Visual feedback decoding during bimanual circle drawing.

The between-hand interference during bimanual tasks is a consequence of the connection between the neural controllers of movement. Previous studies showed the existence of an asymmetric between-hand interference (caused by neural cross talk) when different kinematics plans were to be executed by each hand or when only one was visually guided and received perturbed visual feedback. Here, in continuous bimanual circle drawing tasks, we investigated if the central nervous system (CNS) can benefit from visual composite feedback, i.e., a weighted sum of hands' positions presented for the visually guided hand, to control the nonvisible hand. Our results demonstrated improvement in the nonvisible nondominant hand (NDH) performance in the presence of the composite feedback. When NDH was visually guided, the dominant hand's (DH) performance during asymmetric drawing deteriorated, whereas its performance during symmetric drawing improved. This indicates that the CNS's ability to leverage composite feedback, which can be the result of decoding the nonvisible hand positional information from the composite feedback, is task-dependent and can be asymmetric. Also, the nonvisible hand's performance degraded when DH or NDH was visually guided with amplified error feedback. The results of the amplified feedback condition do not strongly support the asymmetry of the interference during asymmetric circle drawing. Comparing muscle activations in the asymmetric experiment, we concluded that the observed kinematic differences were not due to alternation in muscle co-contractions. Many daily activities involve bimanual coordination while simultaneous movement of the hands may result in interference with their movements. Here, we studied whether the central nervous system could use the relevant information in composite feedback, i.e., a weighted sum of positional information of nonvisible and visible hands, to improve the movement of the nonvisible hand. Our results suggest the ability to decode and associate task-relevant information from the composite feedback.