When multiple methods exist for one goal, correct selection rules determine whether predictions hold
Aliases: selection rule · method branch · GOMS selection
What it is
One goal often has multiple methods: shortcut, menu, context menu, drag, template, or API. GOMS selection rules must state under what condition users choose each method. If the rules mismatch real strategy, later time and step predictions do not hold.
Why it happens
Method choice depends on interface state, object count, visibility, permissions, training, habit, and risk. A rule saying “use the fastest method” fails when users choose the most familiar one. Conditions should be extracted from logs, interviews, and observation, such as “use bulk action when multiple items are selected; use inline delete for one item.”
Studying it
Collect path distributions among skilled users completing the same goal; measure trigger state, usage rate, and duration for each method. Ask users why they chose a method and compare their conditions with model rules. Task experiments on rule-conflict samples can test predictions.
Where it stops holding
Rules are not permanently objective: interface change, training, and defaults alter strategy. Different roles may have different rule sets and should not be averaged into one user. Too-fine rules become hard to maintain; too-coarse rules hide method mixing.
Applying it
- For each goal, list methods, preconditions, permissions, costs, and selection conditions.
- Validate main scenarios with logs and identify unexpected paths.
- Reinforce correct selection rules through entry design and prompts rather than encouraging every method equally.
- Recalibrate rules after changing defaults or adding shortcuts.
Related
- Same group: B4.11.1 The analyzed object is a fixed method; exploration, hesitation, and learning are outside the model · B4.11.3 Decomposition granularity is optional, but alternative rankings should remain consistent across granularities · B4.11.4 The model does not predict error rate, satisfaction, or learnability · B4.11.5 Modeling cost is high; returns are positive only for high-frequency repetitive tasks
- Nearby: B2.26 Directness and Indirection Trade-offs · B4.04 GOMS
- Search terms:
selection rule·method choice·GOMS validation
Cards in the same group
- B4.11.1The analyzed object is a fixed method; exploration, hesitation, and learning are outside the model
- B4.11.3Decomposition granularity is optional, but alternative rankings should remain consistent across granularities
- B4.11.4The model does not predict error rate, satisfaction, or learnability
- B4.11.5Modeling cost is high; returns are positive only for high-frequency repetitive tasks