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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. We are not grading according to strict percentages This class is qualitative not quantitative

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human perception

Human Perception

Christine Robson

September 20, 2007

another word about grading
Another word about grading
  • We are not grading according to strict percentages
  • This class is qualitative not quantitative
    • Assignments are less structured then most CS classes
  • Most of the grade is on the final project
  • Overall, pleased with effort
    • Giving feedback on areas to improve
today
Today
  • Human Information Processing
    • Perception
    • Motor Skills
    • Memory
    • Decision Making
    • Attention
    • Vision
  • Modeling Human Actions
    • Fitts’s Law
    • GOMS
    • KLM
stage theory of human perception memory
Stage Theory of Human Perception & Memory

Sensory Image Store

WorkingMemory

Long Term

Memory

short term sensory store
Short-Term Sensory Store
  • Visual information store
    • Encoded as a physical image
    • Size approx 7-17 letters
    • Decay ~200ms (70-1000ms)
  • Auditory information store
    • Encoded as a physical sound
    • Size 4.4-6.2 letters
    • Decay ~1500ms (900-3500ms)
preattentive processing
Preattentive Processing

http://www.csc.ncsu.edu/faculty/healey/PP/index.html

preattentive processing1
Preattentive Processing

http://www.csc.ncsu.edu/faculty/healey/PP/index.html

preattentive processing2
Preattentive Processing

http://www.csc.ncsu.edu/faculty/healey/PP/index.html

say the colors of these words aloud
Say the colors of these words aloud

Cat

Jacket

Train

Lunch

Knife

Road

do it again
Do it again…

Orange

Purple

White

Red

Yellow

Green

read them in order
Read them in order…

White

Green

Orange

Yellow

Purple

Red

perceptual fusion
Perceptual Fusion
  • 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
  • i.e. That’s how this projector works
stage theory of human perception memory1
Stage Theory of Human Perception & Memory

maintenance rehearsal

Sensory Image Store

WorkingMemory

Long Term

Memory

elaboration

decay

decay,displacement

decay?

interference?

working memory
Working Memory
  • Small capacity: 7 +/- 2 chunks
    • A memory chunk is a small grouping of data eg 800 411 4664 is three chunks
  • Fast decay rate (~7 [5-226] sec)
  • Maintenance Rehearsal fends off delay
  • Interference causes faster delay
long term memory ltm
Long-term Memory (LTM)
  • Huge capacity
  • Little decay
  • Elaborative rehearsal moves chunks from working memory into LTM by making connections with other chunks
recall
Recall
  • Who were the 7 dwarves in Snow White?
recognition
Recognition
  • Does that help?

Grouchy

Sneezy

Smiley

Sleepy

Pop

Grumpy

Cheerful

Dopey

Bashful

Wheezy

Doc

Lazy

Happy

Nifty

Sleepy

power law of practice
Power Law of Practice
  • Task time on the nth trial:

Tn = T1 n-a + cwhere a = 0.4 ; c is a limiting constant

  • You get faster the more times you do it!
  • Applies to skilled behavior
    • eg. Sensory & Motor
    • Not to knowledge acquisition or improving quality
divided attention
Divided Attention
  • Multitasking
    • Attention is a resource that can be shared among different tasks simultaneously
  • Depends on
    • The structure of the tasks (similar tasks interfere, different tasks are easy to share)
      • modality, encoding, and components
    • Difficulty of the task
choice reaction time
Choice Reaction Time
  • 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
Hick’s Law
  • 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
Information Clutter
  • We don’t even need Hick’s Law to see this is a bad idea…
motor processing
Motor Processing
  • Pianist: up to 16 finger movements per second
    • You might faster then you speak
    • You certainly type faster then you click
  • Point of no return for muscle action
fitts s law
Fitts’s Law
  • 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)

    • Depends only on index of difficulty

log (D/S +1)

  • Hand movement based on a series of micro-corrections

D

start

S

implications of fitts s law
Implications of Fitts’s Law
  • Which targets are easier to hit? Why?

A

B

start

start

D

C

start

start

visualization of fitts s law
Visualization of Fitts’s Law
  • 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
Toolbar Example
  • How can you make a simple change to improve this tool bar
    • Apply Fitts’s Law!
  • Targets at screen edge are easy to hit
slide32
GOMS
  • Describe the user behavior in terms of
    • Goals
      • i.e. edit manuscript, locate line
    • Operators
      • Elementary perceptual, motor, or cognative acts
    • Methods
      • Procedure for using operators to accomplish goals
    • Selection rules
      • Used if several methods are available for a given goal
  • Family of methods
    • KLM, CMN-GOMS, NGOMSL, CPM-GOMS
goms example
GOMS Example
  • Goal (the big picture)
    • Go from home to the airport
  • Methods (or subgoals?)
    • Take BART, taxi, airport shuttle
  • Operators
    • Go to BART station, wait for BART…
  • Selection rules
    • BART is cheaper, but I’m running late…
goms how to
GOMS How-To:
  • 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
  • Evaluate description of task
  • Apply results to UI
  • Iterate
goms calculations
GOMS Calculations
  • 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
Using GOMS Analysis
  • 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?
    • Operator Sequence
      • In what order are individual operations done?
keystroke level model
Keystroke Level Model
  • Describe the task using the following Operators
    • K: pressing a key or a pressing (or releasing) of a button
      • T(K) = 0.08~1.2 seconds (~0.2 avg)
    • P: pointing
      • T(P) = 1.1 seconds (without button presses)
    • H: homing (switching device
      • T(H) = 0.4 sec
    • D(n,L): drawing segmented lines
      • T(D) = 0.9n + 0.16L
    • M: mentally prepare
      • T(M) = 1.35s
    • R(t) : system repsonse time
      • T(R) = t
klm heuristic rules raskin
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
Using KLM
  • Encode using all physical operators
    • K, M, P, H, D(n,l), R(t)
  • 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) )
  • Compute the total time by adding each time cost
applications of goms
Applications of GOMS
  • 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
What GOMS Can Model
  • Task must be goal-directed
    • Some activities are more goal-directed then others
    • Creative activities may not be as goal-directed
  • Task must be a routine cognitive skill
    • As opposed to problem solving
    • Good for machine operators
  • Serial and parallel tasks (CMP-GOMS)
advantages of goms
Advantages of GOMS
  • 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
Disadvantages of GOMS
  • Not as easy as other evaluation methods
    • Heuristic evaluation, guidelines, etc.
  • 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
In conclusion
  • 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
assignments
Assignments

Upcoming:

  • Contextual inquiry (Due Sept. 27)
    • Pick appropriate method
    • Group analysis
    • Report
next time
Next time

Design Process: Implement

Low Fidelity Prototyping

  • 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 Forget to pickup:

“Don’t Make Me Think!” handout

A gift for your test subject