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

When old neural networks positively transfer to new swing movements, the result is typically easier learning and reduced practice. This phenomenon occurs because previous experiences and learned motor skills provide a foundation upon which new movements can be built. The neural pathways and connections that were developed during earlier training can facilitate the adaptation to new swing movements, thereby making the learning process smoother and more efficient.

Due to the existing knowledge and muscle memory, individuals may not need to practice as much to achieve competency in the new skill. This is significant as it reduces the time and effort required for skill acquisition, allowing for a quicker transition to performing the new swing movements effectively. The efficiency gained from positive transfer is a crucial factor in skill development and is highly advantageous in training scenarios.

In contrast, the other options suggest outcomes that arise under different circumstances. Increased failure rates, longer training durations, and enhanced physical demands typically indicate challenges in learning new skills or adaptations that require substantial effort and time. These scenarios do not align with the concept of positive transfer, where previous learning actually aids in the mastery of new tasks.