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Cognitive Neurology

Investigation of the Determinants of Visual Perception:
Stimulus and its Interaction with the Human Sensory Apparatus

October, 2020

Summary: Visual perception is referred to the physical interaction of the human senses with an external stimulus. Visual perception can be defined as the most important of all the senses as it informs humans beings about their surroundings and aids in the formation of first impressions from any given object or information. This paper discusses the scientific basis of visual perception through signal detection, based on the physical properties of the objects, analyses’ the biological components of the human vision that process this information to create visual perception. The paper further evaluates a design case, Air India, an aviation web portal, based on these factors.

Image by Harry Quan

Humans perceive objects from the external world based on their distinguishable physical properties. A research based on assessing the visual attention system through eye-tracking experiments (Le Meur et al., 2006), refers to human beings’ ‘involuntary attention’ to the ‘salient spatial-visual features’ of the object. The visual attention is guided through features such as ‘contrast, sensitivity functions, and visual crowding’. This paper examines the connection between the properties of physical objects creating a ‘signal’ and their relation with the human sensory apparatus.

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Signal from the external world 

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Humans perceive the environmental stimuli within the visible spectrum, detecting wavelengths from 380 to 700 nm and recognize objects based on the light reflected by the physical properties of objects. The following elements will be examined:

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Object properties

Luminance is the intensity of light emitted from a surface in a direction. ‘The light reflecting from a surface bears the imprint of both the light illuminating that surface and the reflecting properties of the surface itself’ (Gilchrist & Soranzo, 2019). Objects can be detected and discriminated based on luminance changes”(Brawn & Snowden, 2000). Physical specifications of color are associated with objects through the wavelengths of the light that is reflected from their surface. Some key terms used with respect to color properties of objects are hue, value and saturation. Hue can be defined as a color in its purest form, saturation or chroma is the intensity of color and value refers to the lightness or darkness of a color.

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Signal Properties

1. Salience: Salience can be defined as the property of an object by which it stands out with respect to its surrounds such that it is not consistent with the context of a scene and attracts attention. Various researchers show that an observer has more fixations over such objects. Salience may be contributed due local singularity or a striking feature. ‘An image containing one green circle located among a number of red circles is a classic example. The target is easily seen against the red circles due to its local singularity no matter how many distractors are present’ (Le Meur et al., 2006). Saliency detection is primarily based on the contrast of color, luminance, texture, and depth (Fang et al., 2014).

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2. On-set: Onset is the optimum response to a stimulus when initially presented and the receptors are fully sensitized. “An object appearing abruptly in a previously blank location is efficiently detected in visual search than when it is embedded in an array of objects. The efficiency with which onset stimuli are processed in visual search are due to attentional capture by new perceptual object (Yantis & Jonides, 1996)." A prolonged exposure causes be slow degradation of the on-set response leading to adaptation.

 

3. Duration of the Stimulus: A stimulus is the first step to detect a signal. Research provides an explanation of the stimulus duration and its effects, “The retinal interneurons are in charge of light-adaptive gain control for dynamic and efficient information. The stimulus duration and spatial frequency effects on visual persistence”. The study infers that the efficiency of information processing is improved when the less (more) intense the illumination level is, the longer (shorter) inhibition latency. “A longer stimulus generates shorter persistence and a longer afterimage and a lower spatial-frequency stimulus generates shorter persistence and a stronger afterimage” (Yeonan-kim & Francis, 2019).

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Thus, signals with distinguishing features are discriminated and detected by the human eye.

Signal Detection​

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Anatomy of the human eye: The light from the visible spectrum is reflected from the surface of objects and enters the human eye through (Abramoff et al., 2010) leading to information visualization on the retina.

1. Retina: The first stage of visual processing occurs in the retina, it consists of photoreceptors cells, called rods and cones, and are responsible for the visual perception of the reflected light. These two photoreceptor cells respond differently to light stimulation.  

