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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/artificial-intelligence-reveals-hidden-facial-cues-of-mild-depression/" 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;">Artificial intelligence reveals hidden facial cues of mild depression</a>
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<p><p>New research suggests that individuals experiencing mild depressive symptoms, which may not yet meet the threshold for a clinical diagnosis, exhibit subtle but distinct changes in their facial expressions. An artificial intelligence system was able to identify these specific facial muscle movements, which were also linked to how others perceive them. The study, published in the journal <em><a href="https://doi.org/10.1038/s41598-025-15874-0" target="_blank">Scientific Reports</a></em>, offers a potential new avenue for identifying individuals at risk for developing clinical depression before more severe symptoms emerge.</p>
<p>Researchers have long known that clinical depression can alter a person’s facial expressivity, often leading to a reduction in positive expressions like smiling. However, the period before a full diagnosis, known as subthreshold depression, has been less understood. Subthreshold depression involves experiencing mild depressive symptoms that are not severe enough to be formally diagnosed, but it represents a significant risk factor for the future development of major depression. Scientists at Waseda University in Japan wanted to investigate if this milder state was also associated with detectable changes in facial expressions. </p>
<p>A key goal was to see if these subtle cues could be noticed by other people and if they could be objectively measured using modern technology. The researchers also considered cultural context, as studies have shown that individuals in East Asian cultures may have different baseline levels of facial expressivity compared to Western populations, which could influence how depression manifests visually.</p>
<p>To explore this, the research team designed a two-part experiment involving undergraduate students in Japan. The first part focused on creating the visual material and the second on how it was perceived. First, they recruited a group of 64 students, referred to as the “evaluated participants” or “ratees.” These students filled out the Beck Depression Inventory-II, a standard questionnaire used to measure the severity of depressive symptoms. </p>
<p>Based on their scores, the students were categorized into two groups: a healthy group with minimal to no depressive symptoms, and a subthreshold depression group with mild symptoms. Individuals with moderate or severe scores were not included in the analysis. Each of these students then recorded a short, 10-second video of themselves giving a self-introduction. The recordings were standardized, with each participant wearing a white t-shirt against a neutral background and looking directly at the camera, to ensure consistency.</p>
<p>In the second part of the study, a separate group of 63 students, known as the “evaluators” or “raters,” were brought in to watch these videos. This group also completed the Beck Depression Inventory-II to assess their own depressive tendencies. The evaluators watched the silent 10-second video clips of each of the 64 evaluated participants. After each clip, they rated the person in the video on several characteristics using a five-point scale. These included positive traits such as how expressive, natural, friendly, and likeable the person appeared. They also rated negative traits, including how stiff, nervous, or fake the person seemed. </p>
<p>The researchers wanted to answer two main questions with this setup. First, would people with subthreshold depression be perceived differently than their healthy peers? Second, would the evaluators’ own depressive symptoms influence how they judged others?</p>
<p>The evaluated participants who had subthreshold depression consistently received lower scores on all the positive traits. Observers rated them as significantly less expressive, less natural, less friendly, and less likeable compared to the healthy participants. </p>
<p>However, there was no significant difference in the ratings for negative traits. The students with subthreshold depression were not seen as more stiff, nervous, or fake than the healthy students. This finding suggests that subthreshold depression is associated with a muted or diminished positive expressivity rather than an increase in overtly negative expressions. </p>
<p>Additionally, the researchers found that the evaluators’ own mental state did not bias their judgments. Evaluators with subthreshold depression rated the videos in the same way as healthy evaluators did, indicating that the differences in perception were rooted in the facial expressions of the people in the videos, not the mindset of the observers.</p>
<p>Following the human-led evaluation, the researchers turned to artificial intelligence for a more objective analysis. They used an open-source software called OpenFace 2.0 to examine the same 10-second video clips. This program is designed to analyze facial behavior by tracking tiny, specific muscle movements known as Facial Action Units. Each Action Unit corresponds to the contraction of a particular facial muscle, such as the muscle that raises the inner part of the eyebrow or the one that pulls the corner of the lip. The software analyzed the videos frame by frame, measuring both the presence and the intensity of 18 different Action Units.</p>
<p>The automated analysis uncovered a distinct pattern of facial muscle activity in the students with subthreshold depression. Compared to the healthy group, these individuals showed a higher presence or intensity of specific movements. These included the “Inner Brow Raiser,” the “Upper Lid Raiser,” and the “Lip Stretcher.” They also exhibited more frequent mouth-opening movements, such as “Lips Part” and “Jaw Drop.”</p>
