Human Abilities Part 1 of 3

1
Human Abilities - Part 1 of 3

• Understanding the user Introduction, the
  Senses, the Motor System

This material has been developed by Georgia Tech
HCI faculty, and continues to evolve.
Contributors include Gregory Abowd, Jim Foley,
Diane Gromala, Elizabeth Mynatt, Jeff Pierce,
Colin Potts, Chris Shaw, John Stasko, and Bruce
Walker. Comments directed to foley_at_cc.gatech.edu
are encouraged. Permission is granted to use with
acknowledgement for non-profit purposes. Last
revision January 2004.
2
Outline

• Human capabilities
• Senses
• Motor systems
• Information processing
• Memory
• Cognitive Processes
• Selective attention, learning, problem solving,
  language
• Simple predictive models

3
Typical Person )

• Do we really have limited memory capacity?
• Stay tuned

4
Basic Human Capabilities

• Do not change very rapidly
• Not like Moores law!
• Have limits, which are important to understand
• Our understanding of human capabilities does
  change, ie
• Cognitive neuroscience
• Theories of color perception
• Effect of groups and situation on how we act and
  react

5
Human Capabilities

• Why do we care?
• Better design!
• Want to improve user performance
• Knowing the user informs the design
• Senses
• Information processing systems
• Physical responding

Time and effort expendedto complete tasks
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Overview
I. Senses A. Vision B. Hearing C. Touch
D. Smell
III. Motor system A. Hand movement B.
Workstation Layout
II. Information processing A. Perceptual B.
Cognitive 1. Memory a. Short
term b. Medium term c. Long
term 2. Processes a. Selective
attention b. Learning c.
Problem solving d. Language
7
Senses (Our Input System)

• Sight, hearing, touch important for current HCI
• Smell, taste ???
• Abilities and limitations affect design

8
Key concepts for Senses

• Just noticeable difference (jnd)
• How much of a change in stimulus is needed before
  can be sensed
• Tends to be logarithmic - Webers Law
• Magnitude of physical stimulus versus perceived
  magnitude
• (Doubling number of photons does not double
  perceived intensity)

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Vision (Covered in greater detail in section on
visual design)

• Visual System
• Eye
• Retina
• Neural pathway
• 80 of brains operation

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Color JND
JND, mm
Color, from 400 to 700 mm V I B G Y
O R
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Audition (Hearing)

• Capabilities (best-case scenario)
• pitch - frequency (20 - 20,000 Hz)
• loudness - amplitude (30 - 100dB)
• location (5 source stream separation)
• timbre - type of sound (lots of instruments)
• Often take for granted how good it is(disk
  whirring)
• Implications ?

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Auditory JND

• Pitch
• Loudness
• Location
• Temporal variation (eg, songs) is another
  dimension

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Touch

• Three main sensations handled by different types
  of receptors
• Pressure (normal)
• Intense pressure (heat/pain)
• Temperature (hot/cold)
• Sensitivity, Dexterity, Flexibility, Speed
• Where important?
• Mouse, Other I/O, VR, surgery

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Touch JND

• Spatial - relevant for reading braille
• Pressure
• Temperature

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Smell

• Joseph Kaye, Making scents aromatic output for
  HCI ACM interactions Volume 10, Number 1 (2004),
  Pages 48-61

Solenoid-controlled scent bottles
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Motor System (Our Output System)

• Capabilities
• Range of movement, reach, speed,strength,
  dexterity, accuracy
• Workstation design, device design
• Often cause of errors
• Wrong button
• Double-click vs. single click
• Principles
• Feedback is important
• Minimize eye movement
• See Handbooks for data

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Work Station Ergonomics to Facilitate I/O
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End Part 1
19
Human Abilities - Part 2 of 3

• Understanding the user The Model Human Processor

This material has been developed by Georgia Tech
HCI faculty, and continues to evolve.
Contributors include Gregory Abowd, Jim Foley,
Diane Gromala, Elizabeth Mynatt, Jeff Pierce,
Colin Potts, Chris Shaw, John Stasko, and Bruce
Walker. Comments directed to foley_at_cc.gatech.edu
are encouraged. Permission is granted to use with
acknowledgement for non-profit purposes. Last
revision January 2004.
20
The Model Human Processor

