A review of the field of lightness perception from Helmholtz to the present shows the most adequate theories of lightness perception to be the intrinsic image models. Nevertheless, these models fail on 2 important counts: They contain no anchoring rule, and they fail to account for the pattern of errors in surface lightness. Recent work on both the anchoring problem and the problem of errors has produced a new model of lightness perception, one that is qualitatively different from the intrinsic image models. The new model, which is based on a combination of local and global anchoring of lightness values, appears to provide an unprecedented account of a wide range of empirical results, both classical and recent, especially the pattern of errors. It provides a unified account of both illumination-dependent failures of constancy and background-dependent failures of constancy, resolving a number of long-standing puzzles.

The human visual system devotes a significant proportion of its resources to a very small part of the visual field, the fovea. Foveal vision is crucial for natural behavior and many tasks in daily life such as reading or fine motor control. Despite its significant size, this part of cortex is rarely investigated and the limited data have resulted in competing models of the layout of the foveal confluence in primate species. Specifically, how V2 and V3 converge at the central fovea is the subject of debate in primates and has remained “terra incognita” in humans. Using high-resolution fMRI (1.2 × 1.2 × 1.2 mm³) and carefully designed visual stimuli, we sought to accurately map the human foveal confluence and hence disambiguate the competing theories. We find that V1, V2, and V3 are separable right into the center of the foveal confluence, and V1 ends as a rounded wedge with an affine mapping of the foveal singularity. The adjacent V2 and, in contrast to current concepts from macaque monkey, also V3 maps form continuous bands (∼5 mm wide) around the tip of V1. This mapping results in a highly anisotropic representation of the visual field in these areas. Unexpectedly, for the centermost 0.75°, the cortical representations for both V2 and V3 are larger than that of V1, indicating that more neuronal processing power is dedicated to second-level analysis in this small but important part of the visual field.

The question of how our brains and those of other animals code sensory information is of fundamental importance to neuroscience research. Visual illusions offer valuable insight into the mechanisms of perceptual coding. One such illusion, the tilt after–effect (TAE), has been studied extensively since the 1930s, yet a full explanation of the effect has remained elusive. Here, we put forward an explanation of the TAE in terms of a functional role for adaptation in the visual cortex. The proposed model accounts not only for the phenomenology of the TAE, but also for spatial interactions in perceived tilt and the effects of adaptation on the perception of direction of motion and colour. We discuss the implications of the model for understanding the effects of adaptation and surround stimulation on the response properties of cortical neurons.

Fractals have been very successful in quantifying the visual complexity exhibited by many natural patterns, and have captured the imagination of scientists and artists alike. Our research has shown that the poured patterns of the American abstract painter Jackson Pollock are also fractal. This discovery raises an intriguing possibility – are the visual characteristics of fractals responsible for the long-term appeal of Pollock’s work? To address this question, we have conducted ten years of scientific investigation of human response to fractals and here we present, for the first time, a review of this research that examines the inter-relationship between the various results. The investigations include eye-tracking, visual preference, skin conductance, and EEG measurement techniques. We discuss the artistic implications of the positive perceptual and physiological responses to fractal patterns.