Conference Agenda

In the medial posterior parietal cortex of the primate brain, area V6A has a key role in action monitoring and execution. In the monkey, V6A receives visual and somatosensory input, uses these signals to estimate the state of the arm, and communicates with the frontal cortex to perform accurate arm movements. V6A also contains reach-related and grasp-related cells, being thus provided with a sophisticated apparatus to control the movement. A lesion study where V6A was surgically removed provided the causal role of monkey V6A in reaching and grasping: after V6A removal, monkeys showed impairments during reaching towards targets in the peripersonal space and deficits in the grip aperture and wrist orientation during grasping.

Area V6A was also described in the human brain (hV6A). It shares many functions with monkey V6A. Several works have been performed to assess the causal role of hV6A in reaching and grasping using transcranial magnetic stimulation (TMS). TMS delivered over hV6A during reaching execution impaired reaching corrections to online adapt the movement to shifts of the visual target. TMS delivered during grasping execution impaired prehension of stable objects and grasping corrections performed to adjust the grip aperture and wrist orientation to sudden changes of object size and orientation. These studies highlight the strict homology between the monkey and the human brain and provide information about the causal role of the medial posterior parietal cortex in the motor control of reaching and grasping.

This work was supported by the MAIA project, which received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 951910.

The nervous system is sensitive to statistical regularities of the external world and forms internal models of these regularities to predict environmental dynamics. Given the inherently social nature of human behavior, being capable of building reliable predictive models of others’ actions may be essential for successful interaction. While social prediction might seem to be a daunting task, the study of human motor control has accumulated ample evidence that our movements follow a series of kinematic invariants, which can be used by observers to reduce their uncertainty during social exchanges. Here, we provide an overview of the most salient regularities that shape biological motion, examine the role of these invariants in recognizing others’ actions, and speculate that anchoring perceptual decisions to such kinematic invariants provides a key computational advantage for interpersonal motor coordination.

Sensorimotor control is a complex process that relies on the integration of sensory information to generate goal-directed movements. Virtual reality provides the possibility to investigate sensorimotor control in complex tasks, such as catching a flying ball, in a controlled experimental environment. Using this approach, I examined different aspects of sensorimotor control in healthy individuals across a wide age range, as well as patients with Alzheimer's disease (AD), Mild Cognitive Impairment (MCI), and autism.

In a first study I have investigated the role of the internal model of gravity when intercepting approaching balls in young healthy adults. Participants showed lower points of interception for balls moving at 0g, coherent with the assumption of gravitational acceleration (1). In a second study, healthy individuals showed age-related declines in their ability to intercept or bounce balls towards a target, and these declines were more pronounced in the autistic population (2). Finally, I found significant differences in catching performance of AD and MCI patients compared to healthy individuals. Patients showed reduced catching accuracy in less naturalistic scenarios and altered movement strategies.

These results suggest that virtual reality-based tasks provide a reliable and sensitive measure of sensorimotor performance and valuable insights into the sensorimotor control strategies of both healthy individual and neurological patients. These findings could lead to the development of virtual reality-based training programs to restore sensorimotor function in neurological patients and to enhance sensorimotor skills in healthy individuals.

1 - Russo M, Cesqui B, La Scaleia B, Ceccarelli F, Maselli A, Moscatelli A, Zago M, Lacquaniti F, d’Avella A. Intercepting virtual balls approaching under different gravity conditions: Evidence for spatial prediction. J Neurophysiol 118: 2421–2434, 2017.

2 - Park SW, Cardinaux A, Crozier D, Russo M, Kjelgaard M, Sinha P, Sternad D (2023) Developmental change in predictive motor abilities. iScience 26(2): 106038

Research on movement kinematics has demonstrated that observers can predict several features of a target object through the observation of the reach-to-grasp phase of a movement towards that object (Podda et al., 2017). However, it remains unclear the extent to which the motor similarity between the observer's and agent's kinematics affects the accuracy of reading the action. A first and relevant issue in this sense is the unavailability of a solution to evaluate the degree of similarity between the observer and the agent in a quantitative way, producing what could be defined as a measure of ‘motor distance’. To address this, we developed an innovative approach based on the Procrustes Transformation (Bookstein, 1992). Starting from a set of kinematics features of the upper limb acquired during a grasping task for 17 subjects, we were able to show that the motor distance between different repetitions of the movement made by a same subject (mean = 0.09) is significantly smaller (t-test p<1*10-7) than the motor distance between different repetitions of the movement made by different subjects (mean = 0.37). This result suggests that our approach is suitable for correct quantification of motor distance. As a direct application, we combined motion capture technology with video recording to collect data from further 34 participants performing grasping movements towards light and heavy objects. Procrustes transformation was used to determine the motor distance between participants. Subsequently, we presented observers - who had previously performed the action execution task - with a set of videoclips by manipulating the motor distance (low, intermediate, high) between their kinematics and that presented in the videos. Observers were asked to watch the reach-to-grasp phase of the action and classify the target objects as heavy or light. Preliminary results suggest that higher observer/agent motor similarity leads to better classification performance.