Language is ever evolving—from ancient dialects to modern slang, the words and accents people use are not only expressions of culture and personal identity, but also reflections of our past.

Now, a new study from the University of Portsmouth suggests that these changes may not be as random as first thought. Instead, they may follow predictable patterns.

James Burridge, Professor of Probability and Statistical Physics at the University’s School of Mathematics and Physics, and his team developed a framework to forecast how language patterns spread across regions and generations.

By leveraging statistical physics, scientists are beginning to map the movement of words and accents in ways that are similar to weather forecasting.

“Just as meteorologists use mathematical models to forecast tomorrow’s weather, the same kind of thinking can be applied to language,” Burridge said in a statement. 

“Where you are affects how you speak, and if you map how people use certain words, you see clear geographic patterns—just like a weather map. However, the physics of language is closer to crystals and magnets than the atmosphere.”

“Language change can seem mysterious,” Burridge said, “but my research argues that as well as being driven by individual human behavior it may also obey some of the same broad rules that govern physical systems like magnets, bubbles, and fluids.”

The result looks something like a  “language weather map,” revealing clear geographic patterns in speech. In their research, Burridge and his colleagues decided to focus their study on regional dialects in the United States, using data from the University of Cambridge’s Cambridge Online Survey of World Englishes, created by linguist Bert Vaux.

This large-scale survey enabled Burridge to examine how different terms compete and spread across various communities. Specifically, Burridge looked at common pop culture terms we use daily or weekly, like the word “soda,” while others use the term “pop,” and why some of these popular words spread while others retreat. 

One interesting example is the word used for a small crustacean commonly found in gardens. Depending on the region and area someone lives in, they might call it a “woodlouse” or a “roly-poly.” In the 1950s, “roly-poly” was mainly used in parts of the American South. But by 1995, the term had spread widely across the United States. This rapid spread of common words shows how local expressions can spread far beyond their origins and become the dominant word in that area or region. 

The model also helps explain why some regional terms survive while others die out. In earlier research, Burridge studied the spread of the word “splinter” across England. While “splinter” became standard across most of the country, in the northeast (in regions like Newcastle upon Tyne), the local term “spelk” stayed strong as a word. According to the model, local isolation of a term and low population in those areas can help preserve the local words. 

“Splinter is used across almost all of England, except around Newcastle, where people still say spelk,” says Burridge. “Although Newcastle itself is densely populated, it is surrounded by more sparsely populated areas, which helps the local form hold its ground and prevents splinter from taking over.”

One of the study’s most important findings is the idea of a linguistic “horizon.” Like weather forecasts, language predictions become less trustworthy over time as they keep being picked up by the new generation.

Burridge notes, “My research suggests that language may be much more law-like than it first appears. Beneath the creativity and messiness of human speech, there may be hidden statistical forces shaping how we all end up talking.” 

“For physicists like me, this is particularly exciting, as it suggests that the elegant tools of statistical field theory may help explain not just the natural world, but patterns in human communication as well,” he adds. 

The new framework could have implications beyond linguistics. For example, understanding how language evolves may help sociologists study cultural change and improve technologies such as speech recognition and translation systems.

Chrissy Newton is a PR professional and the founder of VOCAB Communications. She currently appears on The Discovery Channel and Max and hosts the Rebelliously Curious podcast, which can be found on YouTube and on all audio podcast streaming platforms. Follow her on X: @ChrissyNewton, Instagram: @BeingChrissyNewton, and chrissynewton.com. To contact Chrissy with a story, please email chrissy @ thedebrief.org.

Dreams can seem to occur at random, from everyday scenarios to unpredictable, surreal experiences. Now, a new study shows that our personal traits as well as real-life events and experiences actually shape what we dream about, creating patterns in our subconscious.

The study, published in Communications Psychology, analyzed thousands of dream and waking experience reports collected over four years. The researchers used natural language processing tools to quantify the structure of dreams. They found that personal traits like how often someone daydreams, their attitudes about dreams, and their sleep quality all influence dream content. Major shared life events, such as the COVID-19 pandemic, also impacted what people dreamed about.

“Our findings show that dreams are not just a reflection of past experiences, but a dynamic process shaped by who we are and what we live through,” said Valentina Elce, researcher at the IMT School for Advanced Studies Lucca and lead author of the study.

Four Years of Dream Reports

The main dataset included 207 adults aged 18 to 70 who kept a dream diary for two weeks. Each morning, they wrote down everything they remembered from the night’s sleep. Once a day, at a random time, they also recorded what they had been thinking about in the previous 15 minutes. This created a set of waking experience reports to compare with their dream reports.

In addition to the daily records, the researchers collected detailed information about each participant’s sleep habits, cognitive skills, personality, and psychological traits. By the end, they had gathered 1,687 dream reports and 2,843 waking reports from the main group, plus 351 dream reports from 80 people during the first COVID-19 lockdown in Italy in spring 2020.

Dreams Reorganize Reality

When researchers compared participants’ reported dream experiences with situations they reported experiencing while awake, they noticed that dreams don’t simply replay scenarios from our daily lives. Instead, dreams seem to mix familiar places like workplaces, hospitals, and schools into new scenes that blend memories with imagination. Compared to reported waking experiences, the reported dreams tended to focus more on visual details, feature more characters, and make less logical sense. They were also less self-focused and less driven by conscious thinking.

These dream transformations weren’t the same for everyone. Participants who spent more time daydreaming during the day tended to have dreams that jumped rapidly from one scene to another. Those who placed more importance on dreams described them as more vivid and immersive. Sleep quality also played a role: participants who slept poorly showed different patterns in dream content when compared with those who slept better.

Pandemic Influenced Dreams

The lockdown dataset gave researchers a unique opportunity to see how a major external stressor, such as a pandemic, could affect dreams across an entire population.

Dreams recorded during the strict lockdown period were more emotionally intense and mentioned restrictions and limitations more often than dreams from later years. As people adjusted to the new situation, these differences faded. The results suggest that dreams reflect both our personal psychology and the social conditions we share.

AI as a Tool for Studying Consciousness

The team used three large language models, LLaMA 3, ChatGPT-4, and ChatGPT-4 Turbo, to rate dream reports on 16 different features, such as mood, excitement, strangeness, social content, spatial details, and freedom of movement. They combined the scores from the three models and checked them against human ratings. The results showed that these language processing tools could analyze the structure of dream reports as reliably as trained human evaluators. This finding could have uses that extend far beyond this study.

“By combining large-scale data with computational methods, we were able to uncover patterns in dream content that were previously difficult to detect,” Elce said. “This opens new possibilities for studying consciousness, memory, and mental health in a scalable and reproducible way.”

Austin Burgess is a writer and researcher with a background in sales, marketing, and data analytics. He holds an MBA, a Bachelor of Science in Business Administration, and a data analytics certification. His work focuses on breaking scientific developments, with an emphasis on emerging biology, cognitive neuroscience, and archaeological discoveries.