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As a foundation for social interaction, interpersonal coordination is facilitated by positive social qualities (e.g., cooperation), but undermined in negative contexts (e.g., conflict). Exactly how social factors shape coordination is less clear. Previous literature notes that the way people attend to others impacts how interactions unfold. It is possible therefore, that patterns of social attention also govern coordination. We examined this proposition by using virtual reality to investigate how attentional focus (self vs. other) and partner gaze (direct vs. averted) influence the spontaneous emergence of coordination. The results indicated that: (i) coordination was enhanced in the other (cf. self) focus condition; (ii) coordination was diminished in the averted (cf. direct) gaze condition. These findings suggest that changes in social attention impact interpersonal coordination. More broadly, this work provides further evidence that the emergence of interpersonal coordination fluctuates as a function of social context.

Social norms are a hallmark of human social intelligence, yet the reasoning processes involved in norm formation have been difficult to capture with traditional modeling frameworks. We developed a computational model of norm formation as joint planning via theory-of-mind. The model is designed to capture the distinctively human ability to flexibly develop more complex norms in more complex situations, via simulation of joint decision-making with other agents over an extended time horizon. We evaluated the predictions of the model against participant interactions in a 2-player iterated decision-making task. Across 3 conditions our model captured the way participants balanced joint reward, fairness, and complexity when forming norms.

Recent research has identified substantial individual differences in how people solve value-based tasks. Here, we examine such differences in the motivational Go/NoGo task, which orthogonalizes action and valence, using open-source data from 817 participants. Using computational modeling and behavioral analysis, we identified four distinct clusters of people. Three clusters corresponded to previous models of the task, including people with different learning rates for cues that signal rewarding and punishing states and with different sensitives for rewards and punishments. The fourth cluster of people acted like naïve reinforcement learners, with their responses shaped by outcomes in a manner that was independent of the state information provided by the cues. In addition to providing evidence that state-independent learning is a common disposition, we show that not considering such learning can dramatically affect the results of computational modeling. We discuss the implications for the modeling of data from heterogeneous populations.

Contrary to common intuition, groups of people recalling information together remember less than the same number of individuals recalling alone (i.e., the collaborative inhibition effect). To understand this effect in a free recall task, we build a computational model of collaborative recall in groups, extended from the Context Maintenance and Retrieval (CMR) model which captures how individuals recall information alone (Polyn, Norman, & Kahana, 2009). We propose that in collaborative recall, one not only uses their previous recall as an internal retrieval cue, but also listens to someone else's recall and uses it as an external retrieval cue. Attending to this cue updates the listener's context to be more similar to the context of someone else's recall. Over an existing dataset (Gates, Suchow, & Griffiths, 2022), we show that our model successfully captures the collaborative inhibition effects, as well as additional recall patterns such as recency and semantic clustering effects.

Collective improvisation is remarkable. When peopleimprovise—whether dancing, making music, or conversing—they coordinate their behavior while exploring abstract spacesof movements, sounds, and ideas. How do improvisers navigatethese abstract spaces? One possibility is that improvisationbuilds on foraging strategies used to search the physicalworld. Here, we investigate the dynamics of an especiallycomplex and abstract form of collective improvisation: freejazz. We quantify how professional jazz ensembles navigatea space of sounds and show that it resembles a foraging strategyknown as Area Restricted Search. In particular, ensembleschange their playing dynamics in response to encounters withnovel ‘soundworlds.' Before encountering a new soundworld,ensembles engage in widespread exploration; immediately after,they shift to focused exploitation of the new sound. Whilecollective improvisation pushes at our cognitive limits and is aparadigm of human creativity, it may build on evolutionarilyancientstrategies for searching space.

Team coordination is essential for effective performance during critical, stressful events. To better understand processes and states involved at multiple levels of team coordination, we assessed the correspondence between low- and high-level coordination in teams participating in simulation-based medical team training. We computed a measure of low-level team coordination with Multidimensional Recurrence Quantification Analysis, applied to arm movement, heart rate, and skin conductance data. High-level team coordination was captured by annotating video recordings for explicit and implicit, information and action coordination. Three linear mixed-effects model were run, each predicting a type of low-level coordination, based on high-level coordination annotations, accounting for multiple observations per team. Our findings showed that, compared to periods without annotated coordination, explicit- and implicit- information coordination corresponded to significantly different low-level team coordination across each of the studied modalities. Further research is required to assess additional factors related to the temporal variability observed in low-level coordination.

