Post on 25-Dec-2015
Perception
Visual Attention and Information That Pops Out
Scales of Measurement
• 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
Scales Of MeasurementOn 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.
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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
Length Width
Parallelism Curvature
NumberAdded marksSpatial grouping
Shape
Enclosure
• 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
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 Pathwayhttp://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://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.