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\n  \n 2024\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Modeling the Interplay Between Cohesion Dimensions: a Challenge for Group Affective Emergent States.\n \n \n \n \n\n\n \n Maman, L.; Willenbrock, N. L.; Chetouani, M.; Likforman-Sulem, L.; and Varni, G.\n\n\n \n\n\n\n IEEE Transactions on Affective Computing,1-14. 2024.\n \n\n\n\n
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@article{maman-2024,\n  author={Maman, Lucien and Willenbrock, Nale Lehmann- and Chetouani, Mohamed and Likforman-Sulem, Laurence and Varni, Giovanna},\n  journal={IEEE Transactions on Affective Computing}, \n  title={Modeling the Interplay Between Cohesion Dimensions: a Challenge for Group Affective Emergent States}, \n  year={2024},\n  pages={1-14},\n  doi={10.1109/TAFFC.2024.3349910},\n  url = {https://ieeexplore.ieee.org/abstract/document/10417049},\n  url_Paper = {https://lucienmaman.github.io/files/IEEE_TRANSACTION_AC_2024.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n \n Automated analysis of cohesion in small groups interactions.\n \n \n \n \n\n\n \n Maman, L.\n\n\n \n\n\n\n Ph.D. Thesis, 2022.\n Phd Thesis supervised by Laurence Likforman-Sulem, and co-supervised by Giovanne Varni and Mohamed Chetouani\n\n\n\n
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@PHDTHESIS{maman-2022-thesis,\ntitle = {Automated analysis of cohesion in small groups interactions},\nauthor = {Maman, Lucien},\nyear = {2022},\nnote = {Phd Thesis supervised by Laurence Likforman-Sulem, and co-supervised by Giovanne Varni and Mohamed Chetouani},\nurl = {http://www.theses.fr/2022IPPAT030/document},\n}\n\n
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\n \n\n \n \n \n \n \n \n Training Computational Models of Group Processes without Groundtruth: The Self- vs External Assessment’s Dilemma.\n \n \n \n \n\n\n \n Maman, L.; Volpe, G.; and Varni, G.\n\n\n \n\n\n\n In Proceedings of the 2021 International Conference on Multimodal Interaction, of ICMI '22 Companion, pages 18–23, New York, NY, USA, 2022. Association for Computing Machinery\n \n\n\n\n
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@inproceedings{maman-2022,\nauthor = {Maman, Lucien and Volpe, Gualtiero and Varni, Giovanna},\ntitle = {Training Computational Models of Group Processes without Groundtruth: The Self- vs External Assessment’s Dilemma},\nyear = {2022},\nisbn = {9781450393898},\npublisher = {Association for Computing Machinery},\naddress = {New York, NY, USA},\nabstract = { Supervised learning relies on the availability and reliability of the labels used to train computational models. \nIn research areas such as Affective Computing and Social Signal Processing, such labels are usually extracted from multiple self- and/or external assessments. \nLabels are, then, either aggregated to produce a single groundtruth label, or all used during training, potentially resulting in degrading performance of the models. \nDefining a true label is, however, complex. \nLabels can be gathered at different times, with different tools, and may contain biases. \nFurthermore, multiple assessments are usually available for a same sample with potential contradictions. \nThus, it is crucial to devise strategies that can take advantage of both self- and external assessments to train computational models without a reliable groundtruth. \nIn this study, we designed and tested 3 of such strategies with the aim of mitigating the biases and making the models more robust to uncertain labels. \nResults show that the strategy based on weighting the loss during training according to a measure of disagreement improved the performances of the baseline, hence, underlining the potential of such an approach.},\nbooktitle = {Proceedings of the 2021 International Conference on Multimodal Interaction},\npages = {18–23},\nnumpages = {6},\nkeywords = {Group Dynamics, Multimodal Interaction, Self and External Assessment, Cohesion},\nlocation = {Bengaluru, India},\nseries = {ICMI '22 Companion},\nurl = {https://doi.org/10.1145/3536220.3563687},\nurl_Paper = {https://lucienmaman.github.io/files/ICMI2022_nocop.pdf}\n}\n\n
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\n Supervised learning relies on the availability and reliability of the labels used to train computational models. In research areas such as Affective Computing and Social Signal Processing, such labels are usually extracted from multiple self- and/or external assessments. Labels are, then, either aggregated to produce a single groundtruth label, or all used during training, potentially resulting in degrading performance of the models. Defining a true label is, however, complex. Labels can be gathered at different times, with different tools, and may contain biases. Furthermore, multiple assessments are usually available for a same sample with potential contradictions. Thus, it is crucial to devise strategies that can take advantage of both self- and external assessments to train computational models without a reliable groundtruth. In this study, we designed and tested 3 of such strategies with the aim of mitigating the biases and making the models more robust to uncertain labels. Results show that the strategy based on weighting the loss during training according to a measure of disagreement improved the performances of the baseline, hence, underlining the potential of such an approach.\n