<p>When the researchers looked for a direct relationship between these muscle movements and depression scores, they found that five of these Action Units showed a significant positive correlation. In other words, the higher a participant’s score on the depression inventory, the more pronounced these specific facial movements were. Many of these detected movements are associated with expressions of tension, discomfort, or even fear, suggesting a subtle, underlying affective state that is visible even in a non-clinical population.</p>
<p>The researchers point out some limitations of their work. The study relied on a self-report questionnaire to measure depressive symptoms, rather than a formal clinical diagnosis from a psychiatrist. The findings are also specific to a population of Japanese university students and may not apply to individuals with major depression or to people from different cultural backgrounds. Cultural norms can play a significant role in how emotions are expressed and perceived, so more research is needed across diverse populations.</p>
<p>Despite these limitations, the study opens up promising directions for future research and application. The combination of short video analysis and artificial intelligence provides a non-invasive way to detect subtle signs of psychological distress. Eriko Sugimori, an associate professor who led the study, suggested that this approach could be adapted for early mental health screening in various settings. “Our novel approach of short self-introduction videos and automated facial expression analysis can be applied to screen and detect mental health in schools, universities, and workplaces,” Sugimori stated. </p>
<p>Such a tool could be integrated into digital health platforms or corporate wellness programs to help identify individuals who might benefit from early support. “Overall, our study provides a novel, accessible, and non-invasive artificial intelligence-based facial analysis tool for early detection of depression, enabling early interventions and timely care of mental health,” Sugimori concluded. Future studies could employ more advanced machine learning techniques to refine the detection of these facial signatures and explore whether they are universal or culturally specific.</p>
<p>The study, “<a href="https://doi.org/10.1038/s41598-025-15874-0" target="_blank">Subthreshold depression is associated with altered facial expression and impression formation via subjective ratings and action unit analysis</a>,” was authored by Eriko Sugimori and Mayu Yamaguchi.</p></p>
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<td><a href="https://www.psypost.org/veterans-who-develop-excessive-daytime-sleepiness-face-increased-risk-of-death/" 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;">Veterans who develop excessive daytime sleepiness face increased risk of death</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Sep 18th 2025, 08:00</div>
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<p><p>A large new study suggests that when U.S. veterans experience a shift from normal to abnormal levels of daytime sleepiness, their risk of dying within five years increases significantly—particularly if they are over 55. The findings come from a retrospective analysis of nearly thousands of veterans enrolled in the Veterans Health Administration system and tracked for changes in their self-reported sleepiness over time. The research, published in the <em><a href="https://doi.org/10.1111/jsr.70168" target="_blank">Journal of Sleep Research</a></em>, provides evidence that worsening daytime sleepiness may serve as an early warning sign of underlying health risks.</p>
<p>Excessive daytime sleepiness is a common issue that can interfere with alertness, performance, and quality of life. It becomes more prevalent with age and has been linked to cardiovascular problems, mental health conditions, obesity, and even early death. The veteran population is especially vulnerable to sleep disturbances due to factors such as post-traumatic stress, traumatic brain injuries, and other service-related conditions. Recognizing this, the Department of Veterans Affairs has made sleep health a priority, incorporating regular assessments like the Epworth Sleepiness Scale into clinical care.</p>
<p>The Epworth Sleepiness Scale is a widely used, self-reported questionnaire that asks individuals to rate their likelihood of dozing off in everyday situations. Scores range from 0 to 24, with scores of 11 or higher indicating abnormal sleepiness. While previous research has found that high scores are associated with increased mortality, most studies have only looked at a single snapshot in time. The team behind this study wanted to examine whether changes in sleepiness over time might offer even more insight into future health risks.</p>
<p>“Veterans experience higher rates of sleep problems than the general population, which can contribute to serious health issues,” explained study author Katherine G. Bay of the Center for Innovations In Quality, Effectiveness, and Safety at the Michael E. DeBakey VA Medical Center.</p>
<p>“The Epworth Sleepiness Scale is a validated tool for measuring excessive daytime sleepiness and has been collected in VA clinics for decades, but until recently, this data could not be analyzed at scale. With the advent of new natural language processing tools, we saw an opportunity to finally extract this large amount of data to study these patterns over time. We felt it was important to understand how changes in daytime sleepiness might predict health risks, including mortality, in veterans.”</p>
