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The colors that people see depend not only on the surface properties of objects but also on how these properties interact with light as well as on how light reflected from objects interacts with an individual’s visual system. Because individual visual systems vary, the same visual stimulus may elicit different perceptions from different individuals. #thedress phenomenon drove home this point: different individuals viewed the same image and reported it to be widely different colors: blue and black versus white and gold. This phenomenon inspired a collection of demonstrations presented at the Vision Sciences Society 2015 Meeting which showed how spatial and temporal manipulations of light spectra affect people’s perceptions of material colors and illustrated the variability in individual color perception. The demonstrations also explored the effects of temporal alterations in metameric lights, including Maxwell’s Spot, an entoptic phenomenon. Crucially, the demonstrations established that #thedress phenomenon occurs not only for images of the dress but also for the real dress under real light sources of different spectral composition and spatial configurations.

Recent developments in artificial intelligence (AI) and machine learning raise the possibility of screening and early diagnosis for neurodegenerative diseases, using 3D scans of the retina. The eventual value of such screening will depend not only on scientific metrics such as specificity and sensitivity but, critically, also on public attitudes and uptake. Differential screening rates for various screening programmes in England indicate that multiple factors influence uptake. In this narrative literature review, some of these potential factors are explored in relation to predicting uptake of an early screening tool for neurodegenerative diseases using AI. These include: awareness of the disease, perceived risk, social influence, the use of AI, previous screening experience, socioeconomic status, health literacy, uncontrollable mortality risk, and demographic factors. The review finds the strongest and most consistent predictors to be ethnicity, social influence, the use of AI, and previous screening experience. Furthermore, it is likely that factors also interact to predict the uptake of such a tool. However, further experimental work is needed both to validate these predictions and explore interactions between the significant predictors.

A target composed of random dots can be easily segmented from a background of random dots when each is defined by a different, broad distribution of dot speeds, colours, or brightnesses, even if these distributions overlap extensively. This shows that segmentation can be based on global properties of the target and background. If average feature value is the relevant global property, segmentation threshold should be a monotonically decreasing function of dot density. Observers viewed a 6.5 deg × 6.5 deg square array of between 512 and 36 864 randomly positioned dots (dot size 4.7 or 1.6 min arc) in which the target was a vertical band of dots defined by a different distribution of luminance, colour, or motion from the background. The task was to indicate whether the target appeared on the left or on the right of midline. The stimulus was displayed for 67 ms, followed after 233 ms by a 67 ms mask. We found that motion segmentation thresholds decrease monotonically as a function of density. For luminance and colour, segmentation thresholds initially decrease toward a local minimum, then increase to a local maximum. Thereafter, thresholds decrease monotonically as density increases further. These results suggest that the integrative segmentation process for motion is qualitatively different from those for luminance and colour. The local extrema for luminance and colour suggests that segmentation in these domains is based on forming clusters by linking together dots with similar feature properties. Only when interdot distance is sufficiently small are all dots within an area included in the cluster. Behaviour is then determined by difference in average feature value or target — background contrast.