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 cindycc@gmail.com This material has been developed by Georgia Tech HCI faculty, and continues to evolve. 1 1

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!

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

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

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)

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

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)

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)

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

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

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

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

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 http://www.syntagm.co.uk/design/klmcalc.shtml - website with KSLM calculator

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

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

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

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

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

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)

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.

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)

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

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