Visual perception - dbdmg.polito.it
Transcript of Visual perception - dbdmg.polito.it
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Visual perception
Data Management and Visualization
Version 2.2.1© Marco Torchiano, 2021
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Licensing Note
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Your fair use and other rights are in no way affected by the above.
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VISUAL INTEGRITY
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Principles of integrity
▪ Proportionality
Representation as physical quantities should be proportional to the represented numbers
▪ Utility
Graphical element should convey useful information
▪ Clarity
Labeling should counter graphical distortion and ambiguity
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Proportionality
▪ The magnitude of visual attributes should represent faithfully the magnitude of measures
▪ They should allow
Discrimination: are they different?
Comparison: which is larger?
Magnitude Assessment: how much larger?
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Lie Factor
▪ Overstating
LF > 1 Log(LF) > 0
▪ Understating
LF < 1 Log(LF) < 0
▪ Fair
▪ LF = 1 Log(LF) = 0
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Lie Factor
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Understating OverstatingFair
LF
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Lie Factor
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0
Understating OverstatingFair
log(LF)
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Lie Factor
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18.7
2.2= 8.5 on graphic
27.5
18= 1.52 in data
LF = 8.5 / 1.52 = 5.59
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Example
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Example – Lie Factor
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135,2
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Example – Lie Factor
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131,4
135,2
3.5 cm
9 cm
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Example - Redesign
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Debito Pubblico (% PIL)
PD M5S-L
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Example - Redesign
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132,2
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Debito Pubblico (% PIL)
PD M5S-L 131.8 131.6 131.4
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Guidelines for design
▪ Keep the physical Lie Factor = 1
▪ Limit the perceptual Lie Factor as much as possible
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Utility
▪ Every element should convey useful information
▪ Unnecessary visual objects or attributes distract from the message
Different attributes trigger a search for a rationale (e.g. random colors)
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Data-ink
▪ Proportion of a graphic’s ink devoted to the non-redundant display of data information
Or:
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Data-ink
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Data-ink
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Data-ink
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Data-ink
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Tufte’s proposed redesign
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Guidelines for design
▪ Maximize data-ink ratio
Erase non-data-ink
Erase redundant data-ink
▪ “Within reason”
Above all else show the dataE.Tufte
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Use of contrast
▪ Include differences corresponding to actual differences
▪ Effective when one item is different in a context of other items that are the same
Bright saturated color among mid colors
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Chartjunk
▪ The presence of unnecessary elements that distract or hide the message conveyed by the diagram
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Chartjunk
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Clarity
▪ Visual encoding and layout should make perception tasks easy and effortless
▪ Textual and support elements should provide effective support to understanding the information
▪ Any variation in the graph should represent useful information otherwise it is noise obfuscating the message
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Clarity
▪ Textual elements should provide effective support to understanding
Hierarchical
– Size and position reflects importance
Readable
– Large enough
Horizontal
Close to data (avoid legends)
▪ Always label the axes
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Colors
▪ Get it right in black and white
▪ Use medium hues or pastels
Bright colors distract and tire out
▪ Use color only when needed to serve a particular communication goal
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Cognitive Dissonance
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RED BLUE
GREEN YELLOW
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Detection and Separation
Efficiency and efficacy of perception tasks is affected by:
▪ Detection
The capability to visually identify the objects that represent the data to be compared
▪ Separation
The distance between the objects to be compared
– affects negatively the accuracy
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Clarity
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Example
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Analysis
▪ Proportionality
Due to non-zero base bars, it has a large lie factor (2.2):
– ratio of real values: 87.8 : 61.9
– ratio on graph: 37.8 : 11.9
▪ Utility
Most elements appear useful
X-axis ticks can be removed
Y grid could be made less prominent
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Analysis
▪ Clarity
It uses a dual scale that confuses and makes very hard a visual comparison of the values and further distorting the compared values.
The dual scale is not mentioned anywhere and it is not clear which values refer to which scale.
In general the usage of bars is not the most appropriate visual representation if the goal is to show a trend or evolution in time.
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Redesign
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50
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2000 2005 2010 2015
Trends in employment rates of 25-34 with a tertiary degree
Germany
France
Italy
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Case study
▪ Si consideri il seguente grafico
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Assessment
▪ Question:
Is there one (or more) question addressed by the visualization?
▪ Data:
Is the data quality appropriate?
▪ Visual Integrity:
Are the visual features appropriate?
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Visual Integrity
▪ Proportionality:
Are the values encoded in a uniformly proportional way?
▪ Utility:
All the elements in the graph convey useful information?
