M von der Heyde

The effect of cognition on the visually-induced illusion of self-motion (vection)

Stimuli depicting a natural scene can produce faster, stronger, and more convincing sensation of illusory selfmotion The results of the experiment revealed that a stimulus depicting a natural scene can produce faster, stronger, and more convincing sensation of illusory self-motion. Previous studies have typically used abstract stimuli to induce vection. Here, we show that the illusion can be enhanced if a natural scene is used instead. A possible explanation for why this happens is that natural scenes are less likely to be interpreted as moving because of the assumption of a stable environment (Dichgans & Brandt, 1978).

Screen curvature does influence the perception of visually simulated ego-rotations

A repeated-measures ANOVA with turn error as the dependent variable revealed a significant effect of screen curvature, turning velocity, and also an interaction between curvature and turn angle: While target angles were undershot on the curved screen (gain factor 0.84), a surprising overshoot was observed for the flat screen (gain factor 1.08; see Fig. 4). The presentation order of the randomized blocks (flat vs. curved screen) had no significant effect (see Table 1 for F-values).

An architecture for distributed visual memory.

Abstract 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.