 

2. Photoreceptor Cells and Light sensitivity: The rods cells are responsible for vision at low light levels, correspond to scotopic vision and have a low spatial acuity. Rods are more sensitive to light than cones. ‘Rods are responsible for night vision, operating in clusters for increased sensitivity and in very low light conditions. Cones are active at higher light levels, correspond to photopic vision and are responsible for high spatial acuity thus responsible for color perception (Bowmaker & Dartnall, 1980).' Rods do operate within the visible spectrum between approximately 400 and 700 nm. Unlike rods, cones are not sensitive over a large fixed wavelength range, but rather over a small range (Reid & Shapley, 1992).' The small region at the center of the visual axis is known as the fovea, containing only cones, it is the region of sharpest acuity, an angle of 1.5-2 degrees, also defined as foveal vision (Goodchild et al., 1996).

 

3. Color and Contrast sensitivity: The cone cells correspond to color vision (Szikra et al., 2014) and depend on three classes of cone photoreceptors, that are sensitive to short (S), medium (M) or long (L) wavelengths. ‘These three types are associated with color combinations using R (red), G (green), and B (blue). The long-wavelength cones exhibit a spectrum peak at 560 nm, the medium wavelength cones peak at 530 nm, and the short-wavelength cones peak at around 420 nm’(Reid & Shapley, 1992).  Short-wavelength light sensitivity is led by ‘stimulation of photosensitive retinal ganglion cells that are most sensitive to ‘short-wavelength (∼480 nm) blue light and remain functional in the absence of rods and cones’(Zaidi et al., 2007).

‘Cone-photoreceptors are active during daylight and color vision derives from a comparison of signals with different visual pigments. The ganglion cells of the retina have 'color-opponent' (Fuld et al., 1981) visual responses, they are excited by light of one colour and suppressed by another color’ (Joesch & Meister, 2016) thus “exhibiting antagonistic behaviour (Szikra et al., 2014)." Thus color vision is based on rod-cone opponency (Murray et al., 2006). Research concludes that for human foveal vision a sensitivity curve is obtained in the presence of a large white background that exhibits peaks at about 440, 530 and 610 nm and a small dip or notch at about 580 nm (Foster & Snelgar, 1983)."

 

4. Hue and Edge Detection: Edge detection is an important early stage in human visual processing where a retinal image is formed indicating the position of object boundaries and shadows (McIlhagga 2018). A research measured ‘luminance-specified edges and the gaps between them’ to perceive boundary clarity by using experiments to analyse the factors with which observers can detect an object, based on, ‘ (a) Discern the degree of blur of an edge, (b) Distinguish between two different types of blur, and relate the locations of two such edges by judging their (c) Alignment or (d) Separation’(Watt & Morgan, 1983). In this respect, the ‘Mach band effect’ is an important feature to identify objects, it is observed by placing varying values of a color in a sequence. It exaggerates the contrast between the edges of slightly differing values, and appears as an illusionary effect created by a sequence of luminance gradients.(R. Beau Lotto, S. Mark Williams, n.d.).The detection of two elementary visual features occurs on the receptive fields, ‘(a) Hue detection that encodes information about spectral reflectances of surfaces such as red, green, blue and yellow percepts, (2) High acuity edge detection(at the fovea) which encodes the boundaries of objects to form vision (Patterson et al., 2019)."

 

5. Luminance Sensitivity: The human vision responds to changes in luminance and colour contrast. A research (Rucker, 2013) establishes the ability of the eye to use colour and luminance cues to guide the responses in the following ways; (a) Eye maximizes contrast using changes in luminance, effectiveness of luminance cues in monochromatic white light (b) The eye compares the change in luminance and colour contrast (Patterson et al., 2019) and as the eye changes focus, (c) The eye compares relative cone contrast to derive the sign of defocus information from colour cues. ‘In the myopic simulation, the contrast of the red component of a sinusoidal grating was higher than that of the green and blue component. In the hyperopic simulation the contrast of the blue component was higher than that of the green and red components’(Rucker, 2013). Contrast sensitivity (Mullen, 1985) can be explained via the opponent process model (Fuld et al., 1981) and the absorbance spectra of the retina through rods and cones. The retina treats a color as a code and correlates it with the reflectance of objects (Land 1977).