Prior literature shows that some mechanisms, e.g., commitment, could give rise to cooperation. However, participants' diverse propensities to cooperate may limit such mechanisms' effectiveness. Thus, we bring individual differences in their propensities to cooperate into the reasoning of long-term social dynamics of cooperation through an agent-based modeling approach. Our results suggest that commitment may still guarantee cooperation when individuals have different propensities to cooperate but has weaker effects, and the setups of commitment are also important. Our study highlights the importance of integrating individual preferences in analyzing collective dynamics of a population consisting of individuals of heterogeneous characteristics, thus offering implications to facilitate cooperation in rich real-world scenarios.

Large Language models (LLM) exhibit human-like proficiency in various tasks such as translation, question answering, essay writing, and programming. Emerging research explores the use of LLMs in collective problem-solving endeavors, such as tasks where groups try to uncover clues through discussions. Although prior work has investigated individual problem-solving tasks, leveraging LLM-powered agents for group consensus and decision-making remains largely unexplored. This research addresses this gap by (1) proposing an algorithm to enable free-form conversation in groups of LLM agents, (2) creating metrics to evaluate the human-likeness of the generated dialogue and problem-solving performance, and (3) evaluating LLM agent groups against human groups using an open source dataset. Our results reveal that LLM groups outperform human groups in problem-solving tasks. LLM groups also show a greater improvement in scores after participating in free discussions. In particular, analyses indicate that LLM agent groups exhibit more disagreements, complex statements, and a propensity for positive statements compared to human groups. The results shed light on the potential of LLMs to facilitate collective reasoning and provide insight into the dynamics of group interactions involving synthetic LLM agents.

How people reach consensus in social networks with locally distributed interactions is relevant to understanding collective group decision-making and problem-solving. However, while the importance of theory of mind in consensus problems has been hypothesized, little work has been done to test it systematically. We present both computational modeling and behavioral experiments designed to test the impact of theory of mind on individual choices within such consensus networks. We test 2,108 computational models informed by theoretical work on a graph-coloring consensus task to compare models using theory of mind to other behavioral parameters. We then use behavioral responses from 107 participants in a similar task to evaluate support for theory of mind in consensus formation. We find that the computational model that best accounts for prior behavioral data uses theory of mind, and our behavioral results likewise support use of theory of mind over other potential decision-making models.

In this paper, we identify two common assumptions underlying popular accounts of group agency. The first assumption is that paradigmatic cases of agency are to be identified with individual organisms, typically human beings. The second assumption is that cognition requires the manipulation of mental representations. Combining these two assumptions generates the status quo account of group agency, namely that a group's agency ontologically depends upon the mental representations of the individuals that constitute the group. We provide a taxonomy of views about group agency along two axes, each corresponding to the extent to which the view endorses (or rejects) one of these two common assumptions. We believe that none of the standard conceptions of group cognition and agency reject both of these two assumptions. After developing brief arguments against both assumptions, we provide a brief sketch of what an account of group agency that rejects both assumptions might look like.

Conformity refers to phenomena where people match their behavior to others. Much research has focused on cases where people observe others in identical situations, saying little about its depth or generalizability. When conforming, do people revise behaviors only in that specific situation, or do they update more deeply to maintain consistent behaviors across situations? Using simulations, we first show that deep and shallow conformity leads to contrasting group dynamics; only with deep conformity can groups accumulate improvements beyond individual lifespans. We further conduct an experiment using an estimation task to examine the depths of conformity in humans. People generally extended conformist social influence to new situations without direct reference to others. However, those who simply averaged their answer with that of the direct reference showed notable failures in this generalization. Collectively, our research highlights the importance of distinguishing different depths of conformity when studying social influence and resulting group outcomes.

Political polarisation is growing along with its negative consequences – degradation of functional government and increases in stochastic violence. Polarisation can result from both cognitive factors affecting information processing and biased information ecosystems, but their interactions are poorly understood. We present an agent-based model combining a varyingly polarised media landscape with agents driven by homophily and uncertain (political) identity inference processes. Agents were motivated to find similar others to form an ingroup by comparing moral values expressed in response to environmentally imposed moral dilemmas. Media pushed moral values in line with either liberal or conservative values, varying in agreement and influence. Liberal agents were more satisfied (according to homophily motivations), formed larger, more stable clusters, and morally disengaged less than conservatives. Identity aligned media exposure increased liberal agents' satisfaction, but had no, or the opposite effect, on conservative agents. We conclude that media exposure asymmetrically affects political polarisation across political identities.

