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://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.