▪ Clarity:
Are the data in the graph identifiable and understandable (properly described)?
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Question
▪ What are the most popular/favorite NFL teams in our audience?
▪ …
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Data
Team PreferencesPanthers 34%
Cowboys 7%
Packers 7%
Patriots 6%
Steelers 6%
Redskins 5%
Broncos 4%
Total: 69%
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WXII-TV is an NBC–affiliated television station serving North Carolina: home of Panthers
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Full data
Team PreferencesPanthers 34%
Cowboys 7%
Packers 7%
Patriots 6%
Steelers 6%
Redskins 5%
Broncos 4%
Other 31%
Total: 100%
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Integrity - Proportionality
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177°
36.5°
177/36.5 = 4.834/7 = 4.8
34% corresponds to 50% of the pie!
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Utility
▪ Si consideri il seguente grafico
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Clarity
▪ Si consideri il seguente grafico
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Redesign #1
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Redesign #2
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VISUALIZATION PIPELINE
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Visualization Pipeline
Visual PerceptionVisual Properties & Objects
Quantitative ReasoningQuantitative Relationship & Comparison
Information UnderstandingVisual Patterns, Trends, Exceptions
Knowledge Decisions
Data
Representation/Encoding
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Visual Perception
▪ Any variable (measure) must be visually encoded, i.e. we need to identify:
Visual object to represent entity
Visual attribute to represent the measure
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Perception
Reasoning
Understanding
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Example
Votes received by four candidates in recent elections
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Candidate Votes Proportion
Sergio 197800 50.09%
Alberto 140545 35.59%
Giorgio 53748 13.61%
Valter 2759 0.70%
http://www.comune.torino.it/elezioni/2019/regionali/presidente/citta/
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Encoding
▪ Visual object: line
▪ Visual attribute: length
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Alberto
Giorgio
Sergio
Valter
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Visual Reasoning
Layout and visual attributes allow:
▪ Discrimination
Distinguish visual objects or group of -
▪ Comparison
Place visual objects in order
▪ Magnitude assessment
Evaluate the (relative) magnitude of visual objects
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Perception
Reasoning
Understanding
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Reasoning
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Alberto
Giorgio
Sergio
Valter
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Reasoning
▪ Discrimination
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Alberto
Giorgio
Sergio
Valter
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Reasoning
▪ Comparison
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Alberto
Giorgio
Sergio
Valter
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Reasoning
▪ Assessment
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Alberto
Giorgio
Sergio
Valter
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Understanding
▪ Variation within quantitative measures
Distribution
Deviation
Correlation
▪ Variation within category
Ranking
Part-to-whole
Time
Space
▪ Multivariate
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Perception
Reasoning
Understanding
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Understanding
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Alberto
Giorgio
Sergio
Valter
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Understanding
▪ Ranking
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Alberto
Giorgio
Sergio
Valter
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VISUAL PERCEPTION
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Data Visualization
Quantitative ReasoningQuantitative Relationship & Comparison
Information VisualizationVisual Patterns, Trends, Exceptions
Understanding
Data
Representation/Encoding
Visual PerceptionVisual Properties & Objects
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Visual perception
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Sensation(Physical process)
Perception(Cognitive process)
Stimulus Sensory Organ
Eye
Perceptual Organ
Iconic ShortTerm LongTerm
Brain
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Memory Hierarchy
▪ Iconic memory (visual sensory register)
Pre-attentive processing
Detects a limited number of attributes
▪ Short-term memory (working memory)
Store visual chunks
Limited number
▪ Long-term memory
Store high-level knowledge
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Simplified Model
▪ The three levels of memory representa simplified model
does not correspond to “real” physicalbrain structure
▪ Useful to explain a few phenomena
The 7 ± 2 rule
Change blindness
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Change blindness
66http://www2.psych.ubc.ca/~rensink/flicker/download/index.html
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Change blindness
67http://www2.psych.ubc.ca/~rensink/flicker/download/index.html
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Pre-Attentive Attributes
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Pre-Attentive Attributes
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Encoding
▪ Encoding is the key to enable visual perception
Visual object to represent entity
Visual attribute to represent the measure
▪ Two main types
Quantitative (different properties)
Categorical (ordinal or not)
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Perception
Reasoning
Understanding
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Pre-Attentive attributes
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Category Attribute
Form OrientationLength/distanceLine widthSizeShapeCurvatureAdded marksEnclosure
