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Orejudo S, Lozano-Blasco R, Bautista P, Aiger M. Interaction among participants in a collective intelligence experiment: an emotional approach. Front Psychol 2024; 15:1383134. [PMID: 38813562 PMCID: PMC11133684 DOI: 10.3389/fpsyg.2024.1383134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 04/04/2024] [Indexed: 05/31/2024] Open
Abstract
Introduction The construct of collective intelligence assumes that groups have a better capacity than individuals to deal with complex, poorly defined problems. The digital domain allows us to analyze this premise under circumstances different from those in the physical environment: we can gather an elevated number of participants and generate a large quantity of data. Methods This study adopted an emotional perspective to analyze the interactions among 794 adolescents dealing with a sexting case on an online interaction platform designed to generate group answers resulting from a certain degree of achieved consensus. Results Our results show that emotional responses evolve over time in several phases of interaction. From the onset, the emotional dimension predicts how individual responses will evolve, particularly in the final consensus phase. Discussion Responses gradually become more emotionally complex; participants tend to identify themselves with the victim in the test case while increasingly rejecting the aggressors.
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Affiliation(s)
- Santos Orejudo
- Department of Psychology and Sociology, University of Zaragoza, Zaragoza, Spain
| | | | - Pablo Bautista
- Department of Educational Sciences, University of Zaragoza, Zaragoza, Spain
| | - Montserrat Aiger
- Department of Psychology and Sociology, University of Zaragoza, Zaragoza, Spain
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2
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Lei X, Xiang Y, Duan M, Peng X. Exploring the criticality hypothesis using programmable swarm robots with Vicsek-like interactions. J R Soc Interface 2023; 20:20230176. [PMID: 37464802 PMCID: PMC10354469 DOI: 10.1098/rsif.2023.0176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 06/28/2023] [Indexed: 07/20/2023] Open
Abstract
A widely mentioned but not experimentally confirmed view (known as the 'criticality hypothesis') argues that biological swarm systems gain optimal responsiveness to perturbations and information processing capabilities by operating near the critical state where an ordered-to-disordered state transition occurs. However, various factors can induce the ordered-disordered transition, and the explicit relationship between these factors and the criticality is still unclear. Here, we present an experimental validation for the criticality hypothesis by employing real programmable swarm-robotic systems (up to 50 robots) governed by Vicsek-like interactions, subject to time-varying stimulus-response and hazard avoidance. We find that (i) not all ordered-disordered motion transitions correspond to the functional advantages for groups; (ii) collective response of groups is maximized near the critical state induced by alignment weight or scale rather than noise and other non-alignment factors; and (iii) those non-alignment factors act to highlight the functional advantages of alignment-induced criticality. These results suggest that the adjustability of velocity or directional coupling between individuals plays an essential role in the acquisition of maximizing collective response by criticality. Our results contribute to understanding the adjustment strategies of animal interactions from a perspective of criticality and provide insights into the design and control of swarm robotics.
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Affiliation(s)
- Xiaokang Lei
- College of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, People’s Republic of China
| | - Yalun Xiang
- College of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, People’s Republic of China
| | - Mengyuan Duan
- College of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, People’s Republic of China
| | - Xingguang Peng
- School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, People’s Republic of China
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3
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Stangl M, Maoz SL, Suthana N. Mobile cognition: imaging the human brain in the 'real world'. Nat Rev Neurosci 2023; 24:347-362. [PMID: 37046077 PMCID: PMC10642288 DOI: 10.1038/s41583-023-00692-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/03/2023] [Indexed: 04/14/2023]
Abstract
Cognitive neuroscience studies in humans have enabled decades of impactful discoveries but have primarily been limited to recording the brain activity of immobile participants in a laboratory setting. In recent years, advances in neuroimaging technologies have enabled recordings of human brain activity to be obtained during freely moving behaviours in the real world. Here, we propose that these mobile neuroimaging methods can provide unique insights into the neural mechanisms of human cognition and contribute to the development of novel treatments for neurological and psychiatric disorders. We further discuss the challenges associated with studying naturalistic human behaviours in complex real-world settings as well as strategies for overcoming them. We conclude that mobile neuroimaging methods have the potential to bring about a new era of cognitive neuroscience in which neural mechanisms can be studied with increased ecological validity and with the ability to address questions about natural behaviour and cognitive processes in humans engaged in dynamic real-world experiences.
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Affiliation(s)
- Matthias Stangl
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behaviour, University of California, Los Angeles, Los Angeles, CA, USA.
| | - Sabrina L Maoz
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA
| | - Nanthia Suthana
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behaviour, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA.
