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Yamamoto Y, Yokoyama K, Kijima A, Okumura M, Shima H. Interpersonal strategy for controlling unpredictable opponents in soft tennis. Sci Rep 2024; 14:20546. [PMID: 39232140 PMCID: PMC11375079 DOI: 10.1038/s41598-024-71538-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Accepted: 08/28/2024] [Indexed: 09/06/2024] Open
Abstract
Competition in sports, unlike cooperation in everyday life, does not involve a single solution because individuals aim to behave unpredictably, thereby preventing others from predicting their actions. This study determined how individuals in court-based sports attempted to control others' unpredictable behaviors, addressing the gap in previous studies regarding the lack of clarity around strategies employed by individuals in competitive situations. We achieved this by applying a switching hybrid dynamics model, considering external inputs to analyze individual behaviors. Consequently, the study indicates that skilled individuals, in contrast to intermediate players, exhibit greater consistency in their behaviors. These skilled individuals lead others to anticipate their consistency and subsequently employ strategies to disrupt these expectations. This strategy exploits the principles of active human inference, implying that competition involves cooperation. This was revealed by an analysis of both human decision-making and behavior in actual matches as discrete and continuous dynamical systems. This interpersonal strategy could assist policymakers in the field of everyday life to enhance competitiveness. This strategy enables policymakers to adopt new policies that promote cooperation with competitors, ultimately increasing competitiveness in various aspects of our daily lives.
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Affiliation(s)
- Yuji Yamamoto
- Department of Psychological Sciences, Niigata University of Health and Welfare, Niigata, 950-3098, Japan.
| | - Keiko Yokoyama
- Research Center of Health, Physical Fitness, and Sports, Nagoya University, Nagoya, 464-8601, Japan
| | - Akifumi Kijima
- Department of Education, University of Yamanashi, Kofu, 400-0016, Japan
| | - Motoki Okumura
- Faculty of Education, Tokyo Gakugei University, Koganei, 184-8501, Japan
| | - Hiroyuki Shima
- Department of Environmental Sciences, University of Yamanashi, Kofu, 400-0016, Japan
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2
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Kaneko T, Matsumoto J, Lu W, Zhao X, Ueno-Nigh LR, Oishi T, Kimura K, Otsuka Y, Zheng A, Ikenaka K, Baba K, Mochizuki H, Nishijo H, Inoue KI, Takada M. Deciphering social traits and pathophysiological conditions from natural behaviors in common marmosets. Curr Biol 2024; 34:2854-2867.e5. [PMID: 38889723 DOI: 10.1016/j.cub.2024.05.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 05/15/2024] [Accepted: 05/17/2024] [Indexed: 06/20/2024]
Abstract
Nonhuman primates (NHPs) are indispensable animal models by virtue of the continuity of behavioral repertoires across primates, including humans. However, behavioral assessment at the laboratory level has so far been limited. Employing the application of three-dimensional (3D) pose estimation and the optimal integration of subsequent analytic methodologies, we demonstrate that our artificial intelligence (AI)-based approach has successfully deciphered the ethological, cognitive, and pathological traits of common marmosets from their natural behaviors. By applying multiple deep neural networks trained with large-scale datasets, we established an evaluation system that could reconstruct and estimate the 3D poses of the marmosets, a small NHP that is suitable for analyzing complex natural behaviors in laboratory setups. We further developed downstream analytic methodologies to quantify a variety of behavioral parameters beyond motion kinematics. We revealed the distinct parental roles of male and female marmosets through automated detections of food-sharing behaviors using a spatial-temporal filter on 3D poses. Employing a recurrent neural network to analyze 3D pose time series data during social interactions, we additionally discovered that marmosets adjusted their behaviors based on others' internal state, which is not directly observable but can be inferred from the sequence of others' actions. Moreover, a fully unsupervised approach enabled us to detect progressively appearing symptomatic behaviors over a year in a Parkinson's disease model. The high-throughput and versatile nature of an AI-driven approach to analyze natural behaviors will open a new avenue for neuroscience research dealing with big-data analyses of social and pathophysiological behaviors in NHPs.
