The luminance contrast sensitivity function has been investigated using behavioral and electrophysiological methods in many vertebrate species. Some features are conserved across species as a shape of the function, but other features, such as the contrast sensitivity peak value, spatial frequency contrast sensitivity peak, and visual acuity have changed. Here, we review contrast sensitivity across different classes of vertebrates, with an emphasis on the frequency contrast sensitivity peak and visual acuity. We also correlate the data obtained from the literature to test the power of the association between visual acuity and the spatial frequency of the contrast sensitivity function peak.
contrast sensitivity; visual acuity; object vision; animal behavior; psychophysics; visually evoked potentials
SPECIAL SECTION ON CONTRAST SENSITIVITY IN HONOUR OF
EDUARDO OSWALDO CRUZ
Comparative neurophysiology of spatial luminance contrast sensitivity
Givago da Silva Souza; Bruno Duarte Gomes; Luiz Carlos L. Silveira
Universidade Federal do Pará, Belém, PA, Brazil
Correspondence regarding this article should be directed to
The luminance contrast sensitivity function has been investigated using behavioral and electrophysiological methods in many vertebrate species. Some features are conserved across species as a shape of the function, but other features, such as the contrast sensitivity peak value, spatial frequency contrast sensitivity peak, and visual acuity have changed. Here, we review contrast sensitivity across different classes of vertebrates, with an emphasis on the frequency contrast sensitivity peak and visual acuity. We also correlate the data obtained from the literature to test the power of the association between visual acuity and the spatial frequency of the contrast sensitivity function peak.
Keywords: contrast sensitivity, visual acuity, object vision, animal behavior, psychophysics, visually evoked potentials.
Spatial vision and contrast sensitivity
The ecological role of vision is mainly related to object localization and identification in a given environment. Vision helps animals search for food, look for sexual partners, avoid predators, and care for their offspring (Ghim & Hodos, 2006). A variety of eye optics designs, photoreceptor matrices, and post-receptoral retinal, tectal, and cortical mechanisms allows for environmental mapping and the neural representation of the visual information available to the animal. A popular hypothesis for visual system evolution relies on the selective pressure to disclose natural camouflage to other living beings (Regan, 2000). According to Regan (2000), five object attributes make it especially visible against its surrounding environment: luminance, texture, movement, color, and binocular disparity. If an object and its surroundings display the same values for these five parameters, then the visual system cannot distinguish one object from another, and the object is perfectly embedded in the environment.
Spatial vision encompasses both the perception of the spatial distribution of light and the perception of object localization in the environment (De Valois & De Valois, 1980). The present review emphasizes findings regarding the visual system processing of spatial luminance distribution at very low contrast levels. Spatial luminance contrast is the relative difference between the brightness of adjacent regions of space (Campbell & Maffei, 1974; Owsley, 2003). Spatial luminance contrast or simultaneous luminance contrast stands in the domain of space, as opposed to temporal luminance contrast or successive luminance contrast, which stands in the domain of time.
Two measurements of luminance contrast are frequently used in spatial vision: contrast threshold and contrast sensitivity. Contrast threshold is a probabilistic measurement that represents the highest contrast for object identification that is equal to chance. Contrast sensitivity is the inverse of contrast threshold. Measuring both contrast threshold and contrast sensitivity is possible using both periodic and non-periodic stimuli, with the former a measurement performed in the spatial frequency domain and the latter a measurement performed in the space domain. Results obtained using spatial and spatial frequency measurements can be converted to each other using a Fourier transformation, provided the system is linear in the range of the conditions studied.
Contrast transfer functions in natural and manmade optical systems
A common way to express visual system performance in the spatial frequency domain is to plot contrast sensitivity as a function of spatial frequency along most of the range of spatial frequencies that the visual system sees (i.e., contrast sensitivity function [CSF]; Campbell, 1983). This function allows a quick understanding of the animal's visual system performance both under normal and dysfunctional conditions, displaying both peak contrast sensitivity (i.e., the contrast sensitivity in the range where the visual system is more sensitive) and visual acuity (i.e., the highest spatial frequency that the visual system detects at very high contrast). The CSF peak is a good indicator of the spatial frequencies that are more biologically relevant to the animal, whereas visual acuity represents the spatial resolution of the animal's visual system and the highest spatial frequency that is able to evoke a visual response from the animal. The CSF has a bell shape and can be regarded as dividing the spatial frequency world into two halves. Below the curve are all combinations of spatial frequencies and contrast that are seen by the animal. Above the curve resides the unseen world.
The CSF is the visual psychophysics counterpart of a very well known optical measurement, the Modulation Transfer Function (MTF), which together with the Phase Transfer Function (PTF) results in the more general case, the Optical Transfer Function (OTF; Goodman, 2005). The capacity of any optical system-ranging from a simple lens to a composite optical system made from several lens elements to a very complex photonic system made from many different optical elements combined with image recording devices, such as films, photographic paper, and electronic displays-to transfer information from the object space to the image space or to an image recording device can be described by how much the system attenuates spatial contrast (i.e., modulation transfer) and how much it introduces a phase shift (i.e., phase transfer) for each spatial frequency. This is evaluated by careful measurements of the contrast and phase of a periodic object and its image, followed by quantitative comparisons between the two datasets, thus resulting in the aforementioned MTF and PTF, respectively. Sine wave objects are preferable for this type of experiment because linear optical systems only introduce contrast attenuation with no phase shift for all spatial frequencies. The OTF of an optical system can then be expressed either as the MTF plus PTF as separate real functions of a real variable or as a single complex function of a real variable, with the real variable spatial frequency in both cases. With vision, measuring spatial phase shifts is not very common (Westheimer, 1978), and the CSF that physiologically corresponds to the MTF very often remains as the single measured visual system characteristic (Röhler, 1962; Westheimer, 1963).
