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<td><span style="font-family:Helvetica, sans-serif; font-size:20px;font-weight:bold;">PsyPost – Psychology News</span></td>
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<td><a href="https://www.psypost.org/you-dont-just-think-about-politics-you-physically-feel-it-in-your-body/" style="font-family:Helvetica, sans-serif; letter-spacing:-1px;margin:0;padding:0 0 2px;font-weight: bold;font-size: 19px;line-height: 20px;color:#222;">You don’t just think about politics, you physically feel it in your body</a>
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<p><p>A recent study published in the <em><a href="https://doi.org/10.1073/pnas.2534895123" target="_blank">Proceedings of the National Academy of Sciences</a></em> suggests that political emotions are not just abstract thoughts, but are distinctly felt physical experiences that shape democratic engagement. The research provides evidence that people feel politically driven emotions differently in their bodies compared to everyday emotions. These physical sensations reliably predict whether someone will actually participate in political actions like voting or protesting.</p>
<p>Scientists recognize that emotions drive political engagement and public division. Yet, the way people physically experience these feelings remains mostly unexplored in political science. Typically, political emotions are measured by asking people to rate their feelings on a simple numerical scale. This approach treats emotions as detached mental states.</p>
<p>The authors of this study argue that physical sensations form the core of any emotional experience. When people feel an emotion, they experience interoceptive states, which are the brain’s internal perceptions of signals from inside the body, like a racing heart or a tense stomach. The researchers wanted to map these self-aware, physical feelings, known as somatosensory experiences, to see what a state of political anger or political hope actually feels like in the human body.</p>
<p><a href="https://andreavik.carrd.co/" target="_blank">Andrea Vik</a>, a postdoctoral research fellow at <a href="https://www.politics-of-feelings.com/" target="_blank">the Centre for the Politics of Feeling</a> at Royal Holloway and the School of Advanced Study, University of London, helped lead the research. She explained how her academic background inspired the project. </p>
<p>“It really started during my master’s, when I took a political psychology course with Dr. Bert Bakker at the University of Amsterdam, and I became hooked on the question of how emotions shape political behavior,” Vik told PsyPost. “At the same time, I was fascinated by the body’s role in all of this: what our physiology can tell us that self-reports can’t. Then Professor Manos Tsakiris, who leads the Centre for the Politics of Feelings and is the senior author on this paper, introduced me to the emBODY-tool, a body-mapping method, and things clicked into place.” </p>
<p>Understanding these bodily sensations can shed light on how political contexts alter basic psychological responses. To explore these physical patterns, the researchers conducted a study with 992 adult participants from the United States. The sample was designed to be nationally representative in terms of age, gender, ethnicity, and political party affiliation. The median age of the participants was 46 years, and the group included exactly 50 percent women.</p>
<p>The scientists used a validated digital mapping technique called the emBODY tool to measure physical reactions. During the experiment, participants viewed digital silhouettes of the human body and used a coloring tool to indicate exactly where they felt physical sensations. They painted regions red to show increased activation, such as warmth or tension, and they used blue to show decreased activation, such as numbness or physical heaviness.</p>
<p>First, the participants completed this mapping task for five everyday, nonpolitical emotions, which included anger, anxiety, depression, disgust, and hope. Later in the study, participants repeated the exact same mapping process for the political versions of these five emotions. For the political emotions, participants were asked to choose a contemporary political issue from a list that personally made them feel the specific emotion before coloring the body silhouette. They also rated the overall intensity of their emotional response on a numerical scale from zero to 100.</p>
<p>The body maps revealed that adding a political context to an emotion changes how that emotion is physically experienced. For instance, everyday depression tends to cause a sensation of numbness or reduced activation in the arms and legs. Political depression, in contrast, showed a much more widespread pattern of physical activation across the whole body.</p>
<p>Political disgust also produced an entirely different physical map compared to everyday disgust. Everyday disgust, such as the natural physical reaction to spoiled food, tends to be felt heavily in the stomach and throat. When participants mapped political disgust, the physical sensation looked remarkably similar to anger, with high activation concentrated in the head and upper body.</p>
<p>Vik noted that this finding stood out during the analysis. “In this project, we only pre-registered our research questions, because we had no strong predictions about whether politics would alter the embodied signature of an emotion. Either direction, political emotions being similar or different, would have been meaningful,” she said. </p>
<p>“So I was struck by just how much the political context changed how emotions are embodied, particularly how disgust shifted away from something like straightforward pathogen avoidance (such as reacting to something physically repulsive) and towards something closer to moral outrage,” Vik continued. “That shift matters, I think, because it suggests that political context doesn’t just intensify or weaken your emotions. It can fundamentally transform what kind of emotional experience you’re having.”</p>
<p><img fetchpriority="high" decoding="async" src="https://www.psypost.org/wp-content/uploads/2026/05/bodymaps-1024x479.jpg" alt="" width="1024" height="479" class="aligncenter size-large wp-image-232127" srcset="https://www.psypost.org/wp-content/uploads/2026/05/bodymaps-1024x479.jpg 1024w, https://www.psypost.org/wp-content/uploads/2026/05/bodymaps-300x140.jpg 300w, https://www.psypost.org/wp-content/uploads/2026/05/bodymaps-768x359.jpg 768w, https://www.psypost.org/wp-content/uploads/2026/05/bodymaps-750x351.jpg 750w, https://www.psypost.org/wp-content/uploads/2026/05/bodymaps-1140x534.jpg 1140w, https://www.psypost.org/wp-content/uploads/2026/05/bodymaps.jpg 1282w" sizes="(max-width: 1024px) 100vw, 1024px"></p>
<p>Other emotions showed more subtle changes or remained largely similar to their everyday counterparts. Political hope produced weaker physical activation than everyday hope, possibly because political hope is mixed with adversarial feelings toward political opponents. Political anxiety was generally similar to everyday anxiety but featured slightly less sensation in the stomach area, leaning more toward mental vigilance. </p>
<p>The scientists also looked at how individual political differences influenced these physical sensations. Political party affiliation altered the physical experience of these emotions. Democrat-leaning participants reported stronger bodily sensations for negative political emotions compared to Republican-leaning participants. For political anger, anxiety, depression, and disgust, Democrat-leaning individuals showed much higher physical activation, primarily concentrated in the head and upper torso.</p>
<p>When examining how these physical feelings impact real-world behavior, the researchers found a strong link to democratic participation. The physical intensity and physical spread of a political emotion across the body reliably predicted whether a person engaged in real political activities. These activities included voting in elections, signing petitions, posting online advocacy messages, or attending public protests.</p>
<p>Interestingly, the physical intensity of political emotions did not predict affective polarization. Affective polarization refers to the intense emotional dislike or distrust of people belonging to an opposing political party. This suggests that the physical urge of a political emotion drives people toward taking civic action rather than simply disliking the opposing side.</p>
<p>“I hope it gives people a moment to reflect on how their emotions are embodied, and how politics shapes that,” Vik said. “We tend to think of political emotions as something we can simply rate: how angry are you, on a scale of one to ten? But emotions are so much more than a number.” </p>
<p>“They are felt and lived through the body, the butterflies in your stomach, the tension in your chest, the weight in your limbs,” Vik added. “What we find is that politics changes those bodily experiences of anger, anxiety, disgust, and hope. And it may be that embodied experience, not the number someone gives on a survey, that actually moves people to participate. Our bodies, it turns out, are part of our politics too.”</p>
<p>While the study provides extensive evidence regarding how we physically feel politics, the authors acknowledge several boundaries to their findings. The research relies on a cross-sectional design, meaning the data was collected at a single point in time. Because of this, the scientists cannot definitively prove that the physical sensation of an emotion directly causes political action.</p>
<p>Vik emphasized the need for measured interpretations of the results. “I want to be careful not to overstate the effects. This is an initial study, and the findings should be read as such,” Vik said. “There are some important limitations to our study: achieving true equivalence in emotional intensity across political and non-political conditions is inherently difficult, the U.S. context may represent a particularly strong case given the salience of partisan identity, and establishing causal direction will require longitudinal designs. But I’d rather readers see those as directions than dealbreakers.”</p>
<p>Despite these cautions, the researchers said that understanding physical emotional responses could reshape political science. “The practical significance, I think, is real,” Vik said. “If political emotions are embodied, and if that embodiment shapes political behavior in ways that self-reported intensity does not capture, it has genuine implications for how we study political emotions going forward. If political participation depends partly on how politics is felt in the body, then inequalities in that felt experience, who gets to feel it, who has learned not to, whose embodied responses have been suppressed or dismissed, are not just personal.”</p>