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Affiliation(s)
- Takaaki Kaneko
- Center for the Evolutionary Origins of Human Behavior, Kyoto University, Inuyama, Aichi 484-8506, Japan.
| | - Jumpei Matsumoto
- Department of System Emotional Science, Faculty of Medicine, University of Toyama, Toyama 930-0194, Japan; Research Center for Idling Brain Science, University of Toyama, Toyama 930-0194, Japan
| | - Wanyi Lu
- Center for the Evolutionary Origins of Human Behavior, Kyoto University, Inuyama, Aichi 484-8506, Japan
| | - Xincheng Zhao
- Center for the Evolutionary Origins of Human Behavior, Kyoto University, Inuyama, Aichi 484-8506, Japan
| | - Louie Richard Ueno-Nigh
- Center for the Evolutionary Origins of Human Behavior, Kyoto University, Inuyama, Aichi 484-8506, Japan
| | - Takao Oishi
- Center for the Evolutionary Origins of Human Behavior, Kyoto University, Inuyama, Aichi 484-8506, Japan
| | - Kei Kimura
- Center for the Evolutionary Origins of Human Behavior, Kyoto University, Inuyama, Aichi 484-8506, Japan
| | - Yukiko Otsuka
- Center for the Evolutionary Origins of Human Behavior, Kyoto University, Inuyama, Aichi 484-8506, Japan
| | - Andi Zheng
- Center for the Evolutionary Origins of Human Behavior, Kyoto University, Inuyama, Aichi 484-8506, Japan
| | - Kensuke Ikenaka
- Department of Neurology, Osaka University Graduate School of Medicine, Suita, Osaka 565-0871, Japan
| | - Kousuke Baba
- Department of Neurology, Osaka University Graduate School of Medicine, Suita, Osaka 565-0871, Japan
| | - Hideki Mochizuki
- Department of Neurology, Osaka University Graduate School of Medicine, Suita, Osaka 565-0871, Japan
| | - Hisao Nishijo
- Department of System Emotional Science, Faculty of Medicine, University of Toyama, Toyama 930-0194, Japan; Research Center for Idling Brain Science, University of Toyama, Toyama 930-0194, Japan; Faculty of Human Sciences, University of East Asia, Shimonoseki, Yamaguchi 751-8503, Japan
| | - Ken-Ichi Inoue
- Center for the Evolutionary Origins of Human Behavior, Kyoto University, Inuyama, Aichi 484-8506, Japan
| | - Masahiko Takada
- Center for the Evolutionary Origins of Human Behavior, Kyoto University, Inuyama, Aichi 484-8506, Japan; Department of Neurology, Osaka University Graduate School of Medicine, Suita, Osaka 565-0871, Japan.
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Banerjee B, Baruah M. Attention-Based Variational Autoencoder Models for Human-Human Interaction Recognition via Generation. SENSORS (BASEL, SWITZERLAND) 2024; 24:3922. [PMID: 38931706 PMCID: PMC11207823 DOI: 10.3390/s24123922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 06/02/2024] [Accepted: 06/05/2024] [Indexed: 06/28/2024]
Abstract
The remarkable human ability to predict others' intent during physical interactions develops at a very early age and is crucial for development. Intent prediction, defined as the simultaneous recognition and generation of human-human interactions, has many applications such as in assistive robotics, human-robot interaction, video and robotic surveillance, and autonomous driving. However, models for solving the problem are scarce. This paper proposes two attention-based agent models to predict the intent of interacting 3D skeletons by sampling them via a sequence of glimpses. The novelty of these agent models is that they are inherently multimodal, consisting of perceptual and proprioceptive pathways. The action (attention) is driven by the agent's generation error, and not by reinforcement. At each sampling instant, the agent completes the partially observed skeletal motion and infers the interaction class. It learns where and what to sample by minimizing the generation and classification errors. Extensive evaluation of our models is carried out on benchmark datasets and in comparison to a state-of-the-art model for intent prediction, which reveals that classification and generation accuracies of one of the proposed models are comparable to those of the state of the art even though our model contains fewer trainable parameters. The insights gained from our model designs can inform the development of efficient agents, the future of artificial intelligence (AI).
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Affiliation(s)
- Bonny Banerjee
- Institute for Intelligent Systems, and Department of Electrical & Computer Engineering, University of Memphis, Memphis, TN 38152, USA;
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Roth AM, Lokesh R, Tang J, Buggeln JH, Smith C, Calalo JA, Sullivan SR, Ngo T, Germain LS, Carter MJ, Cashaback JGA. Punishment Leads to Greater Sensorimotor Learning But Less Movement Variability Compared to Reward. Neuroscience 2024; 540:12-26. [PMID: 38220127 PMCID: PMC10922623 DOI: 10.1016/j.neuroscience.2024.01.004] [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: 09/18/2023] [Revised: 01/05/2024] [Accepted: 01/09/2024] [Indexed: 01/16/2024]
Abstract
When a musician practices a new song, hitting a correct note sounds pleasant while striking an incorrect note sounds unpleasant. Such reward and punishment feedback has been shown to differentially influence the ability to learn a new motor skill. Recent work has suggested that punishment leads to greater movement variability, which causes greater exploration and faster learning. To further test this idea, we collected 102 participants over two experiments. Unlike previous work, in Experiment 1 we found that punishment did not lead to faster learning compared to reward (n = 68), but did lead to a greater extent of learning. Surprisingly, we also found evidence to suggest that punishment led to less movement variability, which was related to the extent of learning. We then designed a second experiment that did not involve adaptation, allowing us to further isolate the influence of punishment feedback on movement variability. In Experiment 2, we again found that punishment led to significantly less movement variability compared to reward (n = 34). Collectively our results suggest that punishment feedback leads to less movement variability. Future work should investigate whether punishment feedback leads to a greater knowledge of movement variability and or increases the sensitivity of updating motor actions.