Another way to characterize an optical system is to measure its Point Spread Function (PSF; Goodman, 2005; Gubisch, 1967). This is performed by using punctiform objects and recording the amount of blur in the image. A similar measurement can be made using very narrow-line objects. In this case, the resulting function is called a Line Spread Function (LSF), which can then be used to estimate the PSF (Flamant, 1955; Krauskopf, 1962; Westheimer & Campbell, 1962; Campbell & Gubisch, 1966). Both the PSF and LSF are measurements performed in the domain of space, as opposed to the spatial frequency domain where the OTF, MTF, and PTF are measured. A Fourier transformation can then be used to transform the results from one domain to the other. The Fourier theorem establishes that measurements in one domain yield results equal to measurements performed in the related domain followed by Fourier transformation to the first domain, provided the system is linear.
Both spatial frequency and spatial measurements have been used to study visual system contrast transfer. Because the visual system has two very different subsystems, one represented by eye optics and another represented by neural elements (e.g., retinal, subcortical, and cortical), having separate measurements for these two subsystems and measurements for the entire system is desirable. Contrast transfer through the eye optical system has been studied by recording its LSF (Flamant, 1955; Krauskopf, 1962; Westheimer & Campbell, 1962; Campbell & Gubisch, 1966) and MTF (Röhler, 1962; Westheimer, 1963). The eye optical system behaves, in many regards, similarly to manmade optical systems, and the measurement of its LSF or MTF is performed in similar ways and provides equivalent results. The results can then be interpreted using the same rationale. However, an additional practical problem exists when dealing with the eyes of living animals compared with experiments with manmade lenses and optical systems, specifically how to access the image formed by the eye optics. The more common way is called the double-pass method, in which an object is first placed in front of the eye, and its image is formed by the eye optics on the retinal surface. The retinal image is then used as an object by the eye optics, working backward to form a second image outside the eye that can then be measured by the experimenter. Finally, relatively simple mathematics is then used to account for the double-pass and estimate the single-pass effect of the eye optics (Campbell & Gubisch, 1966; Navarro, Artal, & Williams, 1993). Diffraction at the pupil is the main factor that works at very high light levels to degrade the image formed by the eye optics (Campbell & Gubisch, 1966). When the pupil enlarges at progressively lower light levels, diffraction loses importance, and optical aberrations become the main factor that degrades the image formed by the eye optics (Campbell & Gubisch, 1966). The main effect of both diffraction and optical aberrations is an attenuation of image contrast at medium and high spatial frequencies, which can be evaluated by recording the MTF or increasing the light spread in the image of punctiform objects that can be evaluated by recording the PSF or LSF. At intermediate pupil sizes, the eye optics work at optimal conditions to provide a retinal image with minimal blur.
As stated above, the CSF is the psychophysical counterpart of the optical MTF. The CSF has frequently been used to estimate contrast transfer through the entire visual system, comprising both optical and neural subsystems. It has been measured using sine wave gratings placed on a display in front of the subject and recording a behavioral response to determine the contrast threshold for each spatial frequency (Campbell & Green, 1965a; Campbell & Robson, 1968; Robson, 1966; Schade, 1956; Patel, 1966; Van Nes & Bouman, 1967). Additionally, estimating the CSF of the neural part of the visual system is also possible by bypassing the eye optics. This is accomplished by generating a sine wave grating directly on the retina using laser interferometry (Arnulf & Dupuy, 1960; Campbell & Green, 1965a; Westheimer, 1960). The comparison of the eye optics MTF, neural CSF, and CSF of the entire visual system (i.e., optical plus neural parts) made under equivalent conditions from the same animal allows one to distinguish the contribution of each element (i.e., optical and neural) to animal vision. A series of studies by Fergus W. Campbell and colleagues reached the conclusion that the performance of neural elements limits the performance of human vision (Campbell & Green, 1965a; Campbell & Gubisch, 1966; Campbell & Robson, 1968). Experiments in other animals have generally provided similar conclusions (e.g., opossum: Oswaldo-Cruz, Hokoç, & Sousa, 1979; Oswaldo-Cruz, Picanço-Diniz, & Silveira, 1982; Picanço-Diniz, Silveira, & Oswaldo-Cruz, 1983; Silveira, Picanço-Diniz, & Oswaldo-Cruz, 1982).
The shapes of the eye optics MTFs and visual system CSFs reveal some interesting properties. As expected, the eye optics MTF has a low-pass shape with progressive attenuation in the high spatial frequency range, reaching a cut-off frequency at approximately 60 cycles per degree (cpd; Campbell & Gubisch, 1966). The visual system CSF is band-pass, showing attenuation at high spatial frequencies, similar to the eye optics MTF, plus additional attenuation at low spatial frequencies (Campbell & Green, 1965a; Campbell & Robson, 1968). The attenuation of low spatial frequencies is attributable to visual system neural processing and considered to be related to the lateral inhibition and center-surround organization of the receptive fields of visual system neurons (Enroth-Cugell & Robson, 1966).