<p>“They are political,” she added. “They shape who acts, and whose voices are heard. Democracy may depend less on what people think than on what they are able to feel.”</p>
<p>The concept of “ideological bodies” is one area where the authors urge caution. “The finding I think is most vulnerable to misinterpretation is what we call ‘ideological bodies,’ the pattern where Democrat-leaning participants reported stronger and more widespread bodily sensations than Republican-leaning participants for negative political emotions,” Vik said. </p>
<p>“Some might read this as suggesting that one group is more emotional and therefore less rational, a notion that has long been debunked, but stubbornly persists. I want to preempt that: embodying emotions more or less says nothing about your rationality or moral character.”</p>
<p>“What it reflects is that political worldviews are potentially lived not just in how we think, but in how we feel the world from the inside,” she continued. “Here, our case selection and study design also matter; partisan divides might be especially stark in the US, and at the time of data-collection democrats were ‘electoral losers’, so we don’t know how this finding holds up in other contexts.”</p>
<p>Looking ahead, the research team plans to expand their focus to different demographics and societal issues. “The very concrete next steps are a project on how young men experience relative deprivation, i.e., the feeling that their group is unfairly worse off than others, in their bodies, and the consequences this has for violent extremism,” Vik said. </p>
<p>“My long-term goal is to build a fuller picture of how political emotions live in both the brain and the body, and how that shapes our politics,” Vik said. “I’d love to see that knowledge used in ways that are relevant for evidence-based communication, analysis, and policy, whether that’s building emotional resilience in populations, resilience to radicalization, or knowledge resilience to combat misinformation.” </p>
<p>“And perhaps most of all, I hope it can contribute in some way to societies that are better able to channel emotions and frustrations constructively, into participation rather than disenchantment, and into something positive both for individuals and for our democracies.”</p>
<p>The study, “<a href="https://doi.org/10.1073/pnas.2534895123" target="_blank">Politics embodied: How politics shapes and is shaped by the bodily experience of emotions</a>,” was authored by Andrea Vik, Alejandro Galvez-Pol, Sohee Park, and Manos Tsakiris.</p></p>
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<td><a href="https://www.psypost.org/do-manipulative-people-expect-less-from-love/" style="font-family:Helvetica, sans-serif; letter-spacing:-1px;margin:0;padding:0 0 2px;font-weight: bold;font-size: 19px;line-height: 20px;color:#222;">New study links manipulative personality traits to lower relationship intimacy expectations</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">May 22nd 2026, 08:00</div>
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<p><p>New research reveals that people with highly manipulative personalities hold lower expectations for emotional closeness in their romantic relationships, with older women showing the strongest negative association. But the findings suggest that existing views on love and attachment habits shape connection more heavily than negative personality traits alone. The research was published in <a href="https://doi.org/10.1016/j.paid.2026.113692"><i>Personality and Individual Differences</i></a>.</p>
<p>Developing deep intimacy is widely considered a cornerstone of psychological well-being. A supportive and trusting romantic relationship can provide a psychological buffer against life stressors and improve overall mental health. When individuals struggle to form these bonds, they often experience higher rates of loneliness and ongoing emotional distress. </p>
<p>Psychologists identify three socially antagonistic personality profiles collectively called the Dark Triad. Narcissism involves grandiosity, entitlement, and an excessive need for admiration. Psychopathy is characterized by a lack of remorse, impulsive behavior, and emotional coldness. Machiavellianism describes a cynical worldview and a manipulative, strategic approach to interacting with others.</p>
<p>People who score high on these traits often experience relationship difficulties. Past research links these tendencies to infidelity, low commitment, and a tendency to view romance as a game. Less focus has been placed on what these individuals actually anticipate from a partner regarding mutual sharing and emotional trust.</p>
<p>Intimacy goes beyond physical affection. Psychologists define intimate expectations as the anticipation of mutual self-disclosure, deep trust, and a shared sense of understanding. People who score high in intimacy expectations look for a partner who will validate their innermost feelings. Those with low expectations prefer to keep their personal thoughts hidden. </p>
<p>These standards are heavily influenced by a person’s underlying attachment style. Attachment theory was originally developed to describe how infants bond with their caregivers. Psychologists have since adapted this framework to understand how adult romantic partners relate to one another. </p>
<p>Attachment styles are generally divided into secure and insecure categories. People with a secure attachment style feel comfortable with intimacy and are usually warm and loving. Insecure attachment styles, which include anxious and avoidant patterns, tend to create psychological barriers to experiencing a deeply fulfilled romantic life.</p>
<p>The psychological theory proposes that early social experiences create broad mental rules about whether people can be trusted. Individuals with an avoidant attachment style attempt to minimize vulnerability by keeping emotional distance. They often downplay the importance of having a responsive partner. </p>
<p>Those with an anxious attachment style frequently worry about abandonment and remain highly sensitive to rejection. Beliefs about romance also influence how much closeness someone desires. Some people hold highly idealized views of love, believing in concepts like true love or soulmates. These romantic ideals shape how people evaluate the potential for intimacy in their own partnerships.</p>
<p>Researchers Silvija Ručević and Josipa Antunović at the Josip Juraj Strossmayer University of Osijek in Croatia set out to understand how these factors relate to one another. They wanted to evaluate whether Dark Triad profiles, attachment habits, or idealized romantic beliefs were the primary drivers of relationship expectations. They also looked at whether demographic factors like age or gender shifted these emotional patterns.</p>
<p>To investigate this, Ručević and Antunović surveyed 900 adults aged 18 to 74 who were currently in a romantic relationship. The sample was predominantly heterosexual and included a mix of married and dating couples. The participants completed a series of questionnaires designed to measure their levels of Dark Triad traits.</p>
<p>Participants responded to statements such as “I tend to manipulate others” to gauge Machiavellian tendencies. They also answered questions about their relationship anxiety, emotional avoidance, and beliefs regarding idealized romance. Finally, the researchers measured what each participant expected regarding emotional closeness and trust using a standardized intimacy scale.</p>
<p>The researchers analyzed the data using statistical models to see which traits and beliefs carried the most weight. They utilized a layered approach, adding variables step by step to determine which factors uniquely predicted a person’s expectations for intimacy. This layered statistical process is known as hierarchical regression. </p>
<p>Hierarchical regression allows researchers to see whether a newly added variable explains anything fresh about the data. By feeding age and gender into the model first, the scientists ensured that any subsequent findings about personality were not just illusions created by demographic differences. Subsequent steps introduced the personality traits, followed by the relational beliefs and attachment habits.</p>
<p>The results showed that general relational habits were the strongest predictors of intimacy expectations. Avoidant attachment strongly predicted a desire for less emotional closeness. In contrast, holding highly idealized romantic beliefs was the strongest predictor of expecting high levels of intimacy.</p>
<p>When looking specifically at the Dark Triad, the researchers found divergent effects among the three distinct traits. Machiavellianism emerged as the strongest personality predictor of low intimacy expectations. It appears that people who view social interactions as strategic endeavors are less likely to anticipate mutual trust in romance.</p>
<p>Narcissism displayed a slightly different pattern during the analysis. When standing alone as a single data point, narcissism had a small negative association with intimacy expectations. Once the researchers statistically removed the manipulative tendencies of Machiavellianism, narcissism weakly predicted an increase in intimacy expectations. </p>
<p>This statistical phenomenon is known as a suppression effect. The researchers suggest that the need for validation and approval associated with narcissism might drive a basic desire for closeness. Narcissistic individuals may still want admiration and connection, even if that interpersonal desire remains largely self-centered.</p>
<p>Psychopathy did not uniquely predict intimacy expectations once the other personality variables were included in the model. While psychopathy is linked to harmful behavioral outcomes like infidelity, it might not heavily impact the cognitive ideas people hold about closeness. The way individuals act in romantic relationships might simply differ from what they conceptualize in their minds.</p>
<p>The researchers also conducted moderation analyses to see if age or gender changed the mathematical relationships. They found that demographics influenced the connection between Machiavellianism and intimacy expectations. The negative association between manipulative traits and a desire for closeness grew much stronger in older women.</p>