The bystander effect suggests that people are less likely to assist in an emergency when others are present. Traditional theoretical accounts attribute this effect to top-down reflective processes, such as the diffusion of responsibility. However, recent research has proposed a two-system approach, suggesting that an individual's initial response to an emergency is personal distress and avoidance, which is further amplified by the presence of bystanders. In this study, we embed the two-system approach into an evidence accumulation model and argue that a higher distress and avoidance response causes slower evidence accumulation. We conducted a reaction time experiment where participants got exposed to faces or control stimuli and had to judge if a scene was dangerous. Our results confirm our hypothesis: Individuals exposed to faces had slower evidence accumulation for dangerous decisions. These findings contribute to a mechanistic understanding of how the anticipated bystander presence can influence early reflexive reactions to emergencies.

Evolutionary accounts suggest that individuals readily categorize other individuals into an ingroup and an outgroup, and consequently display a strong preference for positive behaviors towards the members of the ingroup relative to the outgroup. In the current study, we tested whether the robust ingroup bias could be modulated at the perceptual level based upon differential contextual information about group characteristics and group relations. Across the four experiments, participants performed a social associative matching task within the minimal group framework. We found that while the ingroup bias is certainly robust, it gets attenuated if the outgroup is portrayed positively and also when the ingroup is depicted negatively. This may have consequences for researchers studying intergroup conflict and consequent policy-making.

Human groups are able to converge to more accurate beliefs through deliberation, even in the presence of polarization and partisan bias --- a phenomenon known as the ``wisdom of partisan crowds.'' Large Language Models (LLMs) are increasingly being used to simulate human collective behavior, yet few benchmarks exist for evaluating their dynamics against the behavior of human groups. In this paper, we examine the extent to which the wisdom of partisan crowds emerges in groups of LLM-based agents that are prompted to role-play as partisan personas (e.g., Democrat or Republican). We find that they not only display human-like partisan biases, but also converge to more accurate beliefs through deliberation, as humans do. We then identify several factors that interfere with convergence, including the use of chain-of-thought prompting and lack of details in personas. Conversely, fine-tuning on human data appears to enhance convergence. These findings show the potential and limitations of LLM-based agents as a model of human collective intelligence.

Environments pose antagonistic demands on individual and collective cognition, such as trading off cognitive stability against cognitive flexibility. Manifestations of this tradeoff have been shown to vary across individuals, leading to differences in individual task switching performance. In this simulation study, we examine how individual differences in cognitive stability and flexibility contribute to collective task switching performance. Specifically, we study whether diversity in cognitive stability and flexibility among members of a group can facilitate collaborative task switching. We test this hypothesis by probing task switching performance of a multi-agent dynamical system, and by varying the heterogeneity of cognitive stability and flexibility among agents. We find that heterogeneous (compared to homogeneous) groups perform better in environments with high switch rates, especially if the most flexible agents receive task switch instructions. We discuss the implications of these findings for normative accounts of cognitive heterogeneity, as well as clinical and educational settings.

Although decision-making processes are typically studied with isolated individuals in the laboratory to control external factors, we mostly make decisions in a social environment in the presence of other individuals. The aim of the current study was to investigate the effects of social conditions on individuals' decision-making performance in economic and moral contexts. Forty-four pairs of participants of the same gender (42 females and 46 males) constituted the sample for this study. Each pair was required to complete both economic and moral tasks under three types of social conditions, namely, “individual,” “joint,” and “joint with gaze-cueing.” Furthermore, eye- and mouse-tracking technologies were utilized to record the participants' responses to the decision tasks. We hypothesized that even a minimal social context would influence people's decisions, as manifested in their gaze and mouse responses. The results revealed that the minimalist social condition in which participants do not communicate or interact with each other affected their decision-making performance. The interplay among social conditions, diverse task types, and stimuli type were identified as some of the factors that impact the decision-making process in this setting.

The interplay between cognition and gaming, notably through educational games enhancing cognitive skills, has garnered significant attention in recent years. This research introduces the CogSimulator, a novel algorithm for simulating user cognition in small-group settings with minimal data, as the educational game Wordle exemplifies. The CogSimulator employs Wasserstein-1 distance and coordinates search optimization for hyperparameter tuning, enabling precise few-shot predictions in new game scenarios. Comparative experiments with the Wordle dataset illustrate that our model surpasses most conventional machine learning models in mean Wasserstein-1 distance, mean squared error, and mean accuracy, showcasing its efficacy in cognitive enhancement through tailored game design.

