For most of us, what we understand about the world relies on borrowed knowledge. We don’t all need to know exactly how vaccines work, how algorithms spit out content, or how climate models generate predictions. Instead, we rely on experts and signals that suggest expertise, such as specialized and highly technical terminology known as jargon.

It’s easy to see why we use these shorthands for credibility. Technical language is a hallmark of expert communities, demonstrating depth, precision, and authority. Simultaneously, it can also make these explanations trickier to comprehend for non-experts, creating a dilemma: if jargon makes ideas harder to understand, why does it also make them feel better? ^{1, 2} 

This question points to a broader reality of our information-dense world—that people are often tasked to evaluate explanations they are not equipped to fully understand. Cruz and Lombrozo’s study steps right into this dilemma, investigating when and why jargon influences how we judge explanations.

Across nine experiments, 6,698 participants were presented with scientific explanations that differed in how complete the explanation was and whether it included jargon.

Some explanations were intentionally weak. They were what the researchers called “circular explanations,” that essentially restated the phenomenon without truly explaining it. Others were more detailed, laying out the step-by-step causal mechanisms. Participants rated these explanations based on how satisfying they found them and how well they understood them.

In addition to varying the strength of explanations, the researchers also manipulated jargon use. In some cases, technical terms were added to an explanation. In others, they replaced simpler language. Later studies introduced entirely made-up jargon to test whether meaning mattered at all. Some participants also answered follow-up questions or generated their own explanations to demonstrate whether their sense of understanding held up under scrutiny.

The central insight is that jargon can make explanations feel better. When explanations were circular (in other words, weak), adding jargon significantly increased how satisfying participants found them. At the same time, jargon consistently reduced how understandable those explanations were, demonstrating a striking disconnect in how laypeople process complex information.

But this effect had limits. When explanations were complete and detailed, jargon no longer made them feel more satisfying. And, when it replaced simple language, it actually made them worse. But why?

Cruz and Lombrozo found that people assume jargon fills in missing gaps in knowledge. When an explanation feels incomplete, technical language acts like a placeholder for missing information. So, even if people don’t understand the technicality, they infer that something meaningful must be there. But when an explanation is already clear and complete, there are no gaps left to fill. In these cases, replacing simple language with jargon just makes the explanation harder to process. Instead of signalling depth, it disrupts understanding which makes a strong explanation feel worse.

This illusion runs deep. Participants rated explanations as more satisfying even when the jargon was entirely made-up, suggesting that this signal of expertise matters more than the content itself.

Crucially, this illusion breaks down when people are forced to test their understanding. When participants were asked to explain the concept themselves or answer follow-up questions, their confidence dropped. In a nutshell, getting them to explain what they understood exposed the gaps that jargon had previously hidden.

The research unveils a subtle downfall in how we process information–we often mistake the appearance of expertise for understanding.

For scientific communication, this creates a delicate balance in how experts portray findings. While jargon can signal credibility, it can also cause people to overestimate the quality of weak explanations. Simplifying language makes knowledge more accessible and helps prevent people from running with questionable information.

In the age of AI, this dynamic becomes more powerful. Large language models that generate fluent, technical-sounding explanations may amplify this bias, producing answers that sound convincing regardless of their actual quality. Thus, an awareness of the fallacy in our ability to process information could help mitigate this effect as we become more accustomed to AI-driven explanations.

If jargon can create an illusion of comprehension, the next challenge is to design ways to counteract it.

One promising approach is to make people explain. The study shows that prompting individuals to generate their own explanations or answer follow-up questions exposes gaps in understanding and reduces overconfidence. AI tools, classrooms, and digital platforms could build in more opportunities for this kind of active engagement.

Zooming out, this research invites us to rethink what counts as understanding in a world where most knowledge is second-hand. If we rely on experts to explain the world to us, how can we tell the difference between when we truly understand and when we’re simply reassured by how technical something sounds?

While we’re far from perfecting the art of knowledge dissemination, Cruz and Lombrozo’s work suggests that language is not just a vehicle for information but also a lever that can either obscure or clarify what we know. Designing communication that reveals, rather than hides, gaps in our understanding is one of the most important steps moving forward.

Jargon can often feel like knowledge. But as this paper shows, it can just as easily mask its absence. When explanations sound sophisticated via technical language, we’re more likely to feel satisfied by them–even when they leave important questions unanswered. Recognizing that gap is the first step toward closing it. Because in a world full of expert language, true understanding doesn’t come from what sounds right but rather, from what we can actually explain on our own.

This article summarizes:

Cruz, F., & Lombrozo, T. (2025). How laypeople evaluate scientific explanations containing jargon. Nature Human Behaviour, 9, 2038–2053. https://doi.org/10.1038/s41562-025-02227-0

1. Bullock, O. M., Colón Amill, D., Shulman, H. C., & Dixon, G. N. (2019). Jargon as a barrier to effective science communication: Evidence from metacognition. Public Understanding of Science, 28(7), 845–853. https://doi.org/10.1177/0963662519865687
2. Weisberg, D. S., Keil, F. C., Goodstein, J., Rawson, E., & Gray, J. R. (2008). The seductive allure of neuroscience explanations. Journal of Cognitive Neuroscience, 20(3), 470–477. https://doi.org/10.1162/jocn.2008.20040