<p>Older women with high levels of Machiavellianism reported the lowest intimacy expectations of any demographic group in the study. Younger women and men of all ages showed a relatively steady pattern. For these groups, a high Machiavellian score predicted lower intimacy expectations, but the effect remained consistent regardless of changing age.</p>
<p>The researchers note that women with high Machiavellianism might develop increasingly pragmatic and emotionally distant views of relationships over time. This psychological distancing could be compounded if they consistently select partners with similar antagonistic traits. Narcissism and psychopathy did not show this age or gender moderation, remaining stable across all demographic groupings.</p>
<p>While the results offer a nuanced look at relationship dynamics, the study has limitations. The research relied entirely on self-reported surveys. This method can introduce psychological bias, as participants might not always answer honestly about socially undesirable motives or actions.</p>
<p>The study also used a cross-sectional design, meaning the data was collected at a single static point in time. Because the data is observational, it cannot prove that these personality traits cause a specific set of intimacy expectations. Longitudinal studies tracking couples over years would be needed to establish how these mental frameworks evolve.</p>
<p>The researchers point out that their non-clinical community sample resulted in relatively low overall scores for the socially antagonistic traits. The statistical effects, while observable, were modest in mathematical size. This indicates that intimacy is a multifaceted concept shaped by a wide variety of personal and environmental factors.</p>
<p>Understanding the roots of low intimacy expectations can help psychologists develop better relationship therapies. If a counselor knows a patient views relationships strictly as strategic alliances, they can tailor their therapy sessions accordingly. Addressing these underlying cognitive frameworks is often necessary before attempting to change outward romantic behaviors. </p>
<p>Future research could explore how cultural backgrounds or specific partner interactions alter these internal relationship maps. Intimacy expectations might be shaped by broader societal norms just as much as individual psychology. By integrating cognitive beliefs and personality analysis, researchers can better map out why some individuals struggle to build healthy romantic bonds.</p>
<p>The study, “<a href="https://doi.org/10.1016/j.paid.2026.113692">Behind the mask of love: Associations among dark triad traits, attachment avoidance and anxiety, romantic beliefs, and intimacy expectations</a>,” was authored by Silvija Ručević and Josipa Antunović.</p></p>
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<td><a href="https://www.psypost.org/brain-changes-during-meditation-begin-within-minutes-and-peak-around-the-7-minute-mark-study-finds/" style="font-family:Helvetica, sans-serif; letter-spacing:-1px;margin:0;padding:0 0 2px;font-weight: bold;font-size: 19px;line-height: 20px;color:#222;">Brain changes during meditation begin within minutes and peak around the 7-minute mark, study finds</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">May 22nd 2026, 06:00</div>
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<p><p>A study published in the journal <a href="https://doi.org/10.1007/s12671-026-02790-1" target="_blank">Mindfulness</a> has found that practicing a brief session of breath-watching meditation can produce changes in brain activity in as little as two minutes. The research indicates that these neural shifts begin within the first few minutes of practice and reach their peak intensity around the seven-minute mark, regardless of a person’s prior meditation experience. </p>
<p>Many people turn to meditative practices to manage daily stress, improve focus, and enhance their emotional well-being. To understand how these practices affect the mind, scientists often use electroencephalography, which is commonly abbreviated as EEG. EEG is a technology that measures the electrical activity of the brain through sensors placed on the scalp. This electrical activity is recorded as brainwaves, which fluctuate at different speeds depending on a person’s mental state.</p>
<p>In past research, scientists often treated the brain during meditation as a static object. They typically collected brainwave data over a full session and averaged the numbers together to find general patterns. While this approach is useful for simplifying data, it misses the moment-to-moment shifts that occur when a person sits down to meditate. The authors of this study wanted to address this gap by tracking the exact timing of brainwave changes from the very beginning of a session.</p>
<p><a href="https://www.linkedin.com/in/saketh-reddy-54657977" target="_blank">Malipeddi Saketh</a>, a senior research fellow at the Centre for Consciousness Studies at the National Institute of Mental Health and Neuro Sciences in Bengaluru, India, explained the motivation behind the project. “Meditation research has traditionally compared broad states such as ‘rest’ versus ‘meditation,’ but we still know surprisingly little about when changes in the brain actually emerge after meditation begins,” Saketh said. “Many people assume meditation effects require long sessions, yet little work has examined the moment-to-moment temporal dynamics of brain activity during meditation.”</p>
<p>Saketh and his colleagues sought to pinpoint these temporal transitions. “We were particularly interested in whether the brain shows specific time windows during meditation where changes become strongest,” Saketh noted. “Since breath-focused practices are widely used across mind-body traditions and mental health interventions, we investigated how neural activity evolves over time during a simple breath-watching meditation practiced in the Isha Yoga tradition.”</p>
<p>By tracking when brainwaves begin to shift and when these shifts reach their peak, the researchers hoped to find the optimal duration of a single session. This information is highly relevant for people who use digital applications or online platforms to practice. If brief sessions of under ten minutes can produce measurable neural changes, meditation could become a more realistic and scalable tool for the general public. Additionally, the researchers wanted to explore whether these timing patterns differ between experienced practitioners and beginners.</p>
<p>To explore these questions, the scientists recruited 103 participants and divided them into three distinct groups based on their level of experience. The first group consisted of 28 individuals with no prior history of meditation, who were classified as meditation-naïve controls. This control group included 16 female participants and had an average age of approximately 31 years. These participants were recruited from a local student community through digital advertisements and word-of-mouth recommendations.</p>
<p>The second group comprised 33 novice meditators, with an average age of nearly 32 years, including 14 female participants. These individuals had completed a foundational training program called Shambhavi Mahamudra, which is a 21-minute practice that incorporates breathing exercises and focus. However, they had not participated in any advanced training programs.</p>
<p>The third group included 42 advanced meditators, with an average age of roughly 35 years, including 18 female participants. These participants had completed an intensive eight-day silent retreat known as Samyama. This advanced retreat requires a strict 60-day preparation period that involves a specific vegan diet and multiple daily yoga practices. All participants in the study were matched to ensure similarities in age, gender, and socioeconomic background.</p>
<p>The researchers applied strict guidelines to select healthy participants. Individuals were excluded if they had a history of neurological disorders, uncorrected vision problems, hearing difficulties, or physical disabilities. Anyone with a history of substance abuse, major mental illness, or recent use of psychiatric medication was also excluded. For the novice and advanced groups, participants had to practice only within the Isha Yoga tradition and could have no experience with other meditation schools.</p>
<p>The study took place in a temperature-controlled, soundproof room to minimize external distractions. The entire experimental session included several phases, beginning with a period of quiet rest and a breathing exercise called pranayama. Next, participants engaged in a 15-minute breath-watching meditation, which was the focus of this specific analysis. To maintain high data quality, the researchers analyzed only the first 10 minutes of this meditation session.</p>
<p>During the breath-watching session, participants were instructed to focus their attention on the natural flow of their breath. If their minds wandered, they were told to simply notice the distraction and gently return their attention to their breathing. The experienced meditators performed this task as they normally would during their personal routines. The control group received a brief training session and a short practice period before the recording began to ensure they understood the instructions.</p>
<p>To record brain activity, the researchers used a specialized net containing 128 electrodes placed across the scalp. Before the session, participants washed their hair to ensure a clean electrical connection between the sensors and the skin. The system recorded brainwaves at a rate of 1,000 measurements per second.</p>
<p>Because biological systems generate non-brain signals, like muscle twitches or eye blinks, the scientists had to clean the data. They used specialized software tools to automatically detect and remove these artifacts, which are essentially electrical noise. They also applied mathematical algorithms to separate true brain signals from muscle movements and heartbeats. Any electrode channels that remained noisy were corrected using mathematical interpolation, which estimates missing data based on neighboring sensors.</p>
<p>The scientists analyzed the cleaned data across several frequency bands, which represent different speeds of brainwaves measured in hertz, or cycles per second. The lowest frequency band is delta, which ranges from 0.5 to 4 hertz and is typically associated with deep sleep or reduced alertness. Theta waves, ranging from 4 to 8 hertz, are associated with deep relaxation and inward mental focus.</p>