While narratives have shaped cognition and cultures for centuries, digital media and online social networks have introduced new narrative phenomena. With increased narrative agency, networked groups of individuals can directly contribute and steer narratives that center our collective discussions of politics, science, and morality. We report the results of an online network experiment on narrative and hashtag generation, in which networked groups of participants interpreted a text-based narrative of a disaster event, and were incentivized to produce matching hashtags with their network neighbors. We found that network structure not only influences the emergence of dominant beliefs through coordination with network neighbors, but also impacts participants' use of causal language in their personal narratives.

Conventions are arbitrary and self-sustaining practices that emerge in a population and facilitate solving coordination problems. A recent study (Formaux et al. 2021) traced the formation of simple conventions in captive baboons in a touch-screen-based colour-matching ‘game'. We replicated this task with human pairs under different conditions (varying the instructions given, visual access to partner's screen, and subjects' previous experience) to assess their effects on convention formation. We found that more information delayed the formation of conventions (arbitrary rankings of colours). Interestingly, pairs maintained their conventions even when given visual access to their partner's screen, despite the availability of a potentially simpler strategy (copying). Although experienced subjects did not transmit their conventions to naïve subjects, they enabled more rapid establishment of a new convention. We hypothesise that these effects are rooted in whether human subjects are prompted to employ cognitively less or more sophisticated processes during behavioural coordination.

People have different intentions when sharing information online. However, are others able to interpret these motives and form accurate impressions of the poster? To investigate this, we put participants (N = 307) in imaginary opinion-based ingroup and outgroup online forums. In each, people were presented with different types of statements and asked for their impressions of the poster as well as of their own ingroup and outgroup. Negative impressions and intentions were more commonly linked to posters thought to be outgroup members, even when they exhibited similar behaviours to posters thought to be members of the ingroup. Notably, most types of contact increased people's liking of the ingroup and disliking of the outgroup. That said, a perceived effort to engage in genuine discussion over group matters by perceived outgroup posters appeared to shift outgroup impressions to be more positive. This highlights the potential benefit of deliberation in mitigating intergroup animosity.

Group‐living in animals comes with a number of benefits associated with predator avoidance, foraging, and reproduction. A large proportion of fish species display grouping behaviour. Fish may also be particularly vulnerable to climate‐related stressors including thermal variation, hypoxia, and acidification. As climate‐related stressors are expected to increase in magnitude and frequency, any effects on fish behaviour may be increased and affect the ability of fish species to cope with changing conditions. Here we conduct a systematic review of the effects of temperature, hypoxia, and acidification on individual sociability and group cohesion in shoaling and schooling fishes. Searches of the published and grey literature were carried out, and studies were included or excluded based on selection criteria. Data from studies were then included in a meta‐analysis to examine broad patterns of effects of climate‐related stressors in the literature. Evidence was found for a reduction in group cohesion at low oxygen levels, which was stronger in smaller groups. While several studies reported effects of temperature and acidification, there was no consistent effect of either stressor on sociability or cohesion. There was some evidence that marine fishes are more strongly negatively affected by acidification compared with freshwater species, but results are similarly inconsistent and more studies are required. Additional studies of two or more stressors in combination are also needed, although one study found reduced sociability following exposure to acidification and high temperatures. Overall, there is some evidence that hypoxia, and potentially other climate‐related environmental changes, impact sociability and group cohesion in fishes. This may reduce survival and adaptability in shoaling and schooling species and have further ecological implications for aquatic systems. However, this synthesis mainly highlights the need for more empirical studies examining the effects of climate‐related factors on social behaviour in fishes. [ABSTRACT FROM AUTHOR]

This paper presents two novel bio-inspired particle swarm optimisation (PSO) variants, namely biased eavesdropping PSO (BEPSO) and altruistic heterogeneous PSO (AHPSO). These algorithms are inspired by types of group behaviour found in nature that have not previously been exploited in search algorithms. The primary search behaviour of the BEPSO algorithm is inspired by eavesdropping behaviour observed in nature coupled with a cognitive bias mechanism that enables particles to make decisions on cooperation. The second algorithm, AHPSO, conceptualises particles in the swarm as energy-driven agents with bio-inspired altruistic behaviour, which allows for the formation of lending–borrowing relationships. The mechanisms underlying these algorithms provide new approaches to maintaining swarm diversity, which contributes to the prevention of premature convergence. The new algorithms were tested on the 30, 50 and 100-dimensional CEC'13, CEC'14 and CEC'17 test suites and various constrained real-world optimisation problems, as well as against 13 well-known PSO variants, the CEC competition winner, differential evolution algorithm L-SHADE and the recent bio-inspired I-CPA metaheuristic. The experimental results show that both the BEPSO and AHPSO algorithms provide very competitive performance on the unconstrained test suites and the constrained real-world problems. On the CEC13 test suite, across all dimensions, both BEPSO and AHPSO performed statistically significantly better than 10 of the 15 comparator algorithms, while none of the remaining 5 algorithms performed significantly better than either BEPSO or AHPSO. On the CEC17 test suite, on the 50D and 100D problems, both BEPSO and AHPSO performed statistically significantly better than 11 of the 15 comparator algorithms, while none of the remaining 4 algorithms performed significantly better than either BEPSO or AHPSO. On the constrained problem set, in terms of mean rank across 30 runs on all problems, BEPSO was first, and AHPSO was third. [ABSTRACT FROM AUTHOR]

