Mutual Skill Learning and Adaptability to Others via Haptic Interaction

When learning a new skill through an unknown environment, should we practice alone, or together with another beginner, or learn from the expert? It is normally helpful to have an expert guiding through unknown environmental dynamics. The guidance from the expert is fundamentally based on mutual interactions. From the perspective of the beginner, one needs to face dual unknown dynamics of the environment and motor coordination of the expert. In a cooperative visuo-haptic motor task, we asked novice participants to bring a virtual mass onto the specified target location under an unknown external force field. The task was completed by an individual or with an expert or another novice. In addition to evaluation of the motor performance, we evaluated the adaptability of the novice participants to a new partner while attempting to achieve a common goal together. The experiment was set in five phases; baseline for skill transfer and adaptability, learning and evaluation for adaptability and skill transfer respectively. The performance of the participants was characterized by using the time to target, effort index, and length of the trajectory. Experimental results suggested that (1) peer-to-peer interactions among paired beginners enhanced the motor learning most, (2) individuals practicing on their own (learning as a single) showed better motor learning than practicing under the expert's guidance, and (3) regarding the adaptability, peer-to-peer interactions induced higher adaptability to a new partner than the novice-to-expert interactions while attempting to achieve a common goal together. Thus, we conclude that the peer-to-peer interactions under a collaborative task can realize the best motor learning of the motor skills through the new environmental dynamics, and adaptability to others in order to achieve a goal together. We suggest that the peer-to-peer learning can induce both adaptability to others and learning of motor skills through the unknown environmental dynamics under mutual interactions. On the other hand, during the peer-to-peer interactions, the novice can learn how to coordinate motion with his/her partner (even though one is a new partner), and thus, is able to learn the motor skills through new environmental dynamics.

[1]  Ruud G. J. Meulenbroek,et al.  Anatomical substrates of cooperative joint-action in a continuous motor task: Virtual lifting and balancing , 2008, NeuroImage.

[2]  Raoul Huys,et al.  Joint dyadic action: Error correction by two persons works better than by one alone. , 2018, Human movement science.

[3]  Hidekazu Yoshikawa,et al.  A basic study on virtual collaborator as an innovative human-machine interface in distributed virtual environment: the prototype system and its implication for industrial application , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[4]  F A Mussa-Ivaldi,et al.  Adaptive representation of dynamics during learning of a motor task , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[5]  Toshiyuki Kondo,et al.  Visuomotor learning by passive motor experience , 2015, Front. Hum. Neurosci..

[6]  Dustin K. Jundt,et al.  Individual adaptive performance in organizations: A review , 2015 .

[7]  Cagatay Basdogan,et al.  Recognition of Haptic Interaction Patterns in Dyadic Joint Object Manipulation , 2015, IEEE Transactions on Haptics.

[8]  M. Kawato,et al.  Two is better than one: Physical interactions improve motor performance in humans , 2014, Scientific Reports.

[9]  Karl J. Friston The free-energy principle: a unified brain theory? , 2010, Nature Reviews Neuroscience.

[10]  Mitsuo Kawato,et al.  Physically interacting individuals estimate the partner’s goal to enhance their movements , 2017, Nature Human Behaviour.

[11]  Léa A S Chauvigné,et al.  Multi-person and multisensory synchronization during group dancing. , 2019, Human movement science.

[12]  Valentina Squeri,et al.  Skill Learning and Skill Transfer Mediated by Cooperative Haptic Interaction , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[13]  P. Morasso,et al.  Trajectory formation and handwriting: A computational model , 1982, Biological Cybernetics.

[14]  J. Royston Some Techniques for Assessing Multivarate Normality Based on the Shapiro‐Wilk W , 1983 .

[15]  J. Patton,et al.  Visual error augmentation for enhancing motor learning and rehabilitative relearning , 2005, 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005..

[16]  G. Wulf,et al.  Effects of attentional focus, self-control, and dyad training on motor learning: implications for physical rehabilitation. , 2000, Physical therapy.

[17]  W. Shebilske,et al.  Motor Learning and Control , 1993 .

[18]  Etienne Burdet,et al.  On the analysis of movement smoothness , 2015, Journal of NeuroEngineering and Rehabilitation.

[19]  Toshiyuki Kondo,et al.  Cooperative visuomotor learning experience with peer enhances adaptability to others , 2021, Adv. Robotics.

[20]  N. Sebanz,et al.  Joint action coordination in expert-novice pairs: Can experts predict novices’ suboptimal timing? , 2018, Cognition.

[21]  R. C. Oldfield The assessment and analysis of handedness: the Edinburgh inventory. , 1971, Neuropsychologia.

[22]  M. Bremmer,et al.  The Role of the Body in Instrumental and Vocal Music Pedagogy: A Dynamical Systems Theory Perspective on the Music Teacher's Bodily Engagement in Teaching and Learning , 2020, Frontiers in Education.

[23]  N. Hogan,et al.  Movement Smoothness Changes during Stroke Recovery , 2002, The Journal of Neuroscience.

[24]  Laura Marchal-Crespo,et al.  Haptic Rendering Modulates Task Performance, Physical Effort and Movement Strategy during Robot-Assisted Training , 2020, 2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob).

[25]  Diego Borro,et al.  Effective Haptic Rendering Method for Complex Interactions , 2012 .

[26]  A. Wing,et al.  Optimal feedback correction in string quartet synchronization , 2014, Journal of The Royal Society Interface.

[27]  Pietro Morasso,et al.  Strategy Switching in the Stabilization of Unstable Dynamics , 2014, PloS one.

[28]  Arvid Q. L. Keemink,et al.  Haptic Human-Human Interaction Through a Compliant Connection Does Not Improve Motor Learning in a Force Field , 2018, EuroHaptics.

[29]  Valentina Squeri,et al.  Human-human physical interaction in the joint control of an underactuated virtual object , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[30]  John W. Krakauer,et al.  Independent learning of internal models for kinematic and dynamic control of reaching , 1999, Nature Neuroscience.

[31]  Junya Masumoto,et al.  Two heads are better than one: both complementary and synchronous strategies facilitate joint action. , 2013, Journal of neurophysiology.

[32]  Robrecht P R D van der Wel,et al.  Let the force be with us: dyads exploit haptic coupling for coordination. , 2011, Journal of experimental psychology. Human perception and performance.

[33]  C. Frith,et al.  Follow you, Follow me: Continuous Mutual Prediction and Adaptation in Joint Tapping , 2010, Quarterly journal of experimental psychology.

[34]  Fakhri Karray,et al.  Human Machine Interaction Platform for Home Care Support System , 2020, 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[35]  L. Ting,et al.  Perspectives on human-human sensorimotor interactions for the design of rehabilitation robots , 2014, Journal of NeuroEngineering and Rehabilitation.