 

6. Visual Crowding: Visual crowding is a process that restricts the visual performance, and such objects are referred as flankers. In a research (Greenwood & Parsons, 2020) it was observed that, ‘on presenting crowded objects experimentally the observers frequently reported the flankers rather than the target’ (Greenwood et al., 2009). This finding reflects that crowding increases the noise in the detection of the object’. The crowding phenomenon in foveal vision can be quantified in two aspects,‘(a) The maximum distance over which interaction occurs (extent) and (b) The amount of loss in acuity’ (Huurneman et al., 2012). Crowding disrupts the recognition of features of the visual system and is modulated by the ‘similarity between target/flanker elements; differences in features, including orientation, position, color and depth’ (Greenwood & Parsons, 2020). In an experiment, observers were unable to report the orientation of the central patch when several grating patches are viewed parafoveally (Parkes et al., 2001).​

Design Review: Air India website 

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The Air India website it is a task-centric website. It is evaluated from the perspective of a user involved in the task of a flight booking. The end goal is to accomplish search for flights and to find the relevant information. Thus it is evaluated for visual signals that lead to assistance in task completion. Success is measured with the ease of signal detection and assistance to conduct the task with speed, accuracy, error rate and user satisfaction.. The use environment is defined as accessing quick information especially during stressful situations. In the midst of constantly changing government regulations during the COVID pandemic, a user undergoing travel stress, seeking to access information on travel advisory and regulation, is unable to access it due to ambiguous signal and hence delay in its detection.


The product is evaluated and the issues are identified in the following order:

1_home page.png

Figure 1: Air India homescreen

1. High-intensity background (Figure 1): The color scheme involves high-intensity red color encompassing the entire screen. Red lies on higher regions of cone sensitivity, as a higher ratio of the cones are red-sensitive (H.D, Baker, n.d.), prolonged exposure to red background screen will cause over simulation due to its superior photosensitivity and lead to bleaching of the red pigment and cause visual fatigue. Exposure to red-dominated color screen, can cause experience delirious effects, as prolonged exposure can cause the opposing cones to activate (Williams & Macleod, 1979).​

4_selection tab_ crop view.png

Figure 2: Top navigation menu

2. Duration of signal: The “Manage your trip”(Figure 2) box is highlighted constantly with orange colour. ‘The change in sensitivity to stimulus contrast results from prolonged exposure to modulation of a visual stimulus’ (Szikra et al., 2014). The continuous highlight of flight status tab in ‘orange highlighted tab’ leads to redundancy, as any new selection is further highlighted with the same color.

3_footer tab and contact us.png

Figure 3: Footer menu

3. Failing to create edge detection (Figure 3): The ‘Bottom navigation bar’ fails to get detected as the difference between two surfaces exists without any sharp change at the edge due to low luminance contrast between the object and the background. It fails to create visual territories. This could be achieved with the effect of either ‘Mach bands’ or ‘Cornsweet illusion (Purves at al), applied to the background (Red) through a value varying with its intensity. The visual system is therefore facing problem of linking the separate edge fragments as one single unit.

 

4. Visual crowding: The website lacks hierarchy to guide a user’s attention in terms of the magnitude of the text size and its color scheme. The text of high magnitude does not play a hierarchical role in its function. In terms of the color scheme, there is competition between multiple warm colors. An empirical research concluded that for websites using a warm color scheme, ‘in a competition between warm colors, the colors with relatively low hue (closer to red) or high saturation is more likely to attract out vision (Pal et al., 2012).' Text appearing in yellow over red background appears to gather attention but is an embedded text message.

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BIBLIOGRAPHY

  • Abramoff, M. D., Garvin, M. K., & Sonka, M. (2010). Retinal imaging and image analysis. IEEE Reviews in Biomedical Engineering, 3, 169–208. https://doi.org/10.1109/RBME.2010.2084567

  • Avidan, G., Harel, M., Hendler, T., Ben-Bashat, D., Zohary, E., & Malach, R. (2002). Contrast sensitivity in human visual areas and its relationship to object recognition. Journal of Neurophysiology, 87(6), 3102–3116. https://doi.org/10.1152/jn.2002.87.6.3102

