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Abstract Background The harmonious cognitive alignment among various netizen groups is pivotal for the spread and amplification of online rumors. This alignment, characterized by shared cognitive inclinations, fosters uniformity in attitudes and perspectives, thereby precipitating synchronized engagement in the dissemination of such rumors. Notably, discernible disparities emerge in group cognition as different types of rumors pertaining to the same event propagate. This research endeavors to dissect the roles of netizen groups through the lens of cognitive variance, thereby attaining a more profound comprehension of the distinctive traits and behavioral dynamics of various netizen factions in the context of online rumor dissemination. Methods By integrating Bloom’s taxonomy and crafting a survey questionnaire, this study captured the cognitive responses of netizens to various online rumor themes across two critical dimensions: (1) Information Cognition: exploring cognitive processing levels from basic recall to application and analysis and (2) Attitude Change: evaluating higher-order cognitive processes such as evaluating and creating in response to complex rumor scenarios. The decision tree classification algorithm was meticulously applied to dissect the catalysts behind the cognitive shifts among netizens. Additionally, the K-Means clustering algorithm was effectively utilized to categorize netizen groups along thematic lines, offering a nuanced view of their cognitive engagement. Results The initial impression of a rumor significantly influences netizens’ final cognitive perceptions. Twelve characteristics were observed in netizen groups during the dissemination of rumors on different themes, and these groups were classified into four categories: knowledge-oriented, competition-oriented, social-oriented, and entertainment-oriented, based on their cognitive differences. Conclusions Throughout the lifecycle of online rumors, from inception to dissemination, diverse netizen groups assume distinct roles, each exerting a unique influence on the spread and reception of information. By implementing tailored governance strategies that are sensitive to the characteristics of these groups, it is possible to attain substantially more effective outcomes in managing the propagation of online rumors. This nuanced approach to governance recognizes the heterogeneity of the online community and leverages it to enhance the efficacy of interventions.

During decision − making, animals consider not only the current but also the past quality of options. For example, when humans evaluate performance (e.g. sales) of employees, they do not only consider the average performance but also the trend of performance; ascending performance is often viewed as more favorable than descending performance. In our study, we test if non-human animals have a similar bias when they are evaluating options using house-hunting by the acorn ant, Temnothorax curvispinosus, as our model system. Our data show that when nest-site quality is static over time, ant colonies tend to prefer the nest site which was better (i.e. darker) between two nest options. However, when the nest quality changes over time—one improves and the other worsens—more colonies choose the low-quality, but improving, nest than the high-quality, but worsening, nest. These results suggest that a continuous change of option quality may influence evaluation. We discuss alternative explanations for our results, possible mechanisms, and potential ecological benefits for keeping track of the nest-site quality. [ABSTRACT FROM AUTHOR]

To introduce our special issue How Minds Work: The Collective in the Individual, we propose "radical CI," a form of collective intelligence, as a new paradigm for cognitive science. Radical CI posits that the representations and processes necessary to perform the cognitive functions that humans perform are collective entities, not encapsulated by any individual. To explain cognitive performance, it appeals to the distribution of cognitive labor on the assumption that the human project runs on countless interactions between locally acting individuals with specialized skills that each retain a small part of the relevant information. Some of the papers in the special issue appeal to radical CI to account for a variety of cognitive phenomena including memory performance, metacognition, belief updating, reasoning, and problem‐solving. Other papers focus on the cultural and institutional practices that make radical CI possible. The notion that cognition is collective has a long pedigree but is enjoying considerable attention in the cognitive sciences as multiple fields of study deliver evidence for the central role of community and culture in cognition. To introduce the special issue of topiCS surveying this groundswell, we propose the concept of "radical collective intelligence," the view that some of the key representations and processes necessary to perform the cognitive functions that humans perform are collective entities, not encapsulated by any individual. This concept clarifies how the volume's contributions either rethink long‐studied cognitive processes (memory, metacognition, reasoning) or contemplate how radical CI can arise. [ABSTRACT FROM AUTHOR]

