user models predicting a user’s behaviour. fitts’ law

User Models

Predicting a user’s behaviour

Fitts’ Law

Objectives

• Define predictive and descriptive models and explain why they are useful

• Describe Fitts’ Law and explain its implications for interface design

• Apply Fitts’ Law and other predictive models to evaluate interfaces

• Explain Guiard’s model of two-handed interaction. Apply this model to evaluate two-handed interaction techniques

Trackpad

Mouse

Fitts’ Law

ID = log2(A/W + 1)

MT = a + b*ID

ID = Index of difficultyMT = movement time (to move hand to a target)A = amplitude (distance to target)W = width of target

Which is faster on average?

Linear menu Pie / marking menu

Aside: marking menus

• Selection is even faster by using a gesture

• Menu doesn’t need to appear

Where are the fastest places to access?

Which is faster?

Why is this menu slow to use?

Action Analysis

• Use mathematical models to predict more complex actions than pointing

• Simple Example: Keystroke-Level Model (KLM)

• List the steps required to complete an operation, and sum up average times for each step

Average Times (seconds)

Physical movements:• Enter one keystroke on a standard keyboard 0.28• Use mouse to point at an object on the screen 1.1• Click mouse or other device 0.2• move hand to pointing device or function key 0.4

Visual perception:• respond to a brief light 0.1• recognize a six letter word 0.34• move eyes to a new location on the screen 0.23

Mental Actions• retrieve a simple item from long-term memory 1.2• learn a single “step” procedure 25• execute a mental “step” 0.075• Prepare for next step (choose a method) 1.35

Example: Bus fare boxes

• List the steps needed to:– Pay your fare by coins– Validate an existing transfer

• Estimate how long each willtake, on average

Example: Bus Fare BoxesFare box 1:

Payment by coins:• Passenger tells driver how many

zones.• Coins drop into glass box. Driver

glances to see if fare seems approx. correct.

• Driver tears off transfer (clip is pre-positioned so transfer will tear off with correct time shown).

• Driver pushes foot pedal to drop money into box

Fare Box 2:

Payment by coins:• Passenger tells driver how many

zones. Driver presses button to indicate.

• Coins dropped into slot are counted by machine.

• Machine prints transfer.

Example: Bus Fare BoxesFare box 1:

To validate an existing transfer• Passenger holds up for driver to see• Driver determines if time is valid

Fare Box 2:

To validate a transfer• Passenger feeds transfer into slot.• Machine reads transfer

electronically and prints ok message.

• Machine returns transfer to user.

Expert vs. novice users

• Fitts’ law and the KLM model only EXPERT performance.

• Novice performance is much harder to model.

Predictive vs. Descriptive models

• Predictive – allow a mathematical prediction of performance (usually time)e.g. Fitts’ law, KLM

• Descriptive – A framework for thinking about a problem e.g. Guiard’s model

Guiard’s Model of Bimanual Control

From Scott Mackenzie

Case studies• See Mackenzie reading for case studies

• E.g. Text entry on mobile phones

Multi-tap

vs. One key + disambiguation

• If you assume one-finger entry (e.g. thumb), can model this using Fitts’ law

More complex user modeling: Eg. Correctly placing menus

• Problem: popup menus can be inconveniently placed on a tabletop display– May be upside down for some users– May be awkward for left-hand users

Solution: neural network

Step 1: Training

HandednessSide of tablePosition & orientation of input device (pen)

Neural network

Mark Hancock - 2003

Solution: neural network

Step 2: Predict handedness & side of table

Use this to position menu correctly

Position & orientation of input device (pen)

Neural network

Mark Hancock - 2003

Handedness

Side of table

Key Points

• Predictive models enable you to predict expert user performance at simple tasks, and consequently design interfaces that will support better performance.

• Predictive models have limited usefulness (only expert users & frequent operations). They should not replace user testing.

• Descriptive models may help you understand a process better.