  • Bowmaker, J. K., & Dartnall, H. J. (1980). Visual pigments of rods and cones in a human retina. The Journal of Physiology, 298(1), 501–511. https://doi.org/10.1113/jphysiol.1980.sp013097

  • Brawn, P. T., & Snowden, R. J. (2000). Attention to overlapping objects: Detection and discrimination of luminance changes. In Journal of Experimental Psychology: Human Perception and Performance (Vol. 26, Issue 1, pp. 342–358). American Psychological Association. https://doi.org/10.1037/0096-1523.26.1.342

  • Dimattina, C., Baker, C. L., Myers, F., Vision, M., & Dimattina, C. (2020). Segmenting luminance-defined texture boundaries. 239.

  • Fang, Y., Wang, J., Narwaria, M., Callet, P. Le, Lin, W., & Member, S. (2014). Saliency Detection for Stereoscopic Images. 23(6), 2625–2636.

  • Foster, D. H., & Snelgar, R. S. (1983). Test and field spectral sensitivities of colour mechanisms obtained on small white backgrounds: Actions of unitary opponent-colour processes? Vision Research, 23(8), 787–797. https://doi.org/https://doi.org/10.1016/0042-6989(83)90201-8

  • Fuld, K., Wooten, B. R., & Whalen, J. J. (1981). The elemental hues of short-wave and extraspectral lights. Perception & Psychophysics, 29(4), 317–322. https://doi.org/10.3758/BF03207340

  • Gegenfurtner, K. R. (2016). The Interaction Between Vision and Eye Movements†. In Perception (Vol. 45, Issue 12, pp. 1333–1357). https://doi.org/10.1177/0301006616657097

  • Gibson, J. J. (1951). The Perception of the Visual World. The Philosophical Review, 60(4), 594. https://doi.org/10.2307/2181436

  • Gilchrist, A., & Soranzo, A. (2019). What is the relationship between lightness and perceived illumination. Journal of Experimental Psychology. Human Perception and Performance, 45(11), 1470–1483. https://doi.org/10.1037/xhp0000675

  • Goodchild, A. K., Ghosh, K. K., & Martin, P. R. (1996). Comparison of photoreceptor spatial density and ganglion cell morphology in the retina of human, macaque monkey, cat, and the marmoset Callithrix jacchus. Journal of Comparative Neurology, 366(1), 55–75. https://doi.org/10.1002/(SICI)1096-9861(19960226)366:1<55::AID-CNE5>3.0.CO;2-J

  • Greenwood, J. A., Bex, P. J., & Dakin, S. C. (2009). Positional averaging explains crowding with letter-like stimuli. Proceedings of the National Academy of Sciences of the United States of America, 106(31), 13130–13135. https://doi.org/10.1073/pnas.0901352106

  • Greenwood, J. A., & Parsons, M. J. (2020). Dissociable effects of visual crowding on the perception of color and motion. In Proceedings of the National Academy of Sciences of the United States of America (Vol. 117, Issue 14, pp. 8196–8202). https://doi.org/10.1073/pnas.1909011117

  • H.D, Baker, R. (n.d.). The red sensitive pigment in normal cones.pdf.

  • Hall, R. H., & Hanna, P. (2004). The impact of web page text-background colour combinations on readability, retention, aesthetics and behavioural intention. Behaviour & Information Technology, 23(3), 183–195. https://doi.org/10.1080/01449290410001669932

  • Hurvich, L. M., & Jameson, D. (1957). An opponent-process theory of color vision. Psychological Review, 64(6 PART 1), 384–404. https://doi.org/10.1037/h0041403

  • Huurneman, B., Boonstra, F. N., Cox, R. F., Cillessen, A. H., & Van Rens, G. (2012). A systematic review on foveal crowding in visually impaired children and perceptual learning as a method to reduce crowding. BMC Ophthalmology, 12(1). https://doi.org/10.1186/1471-2415-12-27

  • Joesch, M., & Meister, M. (2016). A neuronal circuit for colour vision based on rod-cone opponency. Nature, 532(7598), 236–239. https://doi.org/10.1038/nature17158

