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Vision research began more than 100 years before experimental psychology was founded. Over the past 50 years there have been four major developments that have greatly increased our understanding of how vision works. The ﬁrst was the appreciation of the role that internal noise and uncertainty plays in limiting visual performance. The second was the resolution of a century-long conﬂict between two theories of color vision. The third was the discovery that vision, like audition, performs a frequency analysis of the visual stimulus. And the fourth was the realization that there are many parallel streams of visual processing being carried out simultaneously and that diﬀerent characteristics of the stimulus are analyzed by diﬀerent modular mechanisms.
The purpose of vision is to provide us with information about objects in the world around us. This information is obtained from photons radiated from objects. When these photons enter the eye and are absorbed by photopigments in the photoreceptors, the resulting photoreceptor signals inﬂuence the activity of many processes, ultimately giving rise to the experience of seeing. Providing an incontrovertible deﬁnition of ‘seeing’ is not so easy. Seeing usually refers to the generation of conscious experiences through stimulation of the visual system by light. But visual experiences can also be generated as a result of direct electrical or chemical stimulation in the eye and at many locations in the brain. Conscious experiences are taken to mean experiences that can be verbally reported, although some experts ﬁnd this deﬁnition too restrictive, and some ﬁnd it too broad. Disagreement occurs because there is no clear deﬁnition of ‘conscious’ and ‘consciousness.’
Vision involves four realms of human knowledge: the realm of physics (energy and matter); the realm of physiology, and especially neurophysiology (the behavior of the nervous system); the realm of overt behavior (what people say and do); and the realm of the mind and subjective experience. A complete understanding of vision necessarily involves the under- standing of these realms and their interactions. In this essay, however, we will focus on the psychological mechanisms that make up the mind. The characteristics of these mechanisms cannot be determined by direct observation but must be inferred from the outcome of appropriately designed experiments.
1. The Role Of Uncertainty
1.1 Physical Nature Of Light
Light is electromagnetic radiation. It exists in discrete packets of energy called quanta, or photons. Quanta cannot be subdivided, and consequently light always consists of an integer number of quanta. Quanta diﬀer from each other in the amount of energy they contain. Quanta may also be quantiﬁed by two other properties that are directly related to the energy: frequency and wavelength. The energy, frequency, and wavelength of a quantum are interrelated by two fundamental equations: E = hν and c = λν, where E is the energy in Joules, h is Planck’s constant, ν is frequency in cycles per second (Hertz), λ is wavelength in meters, and c is the speed of light in a vacuum. A single quantum therefore has only one unique characteristic, which may be described as energy, frequency, or wavelength.
Light sources are characterized by the distribution of quanta that they produce. Hot sources produce broad spectral distributions, while some sources (lasers, for example) produce only a single wavelength. When a quantum from a source strikes matter (gas, liquid, or solid), one of three things occurs to the quantum: it is reﬂected from the surface; it passes through the medium; or it is absorbed and its energy is transformed into heat. The probability that a quantum is reﬂected, transmitted, or absorbed depends on the energy of the quantum and the physical properties of the material. The distribution of quantal energy from a source is therefore modiﬁed by the material with which it interacts. The human eye takes in quanta from sources, and quanta reﬂected from or transmitted through objects, and forms an optical image on the retina at the back of the eye.
1.2 Beginnings Of Vision
Vision begins when photoreceptors in the retina absorb quanta of light and generate electrochemical signals. Collectively, the human photoreceptors can absorb quanta in the narrow range of wavelengths from approximately 400 to 700 nm (1 nm = 10−9 meter). The probability that a photoreceptor will absorb a quantum varies as a function of quantal wavelength, with a maximum probability occurring at a speciﬁc wavelength, λmax. There are four types of receptors: one rod and three cones. The receptor types diﬀer in their absolute sensitivity (rods are more sensitive than cones), and in their relative sensitivity, to diﬀerent wavelengths. Rods have a λmax at 510 nm, while the three cone types have a λmax at approximately 420 nm (S-cones), 530 nm (M-cones), and 560 nm (L- cones). The normalized sensitivity of each receptor is plotted as a function of wavelength in the upper panel of Fig. 1.
