'Physiological changes are correlated with these biological preparations to protect one-self from danger.' According to the researchers, teaching the algorithm when a person might feel more anxious in a given situation could serve as a guide to help machines avoid risks. 'Our hypothesis is that such reward functions can circumvent the challenges associated with sparse and skewed rewards in reinforcement learning settings and can help improve sample efficiency,' the team explains. The researchers put the autonomous software through a simulated maze filled with walls and ramps to see how they performed with fear instilled in them. And, compared to an AI that was trained based only on wall proximity, the system that had learned fear was much less likely to crash. 'A major advantage of training a reward on a signal correlated with the sympathetic nervous system responses is that the rewards are non-sparse - the negative reward starts to show up much before the car collides,' the researchers wrote. 'This leads to efficiency in training and with proper design can lead to policies that are also aligned with the desired mission.'