Introduction: This article raises the relationship between a neighbourhood and subjectivities shared by young populations, that is, the framework of meanings, ideas, imaginaries, shared conditions in a place that can affect them individually or collectively. Objective: For this reason, this reflection aims to discuss whether the imaginaries of a site have implications for the subjectivity of those who occupy them. Method: It is a qualitative and exploratory research, at this first moment it consisted of group workshops that were carried out in October 2022 with young people who are part of various activities of the Heredia Civic Center for Peace. A recognition was found on the part of young people of neighborhood problems, such as drug use and negative news in which the neighborhood is frequently mentioned. However, those who participate do not intend to delve into these negative ideas of the neighborhood and, on the contrary, express situations of individual and collective development, due to the artistic and educational spaces provided by the Civic Center for Peace. Conclusions: It is concluded that there is an agreement between these young participants in the workshop to develop positive ideas about their neighborhood and show their work within the framework of the Civic Center for Peace; space that they recognize as an instance that opens artistic, educational, sports and animal protection opportunities. [ABSTRACT FROM AUTHOR]

Many real-life decisions, such as booking a vacation or selecting a partner, involve relatively cost-free sampling of options up to a terminal decision point, beyond which the choice becomes costly to reverse. Such problems can be formulated as optimal stopping problems (OSPs), such as the famous secretary problem. Although human behavior on optimal stopping problems has been studied extensively, much of the literature has focused on the behavior of individual decision-makers operating using a binary payoff function. In this study, we use an OSP with a continuous payoff function to study how individuals’ decisions differ from the collective decision of groups of three members working together. An independent threshold model offered the best explanation for the behavior of both individuals and groups. We found groups performed significantly better than individuals, with individuals consistently waiting too long to make a choice relative to the optimal strategy. Groups are also more decisive in following their internal thresholds, which are also different than a simple average of member thresholds. Finally, we also found a lack of long-term learning in OSPs for groups, a trend previously documented in individuals.

What are the computational foundations of social grouping? Traditional approaches to this question have focused on verbal reasoning or simple (low-dimensional) quantitative models. In the real world, however, social preferences emerge when high-dimensional learning systems (brains and bodies) interact with high-dimensional sensory inputs during an animal’s embodied interactions with the world. A deep understanding of social grouping will therefore require embodied models that learn directly from sensory inputs using high-dimensional learning mechanisms. To this end, we built artificial neural networks (ANNs), embodied those ANNs in virtual fish bodies, and raised the artificial fish in virtual fish tanks that mimicked the rearing conditions of real fish. We then compared the social preferences that emerged in real fish versus artificial fish. We found that when artificial fish had two core learning mechanisms (reinforcement learning and curiosity-driven learning), artificial fish developed fish-like social preferences. Like real fish, the artificial fish spontaneously learned to prefer members of their own group over members of other groups. The artificial fish also spontaneously learned to self-segregate with their in-group, akin to self-segregation behavior seen in nature. Our results suggest that social grouping can emerge from three ingredients: (1) reinforcement learning, (2) intrinsic motivation, and (3) early social experiences with in-group members. This approach lays a foundation for reverse engineering animal-like social behavior with image-computable models, bridging the divide between high-dimensional sensory inputs and social preferences.