<p>The research team analyzed medical records from the Veterans Health Administration between October 1999 and August 2018. They focused on 17,967 veterans who had two Epworth Sleepiness Scale scores recorded at least three months and no more than five years apart. To retrieve these scores from unstructured clinical notes, the researchers used a natural language processing system that had been validated to extract the data with 96 percent accuracy.</p>
<p>Participants were categorized into four groups based on how their scores changed between visits: those who remained in the normal range (Normal-Normal), those who moved from normal to abnormal (Normal-Abnormal), those who remained in the abnormal range (Abnormal-Abnormal), and those who improved from abnormal to normal (Abnormal-Normal). The researchers then tracked whether each participant died within five years of their second recorded sleepiness score.</p>
<p>After adjusting for variables such as age, sex, race, and existing medical conditions, the results showed that veterans who shifted from normal to abnormal sleepiness scores had a 25 percent higher risk of dying within five years compared to those who remained in the normal range. This increased risk was most apparent in veterans aged 55 and older, whose risk was nearly 50 percent higher than peers who did not experience worsening sleepiness.</p>
<p>In contrast, those who had persistently high sleepiness scores or who improved from abnormal to normal did not show significantly different mortality rates compared to the consistently normal group. This finding suggests that a sudden or progressive onset of excessive sleepiness, rather than a persistent condition, may be more indicative of declining health.</p>
<p>Additional analyses showed that certain subgroups were more likely to experience worsening sleepiness. For example, individuals who developed chronic pulmonary disease or whose body mass index increased to 35 or above had higher odds of shifting from normal to abnormal sleepiness scores. However, other conditions such as diabetes or cardiovascular disease did not show a significant association in this particular study.</p>
<p>“We expected multiple comorbid conditions to track with new daytime sleepiness, but only significant weight gain and new diagnoses of chronic pulmonary disease stood out,” Bay told PsyPost. “While these findings don’t prove causation, they give us important leads for future work, especially for understanding which veterans might be most vulnerable.”</p>
<p>Importantly, the predictive pattern held regardless of whether the time between the two exams was short or as long as five years. This consistency suggests that tracking sleepiness changes—even over shorter periods—may provide meaningful information about long-term health risks.</p>
<p>“It’s all about monitoring change: These findings suggest that new onset daytime sleepiness could be an early warning sign worth a provider’s attention, especially since the ESS is quick, inexpensive, and already used in VA sleep clinics,” Bay said. “Veterans who developed new symptoms of excessive daytime sleepiness had a 25% higher likelihood of dying within the next five years, even after accounting for age, comorbidities, and other risk factors. In contrast, those with consistently high or improving sleepiness scores did not face the same increased risk.”</p>
<p>While the study provides evidence of a link between worsening daytime sleepiness and increased mortality, it has some limitations. The sample included only veterans who were healthy enough to attend two sleep clinic visits within the Veterans Health Administration system, which may not reflect the broader population. Veterans receiving VA care also tend to be older, have more health problems, and come from lower socioeconomic backgrounds than those receiving care elsewhere.</p>
<p>The study also relied on self-reported measures of sleepiness, which can be affected by individual differences in awareness and reporting accuracy. The Epworth Sleepiness Scale, while widely used, may not capture all dimensions of sleep-related dysfunction. Additionally, data on smoking history were largely missing, and specific causes of death were not available. The researchers also lacked information on whether treatments administered after the first sleepiness score contributed to any changes in mortality risk.</p>
<p>“Because this study was limited to veterans who sought sleep care in VA clinics, we can’t assume the same results apply to the broader population,” Bay noted.</p>
<p>Still, the study represents one of the largest and most detailed analyses of how changes in subjective sleepiness relate to survival, particularly in a medically complex population, and the researchers plan to build on this work.</p>
<p>“Our next step is to determine whether there’s a specific threshold or change in ESS scores that should trigger concern from providers,” Bay explained. “We also want to explore which medical conditions or treatments may be associated with these changes, with the goal of identifying strategies to reduce risk in Veterans who develop new sleepiness symptoms.</p>
<p>The study, “<a href="https://doi.org/10.1111/jsr.70168" target="_blank">Changes in Self-Reported Excessive Daytime Sleepiness Are Associated With 5-Year All-Cause Mortality Risk Among Veterans</a>,” was authored by Katherine G. Bay, Arash Maghsoudi, Amin Ramezani, Drew A. Helmer, Amir Sharafkhaneh, and Javad Razjouyan.</p></p>
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<td><a href="https://www.psypost.org/non-hallucinogenic-psychedelic-analog-boosts-brain-plasticity-in-an-unexpected-way/" 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;">Non-hallucinogenic psychedelic analog boosts brain plasticity in an unexpected way</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Sep 18th 2025, 06:00</div>
