Perception

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Perception

• Visual Attention and Information That Pops Out
• Scales of Measurement

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• 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

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Scales Of MeasurementOn the Theory of
Measurement, S.S. Stevens, Science, 103,
pp.677-680. 1946

• Nominal
• Ordinal
• Interval
• Ratio

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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

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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

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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

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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)

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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

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• 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

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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

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• 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

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• 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

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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

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How to attract users 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.

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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.

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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 McGreneres 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.

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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

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Color
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Orientation
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Motion
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Simple shading
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(No Transcript)
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• 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

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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.

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Vision Pathwayhttp//www.geocities.com/ocular_tim
es/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)

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http//www.geocities.com/ocular_times/vpath.html
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http//www.geocities.com/ocular_times/vpath.html
31
http//nba5.med.uth.tmc.edu/academic/neuroscience/
lectures/section_2/lecture34_04.htm
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http//nba5.med.uth.tmc.edu/academic/neuroscience/
lectures/section_2/lecture34_04.htm
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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.

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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

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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

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• 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.

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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.

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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

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• 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

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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).

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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

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• 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).

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• 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)

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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.

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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.