PRIN 2017 - 2017KZNZLN_005 Stoianov Ivilin Peev (DUS.AD016.095)

Thematic area

Social sciences and humanities, cultural heritage

Project area

Structure responsible for the research project

Institute of cognitive sciences and technologies (ISTC)

Project manager

IVILINPEEV STOIANOV
Phone number: 0498271826
Email: ivilinpeev.stoianov@cnr.it

Abstract

An important feature of our sensorimotor system is its capacity to support adaptive dynamic interactions with the everchanging environment. How can humans catch an object on the fly even when vision for its motion is temporarily precluded by visual obstacles and how do they adapt their behavior based on prediction of others' intentions? Interceptive actions require dynamic visual motion processing and fine predictive motor control that rely on manifold information, principally processed in the medial parieto-frontal network, a circuit linking in humans and non-human primates the medial posterior parietal cortex (PPC), the dorsal premotor cortex (PMd), and the prefrontal cortex (PFC) through direct cortico-cortical connections and, subcortically, via the striatum. In primates, the medial PPC is essential for the sensorimotor transformations required for planning and executing hand movements towards external stimuli.

Goals

The goal of the project PACE is to gain deep understanding of how the human brain successfully achieves accurate control of complex goal-directed sensorimotor actions in a dynamically changing environment, with focus on reaching and grasping. Using innovative multidisciplinary approach involving neurophysiology, TMS, behavior, and neurocomputational modeling analysis, the project will unveil the details of the structure and computational mechanism of the rich neural network analyzing visual information to control actions in dynamic context. The control of interceptive actions relies on the interplay between predictive processing and visual feedback, and predictive processing is essential when the feedback is unreliable or lacking (e.g., occluded object), or when other agents cause changes. However, although motor control and visual perception have been studied for long, also from computational point of view, the neural-level implementation of the mechanism underlying their interplay in predictive motor control is not fully known. Thus, the critical question we address is how the reach-to-grasp network supports visually guided predictive interactions.

Start date of activity

29/08/2019

Keywords

cognition mechanisms, sensorimotor control, computational neuroscience

Last update: 19/04/2024