Introduction
Vision is our most developed sense and unsurprisingly a substantial amount of brain processing is devoted to it, with over half the primate brain involved in vision-related processing. A first step in understanding the nature of this processing involves an appreciation of the capabilities and specialization that define human vision. Such an understanding of our overall visual strengths and weaknesses provides a framework within which the emerging neurophysiology and neuroanatomy of different parts of the pathway can be best understood.
Early visual processing of spatial form has to contend with the fact that information that first impinges on receptors in the eye must be transmitted to the main processing sites in the brain. There are transmission bandwidth considerations to ensure the optic nerve/optic track remains of manageable size. What this means is that of all the information contained in a retinal image, only a fraction of it can be efficiently transmitted and processed; hard choices have to be made. This leads us into the concept of sensory filtering and information compression. The fact that individual cells have limited dynamic ranges also leads to a consideration of distributed parallel processing. These notions, namely filtering, compression, and parallel processing, are important principles in sensory analysis in general and visual analysis in particular.
Foveal window of visibility
It has only been relatively recently that quantitative methods have been developed to help quantify human vision. These developed from the realization of the importance of contrast and object size (or spatial frequency). That contrast is fundamental to the visibility of objects had been known for a long time. Bouguer developed the first quantitative measurements of contrast sensitivity, using the visibility of the shadow of a rod produced by a candle at a specified distance away. That the size of objects (more correctly their retinal image size) also determines their visibility has also been appreciated for a long time, being the basis of acuity testing in the last century. The realization that the relationship between these two variables could provide a more complete description of human vision was a logical consequence of the emerging work on optical transfer functions and was accelerated by the need for calibrated photographic surveillance methods during World War II. Amid great controversy, particularly from certain optical physicists of the time, Campbell and co-workers used this approach to great effect to provide a quantitative description of the capability of the human visual system, which in turn provided a starting point for considerations of optical quality of the human eye and its neural limitations. This function, often referred to as the spatial contrast sensitivity function, provided the first quantitative description of our “window of visibility” and makes explicit the information that our visual system processes best.
Figure 32.1 displays a sinusoidal grating stimulus that was first developed by Fergus Campbell and John Robson at Cambridge in which the spatial frequency (number of cycles subtended in one degree at the eye) is modulated along the abscissa (low spatial frequencies on the left, high spatial frequencies on the right) and contrast is modulated along the ordinate (low contrasts at the top, high contrasts at the bottom). The reader can observe an inverted U-shaped region in which the stripes are visible – this is a demonstration of our window of visibility.
In Figure 32.2 laboratory measurement of the contrast sensitivity function is displayed for a normal observer; its overall shape mirrors that seen in Figure 32.1 . Here, contrast sensitivity, the reciprocal of the contrast needed for threshold detection, is plotted against the spatial frequency of a luminance-defined sinusoidal grating (filled circles), in cycles per degree of angle subtended at the eye. Our window of visibility is defined by an inverted U-shaped function that shows we are best at detecting objects subtending about a half to a third of a degree (approximately half the width of one’s finger nail at arms length). Our sensitivity for detecting smaller objects (i.e. of higher spatial frequency) progressively declines, as it also does for detecting objects larger (i.e. of lower spatial frequency) than one degree. Information in the very low contrast range, particularly if it is of very low or very high spatial frequency, is not processed at all by our visual system; human vision is specialized in the intermediate spatial frequency range and within this limited range our contrast sensitivity is good to one part in 500 (i.e. 0.2 percent) under monocular conditions. Other animals have visual capabilities adapted to match their particular needs; the cat is better than the human at detecting low spatial frequencies and the falcon is better than the human at detecting high spatial frequencies. These benefits, however, come with associated costs. The cat is much worse than us at detecting high spatial frequencies and the falcon much worse than us at detecting low spatial frequencies. In other words the size of the window of visibility is approximately the same for different animals but shifts along the abscissa (i.e. the spatial frequency axis) to best suit the needs of the animal. For the cat, this means things within pouncing distance (resulting in a shift of the function to lower spatial frequencies) and for the falcon, for detecting ground prey when hovering high in the sky (resulting in a shift of the function to higher spatial frequencies).
What limits our contrast sensitivity?
Why are we poor at detecting objects of very high and very low spatial frequency? In terms of the former, an obvious possibility is that the retinal image may not contain this information in the first place due to optical (i.e. cornea, lens) losses. Campbell & Green, using a laser interferometric technique to by-pass the optics, showed that the fall-off of contrast sensitivity at high spatial frequencies only had a small (about one-third) optical component. They argued that two-thirds of the sensitivity loss at high spatial frequencies was neural in nature, due to limitations at the retina/brain ( Box 32.1 ). More recently it has been suggested that the quantal fluctuations in light itself may account for the loss at high spatial frequency. Although not yet resolved, in light of the discussion below in the section on luminance effects, the original suggestion that this fall-off is neural in nature seems more likely. The sensitivity loss at low spatial frequencies only occurs for sinusoidal stimuli (i.e. as opposed to squared-edged stimuli, as shown in Figure 32.1 ) and many different explanations have been put forward including, relative image stabilization, a consequence of reduced cycles or lateral inhibition. None of these hold up to scrutiny; providing a comparable temporal stimulation and having the same number of spatial cycles does not eliminate the low spatial frequency decline in sensitivity. The lateral inhibition explanation can also be rejected for reasons that are outlined in the section below on the relation to single cells. Its explanation has proved elusive.