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4
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Ji P, Wang Y, Peron T, Li C, Nagler J, Du J. Structure and function in artificial, zebrafish and human neural networks. Phys Life Rev 2023; 45:74-111. [PMID: 37182376 DOI: 10.1016/j.plrev.2023.04.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 04/20/2023] [Indexed: 05/16/2023]
Abstract
Network science provides a set of tools for the characterization of the structure and functional behavior of complex systems. Yet a major problem is to quantify how the structural domain is related to the dynamical one. In other words, how the diversity of dynamical states of a system can be predicted from the static network structure? Or the reverse problem: starting from a set of signals derived from experimental recordings, how can one discover the network connections or the causal relations behind the observed dynamics? Despite the advances achieved over the last two decades, many challenges remain concerning the study of the structure-dynamics interplay of complex systems. In neuroscience, progress is typically constrained by the low spatio-temporal resolution of experiments and by the lack of a universal inferring framework for empirical systems. To address these issues, applications of network science and artificial intelligence to neural data have been rapidly growing. In this article, we review important recent applications of methods from those fields to the study of the interplay between structure and functional dynamics of human and zebrafish brain. We cover the selection of topological features for the characterization of brain networks, inference of functional connections, dynamical modeling, and close with applications to both the human and zebrafish brain. This review is intended to neuroscientists who want to become acquainted with techniques from network science, as well as to researchers from the latter field who are interested in exploring novel application scenarios in neuroscience.
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Affiliation(s)
- Peng Ji
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Shanghai 200433, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
| | - Yufan Wang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, 320 Yue-Yang Road, Shanghai 200031, China
| | - Thomas Peron
- Institute of Mathematics and Computer Science, University of São Paulo, São Carlos 13566-590, São Paulo, Brazil.
| | - Chunhe Li
- Shanghai Center for Mathematical Sciences and School of Mathematical Sciences, Fudan University, Shanghai 200433, China; Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China.
| | - Jan Nagler
- Deep Dynamics, Frankfurt School of Finance & Management, Frankfurt, Germany; Centre for Human and Machine Intelligence, Frankfurt School of Finance & Management, Frankfurt, Germany
| | - Jiulin Du
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, 320 Yue-Yang Road, Shanghai 200031, China.
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5
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Galesic M, Barkoczi D, Berdahl AM, Biro D, Carbone G, Giannoccaro I, Goldstone RL, Gonzalez C, Kandler A, Kao AB, Kendal R, Kline M, Lee E, Massari GF, Mesoudi A, Olsson H, Pescetelli N, Sloman SJ, Smaldino PE, Stein DL. Beyond collective intelligence: Collective adaptation. J R Soc Interface 2023; 20:20220736. [PMID: 36946092 PMCID: PMC10031425 DOI: 10.1098/rsif.2022.0736] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Accepted: 02/27/2023] [Indexed: 03/23/2023] Open
Abstract
We develop a conceptual framework for studying collective adaptation in complex socio-cognitive systems, driven by dynamic interactions of social integration strategies, social environments and problem structures. Going beyond searching for 'intelligent' collectives, we integrate research from different disciplines and outline modelling approaches that can be used to begin answering questions such as why collectives sometimes fail to reach seemingly obvious solutions, how they change their strategies and network structures in response to different problems and how we can anticipate and perhaps change future harmful societal trajectories. We discuss the importance of considering path dependence, lack of optimization and collective myopia to understand the sometimes counterintuitive outcomes of collective adaptation. We call for a transdisciplinary, quantitative and societally useful social science that can help us to understand our rapidly changing and ever more complex societies, avoid collective disasters and reach the full potential of our ability to organize in adaptive collectives.
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Affiliation(s)
- Mirta Galesic
- Santa Fe Institute, Santa Fe, NM 87501, USA
- Complexity Science Hub Vienna, 1080 Vienna, Austria
- Vermont Complex Systems Center, University of Vermont, Burlington, VM 05405, USA
| | | | - Andrew M. Berdahl
- School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA 98195, USA
| | - Dora Biro
- Department of Zoology, University of Oxford, Oxford OX1 3PS, UK
| | - Giuseppe Carbone
- Department of Mechanics, Mathematics and Management, Politecnico di Bari, Bari 70125, Italy
| | - Ilaria Giannoccaro
- Department of Mechanics, Mathematics and Management, Politecnico di Bari, Bari 70125, Italy
| | - Robert L. Goldstone
- Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Cleotilde Gonzalez
- Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Anne Kandler
- Department of Mathematics, Max-Planck-Institute for Evolutionary Anthropology, Leipzig 04103, Germany
| | - Albert B. Kao
- Santa Fe Institute, Santa Fe, NM 87501, USA
- Biology Department, University of Massachusetts Boston, Boston, MA 02125, USA
| | - Rachel Kendal
- Centre for Coevolution of Biology and Culture, Durham University, Anthropology Department, Durham, DH1 3LE, UK
| | - Michelle Kline
- Centre for Culture and Evolution, Division of Psychology, Brunel University London, Uxbridge, UB8 3PH, UK
| | - Eun Lee
- Department of Scientific Computing, Pukyong National University, 45 Yongso-ro, Nam-gu, Busan, 48513, Republic of Korea
| | | | - Alex Mesoudi
- Department of Ecology and Conservation, University of Exeter, Penryn TR10 9FE, UK
| | | | | | - Sabina J. Sloman
- Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA
- Department of Computer Science, University of Manchester, Manchester, M13 9PL, UK
| | - Paul E. Smaldino
- Santa Fe Institute, Santa Fe, NM 87501, USA
- Department of Cognitive and Information Sciences, University of California, Merced, CA 95343, USA
| | - Daniel L. Stein
- Santa Fe Institute, Santa Fe, NM 87501, USA
- Department of Physics and Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA
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Tang M, Liao H. Group Structure and Information Distribution on the Emergence of Collective Intelligence. DECISION ANALYSIS 2023. [DOI: 10.1287/deca.2022.0466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
More and more decision-making problems are being solved by groups. Collective intelligence is the ability of groups to perform well when solving complex problems. Thus, it is important to encourage collective intelligence to emerge from groups. In this study, we explore how two critical characteristics of groups, that is, group structure and individual knowledge in groups, influence the emergence of collective intelligence. To do this, we propose a measure for group structure using the collaboration network of a group and a measure for the distribution of individual knowledge in groups. Group structure is measured based on the intensities of links and whether the network is hierarchical or flat. The distribution of individual knowledge is measured from the perspective of whether group information is shared or unique. Social interactions among group members and individual changes in opinion are modeled based on a simulation technique. We find that unbalanced information distribution undermines group performance, whereas group structure can modify the effect of information distribution. We also find that groups with broadly distributed knowledge are good at solving complex problems. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72171158, 71771156 and 71971145].