Photoreceptors signal the rate at which quanta are being absorbed. They do not and cannot signal the wavelength of the quanta that are absorbed. The voltage change that occurs in a receptor when a quantum is absorbed is independent of the wavelength of the quantum. This important principle is known as the ‘Principle of Univariance.’ The visual system operates over a physical intensity range of 1012. The upper limit of 106 is equivalent to a white cumulus cloud illuminated by the noonday sun. The range from 106 to 10−2 is handled by the cone receptors and is called photopic vision. The lower part of the whole range, from 10−2 to 10−6 is handled by the rod receptors and is called scotopic vision. The intensity of 10−2 the boundary between photopic and scotopic vision, corresponds to the illumination provided by the full moon. The lowest level at which rods function is about 100 quanta of 510 nm light striking the front of the eye. The minimum amount of energy for detecting each wavelength with the scotopic and photopic systems is plotted in the lower panel of Fig. 1. The diﬀerence between the energy needed for photopic detection and that needed for scotopic detection is called the photochromatic interval. At short wavelengths, scotopic vision is much more sensitive than photopic vision, but at long wavelengths the two systems are equally sensitive.
1.3 Internal Representation
The component of the mind called ‘the sensory processes’ builds internal representations of the external world by transforming physical stimulation into internal activity. This activity plays an important role in shaping the ﬁnal representation, but memory, attention, and motivation also inﬂuence the ﬁnal outcome. The internal representation is not simply the physical stimulus in a new form. To equate the stimulus with the sensation that it produces is to make the so-called stimulus error. Light, as a physical phenomenon, has no color: it has wavelength distribution and intensity. Color is produced by chromatic mechanisms in the sensory processes, and there are many wavelength combinations that give the same color (see Sect. 1.4).
Internal representations are characterized by uncertainty. This uncertainty derives from many sources but originates in the quantum nature of matter and energy. If short ﬂashes of weak light are used in a vision experiment, it is not possible to know exactly how many photons of light are delivered to the eye on each trial. This uncertainty stems from the random nature of photon generation. The probability of obtaining a speciﬁc number of quanta on a ﬂash can be computed using the Poisson probability distribution if we know the average number of quanta delivered to the eye on each ﬂash:
where x is an integer number ranging from 0 to inﬁnity and λ is the mean number of photons per ﬂash. On some presentations of these stimuli there will be many photons, and on some there will be few, and on some there will actually be zero photons. This uncertainty in the physical stimulus combines with the inherent uncertainty of the many neural synapses and processes involved in constructing internal representations. The result is that the internal representation is not a ﬁxed quantity but ﬂuctuates with time. It is therefore necessary to represent internal representations by probability distributions rather than ﬁxed quantities. The most common one is the Gaussian probability distribution, which is speciﬁed by a mean, µ, and a standard deviation, σ:
The use of Gaussian probability distributions to describe internal representations has a long history that began with Gustav Theodor Fechner’s landmark book Die Elemente der Psychophysik in 1860. It was assumed by Galton when he introduced percentile analysis in 1885 and became more widespread in the early twentieth century through the writings of Thurstone and Urban. Signal detection theory, developed in the 1950s, has introduced this use of probability distributions to a much wider range of applications.
1.5 Psychometric Function
The most immediate consequence of noisy internal representations is that there is no single stimulus intensity above which one will always see a stimulus and below which one will not see it. Rather, as stimulus intensity is increased, the probability that an observer will say that the stimulus is visible increases. This relationship between the probability of a response and the stimulus intensity is called a psychometric function. Psychometric functions are S-shaped (ogive) when stimulus intensity is plotted on a logarithmic axis, as is illustrated at the right side of Fig. 2.
The psychometric function is a powerful tool for inferring the properties of the sensory process and the internal representation. In 1942, Hecht, Shlaer, and Pirenne sought to answer the question ‘how many quanta have to be absorbed by rod receptors in order to say ‘‘yes, I saw the stimulus’’?’ Since the processes of generating quanta and of absorbing quanta are described by Poisson probability distributions, and since the theoretical Poisson psychometric functions have shapes that depend on the number of quanta required to ‘see’ (smooth curves on the left side of Fig. 2), and since only a fraction of the quanta striking the cornea of the eye get absorbed by photoreceptors (quantum eﬃciency), both the number of quanta required for seeing and the quantum eﬃciency may be estimated by ﬁnding the theoretical psychometric function that best ﬁts the observed psychometric functions. In Fig. 2, the data of Hecht, Shlaer, and Pirenne have been shifted to the left by an amount corresponding to the quantum eﬃciency of each observer so that the data coincide as well as possible with a Poisson psychometric function. Each set of data ﬁts extremely well with one, and only one, of the theoretical Poisson functions. It is concluded that from four to 10 quanta are needed, and that the quantum eﬃciency is about 5 percent.