Deepfakes are an effective method of media manipulation because of their realism and also because truth is not a priority when people are consuming and sharing content online. Consumers are more focused on creating their own reality that aligns with their desires, opinions, and values. We explain how deepfakes differ from other sources of information. Their realism and vividness makes them unusually effective at depicting alternative facts, including fake news. Deepfakes are difficult to detect and will be even harder to detect in the future. However, people share deepfakes not necessarily because they believe them but because they want to reinforce their own identity and social position. The threat posed by deepfakes is that they can radicalize people by sowing chaos and confusion. They rarely change minds. We review the consequences of deepfakes in both the social sphere and private lives. We suggest potential solutions to reduce their negative consequences. [ABSTRACT FROM AUTHOR]

Swarm density plays a key role in the performance of a robot swarm, which can be averagely measured by swarm size and the area of a workspace. In some scenarios, the swarm workspace may not be fully or partially observable, or the swarm size may decrease over time due to out-of-battery or faulty individuals during operation. This can result in the average swarm density over the whole workspace being unable to be measured or changed in real-time. The swarm performance may not be optimal due to unknown swarm density. If the swarm density is too low, inter-robot communication will rarely be established, and robot swarm cooperation will not be effective. Meanwhile, a densely-packed swarm compels robots to permanently solve collision avoidance issues rather than performing the main task. To address this issue, in this work, the distributed algorithm for collective cognition on the average global density is proposed. The main idea of the proposed algorithm is to help the swarm make a collective decision on whether the current global density is larger, smaller or approximately equal to the desired density. During the estimation process, the swarm size adjustment is acceptable for the proposed method in order to reach the desired swarm density. [ABSTRACT FROM AUTHOR]

We tested an implication of the community of knowledgehypothesis, that people fail to distinguish their own knowledge fromother people’s knowledge in a collectivist society (China) as they doin individualistic societies like the United States. As predicted,despite the absence of any actual explanatory information, peoplerated their own understanding of novel natural and economicphenomena as higher when they were told that experts understoodthe phenomena than when they were told that experts did not yetunderstand them. This suggests that the community of knowledgeeffect may hold across cultures.

We evaluate whether people will outsource their opinion on public policy to consensus conference participants. The ideal consensus conference brings together a representative sample of citizens and introduces them to the range of perspectives and evidence related to some policy. The sample is given the opportunity to ask questions of experts and to deliberate. Attitudes about each policy are queried before and after the conference to see if the event has changed minds. In general, such conferences do produce opinion shifts. Our hypothesis is that the shift can be leveraged by simply communicating conference results – absent substantive information about the merits of the policies discussed – to scale up the value of conferences to the population at large. In five studies, we tell participants about the impact of a consensus conference on a sample of citizens' opinions for a range of policies without providing any new information about the inherent value of the policy itself. For several of the policies, we see a shift in opinion. We conclude that the value of consensus conferences can be scaled up simply by telling an electorate about its results. This suggests an economical way to bring evidence and rational argument to bear on citizens' policy attitudes. [ABSTRACT FROM AUTHOR]

Ant colonies regulate foraging in response to their collective hunger, yet the mechanism behind this distributed regulation remains unclear. Previously, by imaging food flow within ant colonies we showed that the frequency of foraging events declines linearly with colony satiation (Greenwald et al., 2018). Our analysis implied that as a forager distributes food in the nest, two factors affect her decision to exit for another foraging trip: her current food load and its rate of change. Sensing these variables can be attributed to the forager’s individual cognitive ability. Here, new analyses of the foragers’ trajectories within the nest imply a different way to achieve the observed regulation. Instead of an explicit decision to exit, foragers merely tend toward the depth of the nest when their food load is high and toward the nest exit when it is low. Thus, the colony shapes the forager’s trajectory by controlling her unloading rate, while she senses only her current food load. Using an agent-based model and mathematical analysis, we show that this simple mechanism robustly yields emergent regulation of foraging frequency. These findings demonstrate how the embedding of individuals in physical space can reduce their cognitive demands without compromising their computational role in the group.

