Predictive Evaluation Simple models of human performance.

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Predictive Evaluation Simple models of human performance
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Transcript of Predictive Evaluation Simple models of human performance.

Page 1: Predictive Evaluation Simple models of human performance.

Predictive Evaluation

Simple models of human performance

Page 2: Predictive Evaluation Simple models of human performance.

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

Page 3: Predictive Evaluation Simple models of human performance.

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 interfacePredictive model predictive

evaluation No mock-ups or prototypes!

Page 4: Predictive Evaluation Simple models of human performance.

Two Types of User Modeling

Stimulus-Response Hick’s law Practice law Fitt’s law

Cognitive – human as interpreter/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

Page 5: Predictive Evaluation Simple models of human performance.

Power law of practice

Tn = T1n-a

Tn to complete the nth trial is T1 on the first trial times n to the power -a; a is about .4, between .2 and .6

Skilled behavior - Stimulus-Response and routine cognitive actions

• Typing speed improvement• Learning to use mouse• Pushing buttons in response to stimuli• NOT learning

Page 6: Predictive Evaluation Simple models of human performance.

Power Law: Tn = T1n-a

n T_n1 52 3.795 2.62

10 1.9915 1.6925 1.37

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

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Trial Time

If first trial (T1) takes 5 seconds, how long will future trials take? When will improvements level off? (a = -0.4)

Page 7: Predictive Evaluation Simple models of human performance.

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 usability criteria is realistic

Page 8: Predictive Evaluation Simple models of human performance.

Hick’s law

Decision time to choose among n equally likely alternatives T = Ic log2(n+1)

Ic ~ 150 msec

How can we use this? Explanation on how to calculate base 2

logs:http://mathforum.org/library/drmath/view/55613.html

Page 9: Predictive Evaluation Simple models of human performance.

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

Page 10: Predictive Evaluation Simple models of human performance.

Fitts’ Law

Models movement times for selection (reaching) tasks in one dimension

Basic idea: Movement time for a selection taskIncreases as distance to target

increasesDecreases as size of target increases

Page 11: Predictive Evaluation Simple models of human performance.

Original Experiment

1-Dd w

Page 12: Predictive Evaluation Simple models of human performance.

Components

ID - Index of difficulty

larger target => more information (less uncertainty)

ID = log2 (d/w + 1.0)

bits result

width (tolerance)of target

distance to move

Page 13: Predictive Evaluation Simple models of human performance.

Components

MT - Movement time

MT is a linear function of ID k1 and k2 are experimental constants

MT = k1 + k2*IDMT = k1 + k2 *log2 (d/w + 1.0)

Page 14: Predictive Evaluation Simple models of human performance.

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)

Page 15: Predictive Evaluation Simple models of human performance.

Questions

What do you do in 2D?

h x l rect:one way is ID = log2(d/min(w, l) + 1)

Should take into account direction of approach

Page 16: Predictive Evaluation Simple models of human performance.

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

Example: what would Fitt’s say about multi-level menus? What about pop-up menus?

Page 17: Predictive Evaluation Simple models of human performance.

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

Page 18: Predictive Evaluation Simple models of human performance.

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

Page 19: Predictive Evaluation Simple models of human performance.

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)

Page 20: Predictive Evaluation Simple models of human performance.

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)

Page 21: Predictive Evaluation Simple models of human performance.

Using KSLM - Step Two

Place M operatorsRule 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)

Page 22: Predictive Evaluation Simple models of human performance.

Step Two : MS 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: Pselects command

Rule 0b: Pselects command

Rule 0a: Kis argument

Page 23: Predictive Evaluation Simple models of human performance.

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 rtnRule 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)

Page 24: Predictive Evaluation Simple models of human performance.

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)

Step 3 : MS Word Find Command

Rule 4 Keep M

Rule 1 delete MH anticipates P

Page 25: Predictive Evaluation Simple models of human performance.

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 less than .05

secs

Page 26: Predictive Evaluation Simple models of human performance.

Step 4 : MS Word Find Command

H (Home on mouse)P (Edit)B (click on mouse button - press/release)MP (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, 2M, 7K

Predicted time = 8.06 secs

Page 27: Predictive Evaluation Simple models of human performance.

Example: MS 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

Page 28: Predictive Evaluation Simple models of human performance.

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)

Page 29: Predictive Evaluation Simple models of human performance.

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: Pselects command

Rule 0b: Pselects command

Page 30: Predictive Evaluation Simple models of human performance.

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 Manticipated by P

Page 31: Predictive Evaluation Simple models of human performance.

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

Page 32: Predictive Evaluation Simple models of human performance.

Macintosh 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 0b

Rule 1 Delete H anticipates P

Page 33: Predictive Evaluation Simple models of human performance.

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

Page 34: Predictive Evaluation Simple models of human performance.

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 for all users!

First Kanticipatessecond K

Page 35: Predictive Evaluation Simple models of human performance.

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

Page 36: Predictive Evaluation Simple models of human performance.

Now You Get to Do It

KSLM of the hierarchical menu selection exampleCombine with Hick’s Law

Draw through text and make it boldBy pointing to BOLD icon in floating

paletteBy selecting BOLD from pull-down

menu

Page 37: Predictive Evaluation Simple models of human performance.

Cognitive models - many flavors

More complex than KSLMHierarchical

GOMS - Goals, Operators, Methods, SelectorsCCT - Cognitive Complexity Theory

LinguisticTAG - Task Action GrammarCLG - Command Language Grammar

Cognitive architecturesSOAR, ACT