The optokinetic reaction in foveate and afoveate geckos

Optocollic responses in adult barn owls (Tyto furcata)

Barn owls, like primates, have frontally oriented eyes, which allow for a large binocular overlap. While owls have similar binocular vision and visual-search strategies as primates, it is less clear whether reflexive visual behavior also resembles that of primates or is more similar to that of closer related, but lateral-eyed bird species. Test cases are visual responses driven by wide-field movement: the optokinetic, optocollic, and optomotor responses, mediated by eye, head and body movements, respectively. Adult primates have a so-called symmetric horizontal response: they show the same following behavior, if the stimulus, presented to one eye only, moves in the nasal-to-temporal direction or in the temporal-to-nasal direction. By contrast, lateral-eyed birds have an asymmetric response, responding better to temporal-to-nasal movement than to nasal-to-temporal movement. We show here that the horizontal optocollic response of adult barn owls is less asymmetric than that in the chicken for all velocities tested. Moreover, the response is symmetric for low velocities (< 20 deg/s), and similar to that of primates. The response becomes moderately asymmetric for middle-range velocities (20–40 deg/s). A definitive statement for the complex situation for higher velocities (> 40 deg/s) is not possible.

From "ear" to there: a review of biorobotic models of auditory processing in lizards

The peripheral auditory system of lizards has been extensively studied, because of its remarkable directionality. In this paper, we review the research that has been performed on this system using a biorobotic approach. The various robotic implementations developed to date, both wheeled and legged, of the auditory model exhibit strong phonotactic performance for two types of steering mechanisms-a simple threshold decision model and Braitenberg sensorimotor cross-couplings. The Braitenberg approach removed the need for a decision model, but produced relatively inefficient robot trajectories. Introducing various asymmetries in the auditory model reduced the efficiency of the robot trajectories, but successful phonotaxis was maintained. Relatively loud noise distractors degraded the trajectory efficiency and above-threshold noise resulted in unsuccessful phonotaxis. Machine learning techniques were applied to successfully compensate for asymmetries as well as noise distractors. Such techniques were also successfully used to construct a representation of auditory space, which is crucial for sound localisation while remaining stationary as opposed to phonotaxis-based localisation. The peripheral auditory model was furthermore found to adhere to an auditory scaling law governing the variation in frequency response with respect to physical ear separation. Overall, the research to date paves the way towards investigating the more fundamental topic of auditory metres versus auditory maps, and the existing robotic implementations can act as tools to compare the two approaches.