<p>The researchers also looked at a transitional band called theta-alpha, which ranges from 6 to 10 hertz. This band is thought to reflect a state of calm focus where relaxation and alertness overlap. Alpha waves, ranging from 8 to 12 hertz, represent a state of relaxed wakefulness, such as when someone closes their eyes but remains awake.</p>
<p>The beta1 band, ranging from 13 to 20 hertz, is linked to active, focused attention. Beta2 waves, ranging from 20 to 30 hertz, are associated with high-level cognitive processing or stress, though no changes were found in this band during the study. Finally, the gamma1 band, ranging from 30 to 40 hertz, is associated with active perception and can sometimes reflect mind-wandering or sensory processing.</p>
<p>To track how brain activity changed over time, the researchers compared the data from successive one-minute segments against the first 30 seconds of the meditation session, which served as the baseline. They also ran a separate analysis comparing the meditation state to a period of eyes-closed rest. They used a statistical method called threshold-free cluster enhancement to analyze the data across all 128 sensors simultaneously. This mathematical approach helps identify genuine patterns across neighboring electrodes while reducing the risk of false positives.</p>
<p>The analysis revealed that all three groups experienced brainwave changes during the meditation session. These changes consistently began to emerge around the two- to three-minute mark. Across all groups, the researchers observed increases in theta, theta-alpha, alpha, and beta1 power. At the same time, there was a steady decrease in delta and gamma1 power.</p>
<p>These shifting patterns peaked in intensity between seven and ten minutes into the session. This suggests that the brain gradually transitions into a stable state of relaxed alertness during the practice. The combination of increased alpha and theta waves alongside decreased delta waves provides evidence of this calm, attentive state.</p>
<p>Saketh pointed out that the findings challenged expectations of how brain activity shifts. “One surprising finding was the consistency of the temporal pattern across multiple EEG measures,” he stated. “We observed that several neural changes appeared to intensify around a similar time window rather than increasing linearly throughout the session. This suggests that meditation may involve identifiable transition points in brain dynamics rather than gradual, uniform changes.”</p>
<p>Although the general patterns were similar, the exact timing of these shifts varied slightly by group. In the control group, significant changes across almost all frequency bands emerged precisely at the two-minute mark. Novice meditators showed changes in delta and beta1 waves even earlier, at the one-minute mark, while their theta and alpha waves shifted at two minutes.</p>
<p>Advanced meditators showed a unique pattern in their theta waves. Their theta power initially decreased at the one-minute mark before rising steadily from the second minute onward. The researchers suggest this temporary drop might represent a rapid reorganization of brain networks as experienced practitioners transition quickly into meditation.</p>
<p>When comparing the groups directly, the researchers found that advanced meditators exhibited distinct brainwave signatures. Throughout the entire session, including the very first 30 seconds, advanced meditators had significantly higher theta and theta-alpha power than the other groups. This suggests that long-term practice may produce lasting changes in the brain that remain present even at the start of a session.</p>
<p>Advanced meditators also showed a much stronger decrease in delta power during the first three minutes compared to both novices and controls. This lower delta activity suggests that experienced practitioners may experience less mind-wandering and higher levels of initial alertness. Additionally, advanced meditators showed significantly lower gamma1 power at the nine-minute mark compared to the control group.</p>
<p>The researchers also examined the relationship between theta and gamma1 waves, which tend to fluctuate in opposite directions. They found a negative correlation between these two bands in all three groups, meaning that as theta waves increased, gamma1 waves decreased. This negative correlation was strongest and most stable in the advanced meditators. In contrast, novice meditators showed the weakest relationship, while controls showed a fluctuating pattern over the ten-minute period.</p>
<p>Participation in long meditation retreats has been shown to significantly enhance well-being. However, this may not be practical or feasible for large segments of the population. Digital interventions that provide accessible meditation training through apps or online programs could help bridge this gap, making meditation more accessible to a wider population.</p>
<p>Saketh emphasized that the study offers encouraging news for those with busy schedules. “One important takeaway is that measurable changes in brain activity can emerge within just a few minutes of meditation practice,” he explained. “In our study, several EEG changes appeared to peak around seven minutes into the meditation session. This suggests that even relatively short periods of meditation may meaningfully influence brain dynamics.”</p>
<p>“From a mental well-being perspective, this is encouraging because many people feel they lack sufficient time to meditate or believe they need to practice for very long durations to experience benefits,” Saketh continued. “Our findings suggest that even brief periods of intentional mental training may begin engaging brain processes related to attention and internal awareness.”</p>
<p>These findings support the integration of brief meditation practices into daily routines for mental health and cognitive benefits. Mental health issues such as stress, anxiety, and depression are rising at an alarming rate worldwide. In response to this growing crisis, public health organizations emphasize the importance of investing in preventive care and promoting mental well-being.</p>
<p>But as with all research, there are some limitations to consider when interpreting the results. “This was a controlled laboratory study, so real-world meditation experiences can vary considerably among individuals and contexts,” Saketh noted. First, the researchers categorized the meditators based entirely on their self-reported training history. This approach does not account for individual differences in personality, motivation, or daily lifestyle, which can influence how a person responds to meditation.</p>
<p>Second, the study did not collect real-time descriptions of what the participants were experiencing during the session. Without this subjective feedback, it is difficult to know exactly how the brainwave changes relate to specific feelings of focus, distraction, or calm. Future studies could combine brainwave recordings with periodic questions to capture both physical and mental states simultaneously.</p>
<p>There is also a possibility of selection bias in the study sample. People who volunteer for meditation research often have positive attitudes toward the practice, which might affect their focus and brain activity.</p>
<p>Looking ahead, the research team hopes to expand on these findings by exploring more advanced states of consciousness. “Our broader goal is to better understand how meditation alters brain dynamics across time, especially in relation to attention, consciousness, well-being, and self-experience,” Saketh explained. “We are particularly interested in identifying neural markers associated with advanced meditative states, including non-dual awareness and equanimity.”</p>
<p>To achieve this, the team plans to incorporate a wider array of measurement tools. “Future work will involve combining EEG with other approaches such as MRI, autonomic measures, and longitudinal designs to better understand how short-term brain changes relate to long-term psychological and behavioral outcomes,” Saketh said.</p>
<p>“One aspect we find exciting is that the study moves beyond asking whether meditation changes the brain and instead asks how these changes unfold over time,” Saketh reflected. “Understanding the temporal dynamics of meditation may help bridge mind-body traditions and modern neuroscience in a more mechanistic and experimentally testable way.”</p>
<p>The study, “<a href="https://doi.org/10.1007/s12671-026-02790-1" target="_blank">Temporal EEG Signatures of Meditation Experience: Peak Brainwave Changes at 7 Minutes During Isha Yoga Breath Watching</a>,” was authored by Malipeddi Saketh, Arun Sasidharan, Rahul Venugopal, Prejaas K.B. Tewarie, Ravindra P Nagendra, Georg Northoff, Steven Laureys, Balachundhar Subramaniam, and Bindu M Kutty.</p></p>
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<td><a href="https://www.psypost.org/brain-scans-reveal-how-a-teenagers-reaction-to-loss-connects-impulsivity-and-suicidal-thoughts/" style="font-family:Helvetica, sans-serif; letter-spacing:-1px;margin:0;padding:0 0 2px;font-weight: bold;font-size: 19px;line-height: 20px;color:#222;">Brain scans reveal how a teenager’s reaction to loss connects impulsivity and suicidal thoughts</a>
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<p><p>A neuroimaging study of adolescents found that the association between impulsivity and future suicidal thoughts depends on how the right anterior insula, a region of the brain involved in processing emotions, reacts to loss. In adolescents whose anterior insula reacted with strong activation to a small monetary loss, high impulsivity was associated with elevated suicidal thoughts a year later. In contrast, in adolescents whose anterior insula did not react strongly to loss, higher impulsivity was associated with lower levels of suicidal thoughts. The paper was published in <a href="https://doi.org/10.1016/j.dcn.2026.101726"><em>Developmental Cognitive Neuroscience</em></a>.</p>
<p><em>If you or someone you know is struggling with suicidal thoughts or mental health matters, please call the National Suicide Prevention Lifeline at 988 (or 800-273-8255) or visit the </em><a href="https://suicidepreventionlifeline.org/" target="_blank" rel="noopener"><em>NSPL site</em></a>.</p>
<p>Suicide is currently the second leading cause of death among U.S. youth. Despite ongoing prevention efforts, statistics show that youth suicide rates have increased in recent years. Usually, suicidal behavior is preceded by periods during which a person thinks about death and suicide, a concept known as suicidal ideation. These thoughts can range from general contemplations about ending one’s life to making specific plans.</p>