  • Kim, M. H., Weyrich, T., & Kautz, J. (2009). Modeling human color perception under extended luminance levels. ACM Transactions on Graphics, 28(3). https://doi.org/10.1145/1531326.1531333

  • Korth, M., & Rix, R. (1988). Luminance-contrast evoked responses and color-contrast evoked responses in the human electroretinogram. Vision Research, 28(1), 41–48. https://doi.org/https://doi.org/10.1016/S0042-6989(88)80004-X

  • Le Meur, O., Le Callet, P., Barba, D., & Thoreau, D. (2006). A coherent computational approach to model bottom-up visual attention. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(5), 802–817. https://doi.org/10.1109/TPAMI.2006.86

  • Loop, M. S., Shows, J. F., Mangel, S. C., & Kuyk, T. K. (2003). Colour thresholds in dichromats and normals. Vision Research, 43(9), 983–992. https://doi.org/10.1016/S0042-6989(03)00074-9

  • Mullen, B. Y. K. T. (1985). The contrast sensitivity of human colour vision to red‐green and blue‐yellow chromatic gratings. The Journal of Physiology, 359, 381–400. https://doi.org/10.1113/jphysiol.1985.sp015591

  • Murray, I. J., Parry, N. R. A., & McKeefry, D. J. (2006). Cone opponency in the near peripheral retina. Visual Neuroscience, 23(3–4), 503–507. https://doi.org/10.1017/S0952523806233315

  • Nurminen, L., Kilpeläinen, M., Laurinen, P., & Vanni, S. (2009). Area summation in human visual system: Psychophysics, fMRI, and modeling. Journal of Neurophysiology, 102(5), 2900–2909. https://doi.org/10.1152/jn.00201.2009

  • Pal, R., Mukherjee, J., & Mitra, P. (2012). How do warm colors affect visual attention? ACM International Conference Proceeding Series. https://doi.org/10.1145/2425333.2425357

  • Parkes, L., Lund, J., Angelucci, A., Solomon, J. A., & Morgan, M. (2001). Compulsory averaging of crowded orientation signals in human vision. Nature Neuroscience, 4(7), 739–744. https://doi.org/10.1038/89532

  • Párraga, C. A., Troscianko, T., & Tolhurst, D. J. (2002). Spatiochromatic properties of natural images and human vision. Current Biology, 12(6), 483–487. https://doi.org/10.1016/S0960-9822(02)00718-2

  • Patterson, S. S., Neitz, M., & Neitz, J. (2019). Reconciling Color Vision Models With Midget Ganglion Cell Receptive Fields. Frontiers in Neuroscience, 13(August), 1–13. https://doi.org/10.3389/fnins.2019.00865

  • R. Beau Lotto, S. Mark Williams,  and D. P. 1999. (n.d.). An empirical basis for Mach bands.pdf.

  • Rayner, K., Inhoff, A. W., Morrison, R. E., Slowiaczek, M. L., & Bertera, J. H. (1981). Masking of foveal and parafoveal vision during eye fixations in reading. In Journal of Experimental Psychology: Human Perception and Performance (Vol. 7, Issue 1, pp. 167–179). American Psychological Association. https://doi.org/10.1037/0096-1523.7.1.167

  • Reid, R. C., & Shapley, R. M. (1992). Spatial structure of cone inputs to receptive fields in primate lateral geniculate nucleus. Nature, 356(6371), 716–718. https://doi.org/10.1038/356716a0

  • Rucker, F. J. (2013). The role of luminance and chromatic cues in emmetropisation. Ophthalmic and Physiological Optics, 33(3), 196–214. https://doi.org/10.1111/opo.12050

  • Williams, D. R., & Macleod, D. I. A. (1979). Interchangeable backgrounds for cone afterimages. Vision Research, 19(8), 867–877. https://doi.org/https://doi.org/10.1016/0042-6989(79)90020-8

  • Yantis, S., & Jonides, J. (1996). Attentional Capture by Abrupt Onsets: New Perceptual Objects or Visual Masking? Journal of Experimental Psychology: Human Perception and Performance, 22(6), 1505–1513. https://doi.org/10.1037/0096-1523.22.6.150​

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