Part 2 book “An introduction to the visual system” has contents: Colour constancy, object perception and recognition, face recognition and interpretation, motion perception, brain and space, what is perception.
7 Colour constancy The colour constancy problem One of the most important functions of the visual system is to be able to recognise an object under a variety of different viewing conditions For this to be achieved, the stimulus features that make up that object must appear constant under these conditions If stimulus parameters not form a reliable ‘label’ for an object under different conditions, they are considerably devalued in their use to the visual system For example, if we perceive a square shape on a video screen and the area it covers increases or decreases, we experience a sense of movement The square seems to get closer or further away The visual system assumes that the size of the square will not change, so that changes in its apparent size will signal changes in its relative distance from us This is called object constancy This is a sensible assumption, as under normal conditions, objects seldom change in size Another example is lightness constancy Over the course of a normal day, light levels change significantly, but the apparent lightness of an object will change very little The visual system scales its measure of lightness to the rest of the environment, so that the apparent lightness of an object will appear constant relative to its surroundings A similar problem exists with the perception of colour Over the space of a day, the spectral content of daylight changes significantly (Figure 7.1) This means that the spectral content of light reflected from an object changes too One might expect that objects and surfaces acquire their colour due to the dominant wavelength of the light reflected from them, thus a red object looks red because it reflects more long-wave (red) light However, surfaces and objects retain their colour in spite of wide-ranging changes in the wavelength and energy composition of the light reflected from them This is called colour constancy, and is not only displayed by humans and primates, but by a wide range of species from goldfish to honeybees So it seems there is no prespecified wavelength composition that leads to a colour and to that colour alone If colours did change with every change in illumination, COLOUR CONSTANCY Figure 7:1: (See also colour plate section.) Estimates of the relative spectral power distribution of daylight phases across the visible spectrum, normalized to equal power at 560 nm (reproduced with kind permission from Bruce McEvoy from the website http:// www.handprint.com) north sky light > 20 000K 200 relative spectral power 102 150 illuminant D65 noon daylight 6500K 100 illuminant D55 noon sunlight 5500K 50 sunset sky + sunlight < 4000K 400 500 600 700 wavelength (in nanometres) then they would lose their significance as a biological signalling mechanism since that object could no longer be reliably identified by its colour The Land Mondrian experiments Some of the most important and influential studies on colour constancy were made by Edwin Herbert Land (1909–1991) Land was a Harvard University drop-out, who went on to become one of the most successful entrepreneurs in America He developed a method for producing large sheets of artificial polariser, and in 1937 founded the Polaroid Corporation to market his invention (Mollon, 1991) Polaroid filters, for visible and infra-red light, were soon being used in cameras and sunglasses, and in wartime for range-finders and night adaptation goggles This development was followed up in 1948 with an instant camera, which could produce a picture in 60 seconds, and Land and his company became very rich However, for the last 35 years of his life, Land’s chief obsession was with colour and colour constancy As part of his experiments, he had observers view a multicoloured display made of patches of paper of different colours pasted together (Land, 1964) This display was called a Colour Mondrian, from the resemblance it bore to the paintings of the Dutch artist Piet Mondrian The rectangles and squares composing the screen were of different shapes and sizes, thus creating an abstract scene with no recognisable objects to control for factors such as learning and memory No patch was surrounded by another of a single colour and the patches surrounding another patch differed in REFLECTANCE AND LIGHTNESS colour This was to control for factors such as induced colours and colour contrast The patches were made of matt papers which