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Affiliation(s)
- Ming Tang
- Business School, Sichuan University, Chengdu 610064, China
| | - Huchang Liao
- Business School, Sichuan University, Chengdu 610064, China
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7
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Orejudo S, Cano-Escoriaza J, Cebollero-Salinas AB, Bautista P, Clemente-Gallardo J, Rivero A, Rivero P, Tarancón A. Evolutionary emergence of collective intelligence in large groups of students. Front Psychol 2022; 13:848048. [PMID: 36405219 PMCID: PMC9666766 DOI: 10.3389/fpsyg.2022.848048] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 09/26/2022] [Indexed: 10/20/2023] Open
Abstract
The emergence of collective intelligence has been studied in much greater detail in small groups than in larger ones. Nevertheless, in groups of several hundreds or thousands of members, it is well-known that the social environment exerts a considerable influence on individual behavior. A few recent papers have dealt with some aspects of large group situations, but have not provided an in-depth analysis of the role of interactions among the members of a group in the creation of ideas, as well as the group's overall performance. In this study, we report an experiment where a large set of individuals, i.e., 789 high-school students, cooperated online in real time to solve two different examinations on a specifically designed platform (Thinkhub). Our goal of this paper 6 to describe the specific mechanisms of idea creation we were able to observe and to measure the group's performance as a whole. When we deal with communication networks featuring a large number of interacting entities, it seems natural to model the set as a complex system by resorting to the tools of statistical mechanics. Our experiment shows how an interaction in small groups that increase in size over several phases, leading to a final phase where the students are confronted with the most popular answers of the previous phases, is capable of producing high-quality answers to all examination questions, whereby the last phase plays a crucial role. Our experiment likewise shows that a group's performance in such a task progresses in a linear manner in parallel with the size of the group. Finally, we show that the controlled interaction and dynamics foreseen in the system can reduce the spread of "fake news" within the group.
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Affiliation(s)
- Santos Orejudo
- Department of Psychology and Sociology, University of Zaragoza, Zaragoza, Spain
| | | | | | - Pablo Bautista
- Department of Psychology and Sociology, University of Zaragoza, Zaragoza, Spain
| | - Jesús Clemente-Gallardo
- Department of Theoretical Physics, University of Zaragoza, Zaragoza, Spain
- Institute for Biocomputation and Physics of Complex Systems, University of Zaragoza, Zaragoza, Spain
| | | | - Pilar Rivero
- Department of Specific Didactics, University of Zaragoza, Zaragoza, Spain
| | - Alfonso Tarancón
- Department of Theoretical Physics, University of Zaragoza, Zaragoza, Spain
- Institute for Biocomputation and Physics of Complex Systems, University of Zaragoza, Zaragoza, Spain
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Collective Intelligence in Polish-Ukrainian Internet Projects. Debate Models and Research Methods. FUTURE INTERNET 2020. [DOI: 10.3390/fi12060106] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
In this study, we focus on models of civic debate suitable for use in Polish-Ukrainian internet projects, as well as methods of researching collective intelligence that can help to monitor particular aspects of such debates and consequently create social bridging capital between these groups. The dynamic socio-political situation of recent years, both in Ukraine and in Poland, has created new conditions. Anti-government protests and social turmoil related to the war in Crimea and Donbas, as well as a high level of migration in the region in a short period led to the creation of a multi-ethnic society. This brings opportunities for the development of a new type of social capital: A new participative model of social life based on internet projects, with a relatively low entry barrier, space for creativity, and the widespread use of ICT technologies, can provide the new ways of debating, civic engagement, and collective action. Our research, based on a multidisciplinary literature review, as well as a series of qualitative in-depth interviews (IDIs), proved that the selected collective intelligence (CI) research methods and debate models can help to develop internet communities that will contribute to building bridging capital between Poles and Ukrainians.
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