• Feedback-driven motor learning emphasizes knowledge of results and error feedback as central drivers of skill acquisition, retention, and transfer, contrasting intact vs withdrawn feedback and linking theory with empirical tests [1], [5], [18], [11], [3].

• Fatigue and physical state modulate learning and performance of gross motor tasks across experiments with fatigue induction, exercise loading, and rest structure, showing variable impairment and altered learning curves [7], [6], [15], [14], [12].

• Short-term motor memory is shaped by retention intervals, interference, cue type, and similarity of prior-succeeding responses, revealing structured memory decay and retrieval dynamics in discrete actions [4], [9], [8], [20], [13], [19].

• Cognitive frameworks and schema-based accounts of motor learning frame how rules, abstractions, and generalization underlie discrete skill acquisition, contrasting with closed-loop views and integrating memory effects [16], [1], [3].

• Intervening task structure and memory disruption show how interpolated activities and test conditions alter retention and error detection strategies, highlighting memory-bound constraints in motor learning [10], [4].