How one interprets the probability distributions depends on the model of the detection process one adopts. All models of detection have a sensory process and a decision process. One widely held, but now thoroughly discredited, model of the sensory process is the high threshold model, in which the sensory process has a threshold that must be exceeded by the stimulus before the sensory process generates an internal representation of the stimulus. With such a model, the psychometric function represents the integral of the underlying probability distribution of the ﬂuctuating threshold values.
The decision process of the high threshold model says ‘yes’ on test trials where an internal representation was generated and says ‘yes’ when no representation was generated by guessing some of the time. In order to estimate the guessing rate, experimenters introduced two types of detection trials: those in which a stimulus was presented (signal trials) and those without any stimulus (blank trials). Performance on such a detection task requires two measures to characterize it fully: the hit rate (probability of saying ‘yes’ when the signal is present); and the false alarm rate (probability of saying ‘yes’ when the signal was absent). The relationship between the hit rate and the false alarm rate as the decision process changes its decision strategy is called the receiver operating characteristic (ROC). The high threshold model predicts that the ROC will be a straight line, illustrated on the ROC inserted in the upper panel of Fig. 3. Hit rate–false alarm rate pairs from actual experiments in which decision strategy is manipulated form a bow-shaped ROC, shown in the upper panel of Fig. 3 by the nine ﬁlled circles. The fact that the high threshold model fails to predict the correct ROC is one reason it has been rejected as a viable model of the sensory process.
The widely accepted replacement for the high threshold model is the signal detection model. The sensory process of this model has no sensory threshold and is always generating an output even with no stimulus: any nonzero stimulus adds to this output. The decision process uses one or more decision criteria to decide what response to generate. The ROC predicted by the dual-Gaussian, variable-criterion signal detection model is plotted as the smooth curved line in the insert in the upper panel of Fig. 3 and provides an excellent ﬁt to the data.
Three variations of the Gaussian model are illustrated in Fig. 3. Two measures of sensory process sensitivity are widely used: the distance between the mean of the no-signal distribution and the mean of the signal distribution, da, and the area under the ROC generated by the two distributions, Az. The model in the bottom panel of Fig. 3 describes detection data from an experiment in which on each trial an observer was presented with one of ﬁve visual targets, each of diﬀerent intensity, or no stimulus at all. The observer rated their conﬁdence that a visual target had been presented. The ROCs for detecting these stimuli are shown in the inserts on the graphs of Fig. 4. As stimulus intensity increases, the ROC for that target becomes increasingly bowed. The psychometric function of the sensory process is formed using either da or Az (Fig. 4), since in the model they represent the sensitivity of the sensory process to the stimuli. The variability contributed by the decision process to actual performance is eﬀectively removed.
In the past 20 years, the application of the signal detection model of noisy internal representations has been expanded well outside its humble beginnings as a model of the sensory and decision processes of vision and audition. Fields in which this model has been shown to provide extremely good descriptions of observed data include recognition memory, medical diagnosis, weather forecasting, lie detection, clinical evaluation, drug detection, releasing prisoners on parole, and computer-guided decision making.
2. Color Vision
The ﬁeld of color vision has been marked by controversy and conﬂict that began with Isaac Newton’s discoveries about the nature of light and of color mixing published in his Opticks (1704). By the end of the nineteenth century, two theoretical camps had formed: one based on the ideas of Thomas Young and Hermann von Helmholtz; and the other grounded on the ideas of Ewald Hering and Johann Wolfgang von Goethe. Helmholtz argued that colors are created by the pattern of activity of three types of photoreceptors, each generating a speciﬁc color: red, green, and violet (the trichromatic theory). The Hering camp held that there were three processes that generated opponent pairs of colors: red–green, yellow–blue, and black– white (opponent process theory). These two seemingly incompatible theories coexisted for almost 100 years.