Cognitive science has been traditionally organized around the individual as the basic unit of cognition. Despite developments in areas such as communication, human–machine interaction, group behavior, and community organization, the individual‐centric approach heavily dominates both cognitive research and its application. A promising direction for cognitive science is the study of augmented intelligence, or the way social and technological systems interact with and extend individual cognition. The cognitive science of augmented intelligence holds promise in helping society tackle major real‐world challenges that can only be discovered and solved by teams made of individuals and machines with complementary skills who can productively collaborate with each other. [ABSTRACT FROM AUTHOR]

Understanding the origins and maintenance of cognitive variation in animal populations is central to the study of the evolution of cognition. However, the brain is itself a complex, hierarchical network of heterogeneous components, from diverse cell types to diverse neuropils, each of which may be of limited use to study in isolation or prohibitively challenging to manipulate in situ. Consequently, highly tractable alternative model systems may be valuable tools. Eusocial-insect colonies display emergent cognitive-like properties from relatively simple social interactions between diverse subunits that can be observed and manipulated while operating collectively. Here, we review the individual-scale mechanisms that cause group-level variation in how colonies solve problems analogous to cognitive challenges faced by brains, like decision-making, attention, and search. The origin and maintenance of cognitive variation between individuals is an open problem, central to understanding how cognition emerges and evolves. Mapping neural dynamics to variation in macroscale cognitive patterns remains elusive given the large size of neural populations. Simpler alternative model systems, like eusocial insects, exhibit collective cognitive properties like decision-making, attention, and memory. Individual insects can be observed and manipulated in real time and colonies can be assembled from specifically selected individuals. There is an established framework to study consistent behavioral variation between colonies, which drives evolutionary change. Models from cognitive psychology have produced insights about collective colony function. Eusocial insects as alternative models of 'collective brains' may provide a new lens to study cognitive variation. [ABSTRACT FROM AUTHOR]

Swarm density plays a key role in the performance of a robot swarm, which can be averagely measured by swarm size and the area of a workspace. In some scenarios, the swarm workspace may not be fully or partially observable, or the swarm size may decrease over time due to out-of-battery or faulty individuals during operation. This can result in the average swarm density over the whole workspace being unable to be measured or changed in real-time. The swarm performance may not be optimal due to unknown swarm density. If the swarm density is too low, inter-robot communication will rarely be established, and robot swarm cooperation will not be effective. Meanwhile, a densely-packed swarm compels robots to permanently solve collision avoidance issues rather than performing the main task. To address this issue, in this work, the distributed algorithm for collective cognition on the average global density is proposed. The main idea of the proposed algorithm is to help the swarm make a collective decision on whether the current global density is larger, smaller or approximately equal to the desired density. During the estimation process, the swarm size adjustment is acceptable for the proposed method in order to reach the desired swarm density.

Social network topology can shape collective cognition and group behavior. Different social network topologies can facilitate various forms of collective cognition, leading to diverse collective cognition and group function. We analyzed the characteristics of contract networks and compared the performance of community structure discovery algorithms in social networks, using modularity as the assessment index. By examining the speed and effectiveness of these algorithms, we found that the Louvain algorithm and Girvan–Newman algorithm are suitable for discovering the network structure of sparse social networks. Experimental results have shown that the Louvain algorithm outperforms the Girvan–Newman algorithm on sparse networks across multiple scales. Finally, we learned a close relationship between collective cognition and community structure in contract networks, particularly influenced by the central nodes within these communities. [ABSTRACT FROM AUTHOR]

Human cognition is not solitary, it is shaped by collective learning and memory. Unlike swarms or herds, human social networks have diverse topologies, serving diverse modes of collective cognition and behaviour. Here, we review research that combines network structure with psychological and neural experiments and modelling to understand how the topology of social networks shapes collective cognition. First, we review graph-theoretical approaches to behavioural experiments on collective memory, belief propagation and problem solving. These results show that different topologies of communication networks synchronize or integrate knowledge differently, serving diverse collective goals. Second, we discuss neuroimaging studies showing that human brains encode the topology of one's larger social network and show similar neural patterns to neural patterns of our friends and community ties (e.g. when watching movies). Third, we discuss cognitive similarities between learning social and non-social topologies, e.g. in spatial and associative learning, as well as common brain regions involved in processing social and non-social topologies. Finally, we discuss recent machine learning approaches to collective communication and cooperation in multi-agent artificial networks. Combining network science with cognitive, neural and computational approaches empowers investigating how social structures shape collective cognition, which can in turn help design goal-directed social network topologies. This article is part of a discussion meeting issue 'The emergence of collective knowledge and cumulative culture in animals, humans and machines'. [ABSTRACT FROM AUTHOR]

The article presents the discussion on study of social learning in robots. Topics include playing a significant role in the lives of numerous vertebrate taxa and perhaps of invertebrates raising the prospect of cultural evolution in the world of machines; and integrating different contributions from different individuals and spreading through social learning.

