2. Perception Visual Attention and Information That Pops Out Scales of Measurement

3. Scales of MeasurementScales 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

4. Scales Of Measurement On the Theory of Measurement, S.S. Stevens, Science, 103, pp.677-680. 1946 Nominal Ordinal Interval Ratio

5. 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

6. 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

7. 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

8. 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)

9. 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

10. 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

11. 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

12. 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

13. 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

14. 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

15. 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.

16. 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.

17. 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.

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20. 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.

21. 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

22. Color

23. Orientation

24. Motion

25. Simple shading

26. Length Width Parallelism Curvature Number Added marksSpatial grouping Shape Enclosure

27. 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 pre- attentive –spatially coded information (position on XY plane, stereoscopic depth, shape from shading) and second attribute (colour, shape) DO allow conjunction search

28. 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.

29. Vision Pathway http://www.geocities.com/ocular_times/vpath2.html 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)

30. http://www.geocities.com/ocular_times/vpath.html

32. http://nba5.med.uth.tmc.edu/academic/neuroscience/lectures/section_2/lecture34_04.htm

34. 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.

35. 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

36. Gabor Function Response = C cos(Ox/S)exp(-(x² + y²)/S) Camplitude, or contrast value S overall size of Gabor function Orotation matrix that orients cosine wave orientation, size, and contrast are most significant in modeling human visual processing

37. 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.

38. 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.

39. 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

40. 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

41. 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).

42. 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

43. 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).

44. 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)

45. 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.

46. 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.