The simple resolution came in the late 1960s with the demonstration that each theory described only part of the color vision process. A complete theory of color vision having a Helmholtz component and a Hering component was published by Hans Vos and Pieter Walraven in 1972. To understand the resolution of this conﬂict, one must carefully distinguish between color matching and color appearance.
Two colors form a metameric match when, in spite of diﬀerent wavelength composition, they appear identical and indistinguishable in a side-by-side comparison. For people with normal color vision, all colors can be matched by mixing three suitably chosen primary lights. There are many possible primaries: the only constraint is that each of the three primaries cannot be matched by a mixture of the other two. This restriction means that one of the primaries will be reddish, one will be greenish, and one will be bluish. All colors can be speciﬁed by the amounts of the three primaries that are needed to match them. The speciﬁcations based on one set of three primaries can be changed into any other set of three primaries by simple linear transformation.
When lights are mixed, the amount of the three primaries required to match the mixture is the sum of the amounts of the primaries required to match the individual components of the mixture. This linearity of color matching is remarkable and occurs because the receptors act like quantum counters. Two lights will match if the S-, M-, and L-cones absorb exactly the same number of quanta per second when exposed to the one light as they do when exposed to the others. The three receptors form the Helmholtz zone and govern color matching.
In the above discussion of color matching, no mention was made about the color appearance of the match. This omission is deliberate, because color appearance is not caused directly by cone activity. Visual sensations vary along three fundamental dimensions: hue, saturation, and brightness.
Brightness and saturation are so-called prothetic dimensions: they can be rank ordered from least to greatest (Stevens 1951). For example, white is brighter than gray, which is brighter than black. Hue is a metathetic dimension: diﬀerent hues cannot be rank ordered from least to greatest; one cannot say that red is greater than green, it is just diﬀerent. Color appearance is generated by the activity of two chromatic opponent mechanisms and one luminance mechanism. Each of these mechanisms receives input from the three cone types. The red–green chromatic mechanism can generate red or green experiences but not both at the same time. The yellow–blue chromatic mechanism can generate yellow or blue experiences but not both at the same time. In normal color vision, the hue and saturation of a color experience is determined by the joint activity of these two chromatic mechanisms. A color can be reddish-blue but not reddish-green. A color can be yellow-green but not yellow-blue.
The chromatic mechanisms function by comparing the weighted activity of one cone type with the weighted activity of the other two cone types. The cones’ connection to the chromatic mechanisms and to luminance are estimated to be (from the work of John Werner and Billy Wooton (1979a, 1979b):
There are two important points to be noted: the Scones make almost no contribution to luminance and to the red–green chromatic mechanism; and the yellow–blue mechanism is nonlinear. The activity of the chromatic mechanisms in one part of the visual ﬁeld is altered and inﬂuenced by the activity of the mechanisms in spatially adjacent areas. A ‘neutral gray’ test patch will appear greenish when surrounded by a red ﬁeld.
It is common to read in introductory textbooks that the color opponent processes are located in the ganglion cells of the retina. A careful analysis of the chromatic properties of chromatically sensitive ganglion cells, however, reveals that although many retinal ganglion cells show opponent characteristics by inhibiting their activity to some wavelengths and exciting their activity to other wavelengths, their actual properties do not match those required for the Hering zone chromatic mechanisms. The way the brain actually creates the chromatic mechanisms involves both retinal and cortical mechanisms.
3. Spatial And Spatial Frequency Analysis
Visual stimuli may be described quantitatively in two quite diﬀerent but interchangeable ways: in terms of spatial features, or spatial frequency content. Simple spatial features are, for example, the diameter or height and width of a target, typically measured in degrees of visual angle on the retina of the eye. Description of more complex stimuli, such as letters of the alphabet or human faces, have feature sets to measure the properties of these stimuli. In the early 1950s, the powerful engineering methods of linear systems analysis were introduced into vision. Linear systems analysis characterizes systems by the degree to which sine wave stimuli of various frequencies are passed through it: the modulation transfer function (MTF). In vision, a physical stimulus does not pass through the observer but is transformed into an internal representation; but by ﬁnding the amplitude of a sine wave frequency that will give rise to a constant internal eﬀect (a constant level of detectability, for example) investigators approximated the MTF of the human visual system using the modulation sensitivity function (MSF), or equivalently, the contrast sensitivity function (CSF). A typical photopic contrast sensitivity function is plotted in Fig. 5.