Color HueIntensity
Spatial position 2-D position
Motion FlickerDirectionSpeed
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Perception task
Visual attributes allow:
▪ Discrimination
Distinguish visual objects
▪ Comparison
Place visual objects in order
▪ Magnitude assessment
Evaluate the (relative) magnitude of visual objects
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Just noticeable difference
▪ Given a physical dimension (length, brightness, etc.) x
▪ d is the just noticeable difference if:
difference between x and x+d is perceivable
but not smaller differences
▪ d depends on many factors:
Subject
Environment
Physical dimension
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Weber’s law
▪ Just noticeable difference d is:
▪ Where
x: dimension
dp(x): just noticeable difference
kp: constant– Subjective
– Environmental
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Consequences of Weber’s law
▪ It is easier to compare lengths that differ by a large percentage
▪ The same difference is easier to notice between smaller measures More likely to be larger than just noticeable
difference
▪ Length of non-aligned objects is harder to compare Double comparison
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Non-aligned objects lengths
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Non-aligned objects lengths
▪ Additional references my help comparison
They provide alternative possible comparisons
▪ If lengths range between 0 and a maximum ( L ), e.g. percentages
▪ Comparing l1 and l2 (close to L) that differ by a small amount d Difference L-l1 vs. L-l2 easier to notice
than l1 vs. l2
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Stevens’s law
▪ Perceive scale (magnitude ratio)
▪ Where β depends on spatial dimension
1D: Length → β in [0.9, 1.1]
2D: Area → β in [0.6, 0.9]
3D: Volume → β in [0.5, 0.8]
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Stevens S. S. (1975). Psychophysics, John Wiley & Sons.
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Stevens’s law
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Stevens’s law
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Consequences
▪ Prefer comparing lengths
▪ Avoid comparison between areas
Except for ordinal measures
▪ Never-ever make volume comparisons
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Attributes of form
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Orientation
Line Length
Line Width
Size
Shape
Curvature
Added mark
Enclosure
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Orientation (angle or slope)
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a) b)
c) d)
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Angle vs. Slope
▪ Slope of A-B is b/a
tan(α)
▪ Slope judgment typically falls back to an angle judgment
Given an error ε in the angle judgment
It is reflected in a slope error
– Getting infinite as α approaches to π/2
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A
B
a
bα
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Shape
▪ There is no common quantitative semantics for the shapes
– Unless they are characters…
Fill textures are shapes too
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Length
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Effect of context
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Curvature
▪ There is no common magnitude assessment for the curvature
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Width
▪ Order can be identified
Difficult to appreciate actual magnitude
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A B C D
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Mark
▪ No common quantitative semantics of marks
▪ Number of marks could encode a natural number
Harder to read than a cipher
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Size / Area
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Enclosure
▪ No common quantitative semantics for enclosure
Except counting items enclosed
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Spatial Position
▪ Position along axis
Common scale
Distinct identical scales
– Possibly un-aligned
▪ Distance
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Position
▪ A common scale
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Position
▪ Identical non-aligned scales
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Distance
▪ Points
Use length of imaginary connecting lines
▪ Lines
Distance orthogonal to tangent
– Not what is meant in xy plots
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Detection and Separation
Comparison is affected by:
▪ Detection
The capability to visually identify the objects that represent the data to be compared
▪ Separation
The distance between the objects to be compared
– affects negatively the accuracy
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Attributes of color
▪ Hue
▪ Saturation
▪ Intensity
Luminance
Value
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Hue▪ There is no common ordering semantics
for hues
High spatial frequencies are perceived through intensity changes
Often perceived as separated into bands of almost constant hue, with sharp transitions between hues
▪ Nominal values can be represented by suitably spaced values
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Intensity a.k.a. Luminance, Value
▪ Provides a perceptually unambiguous ordering
Context can affect accuracy
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Saturation
▪ Perceptually difficult to associate an ordered semantics
Can be combined with hue to increase discrimination
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Effect of Context
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Effect of Context
▪ Use uniform background
To make distinct visual objects for the same feature look the same
▪ Use a background color that is contrasting enough with the visual objects’ color
To make visual objects easily seen
▪ Avoid non-uniform background
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Color usage
▪ Ordinal measure should be mapped to increasing saturation and intensity
Avoid rainbow palette
▪ Use sequential or diverging palette
E.g.