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\n \n\n \n \n \n \n \n \n Exploiting the Interplay between Social and Task Dimensions of Cohesion to Predict Its Dynamics Leveraging Social Sciences.\n \n \n \n \n\n\n \n Maman, L.; Likforman-Sulem, L.; Chetouani, M.; and Varni, G.\n\n\n \n\n\n\n In Proceedings of the 2021 International Conference on Multimodal Interaction, of ICMI '21, pages 16–24, New York, NY, USA, 2021. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"ExploitingPaper\n  \n \n \n \"Exploiting paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 14 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{maman-2021-icmi,\nauthor = {Maman, Lucien and Likforman-Sulem, Laurence and Chetouani, Mohamed and Varni, Giovanna},\ntitle = {Exploiting the Interplay between Social and Task Dimensions of Cohesion to Predict Its Dynamics Leveraging Social Sciences},\nyear = {2021},\nisbn = {9781450384810},\npublisher = {Association for Computing Machinery},\naddress = {New York, NY, USA},\nabstract = { Emergent states are behavioral, cognitive and affective processes appearing among\nthe members of a group when they interact together. In the last decade, the development\nof computational approaches received a growing interest in building Human-Centered\nsystems. Such a development is particularly difficult because some of these states\nhave several dimensions interplaying somehow and somewhere over time. In this paper,\nwe focus on cohesion, its dimensions and their interplay. Several definitions of cohesion\nexist, it can be simply defined as the tendency of a group to stick together to pursue\ngoals and/or affective needs. This plethora of definitions resulted in many different\ncohesion dimensions. Social and Task dimensions are the most investigated both in\nSocial Sciences and Computer Science since they both play an important role in a wide\nrange of contexts and groups. To the best of our knowledge, however, no previous work\non the prediction of cohesion dynamics focused on how these 2 dimensions interplay.\nWe leverage Social Sciences to address this issue. In particular, we take advantage\nof the importance of Social cohesion for creating flexible and constructive relationships\nto reinforce Task cohesion. We describe a Deep Neural Network architecture (DNN) for\npredicting the dynamics of Task cohesion by applying transfer learning from a pre-trained\nmodel dedicated to the prediction of Social cohesion dynamics. Our architecture is\nevaluated against several baselines. Results show that it significantly improves the\npredictions of the Task cohesion dynamics, confirming the benefits of integrating\nSocial Sciences insights into models architectures.},\nbooktitle = {Proceedings of the 2021 International Conference on Multimodal Interaction},\npages = {16–24},\nnumpages = {9},\nkeywords = {Group Dynamics, Multimodal Interaction, Cohesion, Social Signal Processing, Transfer Learning},\nlocation = {Montr\\'{e}al, QC, Canada},\nseries = {ICMI '21},\nurl = {https://doi.org/10.1145/3462244.3479940},\nurl_Paper = {https://lucienmaman.github.io/files/ICMI2021_nocop.pdf}\n}\n\n
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\n Emergent states are behavioral, cognitive and affective processes appearing among the members of a group when they interact together. In the last decade, the development of computational approaches received a growing interest in building Human-Centered systems. Such a development is particularly difficult because some of these states have several dimensions interplaying somehow and somewhere over time. In this paper, we focus on cohesion, its dimensions and their interplay. Several definitions of cohesion exist, it can be simply defined as the tendency of a group to stick together to pursue goals and/or affective needs. This plethora of definitions resulted in many different cohesion dimensions. Social and Task dimensions are the most investigated both in Social Sciences and Computer Science since they both play an important role in a wide range of contexts and groups. To the best of our knowledge, however, no previous work on the prediction of cohesion dynamics focused on how these 2 dimensions interplay. We leverage Social Sciences to address this issue. In particular, we take advantage of the importance of Social cohesion for creating flexible and constructive relationships to reinforce Task cohesion. We describe a Deep Neural Network architecture (DNN) for predicting the dynamics of Task cohesion by applying transfer learning from a pre-trained model dedicated to the prediction of Social cohesion dynamics. Our architecture is evaluated against several baselines. Results show that it significantly improves the predictions of the Task cohesion dynamics, confirming the benefits of integrating Social Sciences insights into models architectures.\n