Cognitive neuroscience seeks to discover the biological foundations of the human mind. One goal is to explain how mental operations are generated by the information processing architecture of the human brain. Our aim is to assess whether this is a well-defined objective. Our contention will be that it is not because the information processing of any given individual is not contained entirely within that individual’s brain. Rather, it typically includes components situated in the heads of others, in addition to being distributed across parts of the individual’s body and physical environment. Our focus here will be on cognition distributed across individuals, or on what we call the “community of knowledge,” the challenge that poses for reduction of cognition to neurobiology and the contribution of cognitive neuroscience to the study of communal processes.

Cognitive neuroscience seeks to discover the biological foundations of the human mind. One goal is to explain how mental operations are generated by the information processing architecture of the human brain. Our aim is to assess whether this is a well-defined objective. Our contention will be that it is not because the information processing of any given individual is not contained entirely within that individual's brain. Rather, it typically includes components situated in the heads of others, in addition to being distributed across parts of the individual's body and physical environment. Our focus here will be on cognition distributed across individuals, or on what we call the "community of knowledge," the challenge that poses for reduction of cognition to neurobiology and the contribution of cognitive neuroscience to the study of communal processes. [ABSTRACT FROM AUTHOR]

Cooperative transport of large food loads by Paratrechina longicornis ants demands repeated decision-making. Inspired by the Evidence Accumulation (EA) model classically used to describe decision-making in the brain, we conducted a binary choice experiment where carrying ants rely on social information to choose between two paths. We found that the carried load performs a biased random walk that continuously alternates between the two options. We show that this motion constitutes a physical realization of the abstract EA model and exhibits an emergent version of the psychophysical Weber’s law. In contrast to the EA model, we found that the load’s random step size is not fixed but, rather, varies with both evidence and circumstances. Using theoretical modeling we show that variable step size expands the scope of the EA model from isolated to sequential decisions. We hypothesize that this phenomenon may also be relevant in neuronal circuits that perform sequential decisions.

This paper focuses on the collective cognition by robotic swarms. The robotic swarms expected to perform tasks that are beyond the capability of a single robot by collective behavior that emerge from local interactions, similar to biological swarms. However, the robotic swarms have to rely on the collective cognition more than biological swarms when considering the limitation in sensory capabilities and the cost of each robot. In this paper, we develop controllers for a robotic swarm to accomplish a foraging task that requires collective cognition. In this task, robots have to both collectively distinguish two objects, namely food and poison, and cooperatively transport food objects to the nest. We applied an evolutionary robotics approach with the covariance matrix adaptation evolution strategy to develop controllers for robotic swarms. The results of computer simulations show that collective cognition was successfully developed, which allows the robots to transport only food objects. In addition, we also perform experiments to examine the scalability and the flexibility of the developed controllers. [ABSTRACT FROM AUTHOR]

From close friends to people on a first date, imagining a shared future appears fundamental to relationships. Yet, no previous research has conceptualized the act of imagination as a socially constructed process that affects how connected we feel to others. The present studies provide a framework for investigating imagination as a collaborative process in which individuals cocreate shared representations of hypothetical events-what we call collaborative imagination. Across two preregistered studies ( N = 244), we provide evidence that collaborative imagination of a shared future fosters social connection in novel dyads-beyond imagining a shared future individually or shared experience in general. Subjective ratings and natural language processing of participants' imagined narratives illuminate the representational features of imagined events shaped by collaborative imagination. Together, the present findings have the potential to shift how we view the structure and function of imagination with implications for better understanding interpersonal relationships and collective cognition., Competing Interests: Competing interests statement:B.B.O. was a co-author on a paper with Daniel L. Schacter published in 2022.