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Affiliation(s)
- Adam M Roth
- Department of Mechanical Engineering, University of Delaware, United States
| | - Rakshith Lokesh
- Department of Biomedical Engineering, University of Delaware, United States
| | - Jiaqiao Tang
- Department of Kinesiology, McMaster University, Canada
| | - John H Buggeln
- Department of Biomedical Engineering, University of Delaware, United States
| | - Carly Smith
- Department of Biomedical Engineering, University of Delaware, United States
| | - Jan A Calalo
- Department of Mechanical Engineering, University of Delaware, United States
| | - Seth R Sullivan
- Department of Biomedical Engineering, University of Delaware, United States
| | - Truc Ngo
- Department of Biomedical Engineering, University of Delaware, United States
| | | | | | - Joshua G A Cashaback
- Department of Mechanical Engineering, University of Delaware, United States; Department of Biomedical Engineering, University of Delaware, United States; Kinesiology and Applied Physiology, University of Delaware, United States; Interdisciplinary Neuroscience Graduate Program, University of Delaware, United States; Biomechanics and Movement Science Program, University of Delaware, United States; Department of Kinesiology, McMaster University, Canada.
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Kim SJ. Virtual fashion experiences in virtual reality fashion show spaces. Front Psychol 2023; 14:1276856. [PMID: 38046109 PMCID: PMC10693427 DOI: 10.3389/fpsyg.2023.1276856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Accepted: 11/07/2023] [Indexed: 12/05/2023] Open
Abstract
Introduction Virtual reality (VR) provides a new fashion space and fashion experience. This study focuses on immersive VR and fashion shows to empirically explore the VR fashion space and fashion experience. Insights specific to fashion have not been presented in as much depth in the literature; thus, the current findings are particularly valuable and insightful. Methods This study employed three immersive VR (IVR) fashion show stimuli and in-depth interviews according to a semi-structured questionnaire. Collected data were analyzed based on the concept of VR space and VR experience derived through literature research. Results The VR fashion space was divided into three types and VR experiences of cognitive presence, sensible immersion, emotional immersion, and aesthetic interaction were derived accordingly. First, the physical representation of a fashion show induced a cognitive and emotional sense of presence, in which users felt as though they had moved to the same time and place as those at the fashion show. Second, participants experienced cognitive confusion owing to the differences with a priori experiences in the fashion show space (i.e., reality and imagination coexist). Third, participants transcended the limitations of physical reality while in the fashion show space of pataphysics (which was realized with human imagination), and they moved beyond the stage of confusion that is experienced while facing realistic objects to connect to creative inspiration. Discussion The difference in the properties of VR space may be associated with distinct VR fashion experiences. The findings suggest that (1) a priori elements such as sociocultural contexts and personal experiences differ in the experiential dimension of virtual space, (2) the VR fashion show space induces a psychological experience between brand and consumer, and (3) creative inspiration and exploratory play can be greatly induced in a user if the immersive fashion space is further from the original source.
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Affiliation(s)
- Se Jin Kim
- Department of Clothing and Textiles, Changwon National University, Changwon, Republic of Korea
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Calalo JA, Roth AM, Lokesh R, Sullivan SR, Wong JD, Semrau JA, Cashaback JGA. The sensorimotor system modulates muscular co-contraction relative to visuomotor feedback responses to regulate movement variability. J Neurophysiol 2023; 129:751-766. [PMID: 36883741 PMCID: PMC10069957 DOI: 10.1152/jn.00472.2022] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 02/13/2023] [Accepted: 03/01/2023] [Indexed: 03/09/2023] Open
Abstract
The naturally occurring variability in our movements often poses a significant challenge when attempting to produce precise and accurate actions, which is readily evident when playing a game of darts. Two differing, yet potentially complementary, control strategies that the sensorimotor system may use to regulate movement variability are impedance control and feedback control. Greater muscular co-contraction leads to greater impedance that acts to stabilize the hand, while visuomotor feedback responses can be used to rapidly correct for unexpected deviations when reaching toward a target. Here, we examined the independent roles and potential interplay of impedance control and visuomotor feedback control when regulating movement variability. Participants were instructed to perform a precise reaching task by moving a cursor through a narrow visual channel. We manipulated cursor feedback by visually amplifying movement variability and/or delaying the visual feedback of the cursor. We found that participants decreased movement variability by increasing muscular co-contraction, aligned with an impedance control strategy. Participants displayed visuomotor feedback responses during the task but, unexpectedly, there was no modulation between conditions. However, we did find a relationship between muscular co-contraction and visuomotor feedback responses, suggesting that participants modulated impedance control relative to feedback control. Taken together, our results highlight that the sensorimotor system modulates muscular co-contraction, relative to visuomotor feedback responses, to regulate movement variability and produce accurate actions.NEW & NOTEWORTHY The sensorimotor system has the constant challenge of dealing with the naturally occurring variability in our movements. Here, we investigated the potential roles of muscular co-contraction and visuomotor feedback responses to regulate movement variability. When we visually amplified movements, we found that the sensorimotor system primarily uses muscular co-contraction to regulate movement variability. Interestingly, we found that muscular co-contraction was modulated relative to inherent visuomotor feedback responses, suggesting an interplay between impedance and feedback control.
