For more than seventy years, the scientific consensus regarding human language and the organization of word meaning has rested primarily upon an emotional framework known as the VAD model. Developed in the 1950s by pioneering psychologist Charles Osgood alongside several of his contemporaries, this model dictated that human communication is fundamentally mapped across three emotional axes: valence, which contrasts positive expressions against negative ones; arousal, which distinguishes excited states from calm ones; and dominance, which weighs controlling behaviors against submissive ones. This paradigm quickly became a cornerstone across multiple disciplines, shaping everything from theoretical linguistics and cognitive psychology to modern sentiment analysis and artificial intelligence development. However, a comprehensive new study conducted by researchers at the University of Vermont (UVM) has challenged this long-held foundational theory, presenting empirical evidence that human language is structured not primarily by emotion, but by something far more fundamental to the human experience: our intrinsic understanding of power, danger, and order.
Published in the peer-reviewed journal Science Advances, the study introduces an innovative analytical approach termed "ousiometrics." Derived from "ouisia," an Ancient Greek root meaning "essence," this methodology applies advanced mathematical techniques to measure the essential semantic meaning of language across vast corpora. Through this framework, the research team made a striking discovery that could fundamentally alter how scientists study communication, model human behavior, and train artificial intelligence systems. By analyzing billions of individual word tokens across a diverse array of real-world texts, the researchers uncovered a profound, hidden preference within human language: a pervasive and widespread bias favoring safety over danger. This unexpected revelation offers a compelling new explanation for the Pollyanna principle, a well-documented linguistic phenomenon describing the human tendency to use positive words more frequently than negative ones. For decades, scientists attributed this skew entirely to an inherent emotional inclination toward happiness and optimism. The UVM findings, however, suggest that the Pollyanna principle is merely a superficial reflection of a much deeper, survival-oriented safety bias.
The Origins and Execution of the UVM Study
The investigation was spearheaded by a ten-member multidisciplinary research team led by Peter Dodds and Chris Danforth, both professors in the College of Engineering and Mathematical Sciences at the University of Vermont and principal figures within the UVM Complex Systems Institute. Their collaborators included distinguished researchers from the Santa Fe Institute, the Complexity Science Hub in Austria, the Howard Hughes Medical Center, the University of California, Berkeley, the University of Adelaide, and MassMutual Data Science. Financial and logistical support for the ambitious project was provided by a coalition of public and private entities, including the US National Science Foundation, Google, and MassMutual.
Rather than relying on isolated lists of vocabulary or theoretical constructs, the research team examined billions of individual word uses—referred to in linguistics as tokens—spanning more than 20,000 distinct word categories, or types. This distinction between types and tokens proved vital to the study’s success. While previous linguistic analyses often treated every word in a dictionary with equal weight, the Vermont team incorporated actual word frequency in everyday communication. Because certain words appear millions of times while others are rarely invoked, accounting for token frequency allowed the researchers to capture the true distribution of meaning in human discourse.
The team deployed their computational models across an extraordinarily diverse collection of textual sources. The analyzed corpora included canonical literary works such as Jane Austen’s novels, Arthur Conan Doyle’s Sherlock Holmes stories, and an English translation of Victor Hugo’s Les Misérables, alongside modern informational and communicative mediums including the New York Times, Wikipedia, Twitter, and transcripts of commercial talk radio broadcasts. By processing these massive datasets, the investigators sought to determine whether the traditional three-dimensional VAD model adequately captured the architecture of human meaning or if a more robust underlying structure existed.
Redefining the Dimensions of Meaning: Power, Danger, and Structure
The results of the computational analysis offered a decisive departure from the Osgood framework. The researchers discovered that the three emotional dimensions of the VAD model—valence, arousal, and dominance—are not independent of one another, as scientists had assumed for over half a century. Instead, their mathematical models revealed that word meaning is more accurately organized around three distinct axes: power (measuring the continuum from weak to powerful), danger (measuring the continuum from safe to dangerous), and structure (measuring the continuum from ordered to chaotic).
When applied to the vast textual datasets, this newly formulated three-dimensional framework accounted for more than 90 percent of the total variation in measured word meaning. By comparison, the traditional VAD approach explained approximately 72 percent of that variation. This substantial statistical improvement indicates that human vocabulary is primarily oriented around evaluating agency, threat, and predictability rather than purely emotional valence and arousal.
Furthermore, when the researchers mapped how words actually appear in natural communication, they observed an overwhelming asymmetry. Across books, news reports, social media posts, and spoken dialogue, words associated with safety appeared with dramatically higher frequency than those associated with danger. This persistent imbalance provided the empirical backing for the study’s central conclusion: the positivity bias historically attributed to the Pollyanna principle is, in essence, a one-dimensional projection of an underlying linguistic safety bias. As Peter Dodds, senior author of the study and director of the Complex Systems Institute, noted, expressions of safety are foundational components embedded within the architecture of all human language.
