Overview

The research goals of the STREP "DECISIONS-IN-MOTION" is to describe the neural mechanisms used to guide behaviour in complex visual scenes, in which the observer is in motion and navigates to avoid moving objects. We will measure motion-based image segmentation in the visual cortex, and derive neural models that explicitly make use of a hierarchy of sensory areas (low-, mid-, high-level visual areas) to extract meaningful information about the location and motion of objects in the environment.

The outputs of these units will feed into a decision-making process that will weight these inputs and relations between these inputs based on utility functions. The resulting cognitive architecture will be tested in complex visual environments to determine the efficiency of the image motion segmentation and goal-directed adaptive behaviour. The unique cooperation between several disciplines in the neuro-and cognitive sciences guarantees that the processes revealed in natural neural systems will be endowed into artificial cognitive systems for efficient image segmentation and sensory-guided decision making. This approach will lead to an improved design of augmented cognition systems, since "DECISIONS-IN-MOTION" will model the way the primate brain exploits visual information to segment object from self motion.

A further goal of "DECISIONS-IN-MOTION" is to advance current technologies underlying behavioural monitoring in restricted environments. The advanced capability of the planned technology will capture behavioural information about eye position, shifts in gaze, blinking and fixational eye behaviour during brain-imaging measurements (MRI-Live). A further goal will involve the use of neural network models to support robotic control systems to extract object information from moving scenes. "DECISIONS-IN-MOTION" will exploit this newly gained knowledge to provide real-time guidance systems for artificial cognitive systems. A final goal will involve the use of neural network models to assist patients with visual impairments. "DECISIONS-IN-MOTION" aims to understand how the primate brain extracts object-and self-motion cues from complex dynamic visual scenes. The goal is to use similar algorithms in artificial neural networks. "DECISIONS-IN-MOTION" will exploit this newly gained knowledge to provide computer-assisted guidance systems for the visually impaired.