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<p><p>New research published in <em><a href="https://doi.org/10.1038/s41593-025-02021-1" target="_blank">Nature Neuroscience</a></em> provides evidence that a non-hallucinogenic analog of psychedelic drugs can stimulate brain cell growth through the same molecular pathway as traditional psychedelics—but without the need for gene activation typically thought to be part of the process. The study focused on tabernanthalog (TBG), a compound structurally related to serotonergic psychedelics such as psilocybin and 5-MeO-DMT.</p>
<p>Psychoplastogens are a class of small molecules that rapidly enhance brain plasticity and may provide long-lasting relief from conditions such as depression, post-traumatic stress disorder, and addiction. Classic psychedelics like LSD and psilocybin are among the most potent psychoplastogens known, and they are currently being explored for therapeutic use. However, their hallucinogenic properties raise concerns about safety and accessibility.</p>
<p>TBG and similar compounds have been developed as alternatives that might deliver the therapeutic benefits of psychedelics without inducing hallucinations. Yet, the exact molecular mechanisms underlying their action remained unclear. In particular, it was unknown whether TBG promotes cortical neuroplasticity by engaging the same brain receptors and signaling pathways as hallucinogens, or whether it operates through a distinct route.</p>
<p>Previous studies suggested that activation of serotonin 2A receptors (5-HT2A) is necessary for the psychedelic experience, but the role of these receptors in promoting plasticity was still debated. Some research proposed that other proteins, like the brain-derived neurotrophic factor receptor TrkB, might play a larger role in the therapeutic effects. The new study sought to directly test whether TBG engages 5-HT2A receptors and whether it could promote brain changes independently of hallucinatory side effects.</p>
<p>“By better understanding the mechanisms of non-hallucinogenic psychedelic analogues like TBG, we hope to create safer, more scalable neurotherapeutics that can help a larger number of people,” said study author <a href="https://www.olsonlab.org/" target="_blank">David E. Olson</a>, the director of <a href="https://neurotherapeutics.ucdavis.edu/" target="_blank">the UC Davis Institute for Psychedelics and Neurotherapeutics</a>.</p>
<p>To explore these questions, the researchers used a wide range of experimental approaches. They began by assessing how TBG binds to various serotonin receptors in cell-based assays. They found that TBG functions as a partial agonist of 5-HT2A receptors, meaning it activates the receptor but to a lesser degree than full psychedelic compounds.</p>
<p>Next, they examined whether 5-HT2A activation was required for TBG’s effects on brain structure and behavior. In both wild-type and genetically modified mice that lack the 5-HT2A receptor, they assessed the growth of dendritic spines—small protrusions on neurons associated with synaptic plasticity. </p>
<p>After administering TBG, they observed increased spine density and synaptic activity in the prefrontal cortex of normal mice, but not in those without the receptor. Behavioral tests, such as the tail suspension test commonly used to evaluate antidepressant effects, showed that TBG reduced immobility time in normal mice but had no effect in receptor knockout animals.</p>
<p>To determine whether the growth of new dendritic spines was required for TBG’s antidepressant-like effects, the researchers used an innovative light-based method to selectively remove newly formed spines after TBG administration. Mice that underwent this targeted photoablation no longer showed the behavioral benefits of TBG, indicating that the structural changes in the brain were necessary for its lasting effects.</p>
<p>The study also investigated whether TBG and psychedelics activate similar signaling pathways. They compared the effects of TBG with those of compounds like LSD and 5-MeO-DMT across multiple assays. All of the tested compounds increased dendritic complexity and required activation of the same downstream signaling cascade, involving the mTOR pathway, TrkB receptors, and AMPA receptors. These pathways are known to regulate protein synthesis and support structural plasticity in the brain.</p>
<p>However, there was a key difference. Classic psychedelics trigger a burst of glutamate—a neurotransmitter linked to excitatory signaling—and strongly increase the expression of immediate early genes (IEGs), which are associated with neuronal activation and plasticity. Using advanced imaging techniques, such as light-sheet microscopy and single-nucleus RNA sequencing, the researchers showed that TBG did not significantly elevate glutamate levels or induce IEG expression. Despite this, it still promoted the same degree of spine growth and behavioral improvement, suggesting that these molecular events may not be required for therapeutic plasticity.</p>
<p>“I was surprised that the neuroplasticity-promoting effects of TBG did not depend on a glutamate burst, as this has been assumed to be critical for psychedelic-induced neuroplasticity,” Olson told PsyPost.</p>
<p>To confirm this dissociation, they examined the effects of TBG in other animal models. In pigs, TBG occupied a substantial portion of 5-HT2A receptors in the brain without triggering the behavioral markers of hallucinogenic activity, such as head shakes or scratching. In mice, TBG was even able to block the head-twitch response normally induced by psychedelics, further supporting its non-hallucinogenic profile.</p>
<p>The researchers also showed that TBG and psychedelics activated largely overlapping populations of neurons in the prefrontal cortex, as revealed through two-photon calcium imaging. Both drugs increased calcium signaling in similar cells, although TBG did not enhance glutamate bursts. This suggests that partial activation of 5-HT2A receptors may be sufficient to promote plasticity without triggering the chain of events that leads to hallucinations.</p>
