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Pedestrians with low vision are at risk of injury when hazards, such as steps and posts, have low visibility. This study aims at validating the software implementation of a computational model that estimates hazard visibility. The model takes as input a photorealistic 3D rendering of an architectural space, and the acuity and contrast sensitivity of a low-vision observer, and outputs estimates of the visibility of hazards in the space. Our experiments explored whether the model could predict the likelihood of observers correctly identifying hazards. In Experiment 1, we tested fourteen normally sighted subjects with blur goggles that simulated moderate or severe acuity reduction. In Experiment 2, we tested ten low-vision subjects with moderate to severe acuity reduction. Subjects viewed computer-generated images of a walkway containing five possible targets ahead—big step-up, big step-down, small step-up, small step-down, or a flat continuation. Each subject saw these stimuli with variations of lighting and viewpoint in 250 trials and indicated which of the five targets was present. The model generated a score on each trial that estimated the visibility of the target. If the model is valid, the scores should be predictive of how accurately the subjects identified the targets. We used logistic regression to examine the correlation between the scores and the participants' responses. For twelve of the fourteen normally sighted subjects with artificial acuity reduction and all ten low-vision subjects, there was a significant relationship between the scores and the participant's probability of correct identification. These experiments provide evidence for the validity of a computational model that predicts the visibility of architectural hazards. It lays the foundation for future validation of this hazard evaluation tool, which may be useful for architects to assess the visibility of hazards in their designs, thereby enhancing the accessibility of spaces for people with low vision. [ABSTRACT FROM AUTHOR]

The relationship between biomechanical action and perception of self-motion during walking is typically consistent and well-learned but also adaptable. This perceptual-motor coupling can be recalibrated by creating a mismatch between the visual information for self-motion and walking speed. Perceptual-motor recalibration of locomotion has been demonstrated through effects on subsequent walking without vision, showing that learned perceptual-motor coupling influences a dynamic representation of one's spatial position during walking. Our present studies test whether recalibration of wheelchair locomotion, a novel form of locomotion for typically walking individuals, similarly influences subsequent wheelchair locomotion. Furthermore, we test whether adaptation to the pairing of visual information for self-motion during one form of locomotion transfers to a different locomotion modality. We find strong effects of perceptual-motor recalibration for matched locomotion modalities – walking/walking and wheeling/wheeling. Transfer across incongruent locomotion modalities showed weak recalibration effects. The results have implications both for theories of perceptual-motor calibration mechanisms and their effects on spatial orientation, as well as for practical applications in training and rehabilitation. [ABSTRACT FROM AUTHOR]