• A true classic - see Card, Moran and Newell, The
  Psychology of Human-Computer Interaction,
  Erlbaum, 1983
• Microprocessor-human analogue using results from
  experimental psychology
• Provides a view of the human that fits much
  experimental data
• But is a partial model
• Focus is on a single user interacting with some
  entity (computer, environment, tool)
• Neglects effect of other people

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Block Diagram
LONG-TERM MEMORY
R Semantic D Infinite S Infinite
SHORT-TERM (WORKING) MEMORY
AUDITORY IMAGE STORE
VISUAL IMAGE STORE
R Acoustic or Visual D (one chunk) 73 73-226
s D (3 chunks) 7 5-34 s S 7 5-9 chunks
R Acoustic D 1.5 0.9-3.5 s S 5 4.4-6.2
letters
R Visual D 200 70-1000 ms S 17 7-17
letters
PERCEPTUAL PROCESSOR C 100 5-200 ms
COGNITIVE PROCESSOR C 70 27-170 ms
MOTOR PROCESSOR C 70 30-100 MS
R Representation D Decay Time S Size C
Cycle Time
Eye movement (Saccade) 230 70-700 ms
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Memory

• Perceptual buffers
• Brief impressions
• Short-term (working) memory
• Conscious thought, calculations
• Long-term memory
• Permanent, remember everything that ever happened
  to us

23
Recognize-act cycle

• Contents of WM trigger actions held in LTM
• This idea relates to gulf of execution and gulf
  of interpretation concepts to be introduced in a
  later lecture

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Perceptual

• Memory structures
• Image Stores - Holds fixed image of outside world
  long enough for some analysis(will come back to
  this)
• Processes - Info goes to brain for more
  processing
• e.g. Pattern recognition
• Uses context knowledge

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Perceptual Image Stores

• Visual and auditory impressions
• Visuospatial sketchpad, phonological loop
• Very brief, but accurate representation of what
  was perceived
• Details decay quickly (70 - 1000 ms visual 0.9 -
  3.5 sec auditory)
• Limited capacity (7 - 17 letters visual 4 - 6
  auditory)
• Rehearsal prevents decay
• Another task prevents rehearsal - interference

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Short Term Memory

• Use chunks 5-9 units of information
• Display format (picture, text, sound) should
  match memory system used to perform task
• New info can push out old info - interference

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About Chunks

• A chunk is a meaningful grouping of information
  allows assistance from LTM
• 4793619049 vs. 404 894 7328
• NSAFBICIANASA vs. NSA FBI CIA NASA
• My chunk may not be your chunk
• User and task dependent

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Long-Term Memory

• Seemingly permanent unlimited
• Access is harder, slower
• -gt Activity helps (we have a cache)
• Retrieval depends on network of associations

File system full
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LT Memory Structure

• Episodic memory
• Events experiences in serial form
• Helps us recall what occurred
• Semantic memory
• Structured record of facts, concepts skills
• One theory says its like a network
• Another uses frames scripts (like record
  structs)

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Memory Characteristics

• Things move from STM to LTM by rehearsal
  practice and by use in context
• We forget things due to decay and interference

Unclear if we ever really forget something -
just loose link to the info
Lack of use
Similar gets inway of old
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Processes

• Four main processes of cognitive system
• Selective Attention
• Learning
• Problem Solving
• Language

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Selective Attention

• We can focus on one particular thing
• Cocktail party chit-chat
• Salient visual cues can facilitate selective
  attention
• Examples?

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Learning

• Two types
• Procedural How to do something
• Declarative Facts about something
• Involves
• Understanding concepts rules
• Memorization
• Acquiring motor skills
• Automotization
• Tennis
• Driving to work
• Even when dont want to
• Swimming, Bike riding, Typing, Writing

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Learning

• Facilitated
• By structure organization
• By similar knowledge, as in consistency in UI
  design
• By analogy
• If presented in incremental units
• Repetition
• Hindered
• By previous knowledge
• Try moving from Mac to Windows
• gt Consider users previous knowledge in your
  interface design

35
Observations

• Users focus on getting job done, not learning to
  effectively use system
• Users apply analogy even when it doesnt apply
• Or extend it too far - which is a design problem
• Dragging floppy disk icon to Macs trash can does
  NOT erase the disk, it ejects disk!
• More on this in lecture on structuring help

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Problem Solving

• Storage in LTM, then application
• Reasoning
• Deductive -
• Inductive -
• Abductive -
• Goal in UI design - facilitate problem solving!
• How?? Take time to think about this right now!