Figure 1 shows the several stages of contrast transfer from object generation on a display, such as a cathode ray tube (CRT) display (Figure 1A), modulation transfer through the eye optical elements (Figure 1B), and the end result of contrast transfer through the entire visual system measured behaviorally (Figure 1C). System analysis postulates that system performance depends on the element whose performance is more limited-in this case, the neural part of the visual system.
Vertebrate vision and environmental adaptation
The subphylum Vertebrata (vertebrates) comprises seven classes: Agnatha (jawless fish), Condrichthyes (cartilaginous fish), Osteichthyes (bony fish), Amphibia (amphibians), Reptilia (reptiles), Aves (birds), and Mammalia (mammals). Many features of the vertebrate visual system have been optimized during evolution for appropriate performance in a given set of environmental conditions. Vertebrates occupy terrestrial (both surface and subterranean), aquatic, and aerial environments and interfaces between these compartments. This allows for the evolution of visual systems with different performance for luminance contrast information processing in the spatial frequency domain. The spatial vision of fish, amphibians, reptiles, birds, and mammals has been studied over the years to cover a range of different species to understand the ecological aspects of visual behavior.
Luminance spatial contrast sensitivity of bony fish
The visual response of fish to contrast as a function of spatial frequency has been investigated in several species, some of which are laboratory animals that have been studied for numerous reasons: bluegill sunfish (Lepomis macrochirus), zebrafish (Danio rerio), medaka or Japanese killifish (Oryzias latipes), and goldfish (Carassius auratus; Bilotta & Powers, 1991; Haug, Biehlmaier, Mueller, & Neuhauss, 2010; Mueller & Neuhauss, 2010; Northmore & Dvorak, 1979; Northmore, Oh, & Celenza, 2007; Rinner, Rick, & Neuhauss, 2005; Figure 2D). Northmore and Dvorak (1979) and Bilotta and Powers (1991) used Pavlovian conditioning to suppress respiration upon the presentation of a sinusoidal grating. Both works showed that the fish CSF had a band-pass shape for high mean luminance and stationary stimuli. Bilotta and Powers (1991) showed that temporally modulated stimuli or stimuli with low mean luminance changed the CSF shape from band-pass to low-pass. The fish CSF peaks at 0.2-0.3 cpd and has a relatively high cut-off frequency that might provide behavioral visual acuity of 3.2 cpd at high photopic luminance levels (Bilotta & Powers, 1991). Northmore et al. (2007) estimated the contrast sensitivity of fish based on preferential swimming in response to grating stimuli. They found that the CSF had a band-pass shape, peaked at 0.3-0.4 cpd, and had a cut-off frequency of 5-7 cpd.
Rinner et al. (2005) and Haug et al. (2010) used the optokinetic nystagmus response to estimate the contrast sensitivity of larval zebrafish. These studies immobilized zebrafish larvae and then stimulated the larvae with gratings projected onto a cylindrical screen. Fish eye movements were recorded with a camera, and eye angle and velocity were evaluated in real-time. Rinner et al. (2005) found a band-pass CSF that peaked at 0.07-0.08 cpd, with visual acuity of 0.2-0.4 cpd. Haug et al. (2010) found visual acuity of 0.16 cpd. Also using fish optokinetic nystagmus, Mueller and Neuhauss (2010) studied eye velocity as a function of stimulus contrast and spatial frequency in adult zebrafish and medaka. They found that eye velocity quickly changed with low to medium contrast and was saturated at medium to high contrast. Eye velocity as a function of spatial frequency was well described by a band-pass function that peaked at 0.1-0.12 cpd, and zebrafish had slightly higher visual acuity than medaka (0.4 and 0.6 cpd, respectively).
Luminance spatial contrast sensitivity of amphibians
Amphibians have been studied to solve a number of problems in cell biology and visual neuroscience. Some reference studies of vertebrate vision were performed in amphibians (e.g., Hartline, 1938, 1940a, b, c; Lettvin, Maturana, McCulloch, & Pitts, 1959). Despite several works on the single unit properties of neurons located at different sites along the visual pathways and many behavioral studies of amphibian vision, most of these studies have been performed in frogs. Very few studies have focused on contrast sensitivity and visual acuity in vertebrates (Aho, 1997; Manteuffel & Himstedt, 1978). No complete descriptions of the amphibian CSF have been provided. Himstead (1967) and Manteuffel and Himsted (1978) evaluated visual acuity in both aquatic and aerial environments in the smooth newt (Triturus vulgaris) by measuring optomotor responses and the single unit responses of neurons located in the optic tectum and thalamus. Aho (1997) estimated the visual acuity of frogs (Rana pipiens) using a forced-choice prey-dummy setup. Two dummies were placed in the visual field. Behind the dummies were gratings, but only one of the stimuli drifted. This author found that at high luminance levels, visual acuity reached 2.8 cpd and dropped to approximately 0.7 cpd when the luminance was lowered. He also found, similar to other mammals, a good correlation between behavioral visual acuity and cut-off spatial frequency estimated from the sampling properties of the retinal ganglion cell mosaic. Monroy and Nishikawa (2011) studied the angular head movements of frogs during predatory behavior toward earthworms of different sizes. They found larger angular amplitudes for 2-3 cm prey and a smaller response for both larger-sized prey (low spatial frequencies) and especially smaller-sized prey (high spatial frequencies). This experiment, however informative, was too complex to provide a straightforward description of frog contrast sensitivity and visual acuity. The observed results are doubtless the holistic end product of all of the frog sensory and motor systems working cooperatively.