Evolutionary Implications and the Primal Need for Survival
While the study’s authors emphasize that their findings do not definitively prove a direct causal link between evolutionary pressures and modern linguistic structures, the implications regarding human survival are difficult to ignore. Throughout human history, the ability to rapidly assess environmental conditions, evaluate uncertainty, establish trust, and coordinate collective action has been paramount to survival. Distinguishing between safe and dangerous environments, individuals, and situations represents a baseline cognitive requirement for any organism.
The UVM research suggests that these fundamental biological and evolutionary priorities are deeply mirrored in the symbolic systems humans use to communicate. Words do much more than simply articulate transient emotional states; they serve as critical tools for threat recognition, risk mitigation, and social cohesion. Julia Zimmerman, a postdoctoral researcher in UVM’s Computational Story Lab and a co-author of the study, highlighted the universal relevance of these dimensions, observing that power, danger, and structure are parameters that affect every human life regardless of historical era or cultural background.
By reframing positive language through this lens, the study proposes that many words traditionally classified as expressions of happiness or approval may actually function to convey security, stability, and predictability. Consequently, long-standing linguistic observations regarding humanity’s preference for agreeable, cooperative, and low-aggression vocabulary can be understood not as isolated behavioral quirks, but as downstream shadows cast by a profound, pervasive safety bias.
Introducing the Ousiometer and Ousiometric Trajectories
To operationalize their discoveries and enable granular quantitative analysis of large texts, the research team engineered a novel analytical instrument known as the "ousiometer." This computational tool builds directly upon an earlier invention from the same laboratory: the "hedonometer," a widely recognized happiness meter designed to gauge the overall emotional tone of written works. While the hedonometer focuses specifically on valence and sentiment, the ousiometer evaluates the essential meaning of expansive textual bodies across the newly identified dimensions of power, danger, and structure, ultimately generating an aggregate meaning score.
The utility of the ousiometer was demonstrated through its application to complex narrative structures. By tracking the semantic shifts of a text from beginning to end, the researchers mapped what they termed "ousiometric trajectories." When visualized graphically, these trajectories resemble intricate, multicolored protein structures, tracing how a narrative shifts across a grid defined by contrasting pairs such as dangerous versus safe, weak versus powerful, gentle versus aggressive, and bad versus good.
Applying this technique to Victor Hugo’s Les Misérables, the team was able to visually and mathematically track the shifting thematic currents of the novel as characters moved through cycles of peril, institutional oppression, redemption, and stability. This technological innovation provides literary scholars, historians, and computational sociologists with an unprecedented mechanism for studying how meaning evolves over the course of extended narratives.
Broad Impacts on Artificial Intelligence, Psychology, and Linguistics
The ramifications of the University of Vermont study extend far beyond theoretical linguistics, offering potential paradigm shifts across multiple academic and industrial sectors.
In the field of artificial intelligence, modern systems increasingly rely on natural language processing (NLP), large language models, and automated sentiment analysis tools to interpret human communication, moderate online content, and gauge public opinion. Many of these contemporary AI architectures are explicitly built upon the legacy VAD framework or related sentiment-scoring matrices. If these foundational models fail to account for the dominant underlying structures of power, danger, and structure, they may systematically misinterpret the nuances of human intent, trust, and risk assessment. Integrating the ousiometric framework into AI training protocols could enable machine learning systems to achieve greater interpretive accuracy and provide more transparent explanations for their analytical outputs.
In psychology, decades of behavioral and clinical research have utilized Osgood’s VAD model to investigate emotional perception, cognitive processing, and interpersonal dynamics. The emergence of an empirically superior three-dimensional model based on power, danger, and structure invites researchers to re-examine established psychological datasets through a fresh analytical lens, potentially yielding new insights into how humans process threat and security.
Neuroscience may also find valuable intersections with these findings. The human brain possesses highly specialized neural circuitry dedicated to threat detection and safety signaling—most notably within the amygdala and prefrontal cortex. The identification of a corresponding linguistic safety bias suggests a tangible bridge between neurobiological survival mechanisms and symbolic human communication, opening new avenues for interdisciplinary research into brain activity and language production.
Conclusion and Future Directions
The discovery of a hidden safety bias rooted in power, danger, and order marks a significant milestone in the scientific study of human language. By challenging a seventy-year-old paradigm and introducing advanced measurement tools like the ousiometer, the University of Vermont research team has opened a new chapter in how scholars understand the mechanics of communication. As this research influences subsequent developments in artificial intelligence, cognitive science, and linguistic theory, it reinforces a fundamental truth about human nature: our words, stories, and social structures remain inextricably bound to our most primal quest for safety and stability in an uncertain world.
