively and negatively. I conclude by highlighting current theoretical problems at the forefront of research on networked collective intelligence, as well as vital public policy challenges that require new research efforts.

The puzzle of ideography

An ideography is a general-purpose code made of pictures that do not encode language, which can be used autonomously - not just as a mnemonic prop - to encode information on a broad range of topics. Why are viable ideographies so hard to find? I contend that self-sufficient graphic codes need to be narrowly specialized. Writing systems are only an apparent exception: At their core, they are notations of a spoken language. Even if they also encode nonlinguistic information, they are useless to someone who lacks linguistic competence in the encoded language or a related one. The versatility of writing is thus vicarious: Writing borrows it from spoken language. Why is it so difficult to build a fully generalist graphic code? The most widespread answer points to a learnability problem. We possess specialized cognitive resources for learning spoken language, but lack them for graphic codes. I argue in favor of a different account: What is difficult about graphic codes is not so much learning or teaching them as getting every user to learn and teach the same code. This standardization problem does not affect spoken or signed languages as much. Those are based on cheap and transient signals, allowing for easy online repairing of miscommunication, and require face-to-face interactions where the advantages of common ground are maximized. Graphic codes lack these advantages, which makes them smaller in size and more specialized.

The Dual Challenge of Search and Coordination for Organizational Adaptation: How Structures of Influence Matter

Organizations increasingly need to adapt to challenges in which search and coordination cannot be decoupled. In response, many have experimented with “agile” and “flat” designs that dismantle traditional forms of hierarchy to harness the distributed knowledge of specialized individuals. Despite the popularity of such practices, there is considerable variation in their implementation as well as conceptual ambiguity about the underlying premise. Does effective rapid experimentation necessarily imply the repudiation of hierarchical structures of influence? We use computational models of multiagent reinforcement learning to study the effectiveness of coordinated search in groups that vary in how they influence each other’s beliefs. We compare the behavior of flat and hierarchical teams with a baseline structure without any influence on beliefs (a “crowd”) when all three are placed in the same task environments. We find that influence on beliefs—whether it is hierarchical or not—makes it less likely that agents stabilize prematurely around their own experiences. However, flat teams can engage in excessive exploration, finding it difficult to converge on good alternatives, whereas hierarchical influence on beliefs reduces simultaneous uncoordinated exploration, introducing a degree of rapid exploitation. As a result, teams that need to achieve agility (i.e., rapid satisfactory results) in environments that require coordinated search may benefit from a hierarchical structure of influence—even when the apex actor has no superior knowledge, foresight, or capacity to control subordinates’ actions.

Switching from a continuous to a discontinuous phase transition under quenched disorder

Discontinuous phase transitions are particularly interesting from a social point of view because of their relationship to social hysteresis and critical mass. In this paper, we show that the replacement of a time-varying (annealed, situation-based) disorder by a static (quenched, personality-based) one can lead to a change from a continuous to a discontinuous phase transition. This is a result beyond the state of the art, because so far numerous studies on various complex systems (physical, biological, and social) have indicated that the quenched disorder can round or destroy the existence of a discontinuous phase transition. To show the possibility of the opposite behavior, we study a multistate q-voter model, with two types of disorder related to random competing interactions (conformity and anticonformity). We confirm, both analytically and through Monte Carlo simulations, that indeed discontinuous phase transitions can be induced by a static disorder.

Topological measures for identifying and predicting the spread of complex contagions

The standard measure of distance in social networks - average shortest path length - assumes a model of "simple" contagion, in which people only need exposure to influence from one peer to adopt the contagion. However, many social phenomena are "complex" contagions, for which people need exposure to multiple peers before they adopt. Here, we show that the classical measure of path length fails to define network connectedness and node centrality for complex contagions. Centrality measures and seeding strategies based on the classical definition of path length frequently misidentify the network features that are most effective for spreading complex contagions. To address these issues, we derive measures of complex path length and complex centrality, which significantly improve the capacity to identify the network structures and central individuals best suited for spreading complex contagions. We validate our theory using empirical data on the spread of a microfinance program in 43 rural Indian villages.

Human social sensing is an untapped resource for computational social science

The ability to 'sense' the social environment and thereby to understand the thoughts and actions of others allows humans to fit into their social worlds, communicate and cooperate, and learn from others' experiences. Here we argue that, through the lens of computational social science, this ability can be used to advance research into human sociality. When strategically selected to represent a specific population of interest, human social sensors can help to describe and predict societal trends. In addition, their reports of how they experience their social worlds can help to build models of social dynamics that are constrained by the empirical reality of human social systems.

Probabilistic social learning improves the public's judgments of news veracity

The digital spread of misinformation is one of the leading threats to democracy, public health, and the global economy. Popular strategies for mitigating misinformation include crowdsourcing, machine learning, and media literacy programs that require social media users to classify news in binary terms as either true or false. However, research on peer influence suggests that framing decisions in binary terms can amplify judgment errors and limit social learning, whereas framing decisions in probabilistic terms can reliably improve judgments. In this preregistered experiment, we compare online peer networks that collaboratively evaluated the veracity of news by communicating either binary or probabilistic judgments. Exchanging probabilistic estimates of news veracity substantially improved individual and group judgments, with the effect of eliminating polarization in news evaluation. By contrast, exchanging binary classifications reduced social learning and maintained polarization. The benefits of probabilistic social learning are robust to participants' education, gender, race, income, religion, and partisanship.