A process model of the formation of spatial presence experiences

In order to bridge interdisciplinary differences in Presence research and to establish connections between Presence and “older” concepts of psychology and communication, a theoretical model of the formation of Spatial Presence is proposed. It is applicable to the exposure to different media and intended to unify the existing efforts to develop a theory of Presence. The model includes assumptions about attention allocation, mental models, and involvement, and considers the role of media factors and user characteristics as well, thus incorporating much previous work. It is argued that a commonly accepted model of Spatial Presence is the only solution to secure further progress within the international, interdisciplinary and multiple-paradigm community of Presence research

Robust Models for Optic Flow Coding in Natural Scenes Inspired by Insect Biology

The extraction of accurate self-motion information from the visual world is a difficult problem that has been solved very efficiently by biological organisms utilizing non-linear processing. Previous bio-inspired models for motion detection based on a correlation mechanism have been dogged by issues that arise from their sensitivity to undesired properties of the image, such as contrast, which vary widely between images. Here we present a model with multiple levels of non-linear dynamic adaptive components based directly on the known or suspected responses of neurons within the visual motion pathway of the fly brain. By testing the model under realistic high-dynamic range conditions we show that the addition of these elements makes the motion detection model robust across a large variety of images, velocities and accelerations. Furthermore the performance of the entire system is more than the incremental improvements offered by the individual components, indicating beneficial non-linear interactions between processing stages. The algorithms underlying the model can be implemented in either digital or analog hardware, including neuromorphic analog VLSI, but defy an analytical solution due to their dynamic non-linear operation. The successful application of this algorithm has applications in the development of miniature autonomous systems in defense and civilian roles, including robotics, miniature unmanned aerial vehicles and collision avoidance sensors

Understanding the retinal basis of vision across species

The vertebrate retina first evolved some 500 million years ago in ancestral marine chordates. Since then, the eyes of different species have been tuned to best support their unique visuoecological lifestyles. Visual specializations in eye designs, large-scale inhomogeneities across the retinal surface and local circuit motifs mean that all species' retinas are unique. Computational theories, such as the efficient coding hypothesis, have come a long way towards an explanation of the basic features of retinal organization and function; however, they cannot explain the full extent of retinal diversity within and across species. To build a truly general understanding of vertebrate vision and the retina's computational purpose, it is therefore important to more quantitatively relate different species' retinal functions to their specific natural environments and behavioural requirements. Ultimately, the goal of such efforts should be to build up to a more general theory of vision

Matched filtering and the ecology of vision in insects

In the words of Wehner (J Comp Physiol A 161:511–531, 1987) who first coined the term “matched filter” in the context of sensory systems, matched filters “severely limit the amount of information the brain can pick up from the outside world, but they free the brain from the need to perform more intricate computations to extract the information finally needed for fulfilling a particular task”. In other words, by matching the properties of neurons, circuits and sensory structures to the characteristics of the most crucial sensory stimuli that need to be detected, these stimuli can be rapidly and reliably extracted for further processing, thus drastically improving the efficiency of sensing. And by “severely limiting information picked up by the brain”, the energetic costs that would have been associated with coding superfluous information are effectively eliminated. Thus, “freeing the brain” not only frees it from the need to perform intricate computations, it also frees it from significant (and unnecessary) energetic costs. Not surprisingly, with their small eyes and brains and severely limited energy budgets, visual matched filtering is particularly well developed in small animals like insects. It is most obvious at the visual periphery, in the morphology and physiology of the compound eyes, but remarkable matched filters also occur at higher levels of visual processing. Using a number of case studies, I will show how visual matched filters have evolved for all aspects of insect life, including the detection and pursuit of mates and prey and for locomotion and navigation in the natural habitat