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\n \n\n \n \n \n \n \n \n An Exploratory Computational Study on the Effect of Emergent Leadership on Social and Task Cohesion.\n \n \n \n \n\n\n \n Sabry, S.; Maman, L.; and Varni, G.\n\n\n \n\n\n\n In Companion Publication of the 2021 International Conference on Multimodal Interaction, of ICMI '21 Companion, pages 263–272, New York, NY, USA, 2021. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"AnPaper\n  \n \n \n \"An paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{sabry-2021,\n author = {Sabry, Soumaya and Maman, Lucien and Varni, Giovanna},\n title = {An Exploratory Computational Study on the Effect of Emergent Leadership on Social and Task Cohesion},\n year = {2021},\n isbn = {9781450384711},\n publisher = {Association for Computing Machinery},\n address = {New York, NY, USA},\n abstract = {Leadership is a complex and dynamic phenomenon that has received a lot of attention from psychologists over the last 50 years, primarily due to its relationships with team effectiveness and performances. Depending on the group (e.g., size, relationships among members) and the context (e.g., solving a task under pressure), various styles of leadership could emerge. These styles can either be formally decided or manifest informally. Among the informal types of leadership, emergent leadership is one of the most studied. It is an emergent state that develops over time in a group and that interplays with other emergent states such as cohesion. Only a few computational studies focusing on predicting emergent leadership take advantage of the relationships with other phenomena to improve their models’ performances. These approaches, however, only apply to their models aimed at predicting emergent leadership. There is, to the best of our knowledge, no approach that integrates emergent leadership into computational models of cohesion. In this study, we take a first step towards bridging this gap by introducing 2 families of approaches inspired by Social Sciences’ insights to integrate emergent leadership into computational models of cohesion. The first family consists of amplifying the differences between leaders’ and followers’ features while the second one focuses on adding leadership representation directly into the computational model’s architecture. In particular, for each family, we describe 2 approaches that are applied to a Deep Neural Network model aimed at predicting the dynamics of cohesion across various tasks over time. This study explores whether and how applying our approaches improves the prediction of the dynamics of the Social and Task dimensions of cohesion. Therefore, the performance of a computational model of cohesion that does not integrate the interplay between cohesion and emergent leadership is compared with the same computational models that apply our approaches. Results show that approaches from both families significantly improved the prediction of the Task cohesion dynamics, confirming the benefits of integrating emergent leadership following Social Psychology’s insights to enforce computational models of cohesion at both feature and architecture levels.},\n booktitle = {Companion Publication of the 2021 International Conference on Multimodal Interaction},\n pages = {263–272},\n numpages = {10},\n keywords = {Cohesion, Social Signal Processing, Emergent Leadership, Multimodal Interaction},\n location = {Montreal, QC, Canada},\n series = {ICMI '21 Companion},\n url = {https://doi.org/10.1145/3461615.3485415},\n url_Paper = {https://lucienmaman.github.io/files/IGTD2021_nocop.pdf}\n}\n\n