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Affiliation(s)
- Jan A Calalo
- Department of Biomedical Engineering, University of Delaware, Newark, Delaware, United States
- Department of Mechanical Engineering, University of Delaware, Newark, Delaware, United States
| | - Adam M Roth
- Department of Biomedical Engineering, University of Delaware, Newark, Delaware, United States
- Department of Mechanical Engineering, University of Delaware, Newark, Delaware, United States
| | - Rakshith Lokesh
- Department of Biomedical Engineering, University of Delaware, Newark, Delaware, United States
| | - Seth R Sullivan
- Department of Biomedical Engineering, University of Delaware, Newark, Delaware, United States
| | - Jeremy D Wong
- Department of Kinesiology, University of Calgary, Calgary, Alberta, Canada
- Department of Biomedical Engineering, University of Calgary, Calgary, Alberta, Canada
| | - Jennifer A Semrau
- Kinesiology and Applied Physiology, University of Delaware, Newark, Delaware, United States
- Biomechanics and Movement Science Program, University of Delaware, Newark, Delaware, United States
- Interdisciplinary Neuroscience Graduate Program, University of Delaware, Newark, Delaware, United States
| | - Joshua G A Cashaback
- Department of Biomedical Engineering, University of Delaware, Newark, Delaware, United States
- Department of Mechanical Engineering, University of Delaware, Newark, Delaware, United States
- Kinesiology and Applied Physiology, University of Delaware, Newark, Delaware, United States
- Biomechanics and Movement Science Program, University of Delaware, Newark, Delaware, United States
- Interdisciplinary Neuroscience Graduate Program, University of Delaware, Newark, Delaware, United States
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Coucke N, Heinrich MK, Cleeremans A, Dorigo M. Learning from humans to build social cognition among robots. Front Robot AI 2023; 10:1030416. [PMID: 36814449 PMCID: PMC9939630 DOI: 10.3389/frobt.2023.1030416] [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: 08/28/2022] [Accepted: 01/23/2023] [Indexed: 02/09/2023] Open
Abstract
Self-organized groups of robots have generally coordinated their behaviors using quite simple social interactions. Although simple interactions are sufficient for some group behaviors, future research needs to investigate more elaborate forms of coordination, such as social cognition, to progress towards real deployments. In this perspective, we define social cognition among robots as the combination of social inference, social learning, social influence, and knowledge transfer, and propose that these abilities can be established in robots by building underlying mechanisms based on behaviors observed in humans. We review key social processes observed in humans that could inspire valuable capabilities in robots and propose that relevant insights from human social cognition can be obtained by studying human-controlled avatars in virtual environments that have the correct balance of embodiment and constraints. Such environments need to allow participants to engage in embodied social behaviors, for instance through situatedness and bodily involvement, but, at the same time, need to artificially constrain humans to the operational conditions of robots, for instance in terms of perception and communication. We illustrate our proposed experimental method with example setups in a multi-user virtual environment.
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Affiliation(s)
- Nicolas Coucke
- IRIDIA, Université Libre de Bruxelles, Brussels, Belgium,Consciousness, Cognition and Computation Group, Center for Research in Cognition and Neurosciences, Université Libre de Bruxelles, Brussels, Belgium,*Correspondence: Nicolas Coucke, ; Mary Katherine Heinrich,
| | - Mary Katherine Heinrich
- IRIDIA, Université Libre de Bruxelles, Brussels, Belgium,*Correspondence: Nicolas Coucke, ; Mary Katherine Heinrich,
| | - Axel Cleeremans
- Consciousness, Cognition and Computation Group, Center for Research in Cognition and Neurosciences, Université Libre de Bruxelles, Brussels, Belgium
| | - Marco Dorigo
- IRIDIA, Université Libre de Bruxelles, Brussels, Belgium
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