Mastered rules transfer; the more two interfaces share, the cheaper the second is to learn

What it is

Within Cognitive Complexity Theory, rules a user brings from an old interface keep working on a new one and need no relearning; the higher the share of shared rules, the cheaper the second interface is to learn. Shared structure yields positive transfer, while rules with similar conditions but opposite actions cause negative transfer.

Why it happens

Production rules are stored as condition-action pairs, and transfer depends on condition matching rather than surface similarity: a menu item may be renamed, but the condition "saving requires a selected file" persists and the old rule is reused directly. When an old rule's condition now triggers a different action, the old and new rules compete, interference raises cost, and learning can end up worse than starting fresh.

Studying it

Transfer studies teach participants interface A and then interface B, measuring B's learning time against the share of shared rules; a control group learning an unrelated interface C separates practice effects. Testing how well shared condition structure—rather than shared control appearance—predicts transfer examines whether the condition-action representation captures real knowledge organization.

Where it stops holding

Rules transfer only when conditions match; layout or terminology changes can mask structural sharing or manufacture false similarity, and negative transfer makes cost exceed learning from scratch. The evidence comes from sequential laboratory tasks, whereas real users often alternate between systems, altering the interference pattern.

Applying it

• Maintain a shared-rule inventory across a product family and have new versions inherit condition structures, not merely visual styles.
• Run transfer tests when changing the condition or action mapping of a frequent operation: have experienced users complete the task on the new interface and record first-attempt failures.
• Audit interactions that look like old ones but act oppositely, such as reversed swipe directions; these conflicts cost more to learn than genuinely novel interactions.

Related

• Same group: B4.14.1 Learning time can be estimated from the number of rules the user must newly acquire · B4.14.3 The theory yields quantitative predictions of transfer gains between old and new systems · B4.14.4 Rule counts depend on the representation the modeler chooses, so different modelers may produce different counts
• Nearby: B5.04 Memorability · B4.16 Instrumental versus agentive interaction
• Search terms: transfer of learning · positive transfer · negative transfer