Luminance spatial contrast sensitivity of reptiles
Similar to amphibians, the study of the luminance spatial contrast sensitivity of reptiles is scarce and has been limited to visual acuity measurements in some species of turtles and snakes (e.g., Pseudemys scripta elegans [freshwater turtle], Caretta caretta [loggerhead sea turtle], and Nerodia sipedon pleuralis [midland banded water snake]; Baker, Gawne, Loop, & Pullman, 2007; Bartol, Musick, & Ochs, 2002; Northmore & Granda, 1991). In turtles, experiments measured visually evoked responses recorded from the optic tectum (Northmore & Granda, 1991) and obtained non-invasive recordings directly from surface electrodes placed on the skin of the animal's head (Bartol et al., 2002). The estimated visual acuity ranged from 4.4-9 cpd (Northmore & Granda, 1991) to 3.9-6.7 cpd (Bartol et al., 2002). Snake visual acuity was estimated to be 4.25 cpd (Baker et al., 2007).
Luminance spatial contrast sensitivity of birds
Birds have very sophisticated vision in the spatial, temporal, and chromatic domains. Bird spatial vision has been extensively studied in several species (Blough & Blough, 1989; Dabrowska, 1975; Fite & Rosenfield-Wessels, 1975; Fox, Lehmkuhle, & Westendorf, 1976; Gaffney & Hodos, 2003; Ghim & Hodos, 2006; Gover, Jarvis, Abeyesinghe, & Wathes, 2009; Harmening, Nikolay, Orlowski, & Wagner, 2009; Hirsch, 1982; Hodos, Miller, & Fite, 1991; Hodos, Ghim, Potocki, Fields, & Storm, 2002; Hodos, Potocki, Ghim, & Gaffney, 2003; Jarvis, Abeyesinghe, McMahon, & Wathes, 2009; Lee, Holden, & Djamgoz, 1997; Martin & Gordon, 1974; Nye, 1968; Over & Moore, 1981; Porciatti, Fontanesi, & Bagnoli, 1989; Reymond & Wolfe, 1981; Reymond, 1985, 1987; Schmid & Wildsoet, 1998; Yamamoto, Furuya, & Watanabe, 2001).
Pigeons (Columbia livia) have been widely used in operant conditioned behavior experiments, and their visual system has been the input system of choice in several such experiments mainly because of the high visual acuity of pigeons compared with other commonly studied laboratory vertebrates. Blough (1971) estimated pigeon visual acuity to be 7.5-30 cpd. Hodos et al. (1991) estimated pigeon visual acuity at different ages and found that the youngest individuals (2 years old) had mean visual acuity of 16 cpd, whereas the oldest individuals (10-20 years old) had visual acuity of 2-4 cpd. Blough (1971) and Hodos et al. (2002, 2003) investigated pigeon contrast sensitivity. Blough (1971) used a forced-choice procedure, in which the pigeon had to decide between striped and blank fields to peck. The spatial frequency of the striped field was increased to estimate visual acuity, which ranged from 7.5 to 25.8 cpd. Hodos et al. (2002) used electroretinography and an operant conditioning procedure to estimate the pigeon CSF. They found band-pass functions, but the overall curve was 53% lower for all spatial frequencies when pattern electroretinography was used. The CSF peak was located at a higher spatial frequency, and visual acuity was higher when they used behavioral methods compared with electroretinography (i.e., 0.81 vs. 0.68 cpd and 5.23 cpd vs. 3.31 cpd, respectively).
The visual system of chickens (Gallus gallus domesticus) became quite popular after the famous experiment of experimentally induced myopia in chicks that related this condition to the deprivation of spatial vision during development (Pickett-Seltner, Sivak, & Pasternak, 1988). Over and Moore (1981) found that the visual acuity of 25-day-old chicks was 1.5 cpd. Schmid and Wildsoet (1998) measured the optokinetic response and estimated the visual acuity of 2- to 8-day-old chicks as 6-8 cpd. Jarvis et al. (2009) used a forced-choice procedure, in which the avian response was to peck a correct key. They found that the CSF of adult chickens was higher (approximately 1 cpd), and visual acuity was 7 cpd. Similar results were found by Gover et al. (2009).
The spatial vision of quails (Coturnix coturnix japonica) was studied by Lee et al. (1997) using pattern electroretinography. They found that younger quails had higher contrast sensitivity than older quails, especially in the low spatial frequency range. However, visual acuity was similar in young and old quails (5-6 cpd).
