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Perception Visual Attention and Information That Pops Out Scales of Measurement • Scales of Measurement • Eye Movement • Visual Attention, Searching, and System Monitoring • Reading From the Iconic Buffer • Neural Processing, Graphemes and Tuned Receptors • The Gabor Model and Texture In Visualization • Texture Coding Information • Glyphs and Multivariate Discrete Data Scales Of Measurement On the Theory of Measurement, S.S. Stevens, Science, 103, pp.677-680. 1946 • • • • Nominal Ordinal Interval Ratio Nominal • name only, arbitrary, any one-to-one substitution allowed • words or letters would serve as well as numbers • stats: number of cases, mode, contingency correlation • e.g numbers on sports team, names of classes Ordinal • rank-ordering, order-preserving • intervals are not assumed equal • most measurements in Psychology use this scale • monotonic increasing functions • stats: median, percentiles • e.g. hardness of minerals, personality traits Interval • • • • • quantitative, intervals are equal no “true” zero point, therefore no ratios Psychology aims for this scale general linear group stats: mean, standard deviation, rank-order correlation, product moment correlation • e.g. Centigrade, Fahrenheit, calendar days Ratio • • • • • determination of equality of ratios (true zero) commonly seen in physics stats: coefficient of variation fundamental (additivity: e.g. weights) derived (functions of above: e.g. density, force) Eye Movements • Saccadic Movement – fixation point to fixation point – dwell period: 200-600 msec – saccade: 20-100 msec • Smooth Pursuit Movement – tracking moving objects in visual field • Convergent Movement – tracking objects moving away or toward us • Saccadic suppression – the decrease in sensitivity to visual input during saccadic eye movement • Brain often processing rapid sequences of discrete images • Accommodation – refocusing when moving to a new target at different distances – neurologically coupled with convergent eye movement Visual Attention, Searching, and System Monitoring • Our visual attention is usually directed at what we are currently fixating on. • Supervisory Control – complex semiautonomous systems, only indirectly controlled by human operators – uses searchlight metaphor • Human-Interrupt Signal – effective ways of computer to gain attention • warning • routine change of status • patterns of events • Visual Scanning Strategies – Elements • Channels, Events, Expected Costs – Factors • minimizing eye movement, over-sampling of channels, dysfunctional behaviours, systematic scan patterns • Useful Field of View (UFOV) – expands searchlight metaphor – size of region from which we can rapidly take information – maintains constant number of targets • Tunnel Vision and Stress – UFOV narrows as cognitive load/stress goes up • Role of Motion in Attracting Attention – UFOV larger for movement detection 4 Requirements of User Interrupt • easily perceived signal, even when outside of area of attention • continuously reminds user if ignored • not too irritating • signal conveys varying levels of urgency How to attract user’s attention: problems • Difficult to detect small targets in periphery of visual field. • Colour blind in periphery (rods). • Saccadic suppression allows for the possibility of transitory events being missed. Movement: possible solution • Seen in periphery. • Research supports effectiveness of motion. • Urgency can be effectively coded using motion. • Appearance of new object attracts attention more than motion alone. Reading from the Iconic Buffer • Iconic Buffer – short-lived visual buffer holds images for 1-2 seconds prior to transfer to short-term/working memory • Pre-attentive Processing – theoretical mechanism underlying pop-out – occurs prior to conscious attention Following examples from Joanna McGrenere’s HCI class slides. 85689726984689762689764358922659865986554897689269898 02462996874026557627986489045679232769285460986772098 90834579802790759047098279085790847729087590827908754 98709856749068975786259845690243790472190790709811450 85689726984689762689764458922659865986554897689269898 85689726984689762689764358922659865986554897689269898 02462996874026557627986489045679232769285460986772098 90834579802790759047098279085790847729087590827908754 98709856749068975786259845690243790472190790709811450 85689726984689762689764458922659865986554897689269898 Pop Out • Time taken to find target independent of number of distracters. • Possible indication of primitive features extracted early in visual processing. • Less distinct as variety of distracters increases. • Salience depends on strength of particular feature and context. Pop Out Examples • Form: – line orientation, length, width – spatial orientation, added marks, numerosity (4) • Colour: – hue, intensity • Motion: – flicker, direction of motion • Spatial Position: – stereoscopic depth, convex/concave shape Color Orientation Motion Simple shading Shape Length Width Parallelism Enclosure Curvature Added marks Number Spatial grouping • Rapid Area Judgement – fast area estimation done on basis of colour or orientations of graphical element filling a spatial