Images created from noise filtered to have an approximately 1/f amplitude spectrum were altered by adding excess energy concentrated at various spatial frequencies. The effects of this manipulation on judgements of visual discomfort were studied. Visual noise with a 1/f amplitude spectrum (typical of natural images) was judged more comfortable than any image with a relative increase in contrast energy within a narrow spatial frequency band. A peak centred on 0.375-1.5cycles/degree of spatial frequency was consistently judged as more uncomfortable than a peak at a higher spatial frequency. This finding was robust to slight differences in eccentricity, and when stimuli were matched for perceived contrast across spatial frequency. These findings are consistent with the idea that deviation from the statistics of natural images could cause discomfort because the visual system is optimised to encode images with the particular statistics typical of natural scenes.

BackgroundMigraine is a common neurological condition that often involves differences in visual processing. These sensory processing differences provide important information about the underlying causes of the condition, and for the development of treatments. Review of Psychophysical LiteraturePsychophysical experiments have shown consistent impairments in contrast sensitivity, orientation acuity, and the perception of global form and motion. They have also established that the addition of task-irrelevant visual noise has a greater effect, and that surround suppression, masking and adaptation are all stronger in migraine. Theoretical Signal Processing ModelWe propose utilising an established model of visual processing, based on signal processing theory, to account for the behavioural differences seen in migraine. This has the advantage of precision and clarity, and generating clear, falsifiable predictions. ConclusionIncreased effects of noise and differences in excitation and inhibition can account for the differences in migraine visual perception. Consolidating existing research and creating a unified, defined theoretical account is needed to better understand the disorder.

Our forward-facing eyes allow us the advantage of binocular visual information: using the tiny differences between right and left eye views to learn about depth and location in three dimensions. Our visual systems also contain specialized mechanisms to detect motion-in-depth from binocular vision, but the nature of these mechanisms remains controversial. Binocular motion-in-depth perception could theoretically be based on first detecting binocular disparity and then monitoring how it changes over time. The alternative is to monitor the motion in the right and left eye separately and then compare these motion signals. Here we used an individual differences approach to test whether the two sources of information are processed via dissociated mechanisms, and to measure the relative importance of those mechanisms. Our results suggest the existence of two distinct mechanisms, each contributing to the perception of motion-in-depth in most observers. Additionally, for the first time, we demonstrate the relative prevalence of the two mechanisms within a normal population. In general, visual systems appear to rely mostly on the mechanism sensitive to changing binocular disparity, but perception of motion-in-depth is augmented by the presence of a less sensitive mechanism that uses interocular velocity differences. Occasionally, we find observers with the opposite pattern of sensitivity. More generally this work showcases the power of the individual differences approach in studying the functional organization of cognitive systems.

The processing of visual information by the nervous system requires significant metabolic resources. To minimize the energy needed, our visual system appears to be optimized to encode typical natural images as efficiently as possible. One consequence of this is that some atypical images will produce inefficient, non-optimal responses. Here, we show that images that are reported to be uncomfortable to view, and that can trigger migraine attacks and epileptic seizures, produce relatively non-sparse responses in a model of the primary visual cortex. In comparison with the responses to typical inputs, responses to aversive images were larger and less sparse. We propose that this difference in the neural population response may be one cause of visual discomfort in the general population, and can produce more extreme responses in clinical populations such as migraine and epilepsy sufferers.

This review describes the subjective experience of visual aura in migraine, outlines theoretical models of this phenomenon, and explores how these may be linked to neurochemical, electrophysiological, and psychophysical differences in sensory processing that have been reported in migraine with aura. Reaction–diffusion models have been used to model the hallucinations thought to arise from cortical spreading depolarisation and depression in migraine aura. One aim of this review is to make the underlying principles of these models accessible to a general readership. Cortical spreading depolarisation and depression in these models depends on the balance of the diffusion rate between excitation and inhibition and the occurrence of a large spike in activity to initiate spontaneous pattern formation. We review experimental evidence, including recordings of brain activity made during the aura and attack phase, self-reported triggers of migraine, and psychophysical studies of visual processing in migraine with aura, and how these might relate to mechanisms of excitability that make some people susceptible to aura. Increased cortical excitability, increased neural noise, and fluctuations in oscillatory activity across the migraine cycle are all factors that are likely to contribute to the occurrence of migraine aura. There remain many outstanding questions relating to the current limitations of both models and experimental evidence. Nevertheless, reaction–diffusion models, by providing an integrative theoretical framework, support the generation of testable experimental hypotheses to guide future research.