Haptic human-human interaction does not improve individual visuomotor adaptation

Haptic interaction between two humans, for example, parents physically supporting their child while it learns to keep balance on a bicycle, likely facilitates motor skill acquisition. Haptic human-human interaction has been shown to enhance individual motor improvement in a tracking task with a visuomotor rotation perturbation. These results are remarkable given that haptically assisting or guiding an individual rarely improves their motor improvement when the assistance is removed. We, therefore, replicated a study that reported benefits of haptic interaction between humans on individual motor improvement for tracking a target in a visuomotor rotation. Also, we tested the effect of more interaction time and stronger haptic coupling between the partners on individual performance improvement in the same task. We found no benefits of haptic interaction on individual motor improvement compared to individuals who practised the task alone, independent of interaction time or interaction strength. We also found no effect of the interaction partner's skill level on individual motor improvement

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

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

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

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

Human-Human Sensorimotor Interaction

We investigated the role of sensory feedback in inter-personal interactions when two co-workers are working together. Twenty-five co-workers completed two isometric finger force production experiments. In Experiment 1, co-workers isometrically produced finger forces such that combined force will match a target force and/or torque under different visual and haptic conditions. In Experiment 2, without participants’ knowledge, each performed the same task with the playback of his/her partner’s force trajectory previously recorded from Experiment 1. Results from both experiments indicated that co-workers performed the task worse in the presence of haptic and visual feedback. Since, in latter as opposed to the former condition, they adopted a compensatory strategy to accomplish the task accurately. Further analysis showed that co-workers achieved the same level of motor performance with similar control strategies, suggesting that they did not work synergistically to achieve better performance, but one co-worker processed another as disturbance when they worked together