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

2. 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. 3 Two Types of User Modeling Stimulus-Response  Practice law  Hick’s law  Fitt’s 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

4. 4 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

5. 5 Power law of practice T n = T 1 n -a  T n time to perform a task after n trials  T 1 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

6. 6 Uses for Power Law of Practice Use measured time T 1 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

7. 7 Hick’s law Decision time to choose among n equally likely alternatives – choice reaction time  T = I c log 2 (n+1) where T is decision time  I c ~ 150 msec (constant)  n is number of alternatives

8. 8 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

9. 9 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)

10. 10 Fitts model MT = a +b log 2 (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.

11. 11 Exact Equation Run empirical tests to determine k 1 and k 2 Will get different ones for different input devices and device uses MT log 2 (d/w + 1.0)

12. 12 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

13. 13 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

14. 14 KSLM Accounts for Keystroking T K Mouse Button pressT B Pointing (typically with mouse) T P Hand movement between keyboard and mouse T H Drawing straight line segments T D “Mental preparation” T M – how measure? System Response time T R – ignore (fast)

15. 15 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

16. 16 Step One : 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)

17. 17 Using KSLM - Step Two Place M (mental prep) operators Rule 0a. In front of all K’s that are NOT part of argument strings (ie, not part of text or numbers) Rule 0b. In front of all P’s that select commands (not arguments)

18. 18 Step Two : MSoft Word Find Command H (Home on mouse) MP (Edit) B (click on mouse button) MP (Find) B (click on mouse button) H (Home on keyboard) 6K (Type six characters) MK (Return key on dialogue box starts the find) Rule 0b: P selects command Rule 0a: K is argument

19. 19 Using KSLM - Step 3 Remove M’s according to heuristic rules (Rules relate to chunking of actions) Rule 1. Anticipated by prior operation – PMK ->PK (point and then click is a chunk) Rule 2. If string of MKs is a single cognitive unit (such as a command name), delete all but first – MKMKMK -> MKKK (same as M3K) (type “run rtn is a chunk) Rule 3. Redundant terminator, such as )) or rtn rtn Rule 4. If K terminates a constant string, such as command-rtn, then delete M –M2K(ls)MK(rtn) -> M2K(ls)K(rtn) (typing “ls” command in Unix followed by rtn is a chunk)

20. 20 Step 3 : MS Word Find Command Rule 4 Keep M Rule 1 delete M H anticipates P Rule 1 delete M H anticipates P H (Home on mouse) MP (Edit) B (click on mouse button) MP (Find) B (click on mouse button) H (Home on keyboard) 6K (Type six characters) MK (Return key on dialogue box starts the find)

21. 21 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 - 0.1 secs; click - 0.2 secs  P: 1.1 secs  H: 0.4 secs  M: 1.35 secs  R: depends on system; often negligible

22. 22 Step 4 : 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

23. 23 Example: MSoft Windows Menu Selection Get hands on mouse Select from menu bar with click of mouse button The “pull down” menu appears Select desired item from the pull down menu

24. 24 Step 1: MS Windows Menu H (Home on mouse) P (point to menu bar item) B (left-click with mouse button) P (point to menu item) B (left-click with mouse button)

25. 25 Step 2: MS Windows Menu - Add M’s H (get hand on mouse) MP (point to menu bar item) B (left-click with mouse button) MP (point to menu item) B (left-click with mouse button) Rule 0b: P selects command

26. 26 Step 3: MS Windows Menu - Delete M’s H (get hand on mouse) MP (point to menu bar item) B (left-click with mouse button) MP (point to menu item) B (left-click with mouse button) Keep M Rule 1 M anticipated by P

27. 27 Step 4: MS Windows Menu Calculate Time H (get hand on mouse) P (point to menu bar item) B (left-click with mouse button) MP (point to menu item) B (left-click with mouse button) Textbook timings (all in seconds)  H = 0.40, P = 1.10, B = 0.20, M = 1.35  H, 2P, 2B, 1 M Total predicted time = 4.35 sec

28. 28 Macintosh-style Menu Selection Operator sequence  H(mouse)P(to menu item)B(down)PB(up) Now place Ms  H(mouse)MP(to menu item)B(down)MPB(up) Selectively remove Ms  H(mouse)MP(to menu item)B(down)MPB(up) Textbook timings (all in seconds) –H = 0.40, P = 1.10, B = 0.10 for up or down, M = 1.35 –H, 2P, 2 B, 1 M Total predicted time = 4.15 sec Macintosh is predicted to be.2 secs faster than MS Windows, about 5% Rule 0b Rule 1 Delete: H anticipates P

29. 29 KSLM Comparison Problem Are keyboard accelerators always faster than menu selection? Use MS Windows to compare  Menu selection of File/Print (previous example estimated 4.35 secs.)  Keyboard accelerator –ALT-F to open the File pull down menu –P key to select the Print menu item Assume hands start on keyboard

30. 30 KSLM Comparison: Keyboard Accelerator for Print Use Keyboard for ALT-F P (hands already there)  K(ALT)K(F)K(P)  MK(ALT)MK(F)MK(P)  MK(ALT)K(F)MK(P) –2M + 3K = 2.7 + 3K 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  Time with mouse was 4.35 sec Conclusion: Accelerator keys not necessarily faster than mouse! First K anticipates second K

31. 31 KSLM Example - select a word and replace with new typed text Home on mouseH(mouse) Point to wordP(word) Select wordBB(mouse button) Home on keyboardH Type new wordKKKKK

32. 32 KSLM Example No M’s to add  K’s are part of argument, so rule 0a does not apply  No P’s to use with rule 0b Sequence remains as Home on mouseH(mouse) Point to wordP(word) Select wordBB(mouse button) Home on keyboardH Type new 5-letter word5K T = 5T K +2T B +T P +2T H +T M = 5(.28)+2(.2)+1.1+2(.4)+1.35= 5.05 secs

33. 33 Using KSLM Skilled users Performing routine tasks  The user has done it many times before  No real learning going on  Some modest “thinking” as captured by Ms Rules for placing Ms are heuristics Best use is for comparing alternatives  Sometimes predictions are off  But rankings of faster - slower tend to be accurate

34. 34 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