Working with others creates interdependencies – individual-environment couplings that constrain behaviour but allow for new action possibilities. Teamwork, in this sense, demands behaviour is coordinated appropriately to achieve desired outcomes. The current research investigated whether the dynamics that govern interpersonal coordination also shape group productivity. In pairs, participants (n = 136) performed an object movement task while their behaviour was tracked. We varied task-relevant affordances that restricted the potential for coordination, along with social context (i.e., competitive or cooperative). Results indicated that productivity was enhanced by coordinated behaviour but only when participants were instructed to cooperate and affordances for task completion were restricted. Examination of the underlying dynamics revealed that parameters governing coordination accounted for unique variance in task performance. This suggests that (i): the dynamics governing interpersonal coordination do indeed shape group productivity, (ii) the interdependencies that accompany teamwork help determine whether coordination will benefit (or impede) group performance.

How and why do people share opinions online? In research conducted offline, the social identity of the audience is a key factor: whether they are composed of one’s ingroup or outgroup affects what people share and why. Do people behave similarly and for similar reasons online? To test this, we put participants (N = 326) in imaginary forums belonging to their ingroup and outgroup. In each, people selected statements to share, along with reasons for doing so. The results showed a high degree of heterogeneity; people shared nearly all kinds of statements with both audiences, for a variety of reasons. However, there were also consistent patterns. Identity expression was the most common reason for sharing to both audiences, but this led to different things being shared to each. To the ingroup, people preferred to share statements expressing ingroup beliefs, while to the outgroup, they preferred statements expressing universal beliefs.

Political polarization is driven by many factors, but the role of moral values as both a signal of political identity and a source of internal conflict is understudied. We report an agent-based computational model of polarization that fills this gap. Agents seek to differentiate in- and outgroup neighbors with a slight preference for the former. However, they must do so by inferring neighbors’ identities from visible but transient moral signals. Moreover, agents experience conflicts within their own values, and if difficult to resolve internally, can copy the values of their ingroup or disengage (i.e., act immorally). Results show that liberals form larger, more homogeneous clusters, are happier, and experience less moral conflict than conservatives. Conservatives experience more and higher levels of conflict and morally disengage significantly more often than liberals.

Successful social coordination requires being able to predict how the other people that one depends on are likely to behave. One solution to this dilemma is to establish social conventions, which constrain individuals' behavior but make prediction easier. Here, we develop a multi-agent deep reinforcement learning environment to investigate the costs associated with these conventions. In our produce-and-trade task, agents have varying production skills, but their actions must be predictable in order to be rewarded. Stronger norms improve the overall success of the group by improving the average rewards of the majority, but also systematically disadvantage agents whose specialization is in the minority of the group. Critically, this outcome is magnified by population size: as larger groups make it potentially more difficult to develop individualized representations of agents, minority agents become more likely to conform to a norm that is disadvantageous to them.

Collaborative problem solving capability (CPS) has become more important recently. Accurate measurement of CPS is necessary for developing training programs. However, most existing CPS measurement tools have two weaknesses. They do not account for individual contributions to the output. Also, it is hard to utilize new problems. Addressing these weaknesses, we modified the PISA 2015 framework to account for individual contributions to the output and introduced peer-assessment. To test the validity of our proposition, we had fifty participants perform two tasks in different groups, and analyzed the results. We found that the score from PISA 2015 and our modified score showed a high correlation. And, peer-assessed modified scores approximated expert-assessed modified scores. Furthermore, peer-assessed modified scores between the tasks showed a significant correlation, suggesting that it is consistent across different tasks and groups. These results suggest that the modified method and adopting peer-assessment can be used to measure CPS.

The crowd wisdom effect is a well-established phenomenon that is widely employed for predicting and estimating variables across various domains. Previous research has focused on enhancing the wisdom of crowds by improving individual estimates while maintaining some of the initial opinion diversity. However, it is theoretically possible to increase collective accuracy by largely increasing the diversity in a crowd. In this study, we propose a method that leverages the anchoring effect to extremize individual judgments and thus increase the diversity of opinions in a crowd. This is achieved by dividing the crowd into two groups, anchoring each group to either a low or high value, and aggregating all estimates. We use a mathematical model of anchoring to determine when this strategy is expected to outperform the crowd wisdom effect. Results from three experiments provide converging evidence that the proposed approach outperforms traditional methods in estimating and forecasting unknown quantities.

We provide an empirical test for shared reality theory, which predicts individuals seek to share similar impressions about the world with others in order to connect and reduce uncertainty. We built an online platform permitting synchronous communication between groups of participants time-locked to video stimuli. We first presented participants with the same introduction to a reality show and then seamlessly showed different dyads within a group separate video segments depicting different social interactions between pairs of contestants. People reported feeling more connected to group members who underwent the same video experience compared to those who unknowingly watched a different video. The nature of the interactions also appeared to impact relationships as participants reported feeling more connected to partners who responded to their messages more quickly during the group chat. Together, these results highlight the importance of communication during shared experiences in aligning shared interpretations of reality and facilitating social connection.