The cognitive abilities of biological organisms only make sense in the context of their environment. Here, we study longhorn crazy ant collective navigation skills within the context of a semi-natural, randomized environment. Mapping this biological setting into the ‘Ant-in-a-Labyrinth’ framework which studies physical transport through disordered media allows us to formulate precise links between the statistics of environmental challenges and the ants’ collective navigation abilities. We show that, in this environment, the ants use their numbers to collectively extend their sensing range. Although this extension is moderate, it nevertheless allows for extremely fast traversal times that overshadow known physical solutions to the ‘Ant-in-a-Labyrinth’ problem. To explain this large payoff, we use percolation theory and prove that whenever the labyrinth is solvable, a logarithmically small sensing range suffices for extreme speedup. Overall, our work demonstrates the potential advantages of group living and collective cognition in increasing a species’ habitable range.

As work teams have increasingly become the cornerstone of the post bureaucratic organization, there have been calls for a greater understanding of collective thought and action. Such understanding is deemed important for the design and management of teamwork. Theory suggests that feelings of ownership manifest themselves at the collective level, and positively affect team performance effectiveness. This study illuminates the role played by teamwork complexity and team self-management in the emergence of the psychological processes that are associated with the manifestation of job-focused collective psychological ownership (CPO). In addition, employment of serial mediation suggests that both teamwork dimensions put employees on two routes (intimate knowing of and the collective investment of the team members' selves into the job) that lead to the emergence of a collective sense of ownership, and together these two route variables and CPO sequentially mediated a positive relationship between teamwork design and team performance effectiveness. [ABSTRACT FROM AUTHOR]

An individual's knowledge is collective in at least two senses: it often comes from other people's testimony, and its deployment in reasoning and action requires accuracy underwritten by other people's knowledge. What must one know to participate in a collective knowledge system? Here, we marshal evidence that individuals retain detailed causal information for a few domains and coarse causal models embedding markers indicating that these details are available elsewhere (others' heads or the physical world) for most domains. This framework yields further questions about metacognition, source credibility, and individual computation that are theoretically and practically important. Belief polarization depends on the web of epistemic dependence and is greatest for those who know the least, plausibly due to extreme conflation of others' knowledge with one's own. The knowledge that supports many of our beliefs and attitudes resides not in our own heads, but in a community of knowledge constituted by other people, artifacts, and information repositories (e.g., libraries or the Internet). Individuals are poor at distinguishing knowledge that is in their heads from knowledge that resides in the community. This leads them to overestimate how much they know or understand merely by participating in a community of knowledge. This failure of differentiation has implications for public discourse. Extreme views about science and politics have been found to covary with knowledge overestimation. Modeling individual cognition under collective knowledge is an emerging challenge for cognitive science. [ABSTRACT FROM AUTHOR]

Claire White's introduction to the Cognitive Science of Religion is a valuable research and teaching tool. The uninitiated will truly benefit from the meticulously and clearly formulated expositions of this recent field of research on religion. Although clearly written with students in mind, the volume presents a great opportunity for scholars from other fields to familiarize themselves with the CSR as it has so far been established. A few possible additions and new research avenues are sketched out. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Ant territorial battles self-organize from distributed decisions by many individuals. • Search patterns by unengaged ants strongly affect the spatial dynamics of battles. • Assessment of force ratios by individual ants drives temporal dynamics. • Collective ability to compare numbers is high despite limited individual cognition. • Amplification of initial numerical advantages causes priority effects. Abstract Territorial battles among ants exhibit temporal and spatial patterns that self-organize, arising spontaneously from distributed decisions by large numbers of individuals. We describe agent-based models of inter-group fights in ants and show that two behavioral mechanisms that are rarely quantified have large effects on the dynamics of intraspecific battles; specifically, the pattern of search by unengaged ants, and assessment of relative numbers. In the absence of assessment, recruitment by both colonies rises to steady averages. Alternatively, if ants tend to lay trails only when they detect that their nestmates outnumber opponents, fights can be rapidly resolved as one colony ceases recruiting. If ants tend to lay trails when their nestmates are locally outnumbered, the position of the battle may oscillate. We show that the collective ability of fighting ants to accurately compare group sizes may be high even if each ant has limited perception and memory. However, amplification of small initial numerical advantages can lead to priority effects favoring the first colony to recruit even if it is the smaller colony. [ABSTRACT FROM AUTHOR]

