Psychologists have spent decades trying to classify the complex, unspoken rules of everyday human interaction. Now, artificial intelligence has finally provided a comprehensive, data-driven map of our social lives.
According to a new study published in Psychological Science, researchers from Carnegie Mellon University and the University of Pennsylvania have successfully used large language models (LLMs) to systematically categorise thousands of real-world social exchanges.
By analysing the psychological structures underlying these encounters, the researchers have created a unified taxonomy of human interaction.
The architecture of interaction
Historically, mapping human social behaviour has proven incredibly difficult. While scientists understand that social situations profoundly dictate human cognition, previous frameworks have been fragmented and struggled to capture the sheer diversity of real-life encounters.
To address this, the research team compiled a large dataset comprising more than 20,000 detailed textual descriptions of two-person interactions. The texts were drawn from a wide variety of sources, including autobiographical blogs, published fiction, short stories, and reading comprehension exams.
The researchers fed these narratives into generative AI models, which were tasked with extracting core situational cues — identifying the “who, what, where, and why” of each scenario — and mapping them onto high-level psychological variables such as power, duty, and conflict.
Taya R. Cohen, a professor of organisational behaviour and business ethics at Carnegie Mellon’s Tepper School of Business, who co-authored the study, highlighted the breakthrough nature of the AI-driven approach.
“Researchers have proposed many frameworks for representing social situations, but due to the diversity and complexity of real-life situations, many of these are partial, non-integrated, and not mapped onto situations encountered in everyday life,” Professor Cohen said. “Our work advances the study of social cognition and behaviour by using AI to create a more comprehensive framework for the structure of social situations.”
Refining psychological theory
The AI analysis successfully replicated findings from earlier, much smaller studies while extending the data to a far broader, more representative scale of adult experiences.
Sudeep Bhatia, an associate professor of psychology at Penn who led the study, explained that this new digital catalogue will serve as a foundational tool for the future of psychology.
Professor Bhatia said: “A core challenge in psychology is understanding the structure of social situations — the patterns and psychological features that shape how people think, feel, and behave in social contexts. Our work provides a rigorous and integrative framework for mapping out everyday social situations and relating them to key theoretical dimensions in psychology.”
He added: “Our study offers researchers a rich descriptive catalogue of dozens of classes of situations with which they can test and refine their theories.”
While they claim the study is groundbreaking, the authors did acknowledge several limitations. The analysis relied heavily on short stories, which may exclude more highly nuanced real-world encounters. In addition, the study was conducted exclusively with English-language narratives and current-generation LLMs, which may limit the broader cultural scope of the findings.
