Synchrony in Joint Action Is Directed by Each Participant’s Motor Control System

Noy, Lior; Netta, Weiser; Friedman, Jason

doi:10.3389/fpsyg.2017.00531

Cited by 11 publications

(17 citation statements)

References 68 publications

(124 reference statements)

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“…This is still a low-frequency movement, and one that in previous experiments didn't lend itself easily to smooth movements. Noy et al 18 considered this frequency below the natural human movement repertoire, so this is a positive indicator in our opinion that analogies can improve such movements.…”

Section: Discussionmentioning

confidence: 94%

“…We analyzed the tracking errors of participants to assess their accuracy in tracking the stimuli. Three measures were used based on Noy et al 18 : relative position error (dX, unitless), relative velocity error (dV, unitless) and mean timing error (dT, in seconds). Improvement in all tracking errors was calculated by subtracting the mean error of each participant in the post-test phase from the pre-test phase and combined to calculate group means.…”

Section: Resultsmentioning

confidence: 99%

“…Overall, participants were not able to achieve better movement performance over the duration of the experiment. Previous research in our lab used this task to show the differential performance of movement under different frequencies 18 . This research indicates that people have a harder time moving smoothly in low frequencies.…”

Section: Discussionmentioning

confidence: 99%

“…The protocol and stimuli for this experiment were taken from Noy et al 18 . Participants were instructed to follow the movements of a red oval moving horizontally on the monitor screen.…”

Section: Experimental Protocolmentioning

confidence: 99%

“…The second task is meant to test if the use of analogies can help in motor performance at the limits of the human motor repertoire. Previous studies have shown that humans aren't able to perform slow (low frequency) movements in a smooth fashion, generating "jitter" (i.e., unnecessary acceleration zero crossings) throughout a tracking task 18 named "the mirror game". We used a protocol similar to that from Noy et al 18 , where participants tracked a moving oval with a hand-held stylus on a tablet.…”

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confidence: 99%

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Analogies can speed up the motor learning process

Zacks

Friedman

2020

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Analogies have been shown to improve motor learning in various tasks and settings. in this study we tested whether applying analogies can shorten the motor learning process and induce insight and skill improvement in tasks that usually demand many hours of practice. Kinematic measures were used to quantify participant's skill and learning dynamics. For this purpose, we used a drawing task, in which subjects drew lines to connect dots, and a mirror game, in which subjects tracked a moving stimulus. After establishing a baseline, subjects were given an analogy, explicit instructions or no further instruction. We compared their improvement in skill (quantified by coarticulation or smoothness), accuracy and movement duration. Subjects in the analogy and explicit groups improved their coarticulation in the target task, while significant differences were found in the mirror game only at a slow movement frequency between analogy and controls. We conclude that a verbal analogy can be a useful tool for rapidly changing motor kinematics and movement strategy in some circumstances, although in the tasks selected it did not produce better performance in most measurements than explicit guidance. Furthermore, we observed that different movement facets may improve independently from others, and may be selectively affected by verbal instructions. these results suggest an important role for the type of instruction in motor learning. Using analogies in the acquisition of a motor skill has been shown to improve motor learning in various tasks and settings, from throwing a basketball to performing a high jump, and in populations ranging from young children to older adults 1-4. Analogies in the case of motor learning combine various task-relevant rules into a single biomechanical metaphor, usually given to the learner as a verbal instruction. A survey study among researchers and practitioners 5 defined analogy learning as follows: "Learning facilitated by metaphors. The complex structure of the to-be-learned skill is integrated into a simple metaphor that the learner is provided with". Two examples of this method are given: "reach for an apple up in the tree" (for a jumping pattern) and "putting your hand into the cookie jar" (for a basketball shot). The use of analogies in motor learning has been shown to be beneficial both during the learning process and in maintaining performance under pressure 2,6. Most of the research in this field has been done under the implicit/explicit knowledge paradigm. Researchers have defined explicit motor learning as conscious control of a motor task. This type of knowledge structure relies on working memory and is characterized by learning rules that govern the movement and being able to explicitly state such rules. In contrast, implicit knowledge of a motor task doesn't necessitate conscious awareness of specific rules and isn't as demanding on working memory 7. Experiments show that explicit knowledge can hinder performance during the learning process, although this occurs mostly under stressful con...

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Section: Discussionmentioning

confidence: 94%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…The protocol and stimuli for this experiment were taken from Noy et al 18 . Participants were instructed to follow the movements of a red oval moving horizontally on the monitor screen.…”

Section: Experimental Protocolmentioning

confidence: 99%

mentioning

confidence: 99%

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Analogies can speed up the motor learning process

Zacks

Friedman

2020

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Game theory and partner representation in joint action: toward a computational theory of joint agency

2022

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The sense of agency – the subjective feeling of being in control of our own actions – is one central aspect of the phenomenology of action. Computational models provided important contributions toward unveiling the mechanisms underlying the sense of agency in individual action. In particular, the sense of agency is believed to be related to the match between the actual and predicted consequences of our own actions (comparator model). In the study of joint action, models are even more necessary to understand the mechanisms underlying the development of coordination strategies and how the subjective experiences of control emerge during the interaction. In a joint action, we not only need to predict the consequences of our own actions; we also need to predict the actions and intentions of our partner, and to integrate these predictions to infer their joint consequences. Understanding our partner and developing mutually satisfactory coordination strategies are key components of joint action and in the development of the sense of joint agency. Here we discuss a computational architecture which addresses the sense of agency during intentional, real-time joint action. We first reformulate previous accounts of the sense of agency in probabilistic terms, as the combination of prior beliefs about the action goals and constraints, and the likelihood of the predicted movement outcomes. To look at the sense of joint agency, we extend classical computational motor control concepts - optimal estimation and optimal control. Regarding estimation, we argue that in joint action the players not only need to predict the consequences of their own actions, but also need to predict partner’s actions and intentions (a ‘partner model’) and to integrate these predictions to infer their joint consequences. As regards action selection, we use differential game theory – in which actions develop in continuous space and time - to formulate the problem of establishing a stable form of coordination and as a natural extension of optimal control to joint action. The resulting model posits two concurrent observer-controller loops, accounting for ‘joint’ and ‘self’ action control. The two observers quantify the likelihoods of being in control alone or jointly. Combined with prior beliefs, they provide weighing signals which are used to modulate the ‘joint’ and ‘self’ motor commands. We argue that these signals can be interpreted as the subjective sense of joint and self agency. We demonstrate the model predictions by simulating a sensorimotor interactive task where two players are mechanically coupled and are instructed to perform planar movements to reach a shared final target by crossing two differently located intermediate targets. In particular, we explore the relation between self and joint agency and the information available to each player about their partner. The proposed model provides a coherent picture of the inter-relation of prediction, control, and the sense of agency in a broader range of joint actions.

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