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When people share emotional stories, the intensity of their feelings does not always match the length or detail of their words. A recent study published in PLOS One suggests that the gap between what people express and how much they say is a deliberate communication strategy rather than an error. These findings provide evidence that humans use a wide range of expressive styles that artificial intelligence currently struggles to replicate.
Ryan SangBaek Kim, the founding director and principal investigator of the Ryan Research Institute in Paris, conducted this study to challenge common assumptions in psychology and computer science. Many experts assume that healthy communication requires people to perfectly match their internal feelings with their spoken or written words. Kim noticed that this mismatch is usually dismissed as an error.
“Affective science has long treated the gap between what people feel and what they say as measurement noise,” Kim told PsyPost. “Working across psychology, affective science, and AI ethics, I came to suspect that this gap was not noise but structure.” People often regulate how much of an emotion becomes language, especially in relationship narratives. “I wanted to test whether that regulation leaves a measurable shape in the data,” he noted.
To map out these communication patterns, Kim analyzed exactly 351,734 English language relationship narratives. These stories were collected from public online advice forums and support communities between 2012 and 2023. The data was completely stripped of all personal identifying information to protect the privacy of the original writers. This massive collection provided a window into how real people discuss their relationships in natural, unscripted environments.
Kim measured two main features for every single narrative in the dataset. The first was narrative complexity, which is a structural measure of the writing itself. This concept looks at the total length of the post, the variety of the vocabulary used, and how densely the sentences are constructed. Writing a highly complex narrative requires significant mental effort.
The second feature was linguistically inferred affective intensity. Affect is a term psychologists use to describe the underlying experience of feeling or emotion. The researcher used specialized software to analyze the text and estimate the magnitude of the emotion present in the words. This tool measured how strong the emotional language was, regardless of whether the overall feelings were positive or negative.
By comparing these two measurements, Kim calculated the narrative affect discrepancy. This concept describes the exact mathematical gap between the complexity of the story and the emotional intensity it contains. He did not try to guess the writers’ hidden inner feelings. Instead, he simply measured how much linguistic effort people spent relative to the emotion they put on the page.
“The near-zero correlation surprised me most,” Kim said. “I expected narrative complexity and affective intensity to move together at least weakly, but they were almost orthogonal,” he explained. In statistical terms, variables that are orthogonal are completely independent of one another. “In the data, a story could be psychologically complex without sounding emotionally intense,” Kim added.
“The main takeaway is that emotionally complex experiences do not always sound emotional on the surface,” Kim said. “This challenges a common assumption in both emotion research and affective AI: that stronger or more difficult emotional states should appear as stronger emotional language,” he noted. “In the data, people often described painful or psychologically difficult experiences in calm, restrained, or indirect language rather than highly emotional language.”
“In other words, someone saying ‘I’m fine’ is not always hiding emotion poorly,” Kim explained. “Sometimes restraint is itself part of how humans communicate distress.” These findings provide evidence that humans use a wide range of expressive styles rather than automatically matching complexity to feeling.
Kim identified four distinct patterns of emotional expression in the data. The vast majority of the narratives, about 91.3 percent, fell into a category called coupled expression. In this group, the story complexity and emotional intensity were relatively balanced without extreme gaps. The writing did not show severe signs of overstating or understating emotions.
The remaining narratives fell into three specific mismatch categories. About 20,223 stories showed strategic understatement, where writers expressed intense emotions but used very little narrative structure. Another 2,223 stories demonstrated strategic overstatement, meaning authors used highly complex language to express relatively low emotional intensity. This strategy indicates people are using extensive words to create a protective cognitive distance from a topic.
The final group of 8,040 narratives fell into a pattern Kim called collapse. These writers showed very high emotional intensity but lacked the structural wording to support it. This pattern tends to occur when feelings are so overwhelming that a person cannot organize their thoughts into a cohesive story. The narrative structure effectively breaks down under the weight of the emotion.
After mapping out these human patterns, Kim tested an artificial intelligence system using a safety aligned language model. “For AI, the important finding was that one RLHF-aligned language model occupied a roughly 1.70 times narrower expressive region than humans under the same measurement framework,” Kim said. This type of program is trained using reinforcement learning from human feedback, making it polite and helpful. “The model was especially less present in the parts of emotional language where humans speak indirectly, hold back, or emotionally shut down,” he noted.
“The clearest human signals are not always the loudest ones,” Kim added. “I was also surprised that the model’s contraction was not uniform. It was especially pronounced in regions where humans communicate through strategic understatement or expressive collapse.”
“The 1.70-fold contraction is statistically clear, but its practical importance lies in where the contraction occurs,” Kim said. “If aligned models occupy a narrower expressive space, they may be less sensitive to people who communicate distress through understatement, confusion, silence, or fragmented language rather than direct emotional intensity. This matters for mental health tools, AI companions, and other systems that try to interpret emotional language. A system that only hears intensity will miss the people who speak in restraint.”
Readers might misinterpret the study by assuming the software perfectly captured the writers’ true inner feelings. “The most important caveat is that the study does not claim to measure subjective feeling directly,” Kim cautioned. “It measures the geometry of emotional expression, meaning what people put into language, not the full inner experience underneath it.”
The study also has several limitations regarding its scope and sample. “The data also come from English-language public relationship narratives, so the pattern may differ across languages, cultures, or settings,” Kim said. “Finally, the AI comparison involves one model under a fixed configuration, so it should be read as a baseline result rather than a verdict on all aligned models.” Across these three limits, Kim suggests the safest reading is that the study measures one stable asymmetry between human and aligned-model expressive geometry, not a verdict on emotional AI as a whole.
For future research, Kim plans to look at how these communication styles change over time. “This study is part of a broader research program on narrative-affect geometry and affective sovereignty,” Kim said. “The empirical question is how people structure emotional meaning in language.” The governance question, he noted, is what happens when artificial intelligence systems begin to interpret those meanings for humans.
He aims to study how prolonged interaction with artificial intelligence impacts human behavior. “My next step is longitudinal: whether repeated exposure to aligned models changes how people express, regulate, or interpret their own emotions over time,” Kim said. “The deeper question is whether AI only responds to our emotional language, or whether, over time, it also reshapes how we learn to speak about ourselves.”
To encourage more research, the study resources are completely public. “The dataset and analysis code are openly available on Zenodo,” Kim stated. “Claims about AI and emotion can easily become speculative, so open data and reproducible analysis are especially important here. My hope is that other researchers will test, challenge, and extend the framework.”
The study, “Narrative-affect discrepancy as a regulated degree of freedom in 351,734 relationship narratives,” was authored by Ryan SangBaek Kim.