Predictive Modelling

Learning Objectives

Apply Fitts's Law calculations to compare target layouts
Construct KLM analyses for realistic interactive tasks
Estimate working memory load for different interface designs
Use predictive models to compare designs without human subjects
Understand the role of computational cognitive architectures in usability prediction

Introduction

Chapters 6 and 7 introduced the Model Human Processor and GOMS as theoretical frameworks Card, 1983. This chapter treats them as practical tools: methods for predicting usability without testing with human participants Kieras, 2003. Predictive modelling answers the question "which design is likely to be faster, more accurate, or less cognitively demanding?" using calculations rather than experiments. The appeal is clear: predictions can be made before any code is written, any prototype built, or any participant recruited. The limitation is equally clear: models predict specific aspects of performance (time, cognitive load) for specific tasks under specific assumptions (expert, error-free performance). They do not predict satisfaction, learnability, or the full range of real-world human behaviour.

Fitts's Law in Practice

Calculating Movement Time

Fitts's Law (Chapter 5) [Fitts, 1954; MacKenzie, 1992] predicts the time to move to and acquire a target. MT = a + b × log2(D/W + 1) For mouse pointing, typical values of the constants are a ≈ 0 ms and b ≈ 150 ms (these vary with device and conditions). Using these values:

Distance (D)	Width (W)	ID = log2(D/W + 1)	MT (ms)
100 px	50 px	1.58	237
200 px	50 px	2.32	348
400 px	50 px	3.17	476
200 px	100 px	1.58	237
200 px	25 px	3.17	476

Example

Design comparison: Where should the "Submit" button go? Option A: Submit button at the bottom-right of a form, 400 px from the last input field, 80 px wide. MT = 150 × log2(400/80 + 1) = 150 × 2.58 = 387 ms Option B: Submit button directly below the last input field, 60 px away, 80 px wide. MT = 150 × log2(60/80 + 1) = 150 × 0.81 = 121 ms Option B is predicted to be 266 ms faster per submission. For a form submitted 1,000 times per day across an organisation, this saves 266 seconds (about 4.4 minutes) per day — a modest but measurable improvement at zero development cost.

Comparing Layouts

Fitts's Law becomes most powerful when comparing total movement cost across an entire workflow. For a sequence of actions (click field A, then field B, then button C), the total movement time is the sum of the individual Fitts's Law predictions for each movement.

Design Law

To minimise total interaction time for a sequential workflow, place the controls in the order they will be used and minimise the distance between consecutive controls. The optimal layout clusters frequently used controls and arranges sequential controls along a short path. This can be calculated precisely using Fitts's Law for each transition.

Limitations of Fitts's Law Predictions

Fitts's Law predicts movement time only. It does not account for:

Time to visually locate the target (visual search, which depends on target-distractor similarity)
Time to decide which target to select (Hick's Law for choices)
Error rates (the model predicts optimal, error-free performance)
Learning effects (initial movements are slower than practised ones) A complete prediction requires combining Fitts's Law with visual search models and Hick's Law, as the MHP framework does.

KLM Analysis in Practice

Step-by-Step Method

Describe the task in terms of user goals
Identify the method — the sequence of actions an expert user would perform
Assign operators — K, P, B, H, M, R(t) — to each action
Place mental operators (M) using Card, Moran, and Newell's heuristic rules
Sum the operator times to get the predicted task time

Worked Example: Booking a Meeting Room

Task: Book Conference Room B for 2pm–3pm on Thursday using a calendar application. Method (expert user, mouse-based): Now compare with a keyboard-shortcut method. The keyboard method is predicted to be 1 second faster — a small difference for a single booking, but meaningful for an administrative assistant who books 30 rooms per day (30 seconds saved daily).

When KLM Predictions Are Most Useful

KLM is most valuable for:

Comparing alternative designs for the same task — even if the absolute predictions are imprecise, the relative comparison is usually reliable
Identifying slow steps — the M operator (1.35 s) is often the largest single component; reducing the number of mental preparations (through better defaults, clearer labels, or predictable layouts) offers the greatest time savings
Justifying design decisions — quantitative predictions carry weight with stakeholders who might dismiss qualitative usability arguments

Estimating Cognitive Load

While Fitts's Law and KLM predict time, cognitive load estimation predicts mental effort. A design that is fast but mentally exhausting is not usable in the long run.

