Human decision making anticipates future performance in motor learning.

Area of Science:

• Motor control and learning
• Decision-making under uncertainty
• Human-computer interaction

Background:

• Individuals can optimize reward by considering their error distributions in motor tasks.
• Motor learning involves a decrease in errors over successive trials.
• Understanding anticipatory decision-making in motor learning is crucial for optimizing performance.

Purpose of the Study:

• To investigate whether individuals account for future performance improvements in motor learning when making decisions to maximize reward.
• To determine if people can predict their decreasing error rates to inform reward-optimizing choices.

Main Methods:

• Two virtual tasks were employed: a target selection task and a reward structure selection task under visuomotor rotation.
• Participants performed tasks where movement errors decreased exponentially across trials.
• Optimal decision-making required considering future performance in both tasks.

Main Results:

• Results from both tasks indicated that participants anticipated their future motor performance.
• Participants made decisions that improved their expected future reward based on anticipated performance.
• This suggests a proactive adjustment of strategies in response to learning.

Conclusions:

• Humans proactively adjust their decision-making strategies in motor learning to maximize future rewards.
• Anticipation of improved motor performance plays a key role in optimizing reward-based choices.
• This finding has implications for designing adaptive training systems and understanding human learning.