reflected a constant amount of light in all directions As a result, the display could be viewed from any angle without affecting the outcome of the experiment The display was illuminated by three projectors, each equipped with a rheostat that allowed the intensity of the light coming from the projector to be changed The first projector had a filter so that it only passed red light, the second projector only passed green light and the third projector only passed blue light The intensity of light produced by each projector was measured using a telephotometer, so the relative amounts of the three wavelengths in the illumination could be calculated In one experiment, the intensity of light reflected from a green patch was set so that it reflected 60 units of red light, 30 units of green light and 10 units of blue light Test subjects reported the green patch as being green in colour even though it reflected twice as much red as green light, and more red light than green and blue light put together So, this is a clear example of the perceived colour of the patch not corresponding with the colour of the predominant wavelength reflected from it This experiment was repeated but under slightly different conditions The subject still observed the same patch, illuminated by the same light, but this time the patch was viewed in isolation The surrounding colour patches were not visible This is called the void viewing condition In this case the perceived colour of the patch corresponded to the wavelength composition of the light reflected from it If the surround was then slowly brought into view, the colour of the patch was immediately reported to be green This suggests that the perceived colour of the patch was determined not only by the wavelength composition of the light reflected from it, but also by the wavelength composition of the light reflected from the surrounding surfaces If the position of the green patch was changed within the Mondrian, so that the surrounding patches were different, the perceived colour remained the same This suggested that the relationship between the perceived colour and the wavelength composition of the patch and its surrounding patch or patches was not a simple one Reflectance and lightness: the search for constancy in a changing world To construct a representation of colour that is constant with changes in the spectral illumination of a surface, the visual system must find some aspect of the stimulus which does not change One physical constant of a surface that does not change is its reflectance For example, a red surface will have a high reflectance for red light, and a low reflectance for green and blue light If the intensity of the light 103 104 COLOUR CONSTANCY Figure 7:2: The reflectance of a surface for light of a given wavelength is its efficiency for reflecting light of that wavelength, expressed as the percentage of the incident light of that wavelength which it reflects The reflectance never changes, although the amounts incident on, and relected from, the surface change continually The surface shown here reflects 90%, 20% and 5%, respectively, of red, green and blue light, irrespective of the intensity of the illuminating light (modified from Zeki, 1993) incident upon the object changes, the proportions of red, green and blue light reflected from the object will not (Figure 7.2) Therefore, the visual system must ignore the information related to light intensities and concentrate purely on relative reflectance One way of doing this is to compare the reflectance of different surfaces for light of the same wavelength So, for example, consider two surfaces, a red and a green one The red surface will have a high reflectance for long-wave light and so reflect a high proportion of red light The green surface will have a low reflectance for red light, and therefore only a small proportion of red light will be reflected from it So, if the patches are illuminated by a red light, the red patch will always appear lighter, regardless of the intensity of the red light Thus, the biological correlate of reflectance is lightness (Zeki, 1993) By determining the efficiency of different surfaces in a scene for reflecting light of a given wavelength, the brain builds a lightness record of the scene for that particular wavelength When an entire scene is viewed, each surface will have a different lightness at every wavelength depending upon its efficiency for reflecting light of that wavelength The record of that scene in terms of areas that are lighter or darker, is called its lightness record (Zeki, 1993) In