Abstract
Recovery of heading from optic flow (OF) has been studied extensively by experimental assessment of human performance and by building computational models capable of heading recovery. However, relatively little work has made direct comparisons between models or between models and human performance. Here, we undertake such comparisons investigating heading recovery when OF density and dot direction noise are manipulated. Participants and a range of computational models viewed radial, limited lifetime dot OF fields and made 2AFC judgements about whether heading was to the left or right of a target in the scene. There were four possible horizontal target locations (�2, �4 deg from centre of display) and 10 possible horizontal focus of expansion offsets (�0.2, �0.5, �1, �2, �4 deg relative to target). Dot motion orientation was corrupted by additive, zero mean Gaussian noise with standard deviation at one of three levels (0, 7.5, 15 deg). Dot density was varied by changing the number of dots in the field (5, 50, 100, 200). Thresholds for human observers dropped most sharply (by 50?75%) as number of dots increased from 5 to 50 but then performance stabilised. Furthermore, human observers showed some robustness to noise; when there were at least 50 dots in the display performance in the no noise and 7.5 deg noise conditions was similar but was degraded slightly in the 15 deg noise condition. Performance for the models tested varied greatly. Of these models, Longuet-Higgins & Prazdny (PRSL:Series B; 208(1173); 1980) model performed particularly poorly over the dot density range and showed little robustness to noise. In contrast, the Perrone (JOSAmA; 9(2); 1992) model was considerably more robust to noise and showed a qualitatively similar pattern of dependence on dot density to that seen in humans.
Highlights
Optic flow is the pattern of optical motion available at the eye during relative movement between the observer and the scene
For LHP80 thresholds do appear to depend on the quantity of flow when the stimulus contains no noise this dependence is markedly reduced when noise is added to the stimulus
We note that WC99 appears to differ from the other models in the sense that performance continues to improve beyond the 100 dots per frame level
Summary
Optic flow is the pattern of optical motion available at the eye during relative movement between the observer and the scene. It is known that primates are sensitive to the stereotypical patterns of optic flow which arise when an observer moves through a largely stationary scene (Figure 1). An additional role proposed suggests that that optic flow drives rapid eye movements which act to stabilize the foveal image and maintain correspondence between images on the two retinae (e.g., see Busettini et al, 1997). This optic flowdriven stabilization process helps to preserve foveal visual acuity and stereo vision during observer movement (Angelaki and Hess, 2005). In the flow parsing hypothesis, it has been suggested that optic flow processing plays an important role in the assessment of scene-relative object movement during self movement (Rushton and Warren, 2005; Rushton et al, 2007; Warren and Rushton, 2007, 2008, 2009a,b; Matsumiya and Ando, 2009; Pauwels et al, 2010; Warren et al, 2012)
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