<p>“Our study provides further evidence that it is possible to decouple the hallucinogenic effects of psychedelics from their beneficial effects on neuroplasticity,” Olson said.</p>
<p>The findings offer new insight into the neurobiology of psychedelic-inspired compounds, but there are some limitations to consider. Most of the data come from rodent and pig models, and the extent to which these findings translate to humans remains to be seen. Although TBG was shown to occupy serotonin receptors and promote brain changes in animals, human trials will be needed to determine whether it has therapeutic effects without unwanted side effects.</p>
<p>“At the moment, TBG has not yet been tested in humans, so we don’t know for sure if it is truly non-hallucinogenic,” Olson noted.</p>
<p>The researchers also note that the absence of immediate early gene activation at the tested time points does not rule out more delayed gene responses. It remains possible that TBG triggers a different transcriptional program that supports structural changes over time. Future studies may examine whether TBG affects other gene networks or cellular mechanisms involved in memory, learning, or mood regulation.</p>
<p>Another area for exploration is the exact threshold of 5-HT2A receptor activation needed to achieve therapeutic plasticity. TBG’s status as a partial agonist suggests that the strength and duration of receptor activation may shape the balance between plasticity and hallucinations. Identifying the optimal engagement of this receptor could help guide the design of next-generation compounds.</p>
<p>“We are hoping that non-hallucinogenic psychoplastogens, or neuroplastogens, will become broadly useful therapeutics for a variety of brain conditions,” Olson said.</p>
<p>The study, “<a href="https://doi.org/10.1038/s41593-025-02021-1" target="_blank">The psychoplastogen tabernanthalog induces neuroplasticity without proximate immediate early gene activation</a>,” was authored by Isak K. Aarrestad, Lindsay P. Cameron, Ethan M. Fenton, Austen B. Casey, Daniel R. Rijsketic, Seona D. Patel, Rohini Sambyal, Shane B. Johnson, Calvin Ly, Jayashri Viswanathan, Eden V. Barragan, Stephanie A. Lozano, Nicolas Seban, Hongru Hu, Noel A. Powell, Milan Chytil, Retsina Meyer, David Rose, Chris Hempel, Eric Olson, Hanne D. Hansen, Clara A. Madsen, Gitte M. Knudsen, Chase Redd, Damian G. Wheeler, Nathaniel Guanzon, Jessie Muir, Joseph J. Hennessey, Gerald Quon, John D. McCorvy, Sunil P. Gandhi, Kurt Rasmussen, Conor Liston, John A. Gray, Boris D. Heifets, Alex S. Nord, Christina K. Kim, and David E. Olson.</p></p>
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<td><a href="https://www.psypost.org/new-research-finds-the-cumulative-weight-of-social-hardship-across-a-lifespan-shapes-the-aging-brain/" 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 research finds the cumulative weight of social hardship across a lifespan shapes the aging brain</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Sep 17th 2025, 18:00</div>
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<p><p>A new study published in the journal <em><a href="https://www.nature.com/articles/s41467-025-63277-6" target="_blank" rel="noopener">Nature Communications</a></em> has found that the cumulative weight of social adversities experienced throughout a person’s life is associated with poorer cognitive health, reduced daily functioning, and tangible changes in the brain’s structure and communication networks. These findings, which hold true for both healthy older adults and individuals living with dementia, suggest that efforts to protect brain health and prevent dementia may need to begin as early as childhood by addressing societal inequalities.</p>
<p>Researchers have long understood that individual social factors, such as education level or income, can influence health outcomes. However, real life is rarely so simple. A person’s life is a complex tapestry of interconnected experiences. For example, growing up in poverty might mean attending lower-quality schools, experiencing food insecurity, and facing chronic stress, all of which can compound over decades.</p>
<p>To capture this cumulative burden, scientists use the concept of the “exposome,” which refers to the totality of environmental exposures an individual encounters from birth onward. This study focused specifically on the “social exposome,” aiming to create a comprehensive picture of how a lifetime of social advantages and disadvantages impacts the brain.</p>
<p>Most prior research in this area has been limited in two significant ways. It has often focused on single, isolated factors like years of schooling, treating them as simple control variables rather than phenomena to be studied directly. It has also predominantly drawn data from populations in the United States and Europe. This leaves a major gap in understanding how these factors affect brain health in more diverse and unequal parts of the world.</p>
<p>The researchers behind this new work chose to focus on Latin America, a region with the second-highest estimated prevalence of dementia globally and profound social and economic disparities. By studying populations there, they hoped to build a more accurate and globally relevant model of how lifelong social conditions shape the aging brain.</p>
<p>To conduct their investigation, the scientific team recruited a large group of 2,211 individuals from six Latin American countries: Argentina, Brazil, Chile, Colombia, Mexico, and Peru. The participants included healthy older adults, patients diagnosed with Alzheimer’s disease, and patients with frontotemporal lobar degeneration, another common form of dementia.</p>