<p>Suicidal thoughts tend to appear when a person feels trapped, hopeless, isolated, or in unbearable emotional pain. They can occur alongside depression, trauma, substance use, or other situations of intense distress. Not every person with suicidal thoughts wants to die permanently, as many simply want their pain to stop or wish to escape an unbearable situation.</p>
<p>Lead author Carly J. Lenniger and colleagues note that leading theories propose that behavioral traits such as impulsivity act as background vulnerabilities. These traits can make suicidal thoughts more likely to occur when a person experiences severe emotional distress.</p>
<p>Impulsivity is a general tendency to take action without considering the consequences. Because of this, impulsive teenagers in distress might respond quickly with suicidal thoughts without fully considering other outcomes. However, previous studies have not shown a simple link between impulsivity and suicide, as impulsivity alone does not reliably differentiate between people who think about suicide and those who actually attempt it.</p>
<p>The authors proposed that the way the brain processes negative outcomes and losses might affect how impulsivity relates to suicidal thoughts. They conducted a study to examine whether neural sensitivity to monetary loss influences this relationship during adolescence.</p>
<p>The final study sample consisted of 63 adolescents between the ages of 13 and 17 at the start of the research. They were recruited from the Pittsburgh area, and 59 percent of the participants were female. Although the study originally enrolled 135 participants, the final sample only included those who completed all of the required questionnaires and brain scans.</p>
<p>Importantly, two-thirds of the participants were classified as having a high familial risk for mental health issues because they had a parent with a lifetime history of disorders such as major depression or schizophrenia. The remaining third of the participants had no such family history. Interestingly, the high-risk group showed overall stronger brain activation in the anterior insula when experiencing a loss compared to the low-risk group.</p>
<p>At the start of the study, participants completed assessments of impulsivity, suicidal thoughts, depression symptoms, and anhedonia, which is the inability to feel pleasure. They also underwent a brain scan using functional magnetic resonance imaging. During this scan, they completed an eight-minute guessing task.</p>
<p>In this task, participants won one dollar on winning trials and lost 50 cents on losing trials. Although the participants believed their guesses determined the outcomes, the wins and losses were actually prearranged by the researchers. To ensure fair compensation, everyone received ten dollars at the end of the game regardless of their performance. One year later, participants again completed the assessment of suicidal thoughts.</p>
<p>The brain scans revealed distinct clusters of active neurons in the anterior insula when participants experienced a monetary loss. The strength of this activation differed among participants, with some showing strong responses and others showing much weaker responses in this region during a loss.</p>
<p>Further analyses showed that the link between impulsivity and future suicidal thoughts depended on how the right anterior insula reacted to these loss outcomes. Interestingly, in participants with lower brain activation to the loss, the association between impulsivity and suicidal thoughts was negative. This meant that highly impulsive individuals with low brain reactivity were actually less likely to experience future suicidal thoughts, whereas less impulsive individuals with low brain reactivity faced an elevated risk.</p>
<p>Conversely, in teenagers with high brain activation to a loss, the link between impulsivity and suicidal thoughts was positive, meaning that highly impulsive individuals tended to report increased suicidal thoughts. Overall, seven participants had very low brain activity to the loss, ten had very high activity, and 46 had medium levels of activity. Among the 46 participants in the middle range, impulsivity was not associated with suicidal thoughts.</p>
<p>The authors concluded that the relationship between impulsivity and future suicidal thoughts varied based on the brain’s reaction to a negative outcome. Impulsive adolescents appeared to be at an elevated risk only when they showed a heightened neural sensitivity to loss, whereas those with lower sensitivity appeared to have a reduced risk.</p>
<p>These findings could point to specific targets for clinical therapy. For impulsive adolescents with highly reactive brains, building the capacity to tolerate distress and regulate emotional responses to negative experiences may be especially critical. In contrast, teenagers with a quieter brain response and low impulsivity may benefit more from therapies that encourage active engagement, such as behavioral activation, or methods that stimulate brain networks.</p>
<p>The study contributes to the scientific understanding of the neural pathways underlying suicidal thoughts. However, it is important to note that the final sample size was relatively small due to a high number of participants who did not complete all scans and surveys. Additionally, the monetary loss experienced in the study was very small and might not fully capture how teenagers react to more personally relevant or severe life losses.</p>
<p>The paper, “<a href="https://doi.org/10.1016/j.dcn.2026.101726">Anterior Insula Reactivity to Loss Moderates the Association Between Trait Impulsivity and Future Suicidal Ideation in Adolescents,</a>” was published in 2026. It was authored by Carly J. Lenniger, Kristen L. Eckstrand, T.H. Stanley Seah, Jennifer S. Silk, Jamie L. Hanson, Melissa Nance, Morgan Lindenmuth, Gretchen Haas, Neal Ryan, and Erika E. Forbes.</p></p>
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<td><a href="https://www.psypost.org/how-a-popular-comedy-podcast-transformed-into-a-major-arena-for-presidential-pol/" style="font-family:Helvetica, sans-serif; letter-spacing:-1px;margin:0;padding:0 0 2px;font-weight: bold;font-size: 19px;line-height: 20px;color:#222;">Listening to Joe Rogan was a stronger predictor of a Trump vote than watching Fox News</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">May 21st 2026, 18:00</div>
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<p><p>The popular program “The Joe Rogan Experience” has gradually transformed from a comedy podcast into a highly influential platform with real-world political weight. A pair of new studies reveals that listenership of the show strongly predicts voting for Donald Trump in the 2024 presidential election, even after accounting for past voting habits. These results were published as a recent preprint in <a href="https://doi.org/10.33774/apsa-2025-v05hb"><em>APSA Preprints</em></a>.</p>
<p>The media landscape has fractured over the past two decades in the shadow of the contemporary internet. Audiences now choose from an immense variety of channels, allowing entertainment programming to emerge as a prominent vehicle for political messaging. Shows that traditionally focused on comedy or culture now routinely include policy issues in their scripts, blurring the lines between standard news and entertainment content.</p>
<p>By embedding political concepts into casual conversations, these hybrid programs reach people who might otherwise ignore civic participation. Researchers refer to the expansion of politics into everyday culture as a state of total politics. When apolitical spaces become inundated with policy messages, audiences encounter civic discourse without actively seeking it out.</p>
<p>Modern podcasts operate differently than traditional television or print media. The format allows hosts to foster strong, one-sided relationships with their audience through informal storytelling and a sense of unscripted authenticity. Because listeners feel a personal connection to the host, they often build deep trust and reduce their natural skepticism toward persuasive messages.</p>
<p>Huma Rasheed, a communication researcher at the University of Pennsylvania, and a team of collaborators wanted to examine how this politicization unfolds within a major cultural property. They chose to study “The Joe Rogan Experience,” a podcast that reaches tens of millions of listeners. While the show originated as a lighthearted comedy podcast, it eventually became a routine subject of conversation in the context of national elections.</p>
<p>The podcast revolves around unscripted interviews where the host speaks with guests ranging from tech billionaires like Elon Musk to astrophysicists and heavyweight boxers. In late 2024, the host interviewed Donald Trump and publicly endorsed him on the eve of the election. Political commentators suggested this appearance gave the political campaign momentum to engage younger demographics and undecided voters in swing states.</p>
<p>To map the thematic evolution of the program, Rasheed and her team gathered a nearly complete collection of episode transcripts. They collected the text from 2,175 episodes broadcast between December 2009 and December 2024. Analyzing such an immense volume of text manually is impossible, so the researchers applied computational text models to identify recurring patterns.</p>
<p>The team used an algorithm that groups words into semantic themes to find forty-five distinct topics throughout the history of the show. The researchers then mapped the relationships between these topics to see which subjects frequently appeared together in the same conversations. This structural mapping allowed them to identify six major thematic domains within the podcast.</p>
<p>The computational approach sorted the show’s content into clusters regarding personal narratives, social and political issues, comedy, fitness and combat sports, conspiracy theories, and wildlife. When looking at how these themes interacted with one another, personal storytelling served as the structural center of the program. Intimate accounts of family life, childhood experiences, and career trajectories acted as narrative bridges linking wildly different subject areas.</p>
<p>This narrative anchor helps facilitate smooth transitions across unscripted conversations that routinely extend well past the two-hour mark. The researchers then tracked how the prevalence of these themes changed over the fifteen-year lifespan of the show. Topics related to crude humor and sexual jokes experienced a steady decline.</p>