The visual systems of other birds, together with commonly used laboratory birds (e.g., pigeons and chickens), have rose scientific interest because of their conspicuous cleverness (e.g., crows), their notorious ability to distinguish their prey at a long distance (e.g., eagles, falcons, and hawks), and their sophisticated nocturnal vision (e.g., owls). Dabrowska (1975) estimated the visual acuity of three different species of crows (Corvus frugilegus, Garrulus glandarius, and Coloeus monedula) and found values near 30 cpd. Fite and Rosenfield-Wessels (1975) estimated the visual acuity of a species of crow (Cyanocitta cristata) and found values that ranged from 15 to 19 cpd. Yamamoto et al. (2001) used behavioral methods and estimated the visual acuity of the Japanese jungle crow (Corvus macrorhyncos) to be 8.4 cpd.
Reymond and Wolfe (1981) and Reymond (1985) studied the luminance CSF of the eagle (Aquila audax) using behavioral methods. They estimated that the eagle CSF had a peak at 1 cpd and visual acuity of 137 cpd at high luminance levels. The behavioral visual acuity of falcons (Falco sparverius and Falco berigora) was studied by Fox et al. (1976), Hirsch (1982), and Reymond (1987), who found values between 73 and 160 cpd. Gaffney and Hodos (2003) estimated the visual acuity of falcons (Falco sparverius) using pattern electroretinography and found a value of 29 cpd. These authors argued that electroretinographic visual acuity is 37% lower than behavioral visual acuity, and with the appropriate corrections they predicted falcon visual acuity of 46 cpd, which was still lower than previous behavioral estimations.
Martin and Gordon (1974) and Fite (1973) estimated owl visual acuity of 7.5-15 cpd. Porciatti, Fontanesi, Raffaelli, & Bagnoli (1989) measured the visual acuity of a species of owl (Athene noctua) using pattern electroretinography and found it to be 6 cpd. Martin and Gordon (1974) and Harmening et al. (2009) used behavioral methods to study the contrast sensitivity of three species of owls (Tyto alba pranticola, Strix aluco, and Bubo virginianos). Harmening et al. (2009) found a contrast sensitivity peak of 1-2 cpd and visual acuity of 3-4 cpd.
Ghim and Hodos (2006) used pattern electroretinography to compare the CSF of several bird species, including falcons (Falco sparvarius), owls (Tyto alba), European starlings (Sturnus vulgaris), quails (Coturnix coturnix japonica), red-bellied woodpeckers (Melanerpes carolinus), and pigeons (Columbia livia). They found that these birds had a band-pass CSF that peaked at 3 cpd (falcon), 1-2 cpd (pigeon, starling, and owl), 0.8-1 cpd (quail), and 0.5-0.7 cpd (woodpecker).
Luminance spatial contrast sensitivity of mammals
The measurements of mammalian contrast sensitivity are biologically and evolutionary important. The results in humans can be applied to various subjects, including medicine. Mammals represent a largely diversified and well studied group of vertebrates with different visual system circuitry adapted to many circadian and ecological niches. These animals can process visual information in different ways to make spatially oriented decisions.
Marsupials are among the oldest infraclass mammalian. Their visual system can provide clues about the visual systems of the first mammals. The marsupial CSF was estimated by Silveira et al. (1982) and Hemmi and Mark (1998) using visually evoked potential recordings. Hemmi and Mark (1998) also estimated visual acuity using psychophysical methods. Silveira et al. (1982) studied the vision of opossums (Didelphis marsupialis), and Hemmi and Mark (1998) studied the vision of tammar wallabies (Macropus eugenii). The mean CSF estimated by Silveira and colleagues had a low-pass profile and visual acuity of 1.25 cpd. However, some animals studied by Silveira and colleagues showed a significant attenuation of contrast sensitivity at the lowest spatial frequencies tested (Silveira, 1980). Hemmi and Mark (1998) found a band-pass electrophysiological CSF that peaked at 0.15 cpd and visual acuity of 2.7 cpd. Tammar wallaby behavioral visual acuity ranged from 4 to 5 cpd.
Several studies measured the visual acuity of bats using behavioral methods. The visual acuity of the little brown bat (Myotis lucifugus) was 0.17 cpd, and the visual acuities of the lesser sac-winged bat (Saccopteryx leptura; Suthers, 1966), common vampire bat (Desmodus rotundus; Manske & Schmidt, 1976), big brown bat (Eptesicus fuscus; Bell & Fenton, 1986), and northern bat (Eptesicus nilssonii; Rydell & Eklöf, 2003) were 1.43, 1.25, 1, and 1.25 cpd, respectively.
The nervous systems and especially visual systems of cats and small rodents, such as rats, mice, and hamsters, have been extensively investigated. From the 1950s to 1980s, results obtained from the cat visual system were considered easily transferred to the understanding of primate and human vision. This tenet is no longer accepted, but the large amount of data collected from the cat visual system is still very interesting from the point of view of comparative psychology, physiology, and anatomy. Small rodents, which were initially used as one of the more important models for operant behavior experiments, became progressively more used in different laboratories to study various diseases, drug effects, and the knockout of specific genes that govern neural function.
