Motion coding in the brain undoubtedly reflects the statistics of retinal image motion occurring in the natural environment. Measuring the statistics of motion in natural scenes is an important tool for building our understanding of how the brain works. Unfortunately, there are statistics that are either impossible or prohibitively difficult to measure. For this reason, it is useful to measure scene statistics in artificial movies derived from simulated environments. This is a novel and important methodological approach that allows us to ask questions about optimal coding that are impossible otherwise. This dissertation describes a course of research that develops this research methodology, the simulated scene statistical approach. This dissertation applied the artificial scene statistical approach to understanding the visual statistics of motion during navigation through forest environments. An environmental model of forest scenes was developed based on previously measured range and surface texture statistics. Spatiotemporal power spectra were measured in both simulated and natural scenes for the task of first person motion through a forest environment. These image statistics measurements helped validate the environmental model. Next, the environmental model was used to simulate across-domain statistics to study the ideal aperture size of motion sensors. It was found that across a variety of different scene conditions, the optimal aperture size of motion sensors increases with the speed to which the sensor is tuned. This is an important constraint for understanding both how the brain encodes motion as well as for designing computer motion detectors. This theoretical research inspired a psychophysical experiment estimating the receptive-field size of human foveal motion discrimination. It was found that for narrow-band stimuli the ideal aperture size increases with spatial frequency, but is unchanging with respect to velocity or temporal frequency. This dissertation shows an approach to the study of vision that has applications in psychophysics, neuroscience and computer vision. The emphasis on accurate and validated environmental models for simulating scene statistics can help improve our understanding of the structure and function of the human visual system and also help us build more accurate and robust computer vision systems. / text

The detection of visual stimuli involves neurons which are selectively responsive to components of a visual scene. In the early stages of visual processing, it is commonly accepted that neurons respond to the changes in luminance associated with objects and object boundaries. However, recent experiments have demonstrated that some neurons can also respond to features which are not defined by luminance variations. These features are termed "second-order" because they require more complex processing, and neurons which respond to second-order features are necessarily nonlinear. / In this thesis, I undertook a three dimensional physiological characterization (i.e. tuning of orientation, spatial frequency and temporal frequency) of such nonlinear neurons in order to shed light on their processing capabilities. In particular we sought to address the following issues: (1) whether the temporal and spatial properties underlying second-order motion are similar to those underlying luminance based ("first-order") motion; (2) whether these properties remain constant using different types of second-order stimuli, suggesting that neurons' responses are invariant to the physical attributes comprising the stimulus; and (3) whether second-order processing is a cortical mechanism or can occur at an earlier stage of the visual system (e.g. in the lateral geniculate nucleus). Taken together these results have a dual function; they provide insight into the complex cellular processing of higher order features, and they provide a general framework for the generation of second-order models.

Experiments were conducted examining human interception of projectiles. It was found that fielders tend not maintain a linear optic trajectory (LOT) as advocated by McBeath, Shaffer, and Kaiser (1995) for interception in two-dimensions. Furthermore, it was shown that its curvature provided an ambiguous cue to action. New interception models were proposed based on optic acceleration cancellation (OAC) and the constant δ model significantly improved upon the performance of the LOT model. Awareness of interception strategy was investigated by questioning subjects about their angle of gaze variation during the ball flight. A lack of awareness of the critical information that guides interceptive behaviour was demonstrated. It is proposed that the information is stored implicitly, challenging the position of Shanks and St. John (1994). Subjects were asked to discriminate the visual information that they experienced before running to catch real balls to examine the validity of experiments that test human ability to discriminate the acceleration of simulated trajectories. Discriminative performance found to remain high even when the duration of viewed information is reduced. This intact discriminative ability led to the suggestion that trajectory discrimination occurs very rapidly after ball launch. The movement and gaze angle of fielders running to catch under conditions in which OAC cannot be sustained was analysed. Subjects showed little deviation from the strategy until the final moments of the catch. The overall conclusion to the thesis is that subjects react rapidly to the optic acceleration of a projectile to determine interceptive behaviour but may not be aware of the sensory basis of their decision and use an approximate version of the constant δ strategy to reach the interception point.

No description available.

Thesis (M.S.)--Ohio University, March, 1988. / Title from PDF t.p.

Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2008. / Includes bibliographical references (leaves 131-138). Also available in electronic version.

Thesis (M.S.)--University of Wyoming, 2007. / Title from PDF title page (viewed on Feb. 6, 2009). Includes bibliographical references (p. 66-68).

Thesis (M.S.)--Ohio University, November, 1989. / Title from PDF t.p.

Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.

Thesis (Ph.D.). / Written for the Dept. of Physiology. Title from title page of PDF (viewed 2009/06/10). Includes bibliographical references.