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\n Leadership is a complex and dynamic phenomenon that has received a lot of attention from psychologists over the last 50 years, primarily due to its relationships with team effectiveness and performances. Depending on the group (e.g., size, relationships among members) and the context (e.g., solving a task under pressure), various styles of leadership could emerge. These styles can either be formally decided or manifest informally. Among the informal types of leadership, emergent leadership is one of the most studied. It is an emergent state that develops over time in a group and that interplays with other emergent states such as cohesion. Only a few computational studies focusing on predicting emergent leadership take advantage of the relationships with other phenomena to improve their models’ performances. These approaches, however, only apply to their models aimed at predicting emergent leadership. There is, to the best of our knowledge, no approach that integrates emergent leadership into computational models of cohesion. In this study, we take a first step towards bridging this gap by introducing 2 families of approaches inspired by Social Sciences’ insights to integrate emergent leadership into computational models of cohesion. The first family consists of amplifying the differences between leaders’ and followers’ features while the second one focuses on adding leadership representation directly into the computational model’s architecture. In particular, for each family, we describe 2 approaches that are applied to a Deep Neural Network model aimed at predicting the dynamics of cohesion across various tasks over time. This study explores whether and how applying our approaches improves the prediction of the dynamics of the Social and Task dimensions of cohesion. Therefore, the performance of a computational model of cohesion that does not integrate the interplay between cohesion and emergent leadership is compared with the same computational models that apply our approaches. Results show that approaches from both families significantly improved the prediction of the Task cohesion dynamics, confirming the benefits of integrating emergent leadership following Social Psychology’s insights to enforce computational models of cohesion at both feature and architecture levels.\n
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\n \n\n \n \n \n \n \n \n Using Valence Emotion to Predict Group Cohesion’s Dynamics: Top-down and Bottom-up Approaches.\n \n \n \n \n\n\n \n Maman, L.; Chetouani, M.; Likforman-Sulem, L.; and Varni, G.\n\n\n \n\n\n\n In 2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII), pages 1-8, 2021. IEEE\n \n\n\n\n
\n\n\n\n \n \n \"UsingPaper\n  \n \n \n \"Using paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 18 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{maman-2021-acii,  \n author={Maman, Lucien and Chetouani, Mohamed and Likforman-Sulem, Laurence and Varni, Giovanna},  \n booktitle={2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII)},   \n title={Using Valence Emotion to Predict Group Cohesion’s Dynamics: Top-down and Bottom-up Approaches},   \n year={2021}, \n pages={1-8},\n abstract={Cohesion is an affective group phenomenon. It has received a lot of attention from scholars both in Social Sciences and in Affective Computing that showed that cohesion and emotion influence each other, highlighting the need to jointly analyze them. This study presents 2 deep neural network architectures grounded on multitask learning to jointly predict cohesion and emotion. Inspired by 2 major Social Sciences approaches on group emotion (i.e., Top-down and Bottom-up), these architectures exploit cohesion and emotion interdependencies intending to improve the prediction of the dynamics (i.e. changes over time) of the Social and Task dimensions of cohesion. Emotion, here, is addressed in terms of its valence. Both architectures are evaluated against the performances of a similar model that only predicts the dynamics of both the Social and Task dimensions of cohesion, without integrating valence. Statistical analysis shows that only the deep model implementing the Bottom-up approach significantly improved the predictions of the Task cohesion’s dynamics. This result confirms the theoretical and practical benefits of multitasking as it takes full advantage of the inherent relationships between group emotion and cohesion to improve Task cohesion’s predictions.},\n keywords={Group Cohesion, Group Dynamics, Group Emotion, Multimodal Interaction, Multitask Learning},\n organization={IEEE},\n ISSN={2156-8111},\n url = {https://doi.org/10.1109/ACII52823.2021.9597429},\n url_Paper = {https://lucienmaman.github.io/files/available.pdf}\n}\n\n