– http://colorbrewer2.org/
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Color Blindness
▪ Inability to see colors or perceive color differences
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http://www.color-blindness.com
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Visual Encoding: Quantitative
Object Attribute
Point Position (w.r.t. axis/axes)
LineLengthPosition (w.r.t. axis/axes)Slope
Bar Length
ShapeSize (area)Count
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Visual Encoding: Categorical
Attribute
Position
Size
ColorIntensitySaturationHue
Shape
Fill pattern
Line style
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Ordinal
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VISUAL REASONING
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Perception
Reasoning
Understanding
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Graph layout
Layout and visual attributes allow:
▪ Discrimination
Distinguish visual objects or group of -
▪ Comparison
Place visual objects in order
▪ Magnitude assessment
Evaluate the (relative) magnitude of visual objects
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Perception
Reasoning
Understanding
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Gestalt principles
▪ Visual features that lead us to group visual objects together
Proximity
Similarity
Enclosure
Closure
Continuity
Connection
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Gestalt principles
▪ Visual features that lead the viewer to group visual objects together
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Similarity Connection Closure
Proximity Enclosure Continuity
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Gestalt principles
▪ Visual attributes/patterns that lead observer to group objects together
Proximity
Similarity
Enclosure
Closure
Continuity
Connection
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Gestalt principles
▪ Visual attributes/patterns that lead observer to group objects together
Proximity
Similarity
Enclosure
Closure
Continuity
Connection
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Gestalt principles
▪ Visual attributes/patterns that lead observer to group objects together
Proximity
Similarity
Enclosure
Closure
Continuity
Connection
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Gestalt principles
▪ Visual attributes/patterns that lead observer to group objects together
Proximity
Similarity
Enclosure
Closure
Continuity
Connection
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Gestalt principles
▪ Visual attributes/patterns that lead observer to group objects together
Proximity
Similarity
Enclosure
Closure
Continuity
Connection
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Gestalt principles
▪ Visual attributes/patterns that lead observer to group objects together
Proximity
Similarity
Enclosure
Closure
Continuity
Connection
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Similarity in Shape & Color
600.000
650.000
700.000
750.000
800.000
850.000
Q1 Q2 Q3 Q4
Booking Billing
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Difficult to evaluate the trend
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Similarity+Connection
600.000
650.000
700.000
750.000
800.000
850.000
Q1 Q2 Q3 Q4
Booking Billing
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Easier to evaluatethe trend
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Similarity+Connection+Proximity
600.000
650.000
700.000
750.000
800.000
850.000
Q1 Q2 Q3 Q4
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Direct labeling
Booking
Billing
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Similarity × Proximity
0
100
200
300
400
500
600
Q1 Q2 Q3 Q4
k
2003 Sales
Direct Indirect
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Similarity × Proximity & Enclosure
0
100
200
300
400
500
600
Q1 Q2 Q3 Q4
k
2003 Sales
Direct Indirect
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Continuity replaces axis
0
100
200
300
400
500
600
Q1 Q2 Q3 Q4
k
2003 Sales
Direct Indirect
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Distinct perceptions
▪ The immediacy of any pre-attentive cue declines as the variety of alternative patterns increases
Even if all the distracting patterns are individually distinct from the target
For each single attribute no more than four distinct levels are discernible
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Rainbow Pies
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Attribute Interference
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Attribute Interference
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Cultural conventions
▪ Reading proceed from left to right and from top to bottom
At least in western culture
▪ What is at the top (on the left) precedes what is at the bottom (on the right) in terms of
Importance
Ordering
Time
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EmphasisAttribute Tables Graphs
Line width Boldface text Thicker lines
SizeBigger tablesLarger fonts
Bigger graphsWider barsBigger symbols
Color intensity Darker or brighter colors
2-D positionPositioned at the topPositioned at the left
Positioned in the center
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References
▪ C. Ware. Information Visualization: Perception for Design. Morgan Kaufmann Publishers, Inc., San Francisco, California, 2000
▪ C. Healey, and J. Enns. Attention and Visual Memory in Visualization and Computer Graphics. IEEE Transactions on Visualization and Computer Graphics, 18(7), 2012
▪ I. Inbar, N. Tractinsky and J.Meyer. Minimalism in information visualization: attitudes towards maximizing the data-ink ratio. http://portal.acm.org/citation.cfm?id=1362587
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References
▪ S.Few, “Practical Rules for Using Color in Charts”
http://www.perceptualedge.com/articles/visual_business_intelligence/rules_for_using_color.pdf
▪ D. Borland and R. M. Taylor Ii, "Rainbow Color Map (Still) Considered Harmful," in IEEE Computer Graphics and Applications, vol. 27, no. 2, pp. 14-17, March-April 2007.
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4118486
▪ http://www.color-blindness.com
▪ http://www.csc.ncsu.edu/faculty/healey/PP/index.html
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