<p>Alternatively, discussions regarding social and political issues steadily rose in prominence over the years. Conversations centered on elections, foreign policy, and free speech saw a marked increase beginning around 2016. This change shifted the tone of the show from stereotypical locker room talk to substantive debates about current events.</p>
<p>In the realm of health and medicine, the algorithms inductively grouped conversations about vaccines into the fitness cluster. This placement suggests the podcast framed medical discussions in terms of athletic performance and physical fitness rather than public health policy. This framing matched the host’s previous commentary suggesting that highly fit individuals did not need certain medical interventions.</p>
<p>The researchers also identified a cluster related to altered states and speculative science. This domain included discussions about psychedelics, theoretical physics, ancient civilizations, and space exploration. While conversations about fringe theories and extraterrestrials dipped initially, they garnered a steady increase in popularity on the program after 2016.</p>
<p>After establishing this thematic shift toward serious civic issues, the research team wanted to see if listening to the program correlated with real-world political behavior. They utilized data from a nationally representative survey of 1,600 adults in the United States conducted in early 2025. The survey questioned participants regarding their media consumption habits, demographic background, political leanings, and past voting choices.</p>
<p>Nearly ten percent of the sample reported listening to the podcast at least occasionally. The listener base skewed heavily male, with more than seventy percent of the audience identifying as men. To isolate the specific correlation with the podcast, the researchers mathematically controlled for a wide array of alternative factors.</p>
<p>They accounted for age, income, education, and general interest in government affairs. They also factored in whether the participant regularly consumed specific media, including the Fox News Channel, CNN, MSNBC, national newspapers, or social media platforms. The researchers additionally controlled for party identification and whether the individual voted for Trump in the 2020 election.</p>
<p>Even with these extensive controls in place, tuning into the podcast emerged as a strong predictor of voting for Trump in the 2024 election. The listening habit stood out as the second strongest numeric predictor in their model, trailing only a past vote for the same candidate in 2020. The podcast proved exceptionally predictive compared to traditional political variables like basic party affiliation.</p>
<p>The researchers noted that qualitative anecdotes mirror their quantitative findings. Following the election, focus groups with young voters highlighted how the unscripted audio format shaped perceptions of candidates. Several undecided participants told reporters that the three-hour informal interview made the candidate seem normal and authentic.</p>
<p>It should be noted that the research was published as a preprint. This means the paper has not yet undergone formal peer review—a rigorous process where independent experts evaluate the methodology and conclusions before official publication—so its results should be considered preliminary.</p>
<p>The study authors also note some limitations to their survey design. The data cannot prove that listening to the podcast directly caused individuals to change their voting habits. Unmeasured cultural or psychological factors might drive someone to both listen to the podcast and vote conservatively.</p>
<p>Establishing a direct chain of influence would require specialized experimental setups or tracking the exact same individuals over several years. Future studies might explore the psychological mechanisms that make long-format audio interviews persuasive. Identifying the specific audio features that foster a sense of trust could explain how non-traditional media bypasses typical audience skepticism.</p>
<p>These findings suggest that massive cultural platforms now dictate their own terms of civic engagement. In the past, politicians shaped traditional journalism simply by acting on the public stage. Now, political figures increasingly accommodate the informal style and rules of popular entertainment programs to access vast, unengaged segments of the electorate.</p>
<p>The study, “<a href="https://doi.org/10.33774/apsa-2025-v05hb">From Punchlines to Politics: The Joe Rogan Experience as a Case Study of the Politicization of Apolitical Spaces in the U.S.</a>,” was authored by Huma Rasheed, Liam Cuddy, Brooke Molokach, Jiwon Nam, Scarlett Feuerstein, R. Lance Holbert, and Dannagal G. Young.</p></p>
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<td><a href="https://www.psypost.org/a-new-ai-tool-spots-hidden-signs-of-adult-adhd-months-before-a-formal-diagnosis/" style="font-family:Helvetica, sans-serif; letter-spacing:-1px;margin:0;padding:0 0 2px;font-weight: bold;font-size: 19px;line-height: 20px;color:#222;">A new AI tool spots hidden signs of adult ADHD months before a formal diagnosis</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">May 21st 2026, 16:00</div>
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<p><p>Scientists have developed a new artificial intelligence tool that can predict whether an adult has attention-deficit hyperactivity disorder by looking at their past medical records. The predictive model suggests that subtle patterns in everyday healthcare visits can identify undiagnosed individuals months before a doctor formally spots the condition. This research was recently published in the journal <em><a href="https://doi.org/10.1192/j.eurpsy.2026.10171" target="_blank">European Psychiatry</a></em>.</p>
<p>Attention-deficit hyperactivity disorder is a common neurodevelopmental condition that affects roughly 5 to 7.2 percent of children and about 2.5 percent of adults globally. People with this condition experience varying degrees of inattention, hyperactivity, and impulsivity that interfere with daily life. Getting a proper diagnosis as an adult tends to be quite difficult. </p>
<p>Doctors often struggle to identify the condition in older patients because the symptoms frequently overlap with other mental health challenges. When a diagnosis is delayed, individuals often experience academic or work impairments, increased accident rates, and a lower overall quality of life. An earlier diagnosis provides evidence-based opportunities for pharmacological treatment and therapy, which helps prevent many of these negative outcomes.</p>
<p>Artificial intelligence has recently shown promise in helping doctors spot hidden patterns in patient data. Many previous attempts to use machine learning to detect attention-deficit hyperactivity disorder have mostly relied on brain scans, structured behavioral assessments, or specialized physiological tests. These types of medical data are expensive and not routinely collected for the average patient.</p>
<p>To create a more practical tool, the researchers decided to focus on electronic health records. These records are the standard digital files that clinics and hospitals already maintain for every patient. By training a computer program to read standard medical histories, the authors hoped to create a cost-effective screening method that relies purely on information doctors already have on hand.</p>
<p>The scientists analyzed historical medical data from a regional healthcare system in southwestern Sweden. The database included information from primary care clinics, specialist visits, and hospital admissions between 2011 and 2022. They gathered detailed data on patient demographics, specific medical diagnoses, clinical procedures, and prescribed medications.</p>
<p>To build their model, the researchers started with a group of 3,570 adults who had been formally diagnosed with attention-deficit hyperactivity disorder or prescribed related medications. They also selected a control group of adults who had visited psychiatric outpatient clinics but did not have the disorder. During the design phase, the predictive model struggled to tell the two groups apart when the control group included patients with depression or anxiety.</p>
<p>To fix this issue, the researchers removed individuals with depression and anxiety from the control group. Because the cognitive and behavioral symptoms of depression and anxiety overlap heavily with attention issues, removing them allowed the computer to focus on the unique signatures of attention-deficit hyperactivity disorder. This adjustment left a final control group of 5,126 adults, which still provided plenty of data for the program.</p>
<p>The authors then fed this data into a machine learning system based on a “transformer” architecture. A transformer is a sophisticated type of artificial intelligence technology that excels at understanding sequences of information. Instead of reading words in a sentence, this specific transformer was trained to read the sequence of a patient’s medical visits and prescription codes over time.</p>
<p>These models use a mathematical technique called positional encoding to understand the exact chronological order of events. This allows the system to grasp how a patient’s health trajectory changes over the course of several months or years. The researchers tested whether the model could predict a diagnosis six, twelve, and eighteen months before the actual diagnosis date. </p>
<p>They evaluated the final model on an entirely separate set of 800 patients, splitting this test group evenly with 400 diagnosed individuals and 400 individuals without the condition. Testing the model on a separate group ensures that the artificial intelligence is evaluated on fresh information it has never seen before. The findings suggest that the model can successfully predict adult attention-deficit hyperactivity disorder using routine clinical data. </p>
<p>The artificial intelligence performed best when predicting a diagnosis six months in advance. At this six-month mark, the model correctly identified 80 percent of the patients who actually had the disorder. It also correctly ruled out the condition in 77 percent of the patients who did not have it. </p>
<p>The model achieved a score of 0.79 on a mathematical metric called the Area Under the Curve. This metric evaluates how well a predictive model distinguishes between two groups, with a score of 1.0 being perfect and 0.5 being no better than a random guess. The results remained fairly stable even when predicting diagnoses eighteen months into the past.</p>
