1. ITM 734 Introduction to Human Factors in Information Systems
Simple Human Performance Models: Predictive Evaluation with Hick’s Law, Fitt’s Law, Power Law of Practice, Keystroke-Level Model
Cindy Corritore
This material has been developed by Georgia Tech HCI faculty, and continues to evolve. 1 1

2. 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!

3. Two Types of User Modeling
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
Stimulus-Response
Practice law
Hick’s law
Fitt’s law

4. Keystroke-Level Model (KSLM)
KSLM - developed by Card, Moran & Newell, see their book* and CACM
* The Psychology of Human-Computer Interaction, Card, Moran and Newell, Erlbaum, 1983
Skilled users performing routine tasks
Assigns times to basic human operations - experimentally verified
Based on MHP - Model Human Processor and GOMS
Focuses on very low level actions
Assumes no high level thinking during action

5. KSLM Accounts for Keystroking TK Mouse Button press TB
Pointing (typically with mouse) TP
Hand movement between keyboard and mouse TH
Drawing straight line segments TD
“Mental preparation” for an action TM – how measure?
(fast recall)
System Response time TR – ignore (fast)

6. Using KSLM - Step One
Decompose task into sequence of operations - K, B, P, H, D (no M operators yet; R can be used always or not at all)
Typically system response time appears instantaneous, so can be ignored

7. Step One Example : MS Word Find Command
Use Find Command to locate a six character word
H (Home on mouse)
P (Edit)
B (click on mouse button - press/release)
P (Find)
B (click on mouse button)
H (Home on keyboard)
6K (Type six characters into Find dialogue box)
K (Return key on dialogue box starts the find)

8. Using KSLM - Step Two Place M (mental prep) operators
- In front of all K’s that are NOT part of argument strings (ie, not part of text or numbers)
- In front of all P’s that select commands (not arguments)

9. Step Two Example : MSoft Word Find Command
H (Home on mouse)
MP (Edit)
B (click on mouse button)
MP (Find)
H (Home on keyboard)
6K (Type six characters)
MK (Return key on dialogue box starts the find)
Rule 0b: P selects command
Rule 0b: P selects command
Rule 0a: K is argument

10. Using KSLM - Step 3 Remove M’s according to heuristic rules
(Rules relate to chunking of actions)
Rule 1. If action is anticipated by prior operation – it is a chunk action
change PMK to PK (point and then click is a chunk)
Rule 2. If a string of MKs is a single cognitive unit (such as a command name), delete all MKs except the first
MKMKMK -> MKKK (same as M3K) (again, it is a chunk)
Rule 3. If it is a redundant terminator, such as )) at end of something, then remove M
Rule 4. If the K terminates a constant string, such as command word (such as return after typing in command), then delete M

11. Step 3 Example: MS Word Find Command
H (Home on mouse)
MP (Edit)
B (click on mouse button)
MP (Find)
H (Home on keyboard)
6K (Type six characters)
MK (Return key on dialogue box starts the find)
Rule 1 delete M
H anticipates P
Rule 1 delete M
H anticipates P
Rule 4 Keep M

12. Using KSLM - Step 4 Plug in real numbers from experiments
K: .08 sec for best typists, .28 average, 1.2 if unfamiliar with keyboard
B: down or up secs; click secs
P: 1.1 secs
H: 0.4 secs
M: 1.35 secs
R: depends on system; often negligible

13. Step 4 Example : MS Word Find Command
H (Home on mouse)
P (Edit)
B (click on mouse button - press/release)
P (Find)
B (click on mouse button)
H (Home on keyboard)
6K (Type six characters into Find dialogue box)
MK (Return key on dialogue box starts the find)
Timings
H = 0.40, P = 1.10, B = 0.20, M = 1.35, K = 0.28
2H, 2P, 2B, 1M, 6K
Predicted time = 6.43 secs
- website with KSLM calculator

14. Power law of practice
The logarithm of the reaction time for a particular task decreases linearly with the logarithm of the number of practice trials taken
Time to perform a task based on practice trials
Performance improves based on a “power law of practice”
That is, practice improves performance

15. Power law of practice Tn = T1n-a
Tn time to perform a task after n trials
T1 time to perform a task on first trial
n number of trials (practice time)
a is about .4, between .2 and .6
For learning skills - describes learning curve
Typing speed improvement
Learning to use mouse
Pushing buttons in response to stimuli
NOT learning

16. Uses for Power Law of Practice
Use measured time T1 on trial 1 to predict whether time with practice will meet usability criteria, after a reasonable number of trials
How many trials are reasonable?
Predict how many practices will be needed for user to meet usability criteria
Determine if usabiltiy criteria is realistic

17. Hick’s law
Decision time to choose among n equally likely alternatives – choice reaction time
T = Ic log2(n+1) where
T is decision time
Ic ~ 150 msec (constant)
n is number of alternatives

18. Uses for Hick’s Law Menu selection
Which will be faster as way to choose from 64 choices? Go figure:
Single menu of 64 items
Two-level menu of 8 choices at each level
Two-level menu of 4 and then 16 choices
Two-level menu of 16 and then 4 choices
Three-level menu of 4 choices at each level
Binary menu with 6 levels

19. 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
Function of distance and width (of target)

20. Fitts model MT = a +b log2(d/w +1)
MT is average time taken to complete the movement
a and b are constants and can be determined by fitting a straight line to measured data.
d is the distance from the starting point to the center of the target.
w is the width of the target measured along the axis of motion.

21. Exact Equation Run empirical tests to determine k1 and k2
Will get different ones for different input devices and device uses MT
log2(d/w + 1.0)

22. Uses for Fitt’s Law Menu item size Icon size
Scroll bar target size and placement
Up / down scroll arrows together or at top and bottom of scroll bar
Pie menus

23. Cognitive models - many flavors
More complex than KSLM
Hierarchical
GOMS - Goals, Operators, Methods, Selectors
CCT - Cognitive Complexity Theory
Linguistic
TAG - Task Action Grammar
CLG - Command Language Grammar
Cognitive architectures
SOAR, ACT