Title: Engineering Psychology PSY 378F
1Engineering PsychologyPSY 378F
- University of Toronto
- Fall 2002
- L7 Spatial Displays Graphs
2Outline
- Spatial Displays and Analog Perception
- What Graphs Are, History
- Graph Guidelines
- Use Physical Dimensions Judged Without Bias
- Consider the Task
- Minimize the Number of Mental Operations
- Keep the Data-Ink Ratio High
- Code Multiple Graphs Consistently
3Spatial Displaysand Analog Perception
- In Spatial Displays, Sizes of Objects or
Distances Between Them Used to Communicate
Information - Analog Perception Judgments of Magnitude
(Distance, Position, Extent, Depth, Angle etc.) - Contrast to Digital Displays
- 99 vs. 100 km/h
4Graphical Perception
- What is a Graph?
- A Paper or Electronic Analog Representation of
Numeric Data with Multiple Data Points - Originally developed by Playfair (1786), Lambert
(1765), a political economist and a chemist,
respectively
5Perceptual Biases and Illusions
- Variation of Poggendorf Illusion
- Solution Scales on Each Side
6Perceptual Biases and Illusions
- Cleveland (1985) Illusion--Joining Shortest
Distances Rather than Vertical Distances - Solution Plot the Difference Directly, Draw
Vertical Gridlines
7Area and Volume in Graphs
- Area and Volume Commonly Used to Code Values in
Graphs - Area and Volume Judged Inaccurately (Cleveland)
8Proportion Judgments of 3D Bars
- Cyclical bias patterns occur when observers make
proportion judgments with bars (Spence, 1990) - Why? What is source of bias?
9Bias in Judgments of Area and Volume
Stevens Law ? aPb
- Seen in Magnitude Estimation (Assign Number to
Magnitude of Stimulus Stevens, 1957) - Area and Volume Show Response Compression
(Stevens Exponent, ? lt 1) - Color Saturation Shows Response Expansion (? gt 1)
10Power Model (Spence, 1990 Karmarkar, 1978)
- Power model claims bias seen in magnitude
estimation affects proportion judgments - The model provides a method for estimating
Stevens exponent using proportion judgments
P aPb / (aPb aWb) Pb / (Pb Wb) Pb / Pb
(1-P)b
11Power Model Predictions
- When b lt 1, over-then-under
- When b gt 1, under-then-over
- Can account for one-cycle pattern, but not
multi-cycle
12Cyclical Power Model(Hollands Dyre, 2000,
Psych Review)
- This more general model can account for
multiple-cycle bias patterns - Let P W R, where R is a reference value
- Multiple reference values may be available within
a stimulus
13Cyclical Power Model(Hollands Dyre, 2000)
- With two reference points one cycle (same as
power model)
14Adding Reference Points
- With three reference points two cycle
15Adding Reference Points
- With five reference points four cycle
16Fitting the Model to Judgments with Graphs
- Data from Spence (1990) fit by Hollands Dyre
(2000) - Two-Cycle Patterns
- Stevens Exponents a Bit Larger Than 0.8
(Indicating that Area Judged at Least Part of the
Time)
17Cyclical Power Model Evidence
- Hollands Dyre (2000)
- Conducted experiments to test two assumptions of
CPM - Experiment 1
- Changes in Stevens exponent obtained through
magnitude estimation predicted exponents obtained
from proportion judgments
18Cyclical Power Model Evidence
- Experiment 2
- Changing Available Reference Points (Tickmarks)
Predicts Frequency of Cyclical Bias - Overall Error (Distance from Horizontal Line)
Reduced With More Reference Points
19Cyclical Power Model Extensions
- Same Graph, Different Continua
- Box-and-Whisker Plots Length and Area
- Box Overestimated When Box Small Underestimated
When Large (Behrens et al., 1990) - Solution Use Quartile Plot, Because Length Used
to Code Values
20Response Method(Morton Hollands)
- Experiment 2
- 3 response methods line, dial, numeric
- No tickmarks on pies
21Morton Hollands Results
- 2 and 4 cycle model versions fit
- Best fitting version (largest R2) shown below
- No difference among ? values (0.8)
22Cyclical Power Model Conclusions
- Avoid Continua Whose Stevens Exponents Differ
From One - Increase the Frequency of Tickmarks in the Graph
- Possible to Make less Effective Perceptual
Continua (e.g., Area) More Effective with More
Reference Points - Portray bars at similar depths in 3D bar graphs
if accurate judgments are necessary or use 2D
23Task Dependency and the PCP
- Task Dependency The Choice of the Best Graph
Type Depends on the Judgment Task - Continuum of Tasks Point Reading, Local
Comparisons, Global Comparisons, Synthesis
24Continuum of Tasks
25Proximity Compatibility Principle
- If task requires high processing proximity there
should be high display proximity. - If a task requires low processing proximity there
should be low display proximity (Wickens
Carswell, 1995). - Or,
- For integrated tasks, use more integrated
displays - For specific, point reading tasks, use separated
displays
26PCP Applied to Graphs
- Carswells (1988, 1992a) Metanalysis
- Each Study Classified by Task
- Performance in Task Evaluated as to Whether
Consistent with PCP Prediction
27PCP Applied to Graphs
- Increasing Benefit of Integrated Graphs as Tasks
Require More Integration (Consistent with PCP)
28PCP Applied to Graphs
- Result Confirmed by More Recent Studies Using
Multiple Tasks (e.g., Gillie Berry, 1994
Hollands Spence, 1992, Liu Wickens, 1992,
Wickens et al., 1994, 1995)
29Cleveland vs. PCP
- Cleveland McGill (1984) Hierarchy Applies to
More Focused Tasks--therefore subsumed by PCP - Metanalysis by Carswell (1992b) Supports this
Conclusion
30Minimizing Mental Operations
- Fewer Operations will Reduce Processing Time and
Reduce Likelihood of Error - Two Factors Affect Graph Reading Performance
- 1. Number of operations necessary given a
particular task-graph combination - 2. Effectiveness of the perceptual features used
as input for the operations - Predicts Task Dependent Results (underlies PCP)
Sum Summation (heightA, heightB) Ratio
estimation (heightA,Sum)
B
Ratio estimation (heightA, heightAB)
B
A
A
31The Data-Ink Ratio
- Amount of ink not used to depict data should be
kept to a minimum (Tufte, 1983) - Unnecessary non-data ink slows search for values
- Gillan Richman (1994) found empirical support
for the data-ink ratio concept - Judgments were slower and less accurate with
extra ink
32Multiple Graphs
- Coding variables
- Focused judgments of variable coded across graphs
difficult - Mental representation of coded variables (B and
C) qualitative - Mental representation of variable on x-axis is
quantitative (Shah Carpenter, 1995)
33Multiple Graphs
- Keep format of multiple graphs as consistent as
possible - Identify each graph Identify changing element
- Local and global optimality
Causes of Death Among 25-44 Yr. Olds (Tufte, 1997)
34General Summary
- Spatial Displays and Analog Perception
- What Graphs Are, History
- Graph Guidelines
- Use Physical Dimensions Judged Without Bias
- Consider the Task
- Minimize the Number of Mental Operations
- Keep the Data-Ink Ratio High
- Code Multiple Graphs Consistently