MDPE.10:2.5 - Design practice for later contexts, not only acquisition

When the action must transfer, practice only in one repeated context can make acquisition look better than later use. Characterize the cues available before action, feedback available after action, recent sequence of contexts, and conditions of the receiving Work. The learner may group situations differently from the teacher’s labels.

COIN offers a useful human motor-learning hypothesis: several motor memories can be created or updated while their expression is mixed according to inferred context. Apparent forgetting can therefore be a failure to retrieve or express an available response under the current cues rather than destruction of the earlier memory.

Use that claim as a hypothesis that changes a probe, not as an explanation attached after the fact:

• return to a previously successful context before reteaching;
• alter one cue while preserving the task;
• compare blocked and interleaved practice when flexible switching matters;
• vary cue reliability or the order and frequency of context transitions;
• test unfamiliar but relevant contexts and delayed transfer;
• record recovery, blending, interference, generalization, and protected performance conditions.

Keep rivals live: the target may lie outside the capability envelope; a different contribution may be missing; fatigue, partner, tempo, device, or another condition may explain the result; or the intended coordination may not have been isolated from its surrounding style organization. Stop relying on the contextual-inference hypothesis when another explanation predicts the observations better.

The broader claim that all memory and reasoning work through the same COIN mechanism remains open. Context dependence appears across several memory and decision domains, so the hypothesis is worth testing when it yields a better learning or transfer experiment. It is not a reason to suppress rival explanations. In robot and AI work, domain randomization, curriculum design, context-aware retrieval, and held-out-distribution tests are direct machine-learning Methods; a shared word context does not establish the same mechanism as human motor learning.