Current theory suggests that heterogeneous metapopulation structures can help foster the diffusion of innovations to solve pressing issues including climate change adaptation and promoting public health. In this paper, we develop an agent-based model of the spread of adaptations in simulated populations with minority-majority metapopulation structure, where subpopulations have different preferences for social interactions (i.e., homophily) and, consequently, learn deferentially from their own group. In our simulations, minority-majority-structured populations with moderate degrees of in-group preference better spread and maintained an adaptation compared to populations with more equal-sized groups and weak homophily. Minority groups act as incubators for novel adaptations, while majority groups act as reservoirs for the adaptation once it has spread widely. This suggests that population structure with in-group preference could promote the maintenance of novel adaptations.

Peer-assisted learning (PAL) can have both benefits and costs when compared to individual learning. In an empirical study aimed at testing whether PAL yields better learning outcomes than individual learning in a set of insight problems from the Remote Associates Test (RAT), participants in the PAL condition learned from interacting with scripted peers and outperformed participants in the individual learning condition. Although interaction was limited, there is evidence for knowledge spillover from peers as a cognitive mechanism in PAL. The peer-assisted learning transferred to a final test that was completed individually. The benefit of peer-assisted learning persisted in spite of significantly greater error exposure in the PAL condition. In addition, the superiority of peer-assisted learning over individual learning was more pronounced for the more difficult RAT problems. These findings suggest that PAL may be an effective approach to developing insight problem solving skills.

Within internet there exists the 90-9-1 principle (also called the 1% rule), which dictates that a vast majority of user-generated content in any specific community comes from the top 1% active users, with most people only listening in. When combined with other demographic biases among social media users, this casts doubt as to how well these users represent the wider world, which might be problematic considering how user-generated content is used in psychological research and in the wider media. We conduct three computational studies using pre-existing datasets from Reddit and Twitter; we examine the accuracy of the 1% rule and what effect this might have on how user-generated content is perceived by performing and comparing sentiment analyses between user groups. Our findings support the accuracy of the 1% rule, and we report a bias in sentiments between low- and high-frequency users. Limitations of our analyses will be discussed.

Solving problems with others not only reduces the time required to complete a challenge but may also enable the discovery of novel strategies that qualitatively change how a problem is approached. At the dyadic level, the laboratory-based ‘shepherding task’ demonstrated that, when tasked to contain evasive agents to a centralized location, some participants discover a non-obvious but optimal strategy to solve the task. This paper quantified the interactions between participants engaged in the task using Multidimensional Cross-Recurrence Quantification Analysis (MdCRQA), applied to each participant’s gaze and hand movements. The results demonstrated that strategy discoverers exhibited greater amounts of incidental coupling than non-discoverers prior to discovery. Once discovered, the strategy reduced the strength of coupling between participants, indicating that the strategy also reduced coordination demands. Future work will investigate whether differences in problem-solving can be attributable to differences in the perceptual features participants use which scaffold the discovery of task-optimal solutions.

Collaboration can make collective judgments more accurate than individual judgments, but it also comes with costs in time, effort, and social cohesion. But how do we estimate these costs? In two experiments, we introduce children and adults to two teams in which the teammates disagree about the optimal solution to a novel problem, and ask which team would need more time to reach a consensus decision. We find that all ages expect slower decisions from teams with more people or factions, and expect the number of factions to matter more than the number of people. But only adults expect decisions initially endorsed by a stronger faction to be faster than those endorsed by a weaker faction. Results are discussed in context of children’s reasoning about dominance, and models of time-rational collective decision-making.

Consensus-based social learning strategies often outcompete other strategies in evolutionary models. But while formal proofs suggest that consensus’ reliability is compromised when individual judgments are not independent, this makes for a notoriously implausible assumption in the biological world: the people we learn from are constantly learning from each other as well. How do we avoid being misled by consensus? We present two experiments and a computational model examining commonsense reasoning about how people’s public and private judgments are influenced by the consensus and social status of those around them. Results suggest that while people realize that these two factors can cause others’ public and private judgments to diverge, their own trust in public consensus depends on how accurately they believe it reflects their informants’ true beliefs.

