Human Perception
Christine Robson
September 20, 2007
First Computer “bug”
Self Checkout
love it or hate it?
too much of a good thing?
Another word about
grading
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We are not grading according to strict
percentages
This class is qualitative not quantitative
– Assignments are less structured then most
CS classes
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Most of the grade is on the final project
Overall, pleased with effort
– Giving feedback on areas to improve
Today
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Human Information Processing
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Perception
Motor Skills
Memory
Decision Making
Attention
Vision
Modeling Human Actions
– Fitts’s Law
– GOMS
– KLM
Stage Theory of Human
Perception & Memory
Sensory
Image Store
Working
Memory
Long Term
Memory
Short-Term Sensory Store

Visual information store
– Encoded as a physical image
– Size approx 7-17 letters
– Decay ~200ms (70-1000ms)
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Auditory information store
– Encoded as a physical sound
– Size 4.4-6.2 letters
– Decay ~1500ms (900-3500ms)
Preattentive Processing
http://www.csc.ncsu.edu/faculty/healey/PP/index.html
Preattentive Processing
http://www.csc.ncsu.edu/faculty/healey/PP/index.html
Preattentive Processing
http://www.csc.ncsu.edu/faculty/healey/PP/index.html
Say the colors of these
words aloud
Cat
Jacket
Train
Lunch
Knife
Road
Do it again…
Orange
Purple
White
Red
Yellow
Green
Read them in order…
White
Green
Orange
Red
Yellow
Purple
Perceptual Fusion
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Two stimuli within the same PP cycle
(perceptual processor cycle, approx 100ms)
appear fused
Consequences:
– 10 frames/second seems to be moving (20fps
looks smooth)
– Computer responses in less then 100ms appear
instantaneous
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i.e. That’s how this projector works
Stage Theory of Human
Perception & Memory
maintenance rehearsal
Sensory
Image Store
decay
Working
Memory
Long Term
Memory
elaboration
decay,
displacement
decay?
interference?
Working Memory
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Small capacity: 7 +/- 2 chunks
– A memory chunk is a small grouping of
data eg 800 411 4664 is three chunks
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Fast decay rate (~7 [5-226] sec)
Maintenance Rehearsal fends off delay
Interference causes faster delay
Long-term Memory (LTM)
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Huge capacity
Little decay
Elaborative rehearsal moves chunks
from working memory into LTM by
making connections with other chunks
Recall

Who were the 7 dwarves in Snow
White?
Recognition
Grouchy
Sneezy
Smiley
Sleepy
Pop
Grumpy
Cheerful
Dopey
Bashful
Wheezy
Doc
Lazy
Happy
Nifty
Sleepy
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Does that help?
Power Law of Practice
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Task time on the nth trial:
Tn = T1 n-a + c
where a = 0.4 ; c is a limiting constant
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You get faster the more times you do it!
Applies to skilled behavior
– eg. Sensory & Motor
– Not to knowledge acquisition or improving
quality
Human Actions
Divided Attention
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Multitasking
– Attention is a resource that can be shared
among different tasks simultaneously
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Depends on
– The structure of the tasks (similar tasks
interfere, different tasks are easy to
share)
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modality, encoding, and components
– Difficulty of the task
Choice Reaction Time
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Reaction time is proportional to
information content of stimulus
If the user has to make a choice, it
takes much longer to respond
Double your number of stimuli, double
your reaction time
Hick’s Law
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Time it takes for a user to make a
decision.
Given n equally probable choices, the
average reaction time T required to
choose among them:
T = b log2(n + 1)
Information Clutter
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We don’t even need Hick’s Law to see
this is a bad idea…
Motor Processing
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Pianist: up to 16 finger movements
per second
– You might faster then you speak
– You certainly type faster then you click
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Point of no return for muscle action
Fitts’s Law
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Time T to move your hand to a target of size
S at distance D away is
T = RT + MT = a + b log (D/S +1)
D
start
S
– Depends only on index of difficulty
log (D/S +1)
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Hand movement based on a series of microcorrections
Implications of Fitts’s
Law
A
start
start
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C
start
start
B
D
Which targets are easier to hit? Why?
Visualization of Fitts’s
Law
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Time to move for distances (1 to 10)
and a widths (0.1 to 1.0):
www.mindhacks.com/blog/moving/index.html
Toolbar Example
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How can you make a simple
change to improve this tool bar
– Apply Fitts’s Law!
