There is now compelling evidence that motor imagery (MI) promotes motor learning. While MI has been shown to influence the early stages of the learning process, recent data revealed that sleep also contributes to the consolidation of the memory trace. How such “online” and “offline” processes take place and how they interact to impact the neural underpinnings of movements has received little attention. The aim of the present review is twofold: i) providing an overview of recent applied and fundamental studies (...) investigating the effects of MI practice on motor learning, and ii) detangling applied and fundamental findings in support of a sleep contribution to motor consolidation after MI practice. We conclude with an integrative approach of online and offline learning resulting from intense MI practice in healthy participants, and underline research avenues in the motor learning/clinical domains. (shrink)

Motor Imagery reproduces cognitive operations associated with the actual motor preparation and execution. Postural recordings during MI reflect somatic motor commands targeting peripheral effectors involved in balance control. However, how these relate to the actual motor expertise and may vary along with the MI modality remains debated. In the present experiment, two groups of expert and non-expert gymnasts underwent stabilometric assessments while performing physically and mentally a balance skill. We implemented psychometric measures of MI ability, while stabilometric variables were calculated (...) from the center of pressure oscillations. Psychometric evaluations revealed greater MI ability in experts, specifically for the visual modality. Experts exhibited reduced surface COP oscillations in the antero-posterior axis compared to non-experts during the balance skill. Experts further exhibited reduced length of COP displacement in the antero-posterior axis and as a function of the displacement area during visual and kinesthetic MI compared to the control condition. Predictive relationships were found between the stabilometric correlates of visual MI and physical practice of the balance skill, as well as between the stabilometric correlates of kinesthetic MI and the training experience in experts. Present results provide original stabilometric insights into the relationships between MI and expertise level. While data support the incomplete inhibition of postural commands during MI, whether postural responses during MI of various modalities mirror the level of motor expertise remains unclear. (shrink)

In the near future, vehicles will gradually gain more autonomous functionalities. Drivers’ activity will be less about driving than about monitoring intelligent systems to which driving action will be delegated. Road safety, therefore, remains dependent on the human factor and we should identify the limits beyond which driver’s functional state (DFS) may no longer be able to ensure safety. Depending on the level of automation, estimating the DFS may have different targets, e.g. assessing driver’s situation awareness in lower levels of (...) automation and his ability to respond to emerging hazard or assessing driver’s ability to monitor the vehicle performing operational tasks in higher levels of automation. Unfitted DFS (e.g., drowsiness) may impact the driver ability respond to taking over abilities. This paper reviews the most appropriate psychophysiological indices in naturalistic driving while considering the DFS through exogenous sensors, providing the more efficient trade-off between reliability and intrusiveness. The DFS also originates from kinematic data of the vehicle, thus providing information that indirectly relates to drivers behaviour. The whole data should be synchronously processed, providing a diagnosis on the DFS, and bringing it to the attention of the decision maker in real time. Next, making the information available can be permanent or intermittent (or even undelivered), and may also depend on the automation level. Such interface can include recommendations for decision support or simply give neutral instruction. Mapping of relevant psychophysiological and behavioural indicators for DFS will enable practitioners and researchers provide reliable estimates, fitted to the level of automation. (shrink)