Fig 1.
Possible neural mechanisms to deal with a delayed interaction with a partner.
A: Haptic communication with no compensation, where the CNS understands that the haptic feedback is related to the visual task and uses it (without adjusting for delay) to infer the partner’s motion plan that is then combined with their own motion plan. B: Compensation as noise mechanism in which the CNS, additionally to A, considers the delay as an additional noise to be filtered. C: Compensation by delay prediction mechanism in which the CNS, additionally to A, identifies the temporal delay and uses this knowledge to make a prediction (dashed line) of the partner’s motion using the delayed haptic feedback. This prediction may deviate from the true trajectory if the partner model does not match their behaviour in the time after the delayed feedback.
Fig 2.
A: Participants tracked a randomly moving target with their wrist flexion/extension movement while being connected to a reactive robot partner (RP). B: The experimental protocol for the delay group [15] and noise group. The grey boxes represent the familiarization/washout trials and the colourful blocks are the experimental conditions. Both groups started with the solo condition (i.e. without interacting with a RP). They then were connected with a RP without delay/noise. Subsequently, the delay/noise was increased in every block. After each block, participants were asked to fill in a questionnaire. The order of the blocks was the same for all participants.
Fig 3.
Perception during the interaction with a RP perturbed by delay or noise.
(A) and (C) show the perception of delay and noise, respectively, for the delay group, while (B) and (D) show this perception for the noise group. Note the 5-point Likert scale goes from strongly disagree (‘low’) to strongly agree (‘high’).
Fig 4.
Performance and effort during the interaction with the robotic partner with delay (left) or noise (right) perturbation: Tracking accuracy (A) and co-contraction (B).
Each dot represents the average value in each block for one participant.
Fig 5.
Lag and noisiness during the interaction with the robotic partner with delay (left) or noise (right) perturbation: Cross-correlation delay (A) and smoothness (B).
Each dot represents the average value in each block for one participant.
Fig 6.
Participant specific (and average) tracking performance for the experimental data and in the simulation to analyse the mechanism to compensate for temporal delays.
Experimental results are shown for the delay group (A), as well as for the noise group (B). All simulated results consider only delay compensation, where results are shown for: no compensation (C), compensation as noise (D) and compensation by delay prediction (E). In all subfigures, the grey lines indicate individual participant (real or simulated) performance while the thick solid black line and the dashed black lines denote the median performance and interquartile range of performance across all participants.