ordinary daylight, as in most light sources, there is a mixture of wavelengths, and each set of wavelengths will produce a separate lightness record Land’s Retinex theory (the name is derived THE BIOLOGICAL BASIS OF COLOUR CONSTANCY from retina and cortex) proposes that, in the visual system, the lightness records obtained simultaneously at three different wavelengths are compared in order to construct the colour of a surface (Land, 1964, 1983) This comparison will be unrelated to the wavelength composition of the illuminating light, and therefore will not be affected by the relative intensity of the lights of different wavelengths The colour that we perceive is thus the end product of two comparisons: the comparison of the reflectance of different surfaces for light of the same wavelength (generating the lightness record of the scene for that wavelength), and the comparison of the three lightness records of the scene for the different wavelengths (generating the colour) Colour therefore, is a comparison of comparisons (Zeki, 1993) When the wavelength composition of the light illuminating a surface changes, the intensities of light reflected from all the surfaces in the display will change, but the comparisons will remain the same because the reflectances not themselves change Land has suggested an algorithm for generating these comparisons (Land, 1983) In it, the logarithm of the ratio of the light of a given wavelength reflected from a surface (the numerator), and the average of light of the same wavelength reflected from its surround (the denominator) is taken This constitutes a designator at that wavelength The process is done independently three times for the three wavelengths The biological basis of colour constancy Colour constancy requires the comparison between the light from an object and the light reflected from other objects and surfaces to compensate for the spectral composition of the illuminating light Until recently, it was thought that neurons capable of making this comparison did not occur until V4, where the receptive fields were sufficiently large (Schein & Desimone, 1990) Consistent with this theory, Semir Zeki found cells in V4 which appeared to show colour constancy (so, for example, cells responsive to green would continue to signal green, despite changes in the spectral composition of the illuminating light, as long as a surface continued to be perceived as green) (Zeki, 1983) He called these cells colour-only Cells in V1 seemed to alter their responses with changes in the spectral composition of the illuminating light regardless of the perceived colour, and he called these cells wavelength-only However, recent studies on the responses of visual neurons and their receptive fields have suggested that a large receptive field may not be necessary Visual cells respond to stimuli within their receptive field Stimuli presented outside the receptive field not elicit a direct response from the cell However, stimuli presented in the region surrounding the receptive field can modulate the cell’s response to a stimulus presented within its receptive field (Lennie, 2003) As a result, the region corresponding to the traditional receptive field is often called the classical receptive field, and the surrounding region which 105 106 COLOUR CONSTANCY modulates the cell’s response is called the non-classical or extra-classical receptive field This modulation may form the basis for the initial calculations necessary for colour constancy Consider the simplest example of the background altering colour perceptions If one sees a green patch on a green background, it appears to be less green than a green patch that is observed on a grey background The difference, or contrast, between the colour of the patch and the background alters our perception of the colour of the patch It seems that colour contrast plays an important role in building up a colour constant perception of the world, as factoring out the colour of the background is likely to also factor out the colour of the illuminant (Hurlbert, 2003) Recent studies have found V1 neurons that respond to colour contrast (Wachtler et al., 2003; Hurlbert et al., 2001) When presented with a patch of colour that completely covered the classical receptive field against a neutral grey background, each cell will have a preferred colour Additionally, a background of a cell’s preferred colour will inhibit its response to the preferred colour Thus the cell generates a measure of contrast, which seems to be based on interactions between the classical and extraclassical receptive