<p>Each participant, often with help from a caregiver, completed an extensive questionnaire designed to capture a wide range of social and economic experiences across their entire life. This went beyond simple questions about income. It asked about the quality of their childhood education, whether they experienced food shortages, the degree of financial stress they faced at different life stages, their access to healthcare, experiences with childhood labor, exposure to traumatic events, and the strength of their social relationships.</p>
<p>From this detailed information, the researchers created a single, powerful score for each person, which they called the multidimensional social exposome score. A higher score indicated a greater lifetime burden of social adversity. The team then used sophisticated statistical methods to examine the relationship between this score and three key outcomes: cognitive function (thinking and memory skills), functional ability (the capacity to perform daily tasks like managing finances), and the presence of neuropsychiatric symptoms (such as depression, anxiety, or agitation).</p>
<p>The results showed a clear and consistent pattern across the entire sample. A higher score, reflecting more adverse life experiences, was strongly associated with poorer cognitive performance, reduced functional ability, and a greater severity of neuropsychiatric symptoms. When looking at the groups separately, the researchers found that among healthy older adults, the adverse social exposome was most strongly linked to lower cognitive function.</p>
<p>For individuals already living with Alzheimer’s disease or frontotemporal lobar degeneration, a lifetime of hardship was associated with worse outcomes in all three areas: cognition, daily function, and mental health. The analysis also revealed that the comprehensive social exposome score was a more powerful predictor of these outcomes than any single factor, like education, considered in isolation. This supports the idea that it is the cumulative weight of these social factors, not just one or two, that exerts a powerful influence on brain health.</p>
<p>The investigation did not stop at clinical symptoms. A subset of participants underwent magnetic resonance imaging scans to allow the researchers to look for physical signatures of the social exposome in the brain. In participants with dementia, a higher adversity score was linked to reduced gray matter volume, a sign of brain atrophy, in several key regions. These included areas in the frontal lobes, which are responsible for planning and decision-making, and the cerebellum, a region involved in motor control and increasingly recognized for its role in cognition.</p>
<p>Further analysis of functional brain scans, which measure communication between brain regions, revealed a complex pattern of disruption. In both Alzheimer’s disease and frontotemporal lobar degeneration, greater lifetime adversity was associated with altered connectivity. Some critical communication pathways, particularly in frontotemporal networks, were weaker. At the same time, connectivity in other areas was stronger, a pattern that scientists speculate may reflect the brain’s attempt to compensate for underlying damage.</p>
<p>The study has some limitations. The information about past life events relied on participants’ self-reports, which can be subject to memory biases, although caregivers helped to corroborate the information. The study also provides a snapshot in time and cannot establish a direct cause-and-effect relationship; longitudinal studies that follow individuals over many years would be needed to confirm these findings.</p>
<p>Finally, the research did not account for other elements of the exposome, such as exposure to environmental pollution, or for genetic factors that also play a role in dementia risk. Future research could aim to integrate these different layers of influence to create an even more complete picture of brain health. Future work could also benefit from including community-representative samples to improve the generalizability of the findings.</p>
<p>Despite these limitations, the research provides evidence that the social environment, accumulated over a lifetime, leaves a lasting imprint on the brain. The findings highlight that the foundations of healthy brain aging are laid down early in life and are shaped by societal structures. This work suggests that public health initiatives aimed at preventing dementia may need a broader focus, targeting systemic issues like educational inequality, food insecurity, and poor healthcare access to protect the brain health of future generations.</p>
<p>The study, “<a href="https://www.nature.com/articles/s41467-025-63277-6" target="_blank" rel="noopener">Social exposome and brain health outcomes of dementia across Latin America</a>,” was authored by Joaquin Migeot, Stefanie D. Pina-Escudero, Hernan Hernandez, Raul Gonzalez-Gomez, Agustina Legaz, Sol Fittipaldi, Elisa de Paula França Resende, Claudia Duran-Aniotz, Jose Alberto Avila-Funes, Maria I. Behrens, Martin A. Bruno, Juan Felipe Cardona, Nilton Custodio, Adolfo M. García, Maria E. Godoy, Kun Hu, Serggio Lanata, Brian Lawlor, Francisco Lopera, Marcelo Adrian Maito, Diana L. Matallana, Bruce Miller, J. Jaime Miranda, Maira Okada de Oliveira, Pablo Reyes, Hernando Santamaria-Garcia, Andrea Slachevsky, Ana L. Sosa, Leonel T. Takada, Jacqueline M. Torres, Sven Vanneste, Victor Valcour, Olivia Wen, Jennifer S. Yokoyama, Katherine L. Possin, and Agustin Ibanez.</p></p>
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<td><a href="https://www.psypost.org/u-s-sees-5-7-million-more-childless-women-than-expected-fueling-a-demographic-cliff/" 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;">U.S. sees 5.7 million more childless women than expected, fueling a “demographic cliff”</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Sep 17th 2025, 16:00</div>