Sinex, Burdette, and Pearlman (1979) applied an optokinetic nystagmus method introduced by Wallman (1975) to study the spatial vision of the house mouse (Mus musculus). They investigated the motor response at very low spatial frequencies, such as 0.016 cpd. They found a CSF peak at 0.125 cpd and visual acuity of 0.5 cpd. Birch and Jacobs (1979) estimated the spatial luminance CSF of pigmented and albino rats (Rattus novergicus) using a two-forced-choice behavioral task with a display with a homogeneous field and another display with a sinusoidal grating with a range of spatial frequencies and contrasts. For pigmented rats, they found a low-pass CSF peak at 0.12 cpd and visual acuity of 1.2 cpd. For albino rats, they found that the CSF retained the low-pass profile, but contrast sensitivity was lower compared with pigmented rats at all spatial frequencies tested, and visual acuity was less, ranging from 0.34 to 0.43 cpd. The CSFs of pigmented and albino rats showed no fall-off at spatial frequencies as low as 0.12 cpd. Several studies estimated rat behavioral visual acuity as 0.5-1 cpd (Cowey, Henken, & Perry, 1982; Dean, 1981; Lashley, 1938; Linden, Cowey, & Perry, 1983; Wiesenfeld & Branchek, 1976). Legg (1984) was the first to show the fall-off of the rat CSF at low spatial frequencies. He used spatial frequencies lower than 0.12 cpd. Keller, Strasburger, Cerutti, and Sabel (2000) showed a prominent attenuation of contrast sensitivity at spatial frequencies below 0.1 cpd. They found that the CSF peak occurred at 0.1-0.2 cpd.
Other works used visually evoked potentials to estimate the rat CSF (Silveira, Heywood, & Cowey, 1987; Tejada & Tedó, 1998). Silveira et al. (1987) found that the CSF of pigmented rats was band-pass, peaking at 0.1 cpd with a visual acuity of 1.2 cpd. Tejada and Tedó (1998) used an approach similar to Silveira et al. (1987) but used albino rats. They found lower contrast sensitivity at all spatial frequencies compared with those obtained by Silveira et al. (1987) in the pigmented rat, and visual acuity was 0.48 cpd. Despite the methodological differences, a remarkable similarity was found between the results obtained by Birch and Jacobs (1979) and the results obtained by Silveira et al. (1987) and Tejada and Tedó (1998). Prusky, West, & Douglas (2000) compared the visual acuity of rats and mice and found that rats had two-fold higher visual acuity than mice.
The non-invasive visual investigation of cats (Felis domesticus) was first conducted by Smith (1936) and later widely investigated in 1970-1980 (Berkley & Watkins, 1973; Bisti & Maffei, 1974; Blake, 1988; Blake, Cool, & Crawford, 1974; Campbell, Maffei, & Piccolino, 1973; Harris, 1978; Pasternak & Merigan, 1981).
Smith (1936) found behaviorally that cats could distinguish between horizontally and vertically oriented luminance contrast stripes. This finding allowed researchers, during the 1960s, to relate neural substrates in the retina or visual cortex to psychophysical findings in cats. Enroth-Cugell and Robson (1966) found that the visual acuity of retinal ganglion cells in cats was 5.5 cpd, whereas Campbell and colleagues (Campbell, Cooper, & Enroth-Cugell, 1969) performed the same investigation in thalamic and cortical cells and found visual acuity of 4 cpd.
Campbell et al. (1973) estimated the CSF in anaesthetized cats using visually evoked potential as the investigation method. The cat CSF peaked at 0.2 cpd, and visual acuity ranged from 15 to 20 cpd. Harris (1978) used visually evoked potentials to estimate the CSF of awake cats, which peaked at 0.4 cpd, and visual acuity was approximately 10 cpd. Bisti and Maffei (1974) and Blake et al. (1974) used behavioral methods, in which the cat had to push a pedal when it detected the gratings. Both studies found a CSF that peaked at 0.4 cpd, but visual acuity was approximately 5 cpd (Blake et al., 1974) and 10 cpd (Bisti & Maffei, 1974). Berkley and Watkins (1973) estimated visual acuity using visually evoked potentials, which ranged from 3 to 6 cpd. Pasternak and Merigan (1981) studied the effects of stimulus mean luminance on the cat CSF. The cats were trained under a two-forced-choice paradigm to discriminate vertical sinusoidal gratings from homogeneous fields of equal mean luminance. They found that the CSF profile changed from low-pass to band-pass as the mean luminance decreased. The CSFs peaked at 0.6 cpd and 0.15 cpd at high and low mean luminance, respectively. Visual acuity was approximately 4 cpd at high mean luminance and approximately 1 cpd at low mean luminance.
The spatial vision of other mammals has been investigated. Pak (1984) estimated the pigmented rabbit CSF, which peaked at 0.35 cpd, with visual acuity of 3 cpd. Vaney (1980) measured the visual acuity of wild European rabbits, which ranged from 1.6 to 2.5 cpd. The visual acuity of dogs that were subjected to neuromuscular block was 11.6 cpd and 12.6 cpd, estimated by electroretinography and visually evoked potentials, respectively. Hanke, Scholtyseek, Hanke, and Dehnhardt (2011) studied the contrast sensitivity of harbor seals (Phoca vitulina). They found a CSF peak at 0.7 cpd and visual acuity of 2-3 cpd. Weiffen, Moller, Mauck, and Dehnhardt (2006) measured the underwater visual acuity of harbor seals at different levels of water turbidity. They found a linear loss of visual acuity as turbidity increased. Timney and Keil (1992) estimated the visual acuity of horses to be 23.3 cpd, and Rehkämper, Perrey, Werner, Opfermann-Rüngeler, and Görlach (2000) found that cattle visual acuity for vertical lines was 2.6 cpd and for horizontal lines was 1.6 cpd.
