Motion Plan Changes Predictably in Dyadic Reaching

Takagi, Atsushi; Beckers, Niek; Burdet, Etienne

doi:10.1371/journal.pone.0167314

Cited by 22 publications

(30 citation statements)

References 17 publications

(28 reference statements)

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“…For example, a previous study examined stiffly coupled dyads rotating a crank to perform sequential reaching movements [ 14 ], and found force patterns of one partner purely accelerating the crank and the other slowing it, which the authors ascribed to the adoption of roles. However, it is not clear if these force patterns were evidence of role emergence or were artefacts of different movement speeds [ 13 , 16 ]. Thus, there is a need to understand how people modify their coordination strategy depending on the coupling strength to the partner.…”

Section: Introductionmentioning

confidence: 99%

“…However, rigidly coupled partners have been suggested to adopt roles [ 1 – 4 , 11 ] where one partner follows the lead of the more capable partner. In a previous study, we suggested that reaching tasks may be too short for roles to emerge [ 16 ], but leadership roles could emerge in a longer interactive tracking task where dyadic coordination was observed [ 18 ]. Thus, we hypothesized that hard interactions are governed by a strategy of following the leader or the more capable partner, and compliant interactions can be explained by the sharing of movement goals, as was found in our previous study [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

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Haptic communication between humans is tuned by the hard or soft mechanics of interaction

Takagi

Usai

Ganesh³

et al. 2018

PLoS Comput Biol

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107

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To move a hard table together, humans may coordinate by following the dominant partner’s motion [1–4], but this strategy is unsuitable for a soft mattress where the perceived forces are small. How do partners readily coordinate in such differing interaction dynamics? To address this, we investigated how pairs tracked a target using flexion-extension of their wrists, which were coupled by a hard, medium or soft virtual elastic band. Tracking performance monotonically increased with a stiffer band for the worse partner, who had higher tracking error, at the cost of the skilled partner’s muscular effort. This suggests that the worse partner followed the skilled one’s lead, but simulations show that the results are better explained by a model where partners share movement goals through the forces, whilst the coupling dynamics determine the capacity of communicable information. This model elucidates the versatile mechanism by which humans can coordinate during both hard and soft physical interactions to ensure maximum performance with minimal effort.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Haptic communication between humans is tuned by the hard or soft mechanics of interaction

Takagi

Usai

Ganesh³

et al. 2018

PLoS Comput Biol

Self Cite

107

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“…Social interaction through gaming has shown improvement in motor learning, for instance because it makes the task more fun and motivating [3,8]. Haptic interaction -interaction through exerting forces onto each other -has only recently gained academic interest [10,6,12,15,16,2,11].…”

Section: Introductionmentioning

confidence: 99%

Haptic Human-Human Interaction Through a Compliant Connection Does Not Improve Motor Learning in a Force Field

Beckers

Keemink

Asseldonk

et al. 2018

Haptics: Science, Technology, and Applications

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Humans have a natural ability to haptically interact with other humans, for instance during physically assisting a child to learn how to ride a bicycle. A recent study has shown that haptic humanhuman interaction can improve individual motor performance and motor learning rate while learning to track a continuously moving target with a visuomotor rotation. In this work we investigated whether these benefits of haptic interaction on motor learning generalize to a task in which the interacting partners track a target while they learn novel dynamics, represented by a force field. Pairs performed the tracking task and were intermittently connected to each other through a virtual spring. Motor learning was assessed by comparing each partner's individual performance during trials in which they were not connected to the performance of participants who learned the task alone. We found that haptic interaction through a compliant spring does not lead to improved individual motor performance or an increase in motor learning rate. Performance during interaction was significantly better than when the partners were not interacting, even when connected to a worse partner.

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“…Many activities in daily life involve coordinating our movements with those of a partner 35 or opponent. A couple of dancers, a couple of fighters, a team of players, two workers situations have been often studied in contexts in which there is one common and shared 48 goal -for instance, control of isometric force [5,6], reaching the same fixed [7,8] or optimal collaborative behaviors, but do not explain how they are achieved. Several 86 mechanisms have been proposed [19] to account for learning a collaboration.…”

Section: Introduction 34mentioning

confidence: 99%

“…Dyads gradually develop stable strategies 440 Our results suggest that dyads in all three groups gradually converge to stable 441 interaction strategies, characterized by low interaction forces and low distances from the 442 partner's via-points. When two subjects are rigidly coupled, the development of stable 443 coordination is relatively fast [12] or even instantaneous [8]. When the coupling is softer, 444 learning is more gradual [4,9].…”

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confidence: 99%

Incomplete information about the partner affects the development of collaborative strategies in joint action

Chackochan

Sanguineti

2018

Preprint

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Physical interaction with a partner plays an essential role in our life experience and is 1 the basis of many daily activities. When two physically coupled humans have different 2 and partly conflicting goals, they face the challenge of negotiating some type of 3 collaboration. This requires that both subjects understand their opponent's state and 4 current actions. But, how would the collaboration be affected if information about their 5 opponent were unreliable or incomplete? Here we show that incomplete information 6 about the partner affects not only the speed at which a collaborative strategy is 7 achieved (less information, slower learning), but also the modality of the collaboration. 8In particular, incomplete or unreliable information leads to an interaction strategy 9 characterized by alternating leader-follower roles. In contrast, more reliable information 10 leads to a more synchronous behavior, in which no specific roles can be identified. 11Simulations based on a combination of game theory and Bayesian estimation suggested 12 that synchronous behaviors denote optimal interaction (Nash equilibrium). Roles 13 emerge as sub-optimal forms of interaction, which minimize the need to know about the 14 partner. These findings suggest that physical interaction strategies are shaped by the 15 trade-off of between the task requirements and the uncertainty of the information 16 available about the opponent. 17Author summary 18 Many activities in daily life involve physical interaction with a partner or opponent. In 19 many situations they have conflicting goals. Therefore, they need to negotiate some 20 form of collaboration. Although very common, these situations have rarely been studied 21 empirically. In this study, we specifically address what is a 'optimal' collaboration and 22 how it can be achieved. We also address how developing a collaboration is affected by 23 uncertainty about the partner. Through a combination of empirical studies and 24 computer simulations based on game theory, we show that subject pairs (dyads) are 25 capable of developing stable collaborations, but the learned collaboration strategy 26 depends on the reliability of the information about the partner. High-information dyads 27 converge to the optimal strategies in game-theoretic sense. Low-information dyads 28 converge to strategies that minimize the need to know about the partner. These 29 findings are consistent with a game theoretic learning model which relies on estimates of 30 partner actions, but not partner goals. This similarity sheds some light on the minimal 31 computational machinery which is necessary to an intelligent agent in order to develop 32 stable physical collaborations. 33 July 14, 2018 1/16 36 carrying a load or a therapist interacting with a patient are just the first examples 37 which come to mind. In all these situations, each participant in the interaction needs to 38 know what his/her partner is doing and/or intends to do. On this basis, he/she must 39 then select their own action [1, 2]. To do this, the ...

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Motion Plan Changes Predictably in Dyadic Reaching

Cited by 22 publications

References 17 publications

Haptic communication between humans is tuned by the hard or soft mechanics of interaction

Haptic communication between humans is tuned by the hard or soft mechanics of interaction

Haptic Human-Human Interaction Through a Compliant Connection Does Not Improve Motor Learning in a Force Field

Incomplete information about the partner affects the development of collaborative strategies in joint action

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