We tested zebrafish shoals to examine whether groups exhibit collective spatial learning and whether this relates to the personality of group members. To do this we trained shoals to associate a collective spatial decision with a reward and tested whether shoals could reorient to the learned location from a new starting point. There were strong indications of collective learning and collective reorienting, most likely by memorising distal cues, but these processes were unrelated to personality differences within shoals. However, there was evidence that group decisions require agreement between differing personalities. Notably, shoals with more boldness variation were more likely to split during training trials and took longer to reach a collective decision. Thus cognitive tasks, such as learning and cue memorisation, may be exhibited collectively, but the ability to reach collective decisions is affected by the personality composition of the group. A likely outcome of the splitting of groups with very disparate personalities is the formation of groups with members more similar in their personality.

Few studies have been conducted about team members' individual beliefs of their team's collective efficacy (personal collective efficacy). We argue that this individual belief is a motivational factor for teamwork along with self-efficacy for teamwork. This study investigated relationships between personal collective efficacy, self-efficacy for teamwork, collective orientations and perceptions of self in relation to other team members. In addition, relationships between personal collective efficacy and team members' perceptions of team activities namely evaluating and integrating were explored. Multilevel analysis was employed in order to account for team level variation when testing individual level relationships. Eighty-six university student teams were followed throughout a two-stage study. We found personal collective efficacy was related to self-efficacy for teamwork, horizontal allocentrism, and the extent to which a team member perceived the level of integrating ideas and team tasks to be interdependent. [ABSTRACT FROM AUTHOR]

Mandevillian intelligence is a specific form of collective intelligence in which individual cognitive vices (i.e., shortcomings, limitations, constraints and biases) are seen to play a positive functional role in yielding collective forms of cognitive success. The present paper introduces the concept of mandevillian intelligence and reviews a number of strands of empirical research that help to shed light on the phenomenon. The paper also attempts to highlight the value of the concept of mandevillian intelligence from a philosophical, scientific and engineering perspective. Inasmuch as we accept the notion of mandevillian intelligence, then it seems that the cognitive and epistemic value of a specific social or technological intervention will vary according to whether our attention is focused at the individual or collective level of analysis. This has a number of important implications for how we think about the design and evaluation of collective cognitive systems. For example, the notion of mandevillian intelligence forces us to take seriously the idea that the exploitation (or even the accentuation) of individual cognitive shortcomings could, in some situations, provide a productive route to collective forms of cognitive and epistemic success. [ABSTRACT FROM AUTHOR]

Larger groups often have a greater ability to solve cognitive tasks compared to smaller ones or lone individuals. This is well established in social insects, navigating flocks of birds, and in groups of prey collectively vigilant for predators. Research in social insects has convincingly shown that improved cognitive performance can arise from self-organised local interactions between individuals that integrates their contributions, often referred to as swarm intelligence. This emergent collective intelligence has gained in popularity and been directly applied to groups of other animals, including fish. Despite being a likely mechanism at least partially explaining group performance in vertebrates, I argue here that other possible explanations are rarely ruled out in empirical studies. Hence, evidence for self-organised collective (or ‘swarm’) intelligence in fish is not as strong as it would first appear. These other explanations, the ‘pool-of-competence’ and the greater cognitive ability of individuals when in larger groups, are also reviewed. Also discussed is why improved group performance in general may be less often observed in animals such as shoaling fish compared to social insects. This review intends to highlight the difficulties in exploring collective intelligence in animal groups, ideally leading to further empirical work to illuminate these issues. [ABSTRACT FROM AUTHOR]