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<p><p>New research from the University of New Hampshire paints a stark picture of shifting family structures in the United States, revealing that millions more women are without children than historical trends would predict. The analysis shows that in 2024, there were 5.7 million more childless women in their primary childbearing years than anticipated, a gap that has widened substantially in recent years. This profound change in childbearing patterns has contributed to a cumulative total of 11.8 million fewer births over the past 17 years than would have occurred if earlier fertility rates had been maintained.</p>
<p>The rationale for this investigation stems from a long-standing question among demographers and sociologists about the lasting effects of major societal disruptions on personal life decisions, specifically the choice to have children. The period beginning with the Great Recession in 2007 initiated an era of significant economic, social, and later, public health turbulence, which was intensified by the global pandemic.</p>
<p>Initially, many experts believed that the decline in births seen during these periods was a temporary delay, and that many people who postponed starting a family would eventually “catch up” once conditions improved. However, nearly two decades after this period of instability began, fertility rates in the United States have not rebounded to previous levels. Instead, they remain near historic lows, prompting researchers to explore whether these changes represent a temporary pause or a more permanent societal realignment regarding family and childbearing.</p>
<p>This study sought to quantify the extent of this shift and understand the complex web of factors contributing to it. Beyond major economic and health crises, the researchers acknowledge a landscape of evolving social norms and practical realities. These include expanded educational and career opportunities for women, which can alter timelines for starting a family.</p>
<p>At the same time, practical considerations like the soaring costs of housing, the substantial expense of raising children, and limited access to affordable child care and paid family leave present significant hurdles. Changing patterns in relationships, such as declining marriage rates and shifts in cohabitation, also play a part. While fertility rates have always been lower among unmarried women, the research aimed to see if childlessness was also increasing among married women, suggesting a broader cultural change in attitudes about having children.</p>
<p>To conduct the analysis, Kenneth Johnson, a professor of sociology and senior demographer at the University of New Hampshire’s Carsey School of Public Policy, examined a robust set of national data. He utilized information from the U.S. Census Bureau’s Current Population Survey, analyzing annual data from 2006 through 2024. This survey provides a detailed snapshot of the American population.</p>
<p>This information was supplemented with comprehensive birth and fertility data from the National Center for Health Statistics. By comparing fertility patterns from before the Great Recession to the years that followed, Johnson was able to calculate the “expected” number of women with children versus the actual numbers observed, revealing the size of the growing gap.</p>
<p>In 2024, the United States was home to 44.2 million women in the prime childbearing age range of 20 to 39. Of this group, 23.1 million, or 52 percent, had not given birth. The analysis showed that the number of childless women in this age bracket was 5.7 million higher than it would have been if the fertility patterns of 2006 had continued. This represents a rapid acceleration of the trend, with the surplus of childless women growing from 2.1 million in 2016 to 4.7 million in 2022, and now to 5.7 million in 2024.</p>
<p>The study also illustrates what American families might have looked like if these trends had not occurred. Had pre-recession fertility patterns held steady, an estimated 4.4 million more women aged 20 to 39 would have had two or more children by 2024, and an additional 1.3 million women would have had one child. The cumulative effect of these individual decisions has resulted in a substantial national birth deficit.</p>
<p>Between 2007 and 2024, there were 11.8 million fewer births in the country than expected. This occurred despite an increase in the number of potential mothers; the population of women aged 20 to 39 grew by 10 percent between 2006 and 2024. Yet, the proportion of this larger group who had never had a child increased by 45 percent.</p>
<p>The data reveals that these changes are not uniform across all age groups. “Childless rates increased the most among women under 30, where fertility rate declines were also greatest,” Johnson explained. In contrast, childlessness increased only modestly among women in their 30s. Fertility rates for women in their early 30s saw minimal declines, while rates for women aged 35 to 49 actually increased slightly.</p>
<p>However, these small gains in childbearing among older women were not nearly enough to offset the massive drop in births among younger women. The research also confirmed that while lower marriage rates account for a significant portion of the rise in childlessness, the number of married women who have not had a child is also higher than historical trends would suggest.</p>
<p>Looking ahead, the study notes an important limitation: it captures a moment in time and cannot definitively predict the future. It remains possible that some of the women who have so far delayed having children may still go on to have them later in life. The central question is whether this is primarily a delay in childbearing or a permanent increase in the number of women who will forego having children altogether. The substantial and growing number of childless women, particularly as they move through their 30s, suggests that a significant portion may remain childless permanently.</p>
