I was listening to a speech given by one of the early pioneers of artificial intelligence when he used a word I had heard before but never really thought much about: backpropagation . I looked it up because I was curious about how it related to the way humans learn and develop curiosity. Backpropagation is part of the process used to train neural networks to adjust after seeing how different their output was from the desired result. In very simple terms, the system makes a prediction, discovers that there is an error, and uses information about that error to make adjustments that can lead to a better prediction the next time. That made me curious about what happens when people discover they are wrong and how they respond to being wrong.

What Being Wrong Can Teach Humans About How AI Learns

The basic concept behind backpropagation is simple. Imagine an AI system that is learning to identify images. It produces an answer, compares that answer with the correct one, and determines how far off it was. Backpropagation helps identify which connections inside the neural network contributed to that error, and the training process adjusts those connections so the system has a better chance of producing the right answer in the future. The error is useful because it provides information about what needs to change.

That is an interesting contrast with the way people sometimes respond to being wrong . People can become embarrassed, defensive, frustrated, or determined to prove that their original answer was reasonable. A neural network does not have an ego to protect during training. The information about the error goes back through the system so adjustments can be made. Humans have a choice about whether new information causes them to make an adjustment or causes them to defend what they already believed.

Some AI systems have been designed so that prediction error itself becomes a reason to explore. In research led by Deepak Pathak at the University of California, Berkeley, curiosity was used as an internal reward when an artificial agent had trouble predicting what would happen after it took an action. In simple terms, when something did not happen as expected, the unexpected result gave the system a reason to gather more information.

How Humans Handle Being Wrong Differently Than AI

People experience versions of prediction error all the time, even though they rarely use that term. You expect a customer to react positively to an idea and the customer hates it. Or you might expect an employee to understand what you said and discover that your message was interpreted completely differently. In either case, there is a gap between what you expected to happen and what actually happened.

That gap can be uncomfortable, but it can also generate curiosity. Research in psychology has examined what happens when people receive information that differs from what they expected. Researchers have studied something called an information prediction error, which is the difference between the information someone expects and the information that person actually receives. Their research found that these differences can play an important role in learning. Surprise gets your attention because reality has given you information that your existing expectations did not predict.

What happens next is important because surprise alone does not guarantee curiosity. Someone can encounter unexpected information and immediately dismiss it. Another person can encounter the same information and start asking why it happened, what was missed, what else could explain it, and whether an earlier assumption needs to change. The event is the same, but the response to being wrong is very different.

How Assumptions Affect Being Wrong And Curiosity

Assumptions turned out to be one of the four factors I identified in my research into what can inhibit curiosity. Assumptions are necessary because people could never function effectively if they had to investigate every piece of information from the beginning. Experience creates mental shortcuts that help people predict what is likely to happen. Problems arise when those predictions continue operating long after the conditions that created them have changed.

Many assumptions are simply yesterday’s successful predictions. You learned from experience that customers behaved a certain way, a particular leadership approach worked, a specific skill made someone valuable, or a certain business model produced results. Repeating what worked can be completely reasonable when the environment remains relatively stable. When the environment changes quickly, experience can still be valuable, but people have to become more willing to question whether the conclusions they drew from that experience still apply.

How Being Wrong Can Become Useful Information

Being wrong has traditionally carried a negative connotation in many organizations. Expertise is often associated with knowing the answer, and leadership can create pressure to demonstrate confidence even when information is incomplete. Once someone has publicly supported an idea, changing that position can feel like admitting failure. Those pressures can make people more interested in defending an earlier prediction than learning from the evidence that contradicted it.

The AI comparison offers a different way to look at error. During training, an incorrect output gives the system information. The discrepancy helps identify what needs adjustment. People can use unexpected results in a similar way by asking what the error reveals about the information, assumptions, or experiences that led to the original prediction.

That requires a willingness to become interested in why reality did not match what they expected. If a strategy fails, the useful question is what the result reveals about the assumptions behind the strategy. If an employee responds differently than expected, curiosity can help uncover information about that person’s perspective. If AI suddenly performs a task better than expected, curiosity can turn that surprise into information about where human value may be changing.

Why Curiosity Changes The Response To Being Wrong

Curiosity gives people a way to stay engaged with information that challenges what they already believe. Instead of treating an unexpected result as something that needs to be explained away, they can investigate it. That distinction becomes especially valuable when change happens quickly because people have less time to rely on assumptions before those assumptions are tested by new information. As AI continues changing jobs, skills, and expectations, being right about everything that comes next is probably unrealistic. The more useful capability may be recognizing when reality no longer matches what you expected and becoming curious enough to find out why being wrong can provide information that helps you adapt.