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\n Cohesion is an affective group phenomenon. It has received a lot of attention from scholars both in Social Sciences and in Affective Computing that showed that cohesion and emotion influence each other, highlighting the need to jointly analyze them. This study presents 2 deep neural network architectures grounded on multitask learning to jointly predict cohesion and emotion. Inspired by 2 major Social Sciences approaches on group emotion (i.e., Top-down and Bottom-up), these architectures exploit cohesion and emotion interdependencies intending to improve the prediction of the dynamics (i.e. changes over time) of the Social and Task dimensions of cohesion. Emotion, here, is addressed in terms of its valence. Both architectures are evaluated against the performances of a similar model that only predicts the dynamics of both the Social and Task dimensions of cohesion, without integrating valence. Statistical analysis shows that only the deep model implementing the Bottom-up approach significantly improved the predictions of the Task cohesion’s dynamics. This result confirms the theoretical and practical benefits of multitasking as it takes full advantage of the inherent relationships between group emotion and cohesion to improve Task cohesion’s predictions.\n
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\n \n\n \n \n \n \n \n \n GAME-ON: A Multimodal Dataset for Cohesion and Group Analysis.\n \n \n \n \n\n\n \n Maman, L.; Ceccaldi, E.; Lehmann-Willenbrock, N.; Likforman-Sulem, L.; Chetouani, M.; Volpe, G.; and Varni, G.\n\n\n \n\n\n\n IEEE Access, 8: 124185-124203. 2020.\n \n\n\n\n
\n\n\n\n \n \n \"GAME-ON:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 10 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{maman-2020-gameon,  \n author={Maman, Lucien and Ceccaldi, Eleonora and Lehmann-Willenbrock, Nale and Likforman-Sulem, Laurence and Chetouani, Mohamed and Volpe, Gualtiero and Varni, Giovanna},\n journal={IEEE Access},\n title={GAME-ON: A Multimodal Dataset for Cohesion and Group Analysis},\n year={2020},\n volume={8},\n pages={124185-124203},\n abstract = {This paper presents GAME-ON (Group Analysis of Multimodal Expression of cohesiON), a multimodal dataset specifically designed for studying group cohesion and for explicitly controlling its variation over time. Cohesion is here addressed according to the Severt and Estrada's theoretical multidimensional integrative framework. More specifically, GAME-ON focuses on the social and task dimensions of the instrumental function of cohesion. The dataset consists of over 11 hours of synchronized multimodal recordings (audio, video, and motion capture data) of 17 small groups (3 persons) playing a social game, i.e., an escape game. The game consists of several tasks designed to manipulate the variation of cohesion over time. GAME-ON includes annotations consisting of self-assessment of cohesion and other constructs such as emotions, leadership, and warmth and competence. A first statistical analysis of these annotations shows that we successfully manipulated all the relative variations of cohesion (between tasks) over time. This holds for all tasks except for one where we observed a significant variation of cohesion in the opposite direction than expected. The dataset will be publicly available for research purposes. The motivation of our work is to provide the scientific community with an asset for studying cohesion and other group phenomena.},\n keywords={Cohesion, Group interaction analysis, Multimodal dataset, Social signal processing},\n doi={10.1109/ACCESS.2020.3005719},\n ISSN={2169-3536},\n url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9127943},\n}\n\n
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\n This paper presents GAME-ON (Group Analysis of Multimodal Expression of cohesiON), a multimodal dataset specifically designed for studying group cohesion and for explicitly controlling its variation over time. Cohesion is here addressed according to the Severt and Estrada's theoretical multidimensional integrative framework. More specifically, GAME-ON focuses on the social and task dimensions of the instrumental function of cohesion. The dataset consists of over 11 hours of synchronized multimodal recordings (audio, video, and motion capture data) of 17 small groups (3 persons) playing a social game, i.e., an escape game. The game consists of several tasks designed to manipulate the variation of cohesion over time. GAME-ON includes annotations consisting of self-assessment of cohesion and other constructs such as emotions, leadership, and warmth and competence. A first statistical analysis of these annotations shows that we successfully manipulated all the relative variations of cohesion (between tasks) over time. This holds for all tasks except for one where we observed a significant variation of cohesion in the opposite direction than expected. The dataset will be publicly available for research purposes. The motivation of our work is to provide the scientific community with an asset for studying cohesion and other group phenomena.\n
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\n \n\n \n \n \n \n \n \n Multimodal Groups' Analysis for Automated Cohesion Estimation.\n \n \n \n \n\n\n \n Maman, L.\n\n\n \n\n\n\n In Proceedings of the 2020 International Conference on Multimodal Interaction, of ICMI '20, pages 713–717, New York, NY, USA, 2020. Association for Computing Machinery\n \n\n\n\n