If A, then B
Generalizing from previouscases to learn about
new ones
Reasons from a fact to theaction or state that
caused it
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Observations

• We are more heuristic than algorithmic
• We try a few quick shots rather than plan
• Resources simply not available
• We often choose suboptimal strategies for low
  priority problems
• We learn better strategies with practice

38
Implications

• Allow flexible shortcuts
• Forcing plans will bore user
• Have active rather than passive help
• Recognize waste

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Language

• Rule-based
• How do you make plurals?
• Productive
• We make up sentences
• Key-word and positional
• Patterns
• Should systems have natural language interfaces?
• Stay tuned

40
Recap
I. Senses A. Sight B. Sound C. Touch
D. Smell
II. Information processing A. Perceptual B.
Cognitive 1. Memory a. Short
term b. Medium term c. Long
term 2. Processes a. Selective
attention b. Learning c.
Problem solving d. Language
III. Motor system A. Hand movement B.
Workstation Layout
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Class DiscussionModel Human Processor

• What are the three major subsystems and their
  functions?
• What does it mean to say that certain
  subprocessors have variable rates?
• What is the recognize-act cycle? Is it like
  the fetch-decode-execute of a CPU?
• What are some of the other assumptions underlying
  the MHP model?
• How good is the model?

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People

• Good
• xxx
• yyy
• zzz

• Bad
• aaa
• bbb
• ccc

Fill in the columns - what are people good at and
what are people bad at?
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People

• Good
• Infinite capacity LTM
• LTM duration complexity
• High-learning capability
• Powerful attention mechanism
• Powerful pattern recognition

• Bad
• Limited capacity STM
• Limited duration STM
• Unreliable access to LTM
• Error-prone processing
• Slow processing

Computer is opposite! Allow one who does it best
to do it! (Function allocation)
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End Part 2
45
Human Abilities - Part 3 of 3

• Understanding the user Simple Models of Human
  Performance

This material has been developed by Georgia Tech
HCI faculty, and continues to evolve.
Contributors include Gregory Abowd, Jim Foley,
Diane Gromala, Elizabeth Mynatt, Jeff Pierce,
Colin Potts, Chris Shaw, John Stasko, and Bruce
Walker. Comments directed to foley_at_cc.gatech.edu
are encouraged. Permission is granted to use with
acknowledgement for non-profit purposes. Last
revision January 2004.
46
Simple User Models

• Idea If we can build a model of how a user
  works, then we can predict how s/he will interact
  with the interface
• Predictive model ? predictive evaluation
• No mock-ups or prototypes!

47
Two Types of User Modeling

• Stimulus-Response
• Hicks law
• Practice law
• Fitts law
• Cognitive human as interperter/predictor
  based on Model Human Processor (MHP)
• Key-stroke Level Model
• Low-level, simple
• GOMS (and similar) Models
• Higher-level (Goals, Operations, Methods,
  Selections)
• Not discussed here

48
Power law of practice

• Tn T1n-a
• T on the nth trial is T on the first trial times
  n to the power -a a is about .4, between .2 and
  .6
• Skilled behavior - Stimulus-Response and routine
• But NOT learning
• How can we use this law?
• If watching this lecture on the web, pause and
  reflect

49
Hicks law

• Decision time to choose among n equally likely
  alternatives
• T Ic log2(n1)
• Ic 150 msec
• How can we use this law?
• If watching this lecture on the web, pause and
  reflect

50
Fitts Law

• Models movement times for selection (reaching)
  tasks in one dimension
• Basic idea Movement time for a selection task
• Increases as distance to target increases
• Decreases as size of target increases

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Original Experiment

• 1-D

d
w
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Components

• ID - Index of difficulty
• ID is an information theoretic quantity
• Based on work of Shannon larger target gt more
  information (less uncertainty)

ID log2 (d/w 1.0)
width (tolerance) of target
bits result
distance to move
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Components

• MT - Movement time
• MT is a linear function of ID
• k1 and k2 are experimental constants