Targets at screen edge are
easy to hit
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GOMS
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Describe the user behavior in terms of
– Goals
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i.e. edit manuscript, locate line
– Operators
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Elementary perceptual, motor, or cognative acts
– Methods
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Procedure for using operators to accomplish goals
– Selection rules
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Used if several methods are available for a given goal
Family of methods
– KLM, CMN-GOMS, NGOMSL, CPM-GOMS
GOMS Example
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Goal (the big picture)
– Go from home to the airport
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Methods (or subgoals?)
– Take BART, taxi, airport shuttle
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Operators
– Go to BART station, wait for BART…
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Selection rules
– BART is cheaper, but I’m running late…
GOMS How-To:
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Generate task description
– Pick high-level user Goal
– Write Methods for reaching Goal (may invoke
sub-goals)
– Write Methods for sub-goals
– Iterate recursively until Operators are reached
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Evaluate description of task
Apply results to UI
Iterate
GOMS Calculations
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Execution time
– Add up times from operators
– Assume experts (have mastered tasks)
– Assume error-free behavior
– Very good rank ordering
– Absolute accuracy (~10%-20%)
Using GOMS Analysis
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Check that frequent goals can be
achieved quickly
Making operator hierarchy is often the
value
– Functional coverage & consistency
Does UI contain needed functions?
 Are similar tasks preformed similarly?
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– Operator Sequence
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In what order are individual operations done?
Keystroke Level Model
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Describe the task using the following Operators
– K: pressing a key or a pressing (or releasing) of a button
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T(K) = 0.08~1.2 seconds (~0.2 avg)
– P: pointing
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T(P) = 1.1 seconds (without button presses)
– H: homing (switching device
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T(H) = 0.4 sec
– D(n,L): drawing segmented lines
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T(D) = 0.9n + 0.16L
– M: mentally prepare
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T(M) = 1.35s
– R(t) : system repsonse time
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T(R) = t
KLM Heuristic Rules
(Raskin)
0: Insert M
– in front of all K
– in front of all P’s selecting a command (not in front of P’s ending a
command)
1: Remove M between fully anticipated operators
– MPK  PK
2: if a string of MKs belong to a cognitive unit, delete all M’s except the
first
– 4564.23: MKMKMKMKMKMKMK  MKKKKKKK
3: if K is a redundant terminator, then delete M in front of it
– [enter] [enter]: MKMK  MKK
4a: if K terminates a constant string (command name) delete the M in
front of it
– cd [enter]: MKKMK  MKKK
4b: if K terminates a variable string (parameter) keep the M in front of it
– cd class [enter]: MKKKMKKKKMK  MKKKMKKKKKMK
Using KLM
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Encode using all physical operators
– K, M, P, H, D(n,l), R(t)
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Apply Raskin’s KLM rules
Transform R followed by an M
– If t ≤ T(M) : R(t)  R(0)
– If T(M) < t : R(t)  R(t – T(M) )
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Compute the total time by adding each
time cost
Applications of GOMS
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Compare different UI designs
Profiling (time)
Building a help system? Why?
– Modeling makes user tasks & goals
explicit
– Can suggest questions users will ask &
the answers
What GOMS Can Model
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Task must be goal-directed
– Some activities are more goal-directed then
others
– Creative activities may not be as goal-directed
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Task must be a routine cognitive skill
– As opposed to problem solving
– Good for machine operators
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Serial and parallel tasks (CMP-GOMS)
Advantages of GOMS
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Gives qualitative and quantitative
measures
Model explains the results
Less work then a user study- no users!
Easy to modify when UI is revised
Disadvantages of GOMS
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Not as easy as other evaluation
methods
– Heuristic evaluation, guidelines, etc.
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Takes lots of time, skill & effort
Only works for goal-directed tasks
Assumes expert performance
Does not address several UI issues
– Readability, memorizability of icons, etc
In conclusion
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Know your users capabilities and limits
Models such as Fitts’s and GOMS can
help you test your UI without real
users
But there’s still no substitute for user
studies
Nuts & Bolts
Assignments
Upcoming:
 Contextual inquiry (Due Sept. 27)
– Pick appropriate method
– Group analysis
– Report
Next time
Design Process: Implement
Low Fidelity Prototyping
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Readings
– The Case Against User Interface Consistency
– Norman's The Design of Everyday Things,
Chapter 6
– Steve Krug "Don't make me think" (handout)
Don’t Forget to pickup:
“Don’t Make Me Think!” handout
A gift for your test subject