A recent study published in the journal Psychophysiology provides evidence that the human brain processes taboo words in a completely unique way compared to regular negative or neutral language. The research suggests that the distinct brain patterns triggered by these socially inappropriate words remain detectable even when a person actively tries to regulate their emotional response. These findings help explain how social rules and emotional meaning are deeply intertwined in our neural wiring.
Language serves as a primary tool for communicating emotional experiences in daily life. Emotionally charged language tends to elicit strong reactions, and taboo words represent a highly specific category of such language. People frequently use swearing to express frustration, alleviate pain, or enhance the impact of a message. The authors of the new study wanted to see if specific neural patterns could reveal how people process and manage emotional information.
“One of the major goals of affective neuroscience is understanding whether emotional states can be objectively identified from brain activity,” explained Parisa Ahmadi Ghomroudi, a research fellow at the University of Trento in Italy, and Alessandro Grecucci, a professor at the University of Bari in Italy who directs the Clinical and Affective Neuroscience Lab. “Language provides an ideal model because words can reliably evoke different emotional responses while remaining highly controlled experimentally.”
The scientists were interested in testing whether modern machine learning approaches could detect subtle differences in how the brain processes emotionally relevant language. They specifically focused on taboo words because they occupy a special place in language. The researchers also wanted to see if these neural signatures could provide a window into the broader mechanisms underlying emotional experience and regulation.
“At the neural level, emotional word categories were associated with distinct electrophysiological signatures across multiple stages of processing,” the authors noted. “Differences emerged in early perceptual-attentional components, including the P200, and extended to later components such as the Late Positive Potential (LPP), which is classically associated with sustained emotional evaluation and motivational relevance.”
To understand this, it helps to know that the P200 is a positive spike in electrical brain activity that happens roughly 200 milliseconds after seeing a stimulus. It acts as an automatic marker for early visual attention. The late positive potential is a similar but delayed brain wave occurring around half a second later, reflecting the moment the mind deeply evaluates and pays sustained attention to the word.
To answer their research questions, the scientists recruited forty native Italian speakers. All participants were right-handed and reported no history of neurological or psychiatric disabilities. Before the main experiment, the researchers removed data from five participants due to technical issues or excessive noise in the recordings, leaving a final sample of thirty-five young adults.
The experimental task involved reading a total of 240 words displayed on a computer screen. These words were evenly divided into neutral, negative, and taboo categories. The selected words were matched for their length, how often they appear in everyday language, and how familiar they are to average speakers.
Participants wore a cap fitted with 64 electrodes to measure their electroencephalogram, or brain wave activity. The electrical sensors on the scalp picked up the tiny voltage changes that occur as the brain processes information. The experiment was split into two distinct blocks to test different mental states.
In the first block, called the look condition, participants passively observed the words as they appeared. Afterward, participants rated how pleasant and how emotionally arousing the word felt on a nine-point scale. In the second block, called the accept condition, participants read another set of words but applied an emotion regulation strategy. They were instructed to notice their emotional reactions without judgment and allow those feelings to pass naturally.
To analyze the vast amount of brain wave data, the researchers utilized a machine learning technique known as a support vector machine. This type of artificial intelligence algorithm is trained to recognize complex, hidden patterns within large datasets. The algorithm evaluated the specific voltage spikes tied to the exact moment a word appeared.
“Machine-learning analyses further demonstrated that distributed spatiotemporal EEG patterns could reliably discriminate between neutral, negative, and taboo words,” Ghomroudi and Grecucci told PsyPost. “Notably, taboo words generated the most distinctive neural signatures, suggesting enhanced allocation of attentional and affective processing resources to stimuli carrying both emotional and socio-cultural significance.”
The machine learning algorithm successfully distinguished between the three word categories based solely on the electrical activity in the brain. During the passive looking condition, the model was highly accurate at telling the difference between a neutral word and a taboo word. This distinction was mainly driven by brain activity occurring between 637 and 878 milliseconds after the word appeared.
“One of the most interesting findings was how reliably the algorithms could distinguish different emotional categories from EEG activity alone,” the researchers shared. “We expected some separation between categories, but the fact that the neural patterns were sufficiently distinct to allow above-chance prediction suggests that emotional language leaves a stronger and more structured neural footprint than previously assumed.”
The algorithm’s performance dropped slightly during the accept condition, but it still accurately identified the word categories. This suggests that the brain continues to register the specific emotional and social weight of a word even when a person adopts a non-judgmental stance. Trying to accept an emotion softens the neural response, but the core signature of the taboo or negative word persists.
“Our study suggests that emotional states leave measurable signatures in brain activity that can be detected using artificial intelligence,” the authors explained. “While we are still far from reading thoughts, the results show that the brain responds in systematically different ways to different types of emotionally meaningful information.”
“More broadly, this work contributes to the long-term goal of understanding how emotions are represented in the brain and how these representations may differ across individuals,” they added. “Moreover, we found evidence of the neural signatures of the regulation of the emotional content conveyed by the words.”
There are a few limitations to consider, including the fact that the study relied entirely on written Italian words. This means the findings might not perfectly translate to other cultures, as cultural norms heavily dictate what makes a word taboo. The researchers also stressed that their algorithm is not a mind-reading device.
“The study does not allow us to determine exactly what a person is thinking, nor does it provide a tool for reading private mental content,” they cautioned. “We are identifying broad patterns associated with categories of emotional processing under controlled laboratory conditions. Much more work will be needed before similar approaches can be translated into real-world or clinical settings.”
The researchers noted that their current experiment serves as a starting point for more complex investigations. Brain wave recordings are excellent for tracking the exact millisecond a mental process occurs, but they are poor at pinpointing the exact physical location in the brain. Machine learning models look at broad patterns across the scalp, so the exact brain regions responsible for generating these taboo responses remain somewhat obscured.
“This is primarily a proof-of-concept study rather than a clinical application,” Ghomroudi and Grecucci clarified. “The importance of the findings lies less in the absolute classification accuracy and more in demonstrating that emotional states can be predicted from non-invasive brain recordings. Establishing this principle is an important step toward future efforts aimed at identifying neural biomarkers of emotional functioning and dysfunction.”
Looking ahead, the research team hopes to expand their focus to individuals who struggle with managing their feelings. “Our long-term goal is to develop objective brain-based markers of emotional processing and emotion regulation,” the authors stated. “One particularly promising direction is the study of clinical populations characterized by abnormal emotional responses, such as anxiety disorders, depression, borderline personality disorder, or post-traumatic stress disorder.”
“If we can identify reliable neural signatures of healthy emotional processing, we may eventually be able to detect when these mechanisms become dysregulated and use that information to improve diagnosis, prognosis, and treatment personalization,” they continued.
Despite the limitations, the research highlights the powerful way social context shapes our basic biology. A word is not just a collection of letters. The human brain treats language as a complex social event that requires constant moral and emotional evaluation.
“We believe one of the most exciting aspects of this work is that it illustrates how neuroscience and artificial intelligence can complement each other,” the scientists concluded. “Neuroscience helps us understand how emotions are represented in the brain, while machine learning provides powerful tools for detecting patterns that may be difficult to observe using traditional approaches. Together, these methods may eventually allow us to better understand individual differences in emotional functioning and vulnerability to mental health disorders.”
The study, “EEG Based Decoding of the Perception and Regulation of Taboo Words,” was authored by Parisa Ahmadi Ghomroudi, Michele Scaltritti, Bianca Monachesi, Atefeh Jalali, Peera Wongupparaj, Remo Job, and Alessandro Grecucci.