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@inproceedings{maman-2020-dc,\n author = {Maman, Lucien},\n title = {Multimodal Groups' Analysis for Automated Cohesion Estimation},\n year = {2020},\n isbn = {9781450375818},\n publisher = {Association for Computing Machinery},\n address = {New York, NY, USA},\n abstract = {Groups are getting more and more scholars' attention. With the rise of Social Signal Processing (SSP), many studies based on Social Sciences and Psychology findings focused on detecting and classifying groups? dynamics. Cohesion plays an important role in these groups? dynamics and is one of the most studied emergent states, involving both group motions and goals. This PhD project aims to provide a computational model addressing the multidimensionality of cohesion and capturing its subtle dynamics. It will offer new opportunities to develop applications to enhance interactions among humans as well as among humans and machines.},\n booktitle = {Proceedings of the 2020 International Conference on Multimodal Interaction},\n pages = {713–717},\n numpages = {5},\n keywords = {computational model, emergent state, dataset, multimodality, cohesion},\n location = {Virtual Event, Netherlands},\n series = {ICMI '20},\n url = {https://doi.org/10.1145/3382507.3421153},\n url_Paper = {https://lucienmaman.github.io/files/ICMI2020_dc_nocop.pdf}\n}\n\n
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\n Groups are getting more and more scholars' attention. With the rise of Social Signal Processing (SSP), many studies based on Social Sciences and Psychology findings focused on detecting and classifying groups? dynamics. Cohesion plays an important role in these groups? dynamics and is one of the most studied emergent states, involving both group motions and goals. This PhD project aims to provide a computational model addressing the multidimensionality of cohesion and capturing its subtle dynamics. It will offer new opportunities to develop applications to enhance interactions among humans as well as among humans and machines.\n
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\n \n\n \n \n \n \n \n \n Modeling Dynamics of Task and Social Cohesion from the Group Perspective Using Nonverbal Motion Capture-Based Features.\n \n \n \n \n\n\n \n Walocha, F.; Maman, L.; Chetouani, M.; and Varni, G.\n\n\n \n\n\n\n In Companion Publication of the 2020 International Conference on Multimodal Interaction, of ICMI '20 Companion, pages 182–190, New York, NY, USA, 2020. Association for Computing Machinery\n \n\n\n\n
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@inproceedings{walocha-2020,\n author = {Walocha, Fabian and Maman, Lucien and Chetouani, Mohamed and Varni, Giovanna},\n title = {Modeling Dynamics of Task and Social Cohesion from the Group Perspective Using Nonverbal Motion Capture-Based Features},\n year = {2020},\n isbn = {9781450380027},\n publisher = {Association for Computing Machinery},\n address = {New York, NY, USA},\n abstract = {Group cohesion is a multidimensional emergent state that manifests during group interaction. It has been extensively studied in several disciplines such as Social Sciences and Computer Science and it has been investigated through both verbal and nonverbal communication. This work investigates the dynamics of task and social dimensions of cohesion through nonverbal motion-capture-based features. We modeled dynamics either as decreasing or as stable/increasing regarding the previous measurement of cohesion. We design and develop a set of features related to space and body movement from motion capture data as it offers reliable and accurate measurements of body motions. Then, we use a random forest model to binary classify (decrease or no decrease) the dynamics of cohesion, for the task and social dimensions. Our model adopts labels from self-assessments of group cohesion, providing a different perspective of study with respect to the previous work relying on third-party labelling. The analysis reveals that, in a multilabel setting, our model is able to predict changes in task and social cohesion with an average accuracy of 64%(±3%) and 67%(±3%), respectively, outperforming random guessing (50%). In a multiclass setting comprised of four classes (i.e., decrease/decrease, decrease/no decrease, no decrease/decrease and no decrease/no decrease), our model also outperforms chance level (25%) for each class (i.e., 54%, 44%, 33%, 50%, respectively). Furthermore, this work provides a method based on notions from cooperative game theory (i.e., SHAP values) to assess features' impact and importance. We identify that the most important features for predicting cohesion dynamics relate to spacial distance, the amount of movement while walking, the overall posture expansion as well as the amount of inter-personal facing in the group.},\n booktitle = {Companion Publication of the 2020 International Conference on Multimodal Interaction},\n pages = {182–190},\n numpages = {9},\n keywords = {machine learning, cohesion, nonverbal communication, group interaction analysis, social signal processing},\n location = {Virtual Event, Netherlands},\n series = {ICMI '20 Companion},\n url = {https://doi.org/10.1145/3395035.3425963},\n url_Paper = {https://lucienmaman.github.io/files/IGTD2020_nocop.pdf}\n}\n\n