In the late 1960s, Fergus Campbell and colleagues at Cambridge University reported in two landmark papers that visual detection of complex grating patterns was better predicted by their spatial frequency properties than by their spatial properties (e.g., Campbell and Robson 1968). There ensued an intense period of research investigating the spatial frequency properties of stimuli (as computed by means of the Fourier theorem and transform) and their relationship to various aspects of visual perception. There was also intense debate about whether the visual system carried out a spatial analysis or a spatial frequency analysis of the retinal image.
The controversy was resolved in 1980, when John Daugman (1980) and Stephan Marcelja (1980) pointed out that cells in the visual cortex had spatial-response properties that closely resembled so-called Gabor functions. Denis Gabor had proved in 1946 that products of Gaussian envelopes and sine and cosine waves were optimal for minimizing the joint uncertainty of specifying location in space and spatial frequency simultaneously. Numerous experiments that followed these two papers found that the visual system carries out a simultaneous spatial and spatial frequency analysis of visual input by means of mechanisms that are like Gabor functions. Thus, the visual system has developed an optimal way of carrying out a spatial analysis and a spatial-frequency analysis simultaneously.
4. Modular Processing
The second discovery of the landmark Campbell papers was that the entire span of spatial frequencies to which the visual system responds (see Fig. 5) is handled by numerous mechanisms, each tuned to a diﬀerent and smaller subrange of spatial frequencies. The mechanisms that detect high spatial frequencies act independently from the mechanisms that detect lower spatial frequencies. The spatial-frequency tuning properties of a few of these mechanisms are also shown in Fig. 5. Each of these mechanisms is also tuned to a narrow range of contour orientation. To cover the range of spatial frequencies and orientations to which we are sensitive requires approximately 90 diﬀerent mechanisms at each area of the retinal image, each tuned to a speciﬁc range of frequency and orientation.
The idea of modularity of visual processing, with diﬀerent mechanisms analyzing diﬀerent properties of the visual input, is at the heart of our current concept of the visual system. In fact, based on both anatomical and psychophysical evidence, we seem to have two visual systems, the M-pathway and the P-pathway (named after the magnoand parvo-cellular layers of the lateral geniculate nucleus). These two systems carry quite diﬀerent kinds of information from the retina to the brain. The M-pathway, the older of the two, has high-contrast sensitivity but low spatial resolution. It carries no color information but does carry information about motion and depth and responds rapidly to visual stimulation. The P-pathway has high spatial resolution, but responds slowly and has lower spatial resolution. The P-pathway carries color information encoded in an opponent-process manner.
A complete understanding of vision involves an understanding of physics, physiology, behavior, and the mind. At its simplest, the mind contains a sensory process that converts external physical stimuli into an internal representation and a decision process that uses information available in the internal representation to make decisions about what to say and what to do.
Four major advances in the psychology of vision took place in the last half of the twentieth century. The ﬁrst is the recognition that the internal representation is noisy and unreliable, which has led to the development of powerful models and methods to deal with the resulting uncertainty. The second has been a resolution of the battle between two conﬂicting theories of color vision by combining both into a single uniﬁed theory. The third development has been the discovery that vision, like audition, carries out a frequency analysis on the visual input. Unlike audition, however, it carries out a simultaneous spatial analysis and does it in the theoretically most eﬃcient way. The ﬁnal advance has been the appreciation that in the early stages of visual processing diﬀerent types of information are handled by diﬀerent types of mechanisms, producing a modularity of visual processing.
6. Recommended Reading
There are many oversimpliﬁcations in this research paper. Please consult these works for more detailed information about the material: Sekuler and Blake 2002, Coren et al. 1999, Goldstein 1999, Levine 2000, Schiﬀman 2000. Texts more specialized in speciﬁc topics covered above include: Swets and Picket 1982, de Valois and de Valois 1988, Macmillan and Creelman 1991, Shapley and Lam 1993, Wandell 1995, Backhaus et al. 1998, Rodieck 1998, Oyster 1999.
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