MT k1 k2ID MT k1 k2 log2 (d/w 1.0)
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Exact Equation

• Run empirical tests to determine k1 and k2 in MT
  k1 k2 ID
• Will get different ones for different input
  devices and device uses

MT
ID log2(d/w 1.0)
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Questions

• What do you do in 2D?
• h x l rect one way is ID log2(d/min(w, l) 1)
  
• How can we use this law?
• If watching this lecture on the web, pause and
  reflect

56
Keystroke-Level Model (KSLM)

• KSLM - developed by Card, Moran Newell, see
  their book and CACM
• Skilled users performing routine tasks
• Assigns times to basic human operations -
  experimentally verified
• Based on MHP - Model Human Processor

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KSLM Accounts for

• Keystroking TK
• Mouse button press TB
• Pointing (typically with mouse) TP
• Hand movement betweenkeyboard and mouse TH
• Drawing straight line segments TD
• Mental preparation TM
• System response time TR

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KSLM

• Decompose task into sequence of operations - K,
  B, P, H, D, M, R
• Place M operators
• In front of all Ks that are NOT part of argument
  strings (ie, not part of text or numbers)
• Example - select a word and type new text
• Home on mouse H(mouse)
• Point to word P(word)
• Select word MBB(mouse button)
• Home on keyboard H
• Type new 5-letter word M5K

59
KSLM

• Now remove Ms according to the rules
• Anticipated by prior operation
• PMK -gtPK
• If string of MKs is a single cognitive unit (such
  as a command name), delete all but first
• MKMKMK -gt MKKK (same as M3K)
• Redundant terminator, such as )) or rtn rtn
• If K terminates a constant string, such as
  command-rtn, then delete M
• M2K(ls)MK(rtn) -gt M2K(ls)K(rtn)

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KSLM

• Apply rules to example
• Home on mouse H(mouse)
• Point to word P(word)
• Select word MBB(mouse button)
• Home on keyboard H
• Type new 5-letter word M5K
• T 5TK 2TB TP 2TH TM TR

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KSLM

• Plug in real numbers from experiments
• K .08 sec for best typists, .28 average, 1.2 if
  unfamiliar with keyboard
• B down or up - 0.1 secs click - 0.2 secs
• P 1.1 secs
• H 0.4 secs
• M 1.35 secs
• R depends on system often less than .05 secs
• T 5TK 2TB TP 2TH TM TR
  5(.28)2(.2)1.12(.4)1.35 .05 5.1 secs

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KSLM ExampleMouse for menu selection?

• What is the operator sequence?
• H(mouse)PB(left-click)MPB(left-click)
• Complicated rules for placing Ms but boils
  down to chunking (one M before each chunk of a
  task)
• Candidate Ms before each B, K, and P involved in
  specification or selection of a command
  eliminate the Ms that are fully anticipated or
  in a cognitive unit
• Textbook timings (all in seconds)
• H 0.40, P 1.10, B 0.20, M 1.35
• Total predicted time 4.35 s

63
KSLM Problem

• Consider a KSLM decomposition of selecting File
  / Print from a pull-down menu
• Now consider the same task using only the
  keyboard, with the ALT-F accelerator to open the
  File menu and then the P key to select the Print
  option
• Use texts operator timings for these scenarios
  assume hands start on keyboard

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KSLM ExampleKeyboard for Menu Selection?

• Recall mouse operator sequence over two chunks
  (open File menu, select Print option) HPBMPB
• Assuming same two chunks, you have
  MK(ALT)K(F)MK(P)
• Times for K based on typing speed
• Good typist, K 0.12 s, total time 3.06 s
• Poor typist, K 0.28 s, total time 3.54 s
• Non-typist, K 1.20 s, total time 6.30 s
• Possible moral Shortcut keys not necessarily
  faster than using the mouse

65
Cognitive models - many flavors

• Hierarchical
• GOMS, CCT
• Linguistic
• BNF, TAG, CLG
• Cognitive architectures
• GPS, PUM, ICS

66
Recap

• Human capabilities
• Senses
• Motor systems
• Information processing
• Memory
• Cognitive Processes
• Selective attention, learning, problem solving,
  language
• Simple predictive models

67
End Part 3 of 3