fields These measures can form the basis for the lightness record needed by the retinex theory to generate colour constancy Individual cells cannot represent colour contrast accurately, but the activity of a whole population of such cells could This is not to say that colour constancy is computed in V1 It is probably a gradual process, in which it is calculated by successive computations in V1, V2 and then finally in V4, where full colour constancy is finally realised This would be consistent with lesion studies, which have shown that the removal or damage of V4 in monkeys leaves them able to discriminate wavelength, but impaired on colour constancy (e.g Wild et al., 1985) Colour constancy and the human brain The perception of colour in humans was initially associated with activation of a ventromedial occipital area (in the collateral sulcus or lingual gyrus, see Figure 7.3) in three separate PET studies (Corbetta Figure 7:3: The positions of the lingual and fusiform gyri in the human cerebral cortex (redrawn from Zeki, 1993) COLOUR CONSTANCY AND THE HUMAN BRAIN et al., 1991; Zeki et al., 1991; Gulyas & Roland, 1991) Because V4 contains colour selective cells, it has been speculated that this area is the homologue of V4 The location of this area agreed well with the location of lesions associated with achromatopsia, which is close, but medial to the posterior fusiform area activated by faces That the colour and face-selective areas are close to each other would be consistent with evoked potential studies from chronically implanted electrodes in epilepsy patients (Allison et al., 1993, 1994) The proximity of these two areas would explain the frequent association of achromatopsia with prosopagnosia (the inability to recognise faces) However, the situation seems to be more complicated than this The neurons in monkey V4 are selective for features relevant to object recognition, including shape and colour (Zeki, 1983; Desimone & Schein, 1987), and therefore one would predict that the human homologue of V4 would show the same feature selectivity However, of the two PET studies that examined colour and shape, one found that shape perception also activated the venteromedial occipitotemporal region (Corbetta et al., 1991), but the other did not (Gulyas & Roland, 1991) Moreover, lesions of monkey V4 produce significant impairments in form perception (Schiller & Lee, 1991), but form perception is usually spared in patients with achromatopsia Also, the monkey V4 lesions not seem to produce the profound and permanent colour impairment that is seen in patients with achromatopsia (Schiller & Lee, 1991; Heywood et al., 1992) Thus, although an area in human cerebral cortex has been located that is selective for colour, it may not be the homologue of monkey V4 An alternative candidate has been suggested in a study by Hadjikhani et al (1998) They used fMRI to map brain activity in response to colour, and found a new area that is distinct anatomically from the putative human V4 This area (which they called Visual area or V8) is located in front of human ‘V4’, and responds more strongly to colour than the surrounding areas and, unlike human ‘V4’, is activated by the induction of colour after-effects They suggest that, for humans, V8 may be the neural basis for colour constancy and the conscious perception of colour (Hadjikhani et al., 1998; Heywood & Cowey, Figure 7:4: An illustration of the position of the colour-selective regions in the human fusiform gyrus (the V4-complex) based on functional imaging There are two areas: the posterior area V4 and the anterior area V4a (V8) (a) Left, colour active areas shown in ‘glassbrain’ projections of the brain Right, the colour active regions of a single subject, superimposed on the structural image (b) Projection of the comparison of either upper field (in white) or lower field (in black) stimulation with colour vs their achromatic stimuli onto a ventral view of a human brain (reproduced with permission from Bartels & Zeki (2000) Copyright (2000) Blackwell Publishing) 107 108 COLOUR CONSTANCY 1998) However, Semir Zeki has proposed that ‘V8’ should actually be lumped together with the putative human ‘V4’ into the ‘V4 complex’, and that V8 should be more properly named V4a (Bartels & Zeki, 2000) This latter approach stresses the strong connections between the putative human ‘V4’ and V8, and sees V8 as functionally part of a single colour processing unit along with human ‘V4’(Figure 7.4) Summary of key points (1) Surfaces and objects retain their colour in spite of wide-ranging changes in the wavelength