<p>The scientists also examined which specific medical codes the computer used to make its predictions. To do this, they used an analytical technique called Shapley Additive Explanations. This method helps open the “black box” of artificial intelligence by showing exactly which demographic factors or clinical codes increase or reduce the predicted risk.</p>
<p>The analysis revealed that previous diagnoses related to substance use were strong indicators of a future attention-deficit hyperactivity disorder diagnosis. For example, medical codes indicating the use of stimulants, including heavy caffeine use, were highly predictive. The model also flagged codes related to specific blood alcohol levels ranging from 0.60 to 0.79 milligrams per 100 milliliters. </p>
<p>These findings align with previous research, which indicates that adults with undiagnosed attention-related issues sometimes try to self-medicate with caffeine, alcohol, or other substances. The computer program also picked up on medical codes related to childbirth complications. The data suggests that mothers who experience issues such as obstructed labor or abnormal fetal positions have a slightly higher chance of a later attention-deficit hyperactivity disorder diagnosis. </p>
<p>Researchers suspect this reflects broader physical and psychosocial challenges rather than a direct physical cause. Additionally, the scientists noticed distinct demographic and healthcare utilization patterns between the two groups. The diagnosed individuals tended to be younger, averaging around 31 years old compared to 52 years old in the control group. </p>
<p>They also had significantly more primary care and psychiatrist visits than the control group, but fewer hospital admissions and shorter hospital stays. While these findings are promising, the authors caution against viewing this artificial intelligence as a replacement for human doctors. The tool is not designed to formally diagnose anyone on its own. </p>
<p>Instead, it is meant to act as an early warning system that operates quietly in the background of a hospital’s computer network. By flagging patients who exhibit suspicious patterns of healthcare use, the system can simply notify doctors that a specific person might benefit from a comprehensive psychological evaluation. A trained healthcare professional must still sit down with the patient to conduct structured interviews and confirm the diagnosis.</p>
<p>One limitation of the study is the exclusion of patients with depression and anxiety from the control group. In a real clinical setting, doctors frequently need to distinguish between attention-deficit hyperactivity disorder and depression. Because the model was not trained on patients with these specific overlapping conditions, it might face challenges when deployed in a general psychiatric population.</p>
<p>The researchers also noted a slight discrepancy in how the model treated men and women. The artificial intelligence successfully identified the condition in 75.2 percent of the female patients, but only caught 66.7 percent of the male cases. The false positive rate remained consistent across genders, but the disparity in successful identification highlights the need for further evaluation to ensure equitable performance.</p>
<p>Moving forward, scientists hope to test this model in different healthcare systems outside of Sweden. Medical coding practices can vary significantly from one country to another, so the algorithm must prove its adaptability. The authors also suggest exploring how this data-driven approach might align with newer, more flexible ways of classifying mental health conditions in the future.</p>
<p>The study, “<a href="https://doi.org/10.1192/j.eurpsy.2026.10171" target="_blank">Early detection of adults ADHD using electronic health records: A machine learning study</a>“, was authored by Omar Hamed, Farzaneh Etminani, Peter Jacobsson, and Thomas Davidsson.</p></p>
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<td><a href="https://www.psypost.org/how-a-mother-s-narcissism-might-shape-her-daughter-s-emotional-health/" style="font-family:Helvetica, sans-serif; letter-spacing:-1px;margin:0;padding:0 0 2px;font-weight: bold;font-size: 19px;line-height: 20px;color:#222;">How a mother’s narcissism might shape her daughter’s emotional health</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">May 21st 2026, 14:00</div>
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<p><p>Young women who perceive their mothers as having highly self-centered traits are more likely to struggle with maintaining their own emotional stability. These results suggest that a parent’s inability to show empathy might negatively impact how a daughter learns to process feelings in early adulthood. The research was published in <a href="https://doi.org/10.3389/fpsyg.2025.1629470"><i>Frontiers in Psychology</i></a>.</p>
<p>Narcissism involves a grand sense of self-importance combined with a constant need for admiration. People with highly narcissistic personalities tend to prioritize their own personal desires over the feelings of others. They frequently lack the ability to empathize with the people around them. This creates a difficult environment within a family unit.</p>
<p>A narcissistic parent might view children merely as extensions of themselves. They often struggle to offer genuine emotional support or validate a child’s independent thoughts and feelings. Children growing up in this type of environment frequently learn to suppress their own emotions to avoid harsh criticism. They might learn early on that maintaining outward appearances matters more than addressing genuine emotional pain.</p>
<p>Emotional balance describes a person’s ability to navigate life’s inevitable stresses without swinging into extreme mood states. A person with emotional balance possesses the internal tools to calm down and respond constructively when faced with frustration. They can maintain a sense of equilibrium while evaluating a stressful situation. Those lacking these skills are more susceptible to aggressive reactions or harmful behaviors when under pressure.</p>
<p>The researchers viewed emotional balance through the lens of cognitive and behavioral psychology. From a cognitive perspective, negative emotions often stem from distorted interpretations of reality. When an individual learns to replace these negative thoughts with realistic assessments, they tend to gain better control over their emotional reactions. </p>
<p>Behavioral theorists offer a similar perspective on how stress impacts young adults. They view emotional imbalance as the result of a person losing control over their behavior when confronted with an external problem. A student facing academic pressure might resort to social isolation rather than seeking help, which then feeds back into a cycle of heightened anxiety. </p>
<p>The university years represent a major life transition that tests a young adult’s coping mechanisms. Students face intense academic demands, shifting social dynamics, and the pressure of impending independence. Being unable to regulate emotions during this time can lead to academic failure or severe mental health struggles. </p>
<p>Entesar Alnashmi and Hanem M. Alboray of King Faisal University in Saudi Arabia sought to understand how family dynamics influence developmental outcomes. They wanted to see if the environment a student grew up in correlates with her current emotional coping skills. Alnashmi led the development of specific questionnaires used to measure these specific psychological dynamics. </p>
<p>The researchers recruited 416 female undergraduate students between the ages of eighteen and twenty-four. These participants were enrolled in various academic programs, including agricultural sciences and business administration. The research team collected data over a three-month period using both electronic and paper surveys. Participants completed the forms anonymously to encourage honest responses about their family lives. </p>
<p>To measure family dynamics, the researchers designed the Narcissistic Mother Scale. This tool asked students to rate their mothers on nine distinct behavioral dimensions. These categories included dominance, arrogance, a sense of superiority, excitability, and feelings of entitlement. Students rated statements based on whether the behaviors applied to their mothers occasionally or consistently. </p>
<p>The researchers also developed an Emotional Balance Scale to evaluate the internal lives of the students. This questionnaire measured both personal and social emotional regulation capabilities. It asked students about their cognitive harmony, which refers to the consistency between their thoughts and beliefs. The tool also assessed how well the participants managed contradictory feelings during stressful moments. </p>
<p>Overall, most participants rated their mothers relatively low on the narcissism scale. The one exception was a trait the researchers labeled excitability, which scored in the moderate range. Overall emotional balance for the students independently averaged out to a moderate level as well. </p>
<p>When the researchers analyzed the combined data, they found a predictable mathematical pattern. The analysis revealed a negative correlation between perceived maternal narcissism and the emotional balance of the daughters. Students who reported higher levels of maternal narcissism tended to score lower on emotional balance, while those reporting lower maternal narcissism scored higher. </p>
<p>The researchers broke down the data further to isolate which specific maternal traits were the strongest indicators of emotional distress. They found that a mother’s intolerance was the strongest predictor of a daughter’s emotional imbalance. Exploitative behavior by the mother was the second strongest indicating factor. </p>
<p>The study authors tied these results to established psychological frameworks like attachment theory. Children need secure emotional bonds with their primary caregivers to develop healthy social skills. When a mother fails to provide a secure emotional base, the child can experience a form of emotional neglect. This unseen neglect can leave lasting feelings of emotional loneliness that carry into adulthood. </p>
<p>Children in narcissistic families might also develop maladaptive defense mechanisms to survive their household environment. Some psychologists suggest that these children are never taught to set healthy boundaries. They grow up prioritizing the needs of their parents to gain basic acceptance and avoid conflict. As adults, this childhood conditioning translates into an inability to express their own needs or process negative emotions effectively.</p>