A contrast sensitivity loss at high spatial frequencies may be optical or neural in nature.
What is the relationship between the contrast sensitivity function and the response of single cortical cells?
Neurons at various levels of the visual pathway exhibit spatially overlapping excitatory and inhibitory receptive field properties that endow them with size or spatial frequency dependence, as illustrated in Figure 32.5 . This size selectivity becomes greater as one progresses from retina to cortex owing to the increased strength of the antagonistic surround. These neurons also exhibit contrast thresholds, quasi-linear contrast response regions and contrast saturation responses. Indeed, efforts to outline the optimum spatial frequency response of a sample of contrast-sensitive neurons in the monkey have provided a distribution similar to that of the human behavioral contrast sensitivity function. This can be seen in Figure 32.3 from the histogram of the distribution of cellular contrast sensitivities in monkey V1 relative to human behavioral sensitivities. The contrast sensitivity function represents the envelope of all the contrast-responding cells with the most sensitive ones defining the threshold limit.
Visual cortical neurons have sufficiently strong surrounds to their classical receptive fields to make them selective over a limited part of the spatial frequency range; an example is shown in Figure 32.4 . Thus different populations of cortical cells underlie the contrast sensitivity curve at different locations along the spatial frequency axis; the overall contrast sensitivity curve being the envelope of the sensitivity of the most sensitive cortical cells. This is diagrammatically illustrated in Figure 32.5 .
Psychophysical supporting evidence comes from the finding that adaptation or prolonged viewing of stimuli of one particular spatial frequency desensitizes responses for only stimuli of a similar spatial frequency, also suggesting that the overall curve is composed of a number of more discrete “channels”. At one time it was thought that the low spatial frequency fall-off in sensitivity was a reflection of the surround inhibition of the largest (or lowest spatial frequency-tuned) mechanism whose peak was located around 1 c/deg. There is now ample evidence to doubt this; individual spatial frequency selective mechanisms have been reported down to as low as 0.2 c/deg. Thus individual cortical mechanisms extend across the full extent of the visible spatial frequency range. The reason why the lower spatial frequency mechanisms are less sensitive is unknown but one possibility is that it may be due to a purely retinal cause; larger receptive fields must inevitably contain more inactive rods (contributing no signal but possibly contributing noise) under photopic conditions.
The contribution of M & P pathways to contrast sensitivity
In the retina there are morphologically and functionally different populations of ganglion cells two of which, the parasol (magno; 10%) and midget (parvo; 80%) retinal ganglion cells send their afferents along the retino-geniculate pathway. These projections are kept separate in the geniculate terminating in the magno- and parvo-cellular layers respectively (so called M-cells and P-cells). The afferents of these two geniculate cell types are sent to different layers of 4C of the visual cortex (i.e. 4C alpha and 4C beta) and ultimately make a dominant (though not exclusive) contribution to the dorsal and ventral extrastriate streams respectively.
Do these two parallel systems carry the same or different contrast sensitivity information?
The combination of single cell recording and lesion studies has revealed that these two systems carry different though overlapping visual information to the cortex. The M-cell information is biased to lower spatial and mid-temporal frequencies. P-cell information is biased to mid-higher spatial and lower temporal frequencies. A particular subdivision of the M-cell system, the Y-like M cell, exhibits properties that suggest it may have a special role in perception. The results shown in Figure 32.6 are from the lesion studies of Merigan and colleagues and represent how the spatial and temporal contrast sensitivity function is affected by lesions to parvo- and magno-cellular regions of the LGN of the monkey. Parvo-cellular lesions have a large effect on spatial contrast sensitivity at all spatial frequencies for static stimuli. Magno-cellular lesions reduce temporal contrast sensitivity in the mid range for spatial stimuli of low spatial frequency. Parvo-cellular lesions also produce a loss of color vision since red/green chromatic information is conveyed by this system.
The contribution of different cortical areas to contrast sensitivity
A number of different regions of the visual cortex contain a retinotopic map and are therefore given the status of being “distinct” visual areas. V1 receives the majority of the input from the geniculate although there is evidence that V2 and MT also receive a direct input in primates, although its role may be more modulatory in nature. Although V1 contains cells with both small and large receptive field sizes at each eccentricity (approximately a factor of 4 in receptive field size at any one eccentricity from the data of Hubel & Wiesel ), cells with smaller receptive fields progressively drop out as eccentricity increases, resulting in a shift of the mean receptive field size with eccentricity to larger values. Other visual areas have cells with larger receptive field size and consequently diminished retinotopy. Area V2 has cells that are tuned to lower spatial frequencies than those found in V1, however there is no compelling evidence from lesion studies that cells other than in V1 make a major contribution to spatial contrast sensitivity.