and energy composition of the light reflected from them This is called colour constancy (2) Edwin Land investigated colour constancy by using a multicoloured display made of patches of paper of different colour pasted together ( a Colour Mondrian) (3) When the spectral composition of the light illuminating the Mondrian was altered, the perceived colours of the patches remained the same However, if a patch was viewed in isolation (the void viewing condition), the perceived colour of the patch corresponded to the wavelength composition of the light reflected from it This suggests that the perceived colour of a patch was determined not only by the wavelength composition of the light reflected from it, but also by the wavelength composition of the light reflected from the surrounding surfaces (4) One physical constant of a surface that does not change with changes in the spectrum illumination is its reflectance The biological correlate of reflectance is the perceived lightness of a surface (5) The record of a scene in terms of areas which are lighter or darker, is called its lightness record Land’s Retinex theory proposes that, in the visual system, the lightness records obtained simultaneously at three different wavelengths are compared to construct the colour of a surface (6) Some neurons in monkey V1 and V2 are sensitive to the wavelength composition of light, but not show colour constancy However, the responses of some cells in monkey V4 show the same colour constancy characteristics as those of a human observer viewing the same stimuli (7) The neural basis of human colour constancy is unclear A putative V4 area has been identified, but an additional area, called V8 or V4a, may also play an important role in the development of colour constancy Object perception and recognition From retinal image to cortical representation In the primary stages of the visual system, such as Vl, objects are coded in terms of retinotopic co-ordinates, and lesions of Vl cause defects in retinal space, which move with eye movements, maintaining a constant retinal location Several stages later in the visual system, at the inferior temporal cortex (IT) in non-human primates, the receptive fields are relatively independent of retinal location, and neurons can be activated by a specific stimulus, such as a face, over a wide range of retinal locations Deficits that result from lesions of IT are based on the co-ordinate system properties of the object, independent of retinal location Thus, at some point in the visual system, the pattern of excitation that reaches the eye must be transposed from a retinotopic co-ordinate system to a co-ordinate system centred on the object itself (Marr, 1982) An outline of such a transformation can be seen in Table 8.1 At the same time that co-ordinates become object centred, the system becomes independent of the precise metric regarding the object itself within its own co-ordinate system, that is to say the system remains responsive to an object despite changes in its size, orientation, texture and completeness Single-cell recording studies in the macaque suggest that, for face processing, these transformations occur in the anterior IT The response of the majority of cells in the superior temporal sulcus (STS) is view-selective and their outputs could be combined in a hierarchical manner to produce view-independent cells in the inferior temporal cortex As a result, selective deficits to higher visual areas, such as IT, cause the inability to recognise an object or classes of object This defect in humans is called an agnosia Early visual processing Visual recognition can be described as the matching of the retinal image of an object to a representation of the object stored in memory 110 OBJECT PERCEPTION AND RECOGNITION Table 8.1 A summary of Marr’s model of object recognition Marr viewed the problem of vision as a multi-stage process in which the pattern of light intensities signalled by the retina is processed to form a three-dimensional representation of the objects in one’s surroundings Level Description achieved The raw primal sketch Description of the edges and borders, including their location and orientation The full primal sketch Where larger structures, such as boundaries and regions, are represented The 2½-dimensional A fuller representation of objects, but only in viewer-centred co-ordinates; this is sketch achieved by an analysis of depth, motion and shading as well as from the structures assembled in the primal sketch The threeA representation centred upon the object rather than on the viewer