Phototaxic foraging of the archaepaddler, a hypothetical deep-sea species

An autonomous agent (animat, hypothetical animal), called the (archae) paddler, is simulated in sufficient detail to regard its simulated aquatic locomotion (paddling) as physically possible. The paddler is supposed to be a model of an animal that might exist, although it is perfectly possible to view it as a model of a robot that might be built. The agent is assumed to navigate in a simulated deep-sea environment, where it hunts autoluminescent prey. It uses a biologically inspired phototaxic foraging-strategy, while paddling in a layer just above the bottom. The advantage of this living space is that the navigation problem is essentially two-dimensional. Moreover, the deep-sea environment is physically simple (and hence easier to simulate): no significant currents, constant temperature, completely dark. A foraging performance metric is developed that circumvents the necessity to solve the travelling salesman problem. A parametric simulation study then quantifies the influence of habitat factors, such as the density of prey, and the body-geometry (e.g. placement, direction and directional selectivity of the eyes) on foraging success. Adequate performance proves to require a specific body-% geometry adapted to the habitat characteristics. In general performance degrades smoothly for modest changes of the geometric and habitat parameters, indicating that we work in a stable region of 'design space'. The parameters have to strike a compromise between on the one hand the ability to 'fixate' an attractive target, and on the other hand to 'see' as many targets at the same time as possible. One important conclusion is that simple reflex-based navigation can be surprisingly efficient. In the second place, performance in a global task (foraging) depends strongly on local parameters like visual direction-tuning, position of the eyes and paddles, etc. Behaviour and habitat 'mould' the body, and the body-geometry strongly influences performance. The resulting platform enables further testing of foraging strategies, or vision and locomotion theories stemming either from biology or from robotics

The attentional drift-diffusion model extends to simple purchasing decisions

How do we make simple purchasing decisions (e.g., whether or not to buy a product at a given price)? Previous work has shown that the attentional drift-diffusion model (aDDM) can provide accurate quantitative descriptions of the psychometric data for binary and trinary value-based choices, and of how the choice process is guided by visual attention. Here we extend the aDDM to the case of purchasing decisions, and test it using an eye-tracking experiment. We find that the model also provides a reasonably accurate quantitative description of the relationship between choice, reaction time, and visual fixations using parameters that are very similar to those that best fit the previous data. The only critical difference is that the choice biases induced by the fixations are about half as big in purchasing decisions as in binary choices. This suggests that a similar computational process is used to make binary choices, trinary choices, and simple purchasing decisions

The Effect of Clouds on Air Showers Observation from Space

Issues relating to extensive air showers observation by a space-borne fluorescence detector and the effects of clouds on the observations are investigated using Monte Carlo simulation. The simulations assume the presence of clouds with varying altitudes and optical depths. Simulated events are reconstructed assuming a cloud-free atmosphere. While it is anticipated that auxiliary instruments, such as LIDAR (LIght Detection And Ranging), will be employed to measure the atmospheric conditions during actual observation, it is still possible that these instruments may fail to recognize the presence of a cloud in a particular shower observation. The purpose of this study is to investigate the effects on the reconstructed shower parameters in such cases. Reconstruction results are shown for both monocular and stereo detectors and for the two limiting cases of optically thin, and optically thick clouds.Comment: 31 pages, 13 figure