Interactional leader-follower sensorimotor communication strategies during repetitive joint actions

Abstract

Non-verbal communication is the basis of animal interactions. In dyadic leader-follower interactions, leaders master the ability to carve their motor behaviour in order to 'signal' their future actions and internal plans while these signals influence the behaviour of follower partners, who automatically tend to imitate the leader even in complementary interactions. Despite their usefulness, signalling and imitation have a biomechanical cost, and it is unclear how this cost-benefits trade-off is managed during repetitive dyadic interactions that present learnable regularities. We studied signalling and imitation dynamics (indexed by movement kinematics) in pairs of leaders and followers during a repetitive, rule-based, joint action. Trial-by-trial Bayesian model comparison was used to evaluate the relation between signalling, imitation and pair performance. The different models incorporate different hypotheses concerning the factors (past interactions versus online movements) influencing the leader's signalling (or follower's imitation) kinematics. This approach showed that (i) leaders' signalling strategy improves future couple performance, (ii) leaders used the history of past interactions to shape their signalling, (iii) followers' imitative behaviour is more strongly affected by the online movement of the leader. This study elucidates the ways online sensorimotor communication help individuals align their task representations and ultimately improves joint action performance.

Keywords: model comparison; motor interactions; sensorimotor communication.

Figure 1.

Experimental set-up (a). The letter ‘l’ indicates the LEDs and the letter ‘s’ indicates the starting position. (b) An example trial. (Online version in colour.)

Figure 2.

Experimental structure (a), and two example triplets (b). The left rectangle in b shows a triplet where the leader (black arrow) aimed three times in a row at the higher part of the bottle while the follower (white arrow) aimed at the lower part of the bottle; the right rectangle shows a triplet where the leader (black arrow) aimed at the top, bottom and top of the bottle while the follower (white arrow) executed down, up, down movements. (Online version in colour.)

Figure 3.

Wrist trajectory of an example leader (a) and follower (b). Millimetres on the ordinate, and percentage of movement duration on the abscissa. (Online version in colour.)

Figure 4.

(a,b) Probability of signalling P(L_t|MaxH_t) of the leader of couple 2 given his max height of the wrist, MaxH_t. Probabilities are on the ordinate, values of MaxH_t are on the abscissa. (Online version in colour.)

Figure 5.

(a,b) Probability of imitation P(L_t|MaxH_t) of the follower of couple 4 given his max height of the wrist, MaxH_t. Probabilities are on the ordinate, values of MaxH_t are on the abscissa. (Online version in colour.)

Figure 6.

Results of the grasping asynchrony. The graph shows the reduction of pairs' grasping asynchrony (i.e. their increase in the ability to be synchronous) along the different sessions of the experiment (baseline = value 1). Normalized means ± s.e. mean.

Figure 7.

Results of the MaxH. The graph shows the modulation amplitude of wrist MaxH with respect to the baseline (value 1) for leaders and followers in the three trials of the triplets in up and down grips of test1/2. Normalized means ± s.e. mean.

Figure 8.

(a–c) Model-based analysis of signalling. See main text for explanation. (Online version in colour.)

Figure 9.

(a–c) Model-based analysis of imitation. See main text for explanation. (Online version in colour.)

Figure 10.

(a–c) Model-based analysis of grasping asynchrony. See main text for explanation. (Online version in colour.)