dimensional model Table 8.2 The gestalt principles of organisation Rule Boundaries defined Pragnanz Every stimulus pattern is seen in such a way that the resulting structure is as simple as possible Proximity The tendency of objects near one another to be grouped together into a perceptual unit Similarity If several stimuli are presented together, there is a tendency to see the form in such a way that the similar items are grouped together Closure The tendency to unite contours that are very close to each other Good Neighbouring elements are grouped together when they are potentially connected by continuation straight or smoothly curving lines Common fate Elements that are moving in the same direction seem to be grouped together Familiarity Elements are more likely to form groups if the groups appear familiar or meaningful (Perrett & Oram, 1993) For this to happen, the pattern of different intensity points produced at the level of the retinal ganglion cells must be transformed into a three-dimensional representation of the object, which will enable it to be recognised from any viewing angle The cortical processing of visual information begins in V1, where cells seem to be selective for the orientation of edges or boundaries Boundaries can be defined not just by simple changes in luminance, but also by texture, colour and other changes that occur at 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computerised tomography 8–9 cones 29, 40 (see also blue cones, green cones, red cones) conjunctiva 21 convergence 25, 164–5 cornea 21 corollary discharge theory 147, 152–3 cortical areas 2–4 cortical folding 4, 5, 6–7 critical period 93, 95, 98–9 crystallins 25–6, 28 cyclic guanosine 3’-5’monophosphate (cGMP) 30–2 cytochrome oxidase 66, 67, 68, 84 Dalton, John 47, 53 Daltonism 47 dark adaptation curve 39 dark current 30 delayed matching to sample (DMS) 127 deprivation monocular deprivation 91, 93 binocular deprivation 91 deuteranope 48 dichromacy 44 dioptres 25 direction selectivity 82–4 disparity 168–9 distributed encoding 122 dMRI, see water diffusion MRI dopamine 129–31 dorsal system 70 dorsolateral prefrontal region (DL) 70–1 double-opponent cells 67, 84 duplicity theory 38 egocentre 172 Einstein, Albert 7–8 elaborate cells columnar organisation of 114–16 shape selectivity of 114 electroencephalography (EEG) 14 emmetropia 26, 28 emmetropisation 27 end-inhibited cells 81 end-stopped cells 81 enigma illusion 162 ensemble encoding 121–2 exons 50, 57 extra-classical receptive field 105–6 extra-personal space 170 extrastriate cortex face cells 118–26 far cells 168 far space 170 fibre cells 25–6 figure–ground segregation 127 filling in 23 floaters 22 flicker fusion frequency 40 focus of expansion (FOE) 153–5 focusing the eye 25–8 frontal eye field (FEF) 153 functional magnetic resonance imaging (fMRI) 11–14 fusiform gyrus 106, 134–6, 138–9, 145 Gage, Phineas 143 ganglion cells bistratified ganglion cells 70 M ganglion cells 63 P ganglion cells 63 geons 116–17 gestalt principles 110–11 glaucoma 22 globular (G) proteins 121 gnostic units 121 good continuity 110 gorillas in our midst 182 grandmother cells 121 greebles 138–9 green cones 29, 44, 45, 46 pigment 50, 53 pigment genes 50, 51, 52 guanosine diphosphate (GDP) 31 guanosine triphosphate (GTP) 31 gyri INDEX Hebb, Donald O 89 Hering, Ewald 45 Horopter 168 hybrid genes 50, 51, 52, 55 hypercomplex cells 81 hyperopia 26, 27 hyperpolarisation 30 ice-cube model 85 illusory contours 110–11 inferior temporal cortex (IT) 109, 112–16, 118, 120, 121, 122–5, 127–9, 179 inter-blob region 66, 85 interocular transfer 95 interposition 165–6 interstripes 67–8 introns 50 inverted faces 135–6 iris 21 isomers 29–30 IT, see inferior temporal cortex K cells 69 Kanizsa triangle 112, 165 Kluver-Bucy syndrome 140 koniocellular layers 69–70 lamellae 29 Land, Edwin Herbert 102 lateral geniculate nucleus (LGN) 63 lateral inhibition 34 lateral pretectum 74 lateralisation 3, 136–8, 174 lens 22, 25–6, 28 yellowing of 26 LGN, see lateral geniculate nucleus light adaptation, see adaptation lightness constancy 37, 101 record 104 linear convergence 166 lingual gyrus 106, 136 locus control region (LCR) 52–3, 59 long-term depression (LDP) 94, 98 long-term potentiation (LTP) 90–1, 94, 98–9 lyonisation 55 M ganglion cells 63, 64 M pathway 67, 69–70, 71 magnetic resonance imaging (MRI) 9–10 N-methyl-D-aspartate (NMDA) 90 near space 170 near cells 168 neglect 170–4 night blindness 54 non-classical receptive field 105–6 nyctalopia 54 nystagmus 149 P-B pathway 66, 67, 69 P-I pathway 66, 67 P ganglion cells 63, 64 P pathway 