The effect of disease on contrast sensitivity
Having a more quantitative approach for specifying the quality of normal vision also provides a more sensitive method for documenting visual loss. Given that contrast sensitivity is not a unitary function but one representing the envelope of a large number of more narrowly tuned functions (i.e. the individual sensitivity of different populations of cortical neurons tuned to different ranges of spatial frequency), there was hope that a knowledge of the relative susceptibility of these neural populations might lead to a better understanding of a number of retinal and cortical diseases affecting the visual system. This expectation has been only partially realized because although the specification of the contrast sensitivity function provided more information than before it also has inherent limitations. Without an understanding of these limitations any simple interpretation of contrast sensitivity in disease becomes hazardous. The strengths and weaknesses of the approach can be best appreciated by one or two examples, one involving a developmental condition called amblyopia, the other, an acquired condition called optic neuritis. The reader can imagine how comparable issues could concern the interpretation of anomalous contrast sensitivity in related conditions, for example glaucoma and age-related maculopathy.
One of the first conditions to be studied with the contrast sensitivity approach was amblyopia, a common developmental disorder in which the vision in one eye is compromised in early life. It was shown ( Fig. 32.7 ) that high spatial frequencies were selectively affected. Although in some strabismic amblyopes it was subsequently shown that additional and unrelated losses can occur at low spatial frequencies, the picture today is not substantially different from that originally outlined by Gstalder & Green over 30 years ago. Indeed its application to the study of amblyopia has highlighted some potential pitfalls of using this approach. The first concerns the fact that it is limited to threshold. Amblyopes need much more contrast in order to detect higher spatial frequencies; however at and above their raised thresholds they perceive contrast normally with their amblyopic eye. This is illustrated in Figure 32.8 in which results are shown for contrast matches made between the eyes of the two most common types of amblyopia, strabismic (A) (i.e. turned eye) and (B) non-strabismic (i.e. anisometropia without a turned eye). Thresholds are raised (unfilled symbols) but as the contrast is raised to suprathreshold levels, it is perceived normally (solid diagonal line indicates matches that are veridical). The difference between the raised thresholds and normal perception of suprathreshold contrast is abrupt in the case of strabismic amblyopes ( Fig. 32.8A ) but more gradual in the case of non-strabismic, anisometropic amblyopes ( Fig. 32.8B ). In other words, thresholds are not representative of contrast vision above threshold; the contrast sensitivity function is highly non-linear when it comes to contrast.
In optic neuritis, which commonly occurs as part of multiple sclerosis, similar high spatial frequency loss is observed in the contrast sensitivity function but it is not just limited to threshold. Sufferers of optic neuritis who need, for example, a factor of 10 more contrast to detect a given spatial frequency will perceive that spatial frequency to be a factor of 10 reduced in contrast, no matter what its absolute contrast is. With respect to the results of Figure 32.8 for strabismic and non-strabismic amblyopia, the equivalent contrast-matching function for a person with optic neuritis would run parallel but below the normal matching line (solid diagonal). Amblyopia and optic neuritis can exhibit similar threshold losses but their suprathreshold contrast losses are very different.
Measurement of constant sensitivity has minimal value for differential diagnosis as similar losses occur in different diseases but is of value in monitoring sensitivity changes.
Contrast sensitivity supplies information about threshold detection not suprathreshold perceptions.
Another limitation is that contrast sensitivity may be normal and yet spatial vision may be severely degraded. Some amblyopes perceive severe spatial distortions that would render a 1-D sinusoidal grating unrecognizable (i.e. spatially scrambled), yet the contrast thresholds for detecting such a scrambled stimulus may be normal. Finally the fact that the assessment involves large repetitive patterns means that it supplies no information about how the dysfunction is distributed across the visual field corresponding to the test stimulus. For example, the contrast sensitivity loss of strabismic and anisometropic amblyopes when measured with a relatively large (i.e. 10 degree) centrally fixed stimulus is very similar, yet the visual field is affected very differently in these two conditions; the central field is selectively affected in strabismics and the whole field is evenly affected in anisometropes.
In optic neuritis, initial reports suggested that the loss of contrast sensitivity could be restricted to just a small subset of spatial channels producing a very localized loss of contrast sensitivity within the contrast sensitivity function. Later studies showed that contrast sensitivity could be reduced in a variety of different ways; high-frequency loss alone, low-frequency loss alone, similar loss at all spatial frequencies, or a regional frequency loss (the latter was quite rare). More importantly, all of these different signatures of the contrast sensitivity loss in optic neuritis could be simulated by different types of visual field loss, suggesting that the exact nature of the loss across the visual field could be the primary determinant of the contrast sensitivity loss. To decipher the regional nature of any visual loss necessitates the use of much more localized stimuli (i.e. a Gabor patch) and measurements made at multiple field positions.