Collaboration and teaming are critical for solving complex problems. However, little is known about how group dynamics affect teaming behaviours and, ultimately, problem-solving effectiveness. The present study aimed to validate a novel measure of the dynamics of team communication – here termed turn-taking entropy – and to investigate what aspects of those dynamics affect collaborative-problem-solving performance. Thirty-two teams of 4 were asked to complete a simulated crisis-response task in which they had to rank 15 items in order of their importance to their team’s survival (first individually and then as a team). Group responses were better than the aggregated individual responses of team members (suggesting teaming benefits), and were better when team members had task-relevant skills and knowledge. However, response quality was not significantly related to task completion time. Additionally, the proposed entropy measure appeared to capture group communication dynamics, and appeared to differentiate stable an unstable patterns of communication. Implications and directions for future research are discussed.

Do people engage in “expressive responding” – lying about their beliefs to express support for their in-group? A classic study by Schaffner and Lucks (2018) provided strong evidence of expressive responding in support of Donald Trump among people who voted for him. However, this study was not pre-registered, meaning that the robustness of this finding is uncertain. Our poster reports the results of a pre-registered replication study conducted over two years after the original study. Moreover, we highlight the relevance of work on expressive responding (and other forms of insincere responding) for research in cognitive science which tends to assume that participants report their beliefs sincerely.

We introduce the Cognitive Science Community to the challenges faced in human-in-the-loop (HiTL) decision-making for complex and dynamic industrial chemical processes. A microworld is presented which includes advanced dynamic simulations, an optimal control-based decision-support algorithm and a preliminary human machine interface. Three example applications are discussed, including decision-making in plant startup and shutdown, shared control using human and HiTL supervisory Model Predictive Control (MPC), and a pilot study of operator eye-tracking during a complex startup task. The paper also discusses ongoing and future experimental efforts highlighting the utility of the microworld in understanding human cognition and designing advanced operator training software for various process operation related complex tasks.

Current studies of the serial reproduction paradigm focused on stimuli that were statistically independent of each other. We explored serial reproductions of random walk series and examined whether Bayesian models previously built for independent stimulus could be adapted to autocorrelated stimulus. We found that Bayesian models captured most of the empirical results qualitatively, but could be further improved by incorporating recency effects. Besides, given that the optimal strategy of iterative prediction of random walk was to reproduce the current stimuli, we also compared serial prediction of random walk to serial reproduction. We found that serially reproduced and predicted series both decorrelate as a function of chain position and that the means of the series increase in both tasks, which matched qualitative predictions of the Bayesian models.

Deep neural network representations of entities can serve as inputs to computational models of human mental representations to predict people's behavioral and physiological responses to those entities. Though increasingly successful in their predictive capabilities, the implicit notion of "human" that they rely upon often glosses over individual-level differences in beliefs, attitudes, and associations, as well as group-level cultural constructs. In this paper, we model shared representations of food healthiness by aligning learned word representations with the consensus among a group of respondents. To do so, we extend Cultural Consensus Theory to include latent constructs structured as fine-tuned word representations. We then apply the model to a dataset of people's judgments of food healthiness. We show that our method creates a robust mapping between learned word representations and culturally constructed representations that guide consumer behavior.

We present an analysis of real-life televised Dutch Auctions, where over the course of 12 hours of auctions, bidders made over 5,000 purchases of 81 unique products. In the Dutch Auction format, multiples of an item (traditionally Dutch tulips) are for sale, where prices start high and decrease over time. In the televised version, viewers of the Price-Drop TV program could wait for the price to drop over time but needed to weigh this against the risk of being scooped by the collective bidding behaviour of other viewers. We conduct analyses of the auctions to: 1) demonstrate the features of sell-out auctions where all items are sold, 2) show how time pressure increases sales in the auction, and 3) how the type of price decrease, discrete vs. continuous, affects people's decision to bid on items. We conclude by proposing an iterative process model based on the framework of Prospect Theory to extend theoretically motivated, laboratory-based findings out to real-world purchasing behaviours.

Interpersonal physiological connection is thought to play a crucial role in group activities. A previous study with participants who had little musical experience has shown that the groups with higher heart rate synchronization were better able to perform simple drumming tasks in a coordinated manner. In the present study, we investigated whether heart-rate synchronization is associated with improvement in a complex harmonic performance of an orchestra. The subject of this study was an orchestra composed of several semi-professional and amateur musicians rehearsing a new original piece. The heart rates of sixteen musicians were simultaneously measured during a joint rehearsal. The results indicate that while the average heart rate of the musicians decreased, heart-rate synchronization among the musicians increased over the course of repeated group rehearsals. For a harmonic performance, the musicians may need to have a physiological connection to each other without their physiological arousal being too high.