<p>The current study relies entirely on the daughters’ subjective perceptions of their mothers. The researchers did not conduct clinical psychiatric evaluations of the mothers to confirm a diagnosis of narcissistic personality disorder. The study design is correlational, meaning it cannot definitively prove that maternal traits directly cause the daughters’ emotional struggles. The sample is also limited to a specific demographic of female students at a single university. </p>
<p>To build on this foundational work, the researchers recommend developing targeted intervention programs. They believe universities could offer counseling designed specifically to mitigate the impact of difficult family dynamics on young women. Future studies might also compare the emotional balance of daughters raised by highly narcissistic mothers with that of their typical peers. </p>
<p>Additional research could examine how maternal narcissism relates to other psychological variables in young adults. The authors hope their work will increase awareness among parents about their fundamental role in fostering healthy emotional development. Providing a supportive and empathetic environment at home appears to give children a better chance at navigating the stresses of adult life. </p>
<p>The study, “<a href="https://doi.org/10.3389/fpsyg.2025.1629470">The narcissistic personalities of mothers as perceived by their daughters and its relationship to emotional balance among female students at King Faisal University</a>,” was authored by Entesar Alnashmi and Hanem M. Alboray.</p></p>
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<td><a href="https://www.psypost.org/ai-generated-grokipedia-articles-are-longer-less-readable-and-cite-fewer-sources-than-their-wikipedia-counterparts/" style="font-family:Helvetica, sans-serif; letter-spacing:-1px;margin:0;padding:0 0 2px;font-weight: bold;font-size: 19px;line-height: 20px;color:#222;">AI-generated Grokipedia articles are longer, less readable, and cite fewer sources than their Wikipedia counterparts</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">May 21st 2026, 12:00</div>
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<p><p>A recent study published in the <em><a href="https://doi.org/10.1073/pnas.2603294123" target="_blank">Proceedings of the National Academy of Sciences</a></em> provides evidence that automated encyclopedias differ from human-edited platforms in both structure and political leaning. The research suggests that rather than uniformly removing bias, these automated systems tend to favor longer, more complex narratives while introducing rightward shifts in certain topic areas. These findings raise questions about how artificial intelligence shapes public knowledge and source verification.</p>
<p>In October 2025, the American technology company xAI, founded by Elon Musk, launched <a href="https://grokipedia.com/" target="_blank">Grokipedia</a>. The platform was presented as the world’s first artificial intelligence-written encyclopedia. Musk promised the platform would fix <a href="https://www.psypost.org/wikipedias-news-sources-show-a-moderate-liberal-leaning/" target="_blank">left-leaning biases</a> alleged to exist in the widely used online encyclopedia <a href="https://www.wikipedia.org/" target="_blank">Wikipedia</a>. </p>
<p>Wikipedia’s content is written and maintained by volunteer editors. Grokipedia generates and reviews its content using a large language model, which is a type of artificial intelligence trained on vast amounts of text to predict and generate human-like language. Visitors can suggest edits, but the automated system reviews and implements the changes without traditional human editorial oversight. </p>
<p>To evaluate these claims, researchers at Trinity College Dublin and Technological University Dublin conducted the largest academic analysis of Grokipedia since its launch. Scientists Saeedeh Mohammadi and Taha Yasseri set out to conduct a large-scale computational comparison to map structural and ideological differences objectively. They wanted to determine whether an automated platform could actually correct the perceived biases of human-edited websites. </p>
<p>The team analyzed articles on the same topic across Wikipedia and Grokipedia, using computational text analysis and machine learning methods. They focused on the 20,000 most-edited English Wikipedia pages, ensuring they were analyzing substantive articles by excluding lists and calendar dates. They then downloaded the corresponding 17,790 matching articles from the automated platform.</p>
<p>The authors extracted the main text from each article pair, stripping away menus, sidebars, and formatting code. They analyzed each pair for readability, vocabulary usage, and writing style. To measure reading difficulty, they used a standard formula that estimates the United States school grade level required to understand a text. </p>
<p>The team also calculated structural differences across the collected web pages by counting the exact number of references and hyperlinks present per one thousand words. To measure how closely the automated articles resembled their human-edited counterparts, they combined several similarity metrics into a single test. This score allowed them to directly compare the two versions of the same historical event or public figure. </p>
<p>The researchers found a profound split in how the automated system handled the existing content. While many Grokipedia articles closely mirror Wikipedia, a substantial proportion of the analyzed articles are more extensively rewritten. Roughly 66 percent of the entries diverge markedly in style, sourcing, and political leaning. </p>
<p>These rewritten Grokipedia entries were substantially longer than the Wikipedia versions, and the automated text proved much more difficult to read. On average, Grokipedia articles required a reading comprehension level of 14.5, compared to Wikipedia’s 10.7. The artificial intelligence platform also featured far fewer citations to back up its claims, providing an average of 20 references per one thousand words compared to Wikipedia’s 35. </p>
<p>To assess political bias, the scientists analyzed the external websites cited as references in the articles. They mapped these hyperlinks to an established dataset that assigns political leanings to news media sources based on social media sharing patterns. As a whole, articles on Grokipedia show a similar political leaning to those on Wikipedia, drawing predominantly on left-leaning news sources. </p>
<p>However, the scientists discovered noticeable changes within the highly rewritten group of articles. When it comes to politically and culturally sensitive topics, such as religion, history, literature, and art, Grokipedia shows a consistent shift toward referencing more right-leaning news sources compared to Wikipedia. The findings suggest the automated system provides a localized, topic-specific adjustment rather than a complete overhaul of political bias. </p>
<p>“Rather than systematically ‘correcting’ Wikipedia’s alleged biases, as claimed when first launched, our findings suggest that AI-generated encyclopedias such as Grokipedia selectively reshape existing knowledge,” said Taha Yasseri, director of the joint Centre for Sociology of Humans and Machines at Trinity College Dublin and Technological University Dublin, and principal investigator of the study. “This creates a patchwork system in which some content is copied, while other content is reinterpreted in ways that are less transparent and harder to scrutinize.”</p>
<p>The researchers worry about the long-term impact of relying on automated knowledge generation. “Online encyclopedias are central to public knowledge,” said Saeedeh Mohammadi, lead author of the study and a doctoral candidate at the Centre for Sociology of Humans and Machines and Research Ireland’s Centre for Research Training in Foundations of Data Science. “They are also being used to train future generations of large language models.” </p>
<p>“Our findings raise important questions about how public knowledge is produced, reproduced, verified, and governed,” Mohammadi added. “Unlike Wikipedia, where biases are visible and contested through human editing, AI-generated systems operate largely opaquely. This means shifts in perspective or sourcing may occur without clear accountability or editorial oversight.” </p>
<p>“Simply put AI generation does not remove bias – it changes how and where bias enters the system, often making it less visible.”</p>
<p>While the research provides useful evidence of emerging differences between AI-generated and human-edited encyclopedic knowledge systems, the researchers acknowledge that focusing on Wikipedia’s most-edited English-language pages likely overrepresents high-profile and contentious topics. </p>
<p>The automated similarity metrics also assess textual form and vocabulary alignment, but they cannot verify factual accuracy or detect hallucinated claims. The opaque nature of the automated platform’s training data limits the ability to determine exactly why these specific ideological shifts occur. Yasseri noted that these findings point to broader societal concerns. </p>
<p>“There is a dire need for transparency, oversight, and regulation in this space,” Yasseri said. “Our information landscape is changing rapidly. We have already seen how the lack of editorial responsibility on social media platforms has enabled the generation and circulation of misinformation and disinformation, often with catastrophic consequences for elections, public health, and social stability.” </p>
<p>“Now, we are witnessing the large-scale, black-box regeneration of information by large language models that remain largely closed to public scrutiny,” Yasseri continued. In future research, scientists might explore the underlying mechanics of how these models select information to cite and display. They could also study the inclusion of academic and governmental sources to build a more comprehensive picture of automated knowledge generation.</p>
<p>The study, “<a href="https://doi.org/10.1073/pnas.2603294123" target="_blank">Selective divergence between Grokipedia and Wikipedia articles</a>,” was authored by Saeedeh Mohammadi and Taha Yasseri.</p></p>
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<p><strong>Forwarded by:<br />
Michael Reeder LCPC<br />
Baltimore, MD</strong></p>
<p><strong>This information is taken from free public RSS feeds published by each organization for the purpose of public distribution. Readers are linked back to the article content on each organization's website. This email is an unaffiliated unofficial redistribution of this freely provided content from the publishers. </strong></p>
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