Target reward influences motor planning strategies through modulation of movement vigor. Considering current theories of sensorimotor control suggesting that movement planning consists in selecting a goal-directed control strategy, we sought to investigate the influence of reward on feedback control. Here we explored this question in three human reaching experiments. First, we altered the explicit reward associated with the goal target and found an overall increase in feedback gains for higher target rewards, highlighted by larger velocities, feedback responses to external loads, and background muscle activity. Then, we investigated whether the differences in target rewards across multiple goals impacted rapid motor decisions during movement. We observed idiosyncratic switching strategies dependent on both target rewards and, surprisingly, the feedback gains at perturbation onset: the more vigorous movements were less likely to switch to a new goal following perturbations. To gain further insight into a causal influence of the feedback gains on rapid motor decisions, we demonstrated that biasing the baseline activity and reflex gains by means of a background load evoked a larger proportion of target switches in the direction opposite to the background load associated with lower muscle activity. Together, our results demonstrate an impact of target reward on feedback control and highlight the competition between movement vigor and flexibility.

This dataset was collected on a Kinarm end-point robot. EMG data were collected with Delsys surface EMGs. More details about the methods and the experimental paradigms are detailed in the manuscript.

Kinematics data were sampled at 1kHz and low-pass filtered (4th order butterworth, double pass) with a cutoff frequency of 20Hz.

EMG data were sampled at 1kHz and band-pass filtered (4th order butterworth, double pass) with cutoff frequencies of 20-250Hz. EMG data were normalized for each participant to the average activity collected when they maintained postural control against a constant force of 9N at the starting position.

Datasets structure :

DSExpe1.mat

- Contains 14 fields (one for each participant) each composed of

    - "id" : anonymised subject identifier

    - "matrix_timing" : Timing of the triggered events

    - "array_timing" : Name of the triggered events

    - "vector_TP" : Type of trials

    - "matrix_kinematics" : Individual kinematics (position and velocity of the end point)

    - "matrix_EMG" : Individual EMG data (along the third dimension, 1st and 2nd entries are for Pectoralis Major and Posterior Deltoid)

    - "mean_kine" : Individual mean kinematics trace

    - "std_kine": Individual standard deviation of the kinematics

    - "time_matrix": Individual time vector

    -"mean_EMG": Individual mean EMG trace

    -"std_EMG": Individual standard deviation of the EMG

DSExpe2.mat

-Contains 20 fields (one for each participant) see DSExpe1.mat for the different fields

DSExpe3.mat

-Contains 19 fields (one for each participant) see DSExpe1.mat for the different fields