Jacobs, Blakeslee, McCourt, and Tootell (1980) estimated the CSF of ground squirrels, which peaked at 0.7-0.8 cpd, with visual acuity of 4 cpd. Jacobs, Birch, and Blakeslee (1982) compared the CSF of three different species of tree squirrels (western gray squirrel, Sciurus griseus; fox squirrel, Sciurus niger; eastern gray squirrel, Sciurus carolinensis). No difference in the CSF was found between these species. The squirrel CSF peaked at 0.5 cpd, and visual acuity was 1.8-3.8 cpd.
The study of spatial contrast sensitivity in primates is a hot field of spatial vision investigation. The large amount of data obtained from primates has occurred because of their similarity to humans. Petry, Fox, and Casagrande (1984) estimated the CSF of prosimians tree shrew (Tupaia belangeri) using a forced-choice discrimination task. Two of the three specimens had a CSF peak at 0.7 cpd and visual acuity of approximately 2-2.4 cpd, whereas the third specimen had a CSF peak at 0.3 cpd and visual acuity of 1.25 cpd. Similar experiments were performed with galagos (Galago crassicaudatus; Langston, Casagrande, & Fox, 1986), southern pig-tailed macaques (Macaca nemestrina; De Valois, Morgan, & Snodderly, 1974; Merigan, Pasternak, & Zehl, 1981), crab-eating macaques (Macaca fascicularis; De Valois et al., 1974), owl monkeys (Aotus trivirgatus; Jacobs, 1977), and squirrel monkeys (Saimiri sciureus Merigan, 1976). The results obtained from Macaca and Saimiri were not different. Their CSFs peaked at 3-5 cpd, with visual acuity of 30-40 cpd (De Valois et al., 1974; Merigan, 1976). The owl monkey CSF peaked at 2-3 cpd, with visual acuity of 12-15 cpd (Jacobs, 1977). The galago CSF peaked at 0.7-0.9 cpd, with visual acuity of 3-4 cpd (Langston et al., 1986). Bonds, Casagrande, Norton, and DeBruyn (1987) also estimated the galago CSF using visually evoked potentials, and their results were slightly different from Langston et al. (1986). Bonds at al. (1987) found a CSF peak at 0.2-0.4 cpd and visual acuity of 1.6-3 cpd.
The spatial luminance CSF in humans (Homo sapiens) was first investigated by Schade (1956). During the 1960s and 1970s, a series of studies was performed that unveiled important aspects of human vision using psychophysical and non-invasive electrophysiological methods (Atkinson & Campbell, 1974; Bain & Kulikowski, 1976; Blakemore & Campbell, 1969a, b; Blakemore, Carpenter, & Georgeson, 1970; Campbell & Gubisch, 1966; Campbell & Green, 1965a, b; Campbell & Gregory, 1960a, b; Campbell, Howell, & Robson, 1971; Campbell, Kulikowski, & Levinson, 1966; Campbell & Kulikowski, 1966; Campbell & Kulikowski, 1971; Campbell & Kulikowski, 1972; Campbell & Maffei, 1970; Campbell, Nachmias, & Jukes, 1970; Campbell & Robson, 1968; Campbell et al., 1969; Gubisch, 1967; King-Smith & Kulikowski, 1973a, b; King-Smith & Kulikowski, 1975; Kulikowski, 1978; Kulikowski, 1971a, b; Kulikowski, Abadi, & King-Smith, 1973; Kulikowski & Campbell, 1971; Kulikowski & King-Smith, 1973; Kulikowski & Tolhurst, 1973; Maffei & Campbell, 1970; Robson, 1966; Tolhurst, 1972a, b; Tolhurst, 1973; Tolhurst & Hart, 1972; Wood & Kulikowski, 1978). The legacy of these studies, in addition to those performed in animals using invasive and non-invasive methods, elicited a theory of visual processing by parallel channels that were responsible for detecting narrow bands of spatial frequencies that together represent the CSF. The human CSF in photopic conditions peaks at 2-6 cpd and falls off at low and high spatial frequencies. Visual acuity can reach 60 cpd using foveal vision.
Fundamental theory of the spatial luminance contrast sensitivity function
Some features of spatial CSFs are shared by all species. The CSFs described above show a spatial frequency range with high contrast sensitivity that decreases at lower and higher spatial frequencies. This band-pass profile of the function can be altered by other parameters, such as mean luminance and temporal frequency. Non-linear mechanisms involved in the receptive field at different levels of the visual system should be present in different species to generate similar CSF patterns.
Other features of the CSF are very different among species. They widely depend on the morphophysiological organization of the different visual systems. Some characteristics, such as eye optics, the photoreceptor mosaic, the density of retinal neurons, post-receptoral mechanisms, and the number of neurons at different stages of visual processing, have been selected in each species that together contribute to the generation of different contrast sensitivity peaks, spatial frequency peaks, and visual acuity (Hughes, 1977; Jacobs et al., 1982).