Collective cognition has received much attention in recent years but most of the empirical work has focused on comparing individuals and groups within single populations, thereby not addressing evolutionary origins of collective cognition. Here, we investigated collective cognition in multiple populations that are subject to different levels of predation. Guppies (Poecilia reticulata) were given a simultaneous choice between an edible and a nonedible stimulus. We found evidence for an improvement in decision accuracy when in groups but only in low-predation guppies. This performance increase was due to a combination of increased private sampling behavior when in groups (compared to being alone) and social information use. In contrast, high-predation fish did not sample more when in groups, nor used social information; hence did not improve decision-accuracy when in groups. The improvement of groups in foraging accuracy in low but not in high-predation sites, suggests that these populations differ in their trade-off between attention dedicated to food and predators. In high-predation sites, investing time in predator detection is more crucial than in low-predation sites, thereby possibly conflicting with food detection. Our results highlight the importance of considering the effects of ecological gradients on collective cognition. [ABSTRACT FROM AUTHOR]

This paper offers a critical review of the issue of assessing the quality of group thinking, describes the development of a Group Thinking Measure that fills a gap revealed by the literature and illustrates the use of this measure, in combination with interpretative discourse analysis, as a way of distinguishing those behaviors that add value to group thinking from those behaviors that detract value. The Group Thinking Measure combines two tests of equal difficulty, one for individual use and one for use by triads. This enables a measure not only of how well groups are thinking together but also a correlation between individual thinking and group thinking. This innovation gives an indication of whether or not working in a group adds value and so the extent to which a classroom culture supports collaborative thinking. [ABSTRACT FROM AUTHOR]

This article develops a co-citation analysis covering the period 1991–2014 outlining the collective logic of authors conducting studies of entrepreneurship education. The core themes, major contributions and topological features characterising their relationships are identified. The findings indicate that the field has a polycentric structure with five core themes of which entrepreneurial intentions emerges as the most influential while the entrepreneurial learning and evaluation themes have emerged in recent years. These two core themes appear to foster changes within the current structure with the introduction of new theoretical inputs and the formation of new theoretical hubs. We argue that future research should attempt to organise focal themes into a theoretical framework that allows a comprehensive picture of entrepreneurship education. [ABSTRACT FROM AUTHOR]

Information sharing is a critical task for group-living animals. The pattern of sharing can be modeled as a network whose structure can affect the decision-making performance of individual members as well as that of the group as a whole. A fully connected network, in which each member can directly transfer information to all other members, ensures rapid sharing of important information, such as a promising foraging location. However, it can also impose costs by amplifying the spread of inaccurate information (if, for example the foraging location is actually not profitable). Thus, an optimal network structure should balance effective sharing of current knowledge with opportunities to discover new information. We used a computer simulation to measure how well groups characterized by different network structures (fully connected, small world, lattice, and random) find and exploit resource peaks in a variable environment. We found that a fully connected network outperformed other structures when resource quality was predictable. When resource quality showed random variation, however, the small world network was better than the fully connected one at avoiding extremely poor outcomes. These results suggest that animal groups may benefit by adjusting their information-sharing network structures depending on the noisiness of their environment. [ABSTRACT FROM AUTHOR]

While most technological positioning studies were traditionally addressed by comparing firms technological patents classes and portfolios, only a few of them adopted science mapping patent co-citation techniques and none of these seeks to understand the impact of collective cognition on the technology structure of an entire industry. What is the firms technological positioning landscape within an high collective cognition sector? What is the groups technological positioning evolution? How do technology structures shift according to different economic scenarios? Through a strategic lens we contribute to technology strategy literatures by proposing an invention behavior map of automotive actors at a firm, groups and industry level. From Derwent Innovation Index, about 581,000 patents, 1,309,356 citations and 1,287,594 co-citations relationships between (a) the main 49 firms assignees of 1991-2013 and (b) the main 28 or 34 groups assignees by considering three timespan 1991-1997, 1998-2004, 2005-2013, were collected. Results: (1) most of the companies are located close together, depicting the sector technology structure as highly dense; (2) the market leaders do not coincide with technology production leaders and not necessarily occupy central technological positions; (3) the automotive groups considerably varies in the three timespan in terms of position and composition; (4) the market leaders groups occupy technological remoteness positions during economic growth timespan; (5) the sector technology structure is highly dense during growth, strongly scattered and lacking of technologically center positioned actors after economic decline. Finally, strategic implications supporting central locating or suburb R&D positioning planning and M&As recombinational partners decision making are discussed. [ABSTRACT FROM AUTHOR]