region • Conjunction Search – combination of features not generally preattentive – spatially coded information (position on XY plane, stereoscopic depth, shape from shading) and second attribute (colour, shape) DO allow conjunction search Neural Processing, Graphemes, and Tuned Receptors • Cells in Visual Areas 1 and 2 differently tuned to: – – – – orientation and size (with luminance) colour (two types of signal) stereoscopic depth motion • Massively parallel system with tuned filters for each point in visual field. Vision Pathway http://www.geocities.com/ocular_times/vpath2.html • Signal leaves retina, passes up optic nerve, through neural junction at geniculate nucleus (LGN), on to cortex. • First areas are Visual Area 1 and Visual Area 2: these areas have neurons with preferred orientation and size sensitivity (not sensitive to colour) http://www.geocities.com/ocular_times/vpath.html http://www.geocities.com/ocular_times/vpath.html http://nba5.med.uth.tmc.edu/academic/neuroscience/lectures/section_2/lecture34_04.htm http://nba5.med.uth.tmc.edu/academic/neuroscience/lectures/section_2/lecture34_04.htm Grapheme • Smallest primitive elements in visual processing, analogous to phonemes. • Corresponds to pattern that the neuron is tuned to detect (‘filter’). • Assumption: rate of neuron firing key coding variable in human perception. Gabor Model and Texture in Visualization • Mathematical model used to describe receptive field properties of the neurons of visual area 1 and 2. • Explains things in low-level perception: – detection of contours at object boundaries – detection of regions with different visual textures – stereoscopic vision – motion perception Gabor Function • • • • Response = C cos(Ox/S)exp(-(x² + y²)/S) C amplitude, or contrast value S overall size of Gabor function O rotation matrix that orients cosine wave • orientation, size, and contrast are most significant in modeling human visual processing • Gabor model helps us understand how the visual system segments the visual world into different textual regions. • Regions are divided according to predominant spatial frequency(grain or coarseness of a region) and orientation information • Regions of an image are analyzed simultaneously with Gabor filters, texture boundaries are detected when best-fit filters for one area are substantially different from a neighbouring area. Trade-Offs in Information Density • The second dogma (Barlow, 1972) – visual system is simultaneously optimized in both spatial-location and spatial-frequency domains • Gabor detector tuned to specific orientation and size information in space. • Orientation or size can be specified exactly, but not both, hence the trade-off. Texture Coding Information • Gabor model can be used to produce easily distinguished textures for information display (used to represent continuous data). • Human neural receptive fields couple the gaussian and cosine components, resulting in three parameter model: – O orientation – S scale / size – C contrast / amplitude • Textons – combinations of features making up small graphical shapes • Perceptual Independence – independence of different sources of information, increase in one does not effect how the other appears • Orthogonality – channels that are independent are orthogonal – textures differing in orientation by +/- 30 degrees are easily distinguishable Texture Resolution • Resolvable size difference of a Gabor pattern is 9%. • Resolvable orientation difference is 5°. • Higher sensitivity due to higher-level mechanisms. • No agreement on what makes up important higher order perceptual dimensions of texture (randomness is one example). Glyphs and Multivariate Discrete Data • Multivariate Discrete Data – data objects with a number of attributes that can take different discrete values • Glyph – single graphical object that represents a multivariate data object • Integral dimensions – two or more attributes of an object are perceived holistically (e.g.width and height of rectangle). • Separable dimensions – judged separately, or through analytic processing (e.g. diameter and colour of ball). • Restricted Classification Tasks – Subjects asked to group 2 of 3 glyphs together to test integral vs. separable dimensions. • Speeded Classification Tasks – Subjects asked to rapidly classify glyphs according to only one of the visual attributes to test for interference. • Integral-Separable Dimension Pairs – continuum of pairs of features that differ in the extent of the integral-separable quality – integral(x/y size)…separable(location/colour) Multidimensional Discrete Data • Using glyph display, a decision must be made on the mapping of the data dimension to the graphical attribute of the glyph. • Many display dimensions are not independent (8 is probably maximum). • Limited number of resolvable steps on each dimension (e.g. 4 size steps, 8 colours..). • About 32 rapidly distinguishable alternatives, given limitations of conjunction searches. Conclusion • What is currently known about visual processing can be very helpful in information visualization. • Understanding low-level mechanisms of the visual processing system and using that knowledge can result in improved displays.