66, 68, 69–70, 71 Pa cells, see M ganglion cells Pb cells, see P ganglion cells pale stripes 67 parvocellular (P) layers 64, 65 pentachromacy 44 perceptual learning 179–80 peripersonal 170 personal space 170 perspective 166 phosphodiesterase (PDE) 31 photoisomerisation 29–30 photons 18 Piazza del Duomo, Milan 171–2 pigment epithelial layer 23 Ponzo illusion 166 pop out 111 population encoding 121–2 positron emission tomography (PET) 10–11 posterior parietal cortex (PPC) 20–1, 129, 169–70, 173–4, 176–7, 178 inferior lobule 170 superior lobule 170 pre-attentive processes 126–9 presbyopia 25 prestriate primary visual cortex, see visual area promotor region 52–3, 59 prosopagnosia 107, 133 protanope 48 pupil 21 Purkinje shift 38 pursuit eye movements 148 object constancy 101 ocular apraxia 69 ocular dominance column 79, 91–3 oculomotor cues 164–5 OFF-surround 34–5, 36 ON-centre 34–5, 36 opponent colour mechanisms 45, 84 opsin 29 optic ataxia 69 optic chiasm 63 optic disc 23 optic flow 153–5 optic nerve 23 opto-kinetic nystagmus (OKN) 149–50 orbitofrontal cortex (OFC) 143–4 oscillations, neuronal 176–8 Rayleigh match 58 receptive field 34–5 red cones 29, 44, 45, 46 pigment 50, 53 pigment genes 50, 51, 52 reflectance 103–4 refraction of light 25 relative size 166 Rembrandt 96 retina 22–3, 29, 33, 63 retinal 29–30 retinal densitometry 39 retinex theory 104 retinitis pigmentosa (RP) 53–5 retinopathy 54 rhodopsin 29, 30, 31 magnetoencephalography (MEG) 14–15 magnocellular (M) layers 64, 65 medial superior temporal (MST) 154–5, 157, 160–1 medio-dorsal nucleus (MDN) 153 melanin 23 memory fields 130 metarhodopsin II 31 microelectrode recording 160 microsaccades 148 microspectrophotometry (MSP) 58 microstimulation 160 monocular deprivation 91–3 monocular rivalry 182 motion aftereffect 83–4 motion detection component motion 158 global motion 158 illusion 162–3 neural basis in V1 82–4 neural basis in V3 156–7 neural basis in V5 157–60, 161–3 motion parallax 168 MT, see visual area Muller-Lyer illusion 167 multistable perception 182 myopia 26–8 211 212 INDEX rhodopsin gene 50, 54 rods 29, 30, 40–1 saccades 148, 149–53 sclera 19 scotoma 73 selective rearing sensitive period, see critical period shading 167 signal efficiency simple cell 79–81 simultagnosia 69 single unit recording, see microelectrode recording smooth eye movements, see pursuit eye movements sparse encoding 122 spatial frequency stereopsis 65, 168–9 stereoscope 168 stochastic resonance strabismus 94–6 striate cortex, see visual area stripes, see interstripes, pale stripes, thick stripes, thin stripes STS, see superior temporal sulcus sulci superior colliculus (SC) 74–5, 152–3 superior temporal gyrus 173–4 superior temporal sulcus (STS) 109, 118–19, 120–1, 124–5, 133 suppression of perception during blinking 19–21 during saccades 150–1 tapetum 23 temporal binding mechanism 175–80 tetrachromatic colour vision 56 texture 82 texture gradients 166 thick stripes 67–8 thin stripes 67–8 three-dimensional structure from motion (3D-SFM) 156 tilt aftereffect 95 top-down processes 126 transcranial magnetic stimulation (TMS) 15, 131, 162 transduction 29, 30–3 tremors, see microsaccades trichromacy 44–5, 58, 59 tritanope 47 tuned-excitatory neurons 168 tuned-inhibitory neurons 168 ultra-violet light 19 univariance 44 Urbach-Wiethe disease 140 V4 complex 107 V4a 107 ventral system 70 visual alphabet 116 visual area (V1) 3, 62, 64, 73, 74, 75, 111, 131 visual area (V2) 62, 65, 67–8, 73, 74, 75, 111, 131 visual area (V3) 20, 69, 156–7 visual area (V4) 68–9, 105–7, 112 visual area (V5) 3, 65, 69 visual area (V8) 107 visual imagery 131 visual memory long-term 131 short-term 126–9 vitamin A 50 vitreous humour 22 void viewing condition 103 voltage-sensitive dyes 85–6 water diffusion MRI (dMRI) 9, 10 waterfall illusion 83 wavelength 18–19 what versus how pathways 71–3 what versus where pathways 62, 70–1 Wheatstone, Sir Charles 168 Working memory 126–9 Young, Thomas 44 X-chromosome 50, 51, 52, 55, 59 ... pasted together (Land, 1964) This display was called a Colour Mondrian, from the resemblance it bore to the paintings of the Dutch artist Piet Mondrian The rectangles and squares composing the screen... within the face; particularly important is inter-eye distance, distance from eyes to mouth and the amount and style of hair on the forehead (e.g Yamane et al., 1988; Young & Yamane, 19 92) Moreover,... about the extent of the re-activation of the visual system and whether it involves the early visual areas, such as V1 and V2 Kosslyn and Oschner (1994) have argued that mental imagery requires the