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\n Group cohesion is a multidimensional emergent state that manifests during group interaction. It has been extensively studied in several disciplines such as Social Sciences and Computer Science and it has been investigated through both verbal and nonverbal communication. This work investigates the dynamics of task and social dimensions of cohesion through nonverbal motion-capture-based features. We modeled dynamics either as decreasing or as stable/increasing regarding the previous measurement of cohesion. We design and develop a set of features related to space and body movement from motion capture data as it offers reliable and accurate measurements of body motions. Then, we use a random forest model to binary classify (decrease or no decrease) the dynamics of cohesion, for the task and social dimensions. Our model adopts labels from self-assessments of group cohesion, providing a different perspective of study with respect to the previous work relying on third-party labelling. The analysis reveals that, in a multilabel setting, our model is able to predict changes in task and social cohesion with an average accuracy of 64%(±3%) and 67%(±3%), respectively, outperforming random guessing (50%). In a multiclass setting comprised of four classes (i.e., decrease/decrease, decrease/no decrease, no decrease/decrease and no decrease/no decrease), our model also outperforms chance level (25%) for each class (i.e., 54%, 44%, 33%, 50%, respectively). Furthermore, this work provides a method based on notions from cooperative game theory (i.e., SHAP values) to assess features' impact and importance. We identify that the most important features for predicting cohesion dynamics relate to spacial distance, the amount of movement while walking, the overall posture expansion as well as the amount of inter-personal facing in the group.\n
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\n \n\n \n \n \n \n \n \n GRACE : Un projet portant sur l'étude automatique de la cohésion dans les petits groupes d'humains.\n \n \n \n \n\n\n \n Maman, L.; and Varni, G.\n\n\n \n\n\n\n In Workshop sur les Affects, Compagnons artificiels et Interactions, Saint Pierre d'Oléron, France, June 2020. CNRS, Université Toulouse Jean Jaurès, Université de Bordeaux\n \n\n\n\n
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@inproceedings{maman-2020-wacai,\n TITLE = {{GRACE : Un projet portant sur l'{\\'e}tude automatique de la coh{\\'e}sion dans les petits groupes d'humains}},\n AUTHOR = {Maman, Lucien and Varni, Giovanna},\n BOOKTITLE = {{Workshop sur les Affects, Compagnons artificiels et Interactions}},\n ADDRESS = {Saint Pierre d'Ol{\\'e}ron, France},\n ORGANIZATION = {{CNRS, Universit{\\'e} Toulouse Jean Jaur{\\`e}s, Universit{\\'e} de Bordeaux}},\n YEAR = {2020},\n MONTH = Jun,\n KEYWORDS = {Analyse des signaux sociaux ; {\\'e}tats {\\'e}mergents ; coh{\\'e}sion},\n PDF = {https://hal.archives-ouvertes.fr/hal-02933474/file/MAMAN_WACAI2020.pdf},\n HAL_ID = {hal-02933474},\n HAL_VERSION = {v1},\n ABSTRACT = {Cet article présente le projet GRACE (GRoups’ analysis for Automated Cohesion Estimation), un projet de recherche fondamentale JCJC financé par l’Agence Nationale de la Recherche française qui vise à développer un modèle informatique de la cohésion dans les interactions humain-humain. Premièrement, les objectifs du projet sont décrits. Ensuite, une brève revue de l’état de l’art sur la cohésion est détaillée. Enfin, la méthodologie adoptée est présentée},\n url = {https://hal.archives-ouvertes.fr/hal-02933474},\n}\n
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\n Cet article présente le projet GRACE (GRoups’ analysis for Automated Cohesion Estimation), un projet de recherche fondamentale JCJC financé par l’Agence Nationale de la Recherche française qui vise à développer un modèle informatique de la cohésion dans les interactions humain-humain. Premièrement, les objectifs du projet sont décrits. Ensuite, une brève revue de l’état de l’art sur la cohésion est détaillée. Enfin, la méthodologie adoptée est présentée\n
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