Scientists Challenge a 70-Year-Old Theory of Language With a Surprising Discovery

A new study challenges a 70-year-old idea about how word meanings are organized. Instead of emotion, scientists found that language might be shaped by a more basic need: safety.

Researchers at the University of Vermont made this discovery. It goes against a major assumption in psychology, linguistics, and artificial intelligence that has been around for over 70 years.

A New Way to Understand Language

The study, published in Science Advances, introduces "ousiometrics." This is a new way to study the core meaning of words. It suggests that language isn't mainly about emotion. Instead, it follows a deeper pattern related to power, danger, and order.

The main finding is that people consistently lean towards safety in language.

For decades, many researchers used three emotional measures to describe meaning: positive vs. negative (valence), excited vs. calm (arousal), and controlling vs. submissive (dominance). This is known as the VAD framework. It started with work by Charles Osgood in the 1950s and is used widely in psychology, linguistics, and AI systems that analyze feelings.

The new study looked at billions of uses of over 20,000 words in real texts. It shows that the VAD framework has big weaknesses. With support from the US National Science Foundation, Google, and others, the researchers used modern computer methods. They found a different set of basic meaning dimensions. The VAD dimensions are not truly separate and can hide a more fundamental structure in language.

The researchers say that meaning is better understood through three independent dimensions: weak vs. powerful (power), safe vs. dangerous (danger), and ordered vs. chaotic (structure).

This is important because language technologies, like large language models, are increasingly shaping how people communicate. Understanding the structure of meaning is now more urgent. The new framework explains over 90% of the differences in meaning. The traditional VAD model explains about 72%.

When researchers studied words in books, news, social media, and spoken language, one pattern kept appearing. Language strongly favors words linked with safety over words linked with danger.

This "safety bias" offers a new way to understand the Pollyanna principle. This old idea in linguistics says that language tends to be positive. The new work suggests this isn't just about positive emotion. Instead, it shows a deeper bias towards safety. The study concludes that the Pollyanna principle's positivity bias is actually a reflection of an underlying safety bias.

Peter Dodds, director of UVM’s Complex Systems Institute and a lead author, called this a "big observation." He noted that expressions of safety are vital to all language.

Language as a Survival System

These findings have wide implications. If language leans towards safety, then communication might have been shaped by evolution and survival needs. Words do more than show emotions. They help people judge risk, spot threats, and work together when things are uncertain.

This idea helps explain why people often communicate whether something is safe or dangerous. Across cultures, humans regularly signal the risk level of places, actions, people, and events. The study suggests that this safety dimension is not secondary to emotion. It might be one of the core foundations of meaning.

From this view, positivity in language isn't just about happiness or optimism. It can also signal predictability and safety in a shared environment. Julia Zimmerman, a researcher at UVM’s Computational Story Lab and coauthor, said the framework points to a basic part of human experience. She noted that power, danger, and structure are relevant to everyone who has ever lived.

Linguists have also noticed that language favors expressions of goodness and low aggression. The team writes that these are "shadows of an underlying linguistic safety bias."

Rethinking Meaning in Different Fields

The results challenge ideas in several fields.

For artificial intelligence, the impact is direct. Many natural language processing systems use sentiment analysis based on frameworks like VAD. If these models don't capture the deeper structure of meaning, AI systems might be misunderstanding human language. Adding power, danger, and structure to these systems could make them more accurate. It could also make them easier to understand, especially for tasks involving risk, trust, and decisions.

For linguistics, the study changes how researchers might think about the basic organization of meaning. Instead of emotional tone being the main structure, the work points to survival-related differences. These include what is powerful, what is dangerous, and what is orderly.

For psychology, the findings question decades of research built on the VAD model. If the core dimensions of meaning are different, then some ideas about emotion, perception, and behavior might need to be rethought.

For neurobiology, the results connect with what is known about the brain's strong sensitivity to threat and safety. A safety bias in language might reflect biological priorities in communication. This helps link brain processes with how humans use words.

Ousiometrics: A New Framework

To find these patterns, the researchers created new tools to measure meaning on a large scale. One key tool is the "ousiometer." This instrument quickly measures the core meaning of large texts and gives an average meaning score.

The word "ouisa" comes from Ancient Greek and means "essence." Building on the team's earlier "hedonometer" (a happiness meter), the new tool can find broad meaning patterns in texts. These texts range from Jane Austen novels to The New York Times, Wikipedia, talk radio, and Twitter.

One example in the study shows the "ousiometric trajectory" of an English translation of Victor Hugo’s Les Misérables. The book's path winds over a grid with four opposing pairs: dangerous and safe, weak and powerful, gentle and aggressive, and bad and good. This method condenses the core meaning of different parts of the novel as the story unfolds.

The study also makes an important difference between words as categories ("types") and words as they are actually used ("tokens"). For example, "apple" as a category is a type. Every time "apple" is used in a sentence is a token. Earlier work often treated words as if they mattered equally, no matter how often they appeared.

By considering how often words are used, the team of 10 scientists uncovered patterns that only appear in real language use. This includes the safety bias. The team was led by Peter Dodds and Chris Danforth, professors at UVM’s College of Engineering and Mathematical Sciences. They worked with colleagues from the Santa Fe Institute, the Complexity Science Hub in Austria, Howard Hughes Medical Center, University of California, Berkeley, University of Adelaide, and MassMutual Data Science.

Why This Matters

If language consistently leans towards safety, this finding could change how researchers understand how information spreads. It could also affect how narratives are built and how people interpret the world. It might be important for political discussions, mental health communication, and the design of AI systems that respond to human language.

More broadly, the study suggests a shift in how meaning should be understood. Meaning is not only about emotion or sentiment. It is also rooted in the need to handle risks, relationships, and social order. By showing a deeper structure of meaning, the team offers a new way to see language. It's not just a system of symbols, but a record of what humans need to survive in a social and dangerous world.

Deep Dive & References

Ousiometrics: The essence of meaning aligns with a power-danger-structure framework instead of valence-arousal-dominance - Science Advances, 2026