Existing theories suggest that evidence is accumulated before making a decision with competing goals. In motor tasks, reward and motor costs have been shown to influence the decision, but the interaction between these two variables has not been studied in depth. A novel reward-based sensorimotor decision-making task was developed to investigate how reward and motor costs interact to influence decisions. In human subjects, two targets of varying size and reward were presented. After a series of three tones, subjects initiated a movement as one of the targets disappeared. Reward was awarded when participants reached through the remaining target within a specific amount of time. Subjects had to initiate a movement before they knew which target remained. Reward was found to be the only factor that influenced the initial reach. When reward was increased, there was a lower probability of intermediate movements. Both target size and reward lowered reaction times individually and jointly. This interaction can be interpreted as the effect of the expected value, which suggests that reward and target size are not evaluated independently during motor planning. Curvature, or the changing of motor plans, was driven primarily by the target size. After an initial decision was made, the motor costs to switch plans and hit the target had the largest impact on the curvature. An interaction between the reward and target size was also found for curvature, suggesting that the expected value of the target influences the changing of motor plans. Reward, target size, and the interaction between the two were all significant factors for different parts of the decision-making process.

Myoelectric artificial limbs can significantly advance the state of the art in prosthetics, since they can be used to control mechatronic devices through muscular activity in a way that mimics how the subjects used to activate their muscles before limb loss. However, surveys indicate that dissatisfaction with the functionality of terminal devices underlies the widespread abandonment of prostheses. We believe that one key factor to improve acceptability of prosthetic devices is to attain human likeness of prosthesis movements, a goal which is being pursued by research on social and human–robot interactions. Therefore, to reduce early abandonment of terminal devices, we propose that controllers should be designed so as to ensure effective task accomplishment in a natural fashion. In this work, we have analyzed and compared the performance of three types of myoelectric controller algorithms based on surface electromyography to control an underactuated and multi-degrees of freedom prosthetic hand, the SoftHand Pro.

The goal of the present study was to identify the myoelectric algorithm that best mimics the native hand movements. As a preliminary step, we first quantified the repeatability of the SoftHand Pro finger movements and identified the electromyographic recording sites for able-bodied individuals with the highest signal-to-noise ratio from two pairs of muscles, i.e., flexor digitorum superficialis/extensor digitorum communis, and flexor carpi radialis/extensor carpi ulnaris. Able-bodied volunteers were then asked to execute reach-to-grasp movements, while electromyography signals were recorded from flexor digitorum superficialis/extensor digitorum communis as this was identified as the muscle pair characterized by high signal-to-noise ratio and intuitive control. Subsequently, we tested three myoelectric controllers that mapped electromyography signals to position of the SoftHand Pro. We found that a differential electromyography-to-position mapping ensured the highest coherence with hand movements. Our results represent a first step toward a more effective and intuitive control of myoelectric hand prostheses.