Working Memory Load Analysis

For each step in a task, count the number of items the user must hold in working memory simultaneously. Compare this count against Cowan's estimate of ~4 items (Chapter 3) Cowan, 2001.

Example

Task: Manually calculate a drug dose. The user must simultaneously hold:

The patient's weight (72.5 kg)
The dose per kilogram (5 mg/kg)
The intermediate calculation (72.5 × 5 = 362.5 mg)
The available concentration (50 mg/mL)
The volume to administer (362.5 / 50 = 7.25 mL) This task requires holding 5 items — exceeding typical working memory capacity. The prediction: errors are likely, especially under time pressure or interruption. The design intervention: a dose calculator that accepts weight and drug, computes the volume automatically, and displays all intermediate values. This externalises the memory load.

Split-Attention Analysis

For each step, identify whether the required information is co-located (visible in a single glance) or split across locations (requiring the user to look back and forth or remember information across screens). Split attention increases cognitive load by requiring mental integration of separated information (Chapter 3) Sweller, 1998. A systematic split-attention analysis walks through each task step and notes: "What information does the user need? Where is it displayed? Can the user see all needed information without scrolling, switching screens, or remembering values?"

Computational Cognitive Architectures

Beyond the hand-calculated models, computational cognitive architectures provide more detailed (and more effortful) predictions.

ACT-R

ACT-R (Adaptive Control of Thought — Rational), developed by John Anderson at Carnegie Mellon Anderson, 2007, is a cognitive architecture that models human cognition at the level of individual memory retrievals, perceptual encodings, and motor actions. ACT-R models can predict not only task times but also error patterns, learning curves, and the effects of cognitive load. ACT-R has been used to model tasks ranging from menu selection to air traffic control. Its predictions are often within 10–15% of observed human performance Newell, 1992. However, building an ACT-R model requires substantial expertise and effort — it is a research tool rather than a practitioner tool.

CogTool and Cogulator

CogTool (developed at Carnegie Mellon) John, 2004 and Cogulator (developed at the US Census Bureau) are practical tools that lower the barrier to cognitive modelling. Cogulator, in particular, provides a web-based interface where practitioners can specify a task as a sequence of operators and receive predictions of task time, working memory load, and a CPM-GOMS schedule — without needing to understand the underlying cognitive theory.

Think About It

Predictive models work best for routine, well-defined tasks performed by experts. But many important usability questions concern non-routine situations: What happens when the user encounters an unexpected error? How does a novice explore an unfamiliar interface? What happens when the user is interrupted mid-task? Can predictive models be extended to these situations, or are they fundamentally limited to the expert, error-free case?

Combining Predictive Models with Other Methods

Predictive modelling is most effective when integrated into a broader evaluation strategy:

Use predictive models early — during design, before prototypes exist — to compare alternative layouts, workflows, and interaction patterns
Use heuristic evaluation (Chapter 16) to catch problems that models do not address (consistency, error recovery, aesthetic quality)
Use usability testing (Chapter 15) to validate predictions and discover problems that models cannot predict
Use analytics and A/B testing (Chapter 18) to measure actual performance at scale No single method provides a complete picture. Predictive models provide precision for specific questions; other methods provide coverage for the broader usability landscape.

Key Takeaways

Fitts's Law can be applied directly to compare target layouts: larger targets and shorter distances produce faster acquisition. Total workflow time is the sum of individual movement predictions.
KLM analysis predicts expert task time by summing operator times. The mental operator (M = 1.35 s) is often the largest component and the best target for optimisation.
Cognitive load can be estimated by counting working memory items per step and identifying split-attention situations.
Computational tools (Cogulator, CogTool) make predictive modelling accessible to practitioners.
Predictive models are most valuable for comparing alternatives early in design; they should be combined with expert review and user testing for comprehensive evaluation.