Figure 2 shows the CSFs of several species. We can observe the similar shapes and different positions of the contrast sensitivity and spatial frequency coordinates. Figure 3 shows a crescent order of visual acuity in different species. The data presented in Figure 3 show the average of several studies for each species under similar experimental conditions. The species with the highest visual acuity are bird raptors and primates. Both groups are diurnal, and they have very large eyes. Higher visual acuity can be supported in large eyes by spreading the image over a large number of receptors (Fite & Rosenfield-Welles, 1975; Hughes, 1977; Reymond, 1985; Ross, 2000; Schultz, 1940; Troilo, Howland, & Judge, 1993). In large eyes, the contrast of the image decreases, but this impairment is compensated for by the amount of light that enters the eyes of diurnal animals. Both groups also have high foveal neuronal densities (Andrade da Costa & Hokoç, 2000; Curcio, Sloan, Kalina, & Hendrikson, 1990; DeBruyn, Wise, & Casagrande, 1980; Fite & Rosenfield-Welles, 1975; Reymond, 1985; Troilo et al., 1993; Wikler, & Rakic, 1990; Yamada, Marshak, Silveira, & Casagrande, 1998; Yamada, Silveira, Perry, & Franco, 2001). This high visual acuity has been associated with the ability to locate prey or predators from long distances (Tisdale & Fernández-Juricic, 2009). Most primates have other adaptations, such as the absence of tapeta lucida, no vessels in the central retina, and short-wavelength filters that support high visual acuity (Dartnall et al., 1965; Martin, 1990). The specialization of the primate visual nervous system to detect small details is very significant. Even in nocturnal primates, such as the nocturnal pattern activity of the owl monkey (Aotus), visual acuity reaches approximately 10 cpd (Jacobs, 1977). Compared with other primates, owl monkeys have poor vision, but compared with other large-eye mammals, Aotus has better visual acuity or visual acuity that is as good as horses, cats, and even some diurnal birds. Another primate example of nervous system specialization is Callithrix jacchus. Even with small eyes, it has an estimated visual acuity of 30 cpd, which is higher than other mammals with larger eyes (Troilo et al., 1993). The visual acuity of Callithrix has not been estimated using behavioral methods, but rather from the microanatomy of the retina, which is similar to other primates and matches the behavioral results (Curcio et al., 1990; Andrade da Costa & Hokoç, 2000; Yamada et al., 2001).
Natural selection acts in the visual system to not only increase visual acuity. Visual acuity is only the maximum spatial frequency of detection at high contrast (i.e., the last point of CSF). Visual acuity likely co-evolves with other factors that are more important to the survival of the species. Many studies have associated visual acuity with other visual features that emphasize how other features converge to increase visual acuity (Kay & Kirk, 2000; Kiltie, 2000; Kirk & Kay, 2004). Other spatial frequencies could be ecologically more important than visual acuity in the recognition of other individuals from the same species or group or in finding prey or food in a low-contrast environment. The main spatial frequency range to be naturally selected would reasonably be the spatial frequency range of the CSF peak, and all other optical and neural changes may be associated with that selection. We tested the relationship between the spatial frequency of the CSF peak and visual acuity (Figure 4). We found a good correlation (R2 = .91) using an exponential model, suggesting that both parameters may co-evolve. Small changes in the spatial frequency of the CSF peak are related to small changes in visual acuity until a range of 1-2 cpd for the spatial frequency of the CSF peak. After 1-2 cpd, the rate of change of visual acuity increases for each change in the spatial frequency of the CSF peak. Our analysis suggests that after the establishment of neural circuitry selected to be tuned for a spatial frequency, its high spatial frequency cut-off would also be automatically selected. The reason why the rate of change of visual acuity increases after a CSF peak at 1-2 cpd is still unclear. Other comparisons between the spatial frequency of the CSF peak and other optical and neural factors could be made to support our hypothesis.
Table 1 summarizes the data from the literature regarding the spatial frequency of the CSF peak, contrast sensitivity value at the CSF peak, and visual acuity. This table may be useful for the study of vertebrate contrast sensitivity.
The authors dedicate this work to Eduardo Oswaldo Cruz, physician, physiologist, and visual neuroscientist, who was awarded the 2009 Brazilian Neuroscience Medal. Supported by: FINEP IBN Net; CNPq-PRONEX/FAPESPA #2268 and #316799/2009; CNPq #476744/2009-1, #620037/2008-3, and #475860/2010-1; and CAPES-PROCAD #182/2007. LCLS is a CNPq research fellow. LCLS would like to thank Eduardo Oswaldo Cruz, who guided him through the first steps of his scientific career and taught him all that matters about Physiological Optics and Visual Neuroscience.
Oswaldo-Cruz, E., Picanço-Diniz, C.W., & Silveira, L.C.L. (1982). Optical and neural factors involved in spatial resolution by the visual system of the opossum Didelphis marsupialis. Proceedings of the Third Japan-Brazil Symposium on Science and Technology (pp. 147-161). Tokyo.
Received 25 May 2011; received in revised form 17 June 2011; accepted 29 June 2011. Available on line 30 June 2011
Givago da Silva Souza and Luiz Carlos L. Silveira, Universidade Federal do Pará, Núcleo de Medicina Tropical and Instituto de Ciências Biológicas, Belém, Brasil. Bruno Duarte Gomes, Universidade Federal do Pará, Instituto de Ciências Biológicas, Belém, Brasil.
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