What is expected when old neural networks positively transfer to new skills?

When old neural networks positively transfer to new skills, a faster rate of learning is expected because the knowledge and experience gained from previous learning experiences can facilitate the acquisition of new skills. This phenomenon, known as positive transfer, occurs when previously learned information or skill sets enhance the ability to learn additional, related concepts or skills more efficiently.

For example, a golfer who has mastered the fundamentals of putting may find it easier to learn chipping or pitching because the skills and muscle memory related to putting can be adapted for those new areas. The neural pathways that have already been developed for similar tasks allow for quicker adaptation to the nuances of the new skill, thereby speeding up the overall learning process. This is an important aspect in skill acquisition and teaching strategies, emphasizing the value of building on existing knowledge to advance learning further.