Driving in the future: temporal visuomotor adaptation and generalization.

Rapid and accurate visuomotor coordination requires tight spatial and temporal sensorimotor synchronization. The introduction of a sensorimotor or intersensory misalignment (either spatial or temporal) impairs performance on most tasks. For more than a century, it has been known that a few minutes of exposure to a spatial misalignment can induce a recalibration of sensorimotor spatial relationships, a phenomenon that may be referred to as spatial visuomotor adaptation. Here, we use a high-fidelity driving simulator to demonstrate that the sensorimotor system can adapt to temporal misalignments on very complex tasks, a phenomenon that we refer to as temporal visuomotor adaptation. We demonstrate that adapting on a single street produces an adaptive state that generalizes to other streets. This shows that temporal visuomotor adaptation is not specific to a single visuomotor transformation, but generalizes across a class of transformations. Temporal visuomotor adaptation is strikingly parallel to spatial visuomotor adaptation, and has strong implications for the understanding of visuomotor coordination and intersensory integration.

An architecture for distributed visual memory.

The development of autonomous as well as situated robots is one of the great remaining challenges and involves a number of different scientific disciplines. In spite of recent dramatic progress, it remains worthwhile to examine natural systems, because their abilities are still out of reach. Motivated by research work done in the fields of cognitive systems, visual perception, and psychology of memory we designed and implemented a memory architecture for visual tasks. Structural and functional concepts of the memory architecture were modeled on the ones found in natural systems. We present an efficient implementation based on parallel programming techniques. The memory module is integrated into a distributed system for speech and image analysis, which is currently developed in the Sonderforschungsbereich (SFB) 360, Situated Artificial Communicators, where a hybrid vision system combining neural and semantic networks is used.