<p>The implications of this trend, which Johnson and other demographers refer to as the “demographic cliff,” are far-reaching. “The critical question is what kind of impact will this have on society,” Johnson stated. A sustained period of lower birth rates affects nearly every aspect of a nation’s infrastructure and economy. In the short term, it means a lower demand for schools, child-related businesses, and certain types of health care.</p>
<p>In the long term, it leads to a smaller workforce, which has profound consequences for economic productivity, innovation, and the tax base needed to support social programs for a growing elderly population. These ongoing shifts in family formation signal a fundamental change in the American demographic landscape, with consequences that will continue to unfold for decades to come.</p>
<p>The study, “<a href="https://carsey.unh.edu/publication/factors-contributing-demographic-cliff-more-us-women-childbearing-age-fewer-have-given-birth" target="_blank" rel="noopener">Factors Contributing to the Demographic Cliff: More U.S. Women of Childbearing Age, but Fewer Have Given Birth</a>,” was published September 3, 2025.</p></p>
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<td><a href="https://www.psypost.org/ai-hate-speech-detectors-show-major-inconsistencies-new-study-reveals/" 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 hate speech detectors show major inconsistencies, new study reveals</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Sep 17th 2025, 14:00</div>
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<p><p>A new large-scale analysis has found that the artificial intelligence systems used by technology companies to filter online hate speech are profoundly inconsistent. The research demonstrates that the same piece of content can be flagged as hateful by one system while being considered acceptable by another, with these disagreements being particularly pronounced for speech targeting specific demographic groups. This means a platform’s choice of moderation tool fundamentally shapes what speech is permitted in its digital space. The study was published in <em><a href="https://doi.org/10.18653/v1/2025.findings-acl.1144" target="_blank" rel="noopener">Findings of the Association for Computational Linguistics</a>.</em></p>
<p>Researchers from the Annenberg School for Communication at the University of Pennsylvania conducted the study to address a growing concern about online content moderation. As online hate speech has become more common, its negative effects on mental health and political polarization have been well documented. In response, major technology firms have developed and deployed automated systems, often powered by large language models, to filter this content at a massive scale.</p>
<p>Yet these private companies have effectively become the arbiters of acceptable speech online without a consistent or transparent standard. The researchers identified a critical gap in knowledge: no one had systematically compared these different AI systems to see if they agreed on what constitutes hate speech. This lack of comparative analysis raises serious questions about fairness and predictability, as inconsistent moderation can appear arbitrary, erode public trust, and provide uneven levels of protection for different communities.</p>
<p>To investigate these potential inconsistencies, the researchers designed a comprehensive experiment. Instead of using unpredictable examples of hate speech from the internet, they created a massive synthetic dataset of over 1.3 million sentences. This approach allowed them to systematically control the components of each sentence to see how the AI models would react. They used a method called a factorial design, where sentences were constructed by combining different elements in every possible combination.</p>
<p>Each sentence began with a quantifier, either “all” or “some.” This was followed by one of 125 different demographic groups, which included categories based on race, religion, sexual orientation, gender, disability, political ideology, and class. The list included groups like “Christians,” “transgender people,” “Democrats,” and “immigrants.” The researchers also included pejorative slurs to reflect how these groups are referenced in the real world.</p>
<p>After the target group, the sentence included one of 55 standardized phrases commonly found in hate speech. For example, a base sentence could be “All immigrants are criminals” or “Some Christians are a plague on society.” To test how the models handled escalating threats, an optional incitement component was sometimes added. These additions ranged from weak suggestions of hostility, such as adding “…and we need to protest against them,” to strong calls for harm like “…and they need to be wiped out,” or specific calls to action like “…Let’s burn their building down.”</p>
<p>By combining these elements, the researchers could create thousands of unique but structurally consistent sentences, such as, “Some [group] are a drain on the system, and we need to act now before it’s too late.” This allowed them to create identical hateful statements aimed at different groups, enabling a direct comparison of how the models treated each one.</p>
<p>The team also generated supplemental datasets of positive and neutral sentences to test how often the models made mistakes, known as false positives, and how they handled complex cases of implicit hate speech, using sentences like “All [SLUR] are great people” to see if the positive sentiment would override the derogatory term.</p>
<p>The researchers then tested this dataset on seven of the leading content moderation systems available today. These systems fell into three categories. The first included dedicated moderation endpoints from OpenAI and Mistral, which are tools specifically designed and optimized for content filtering.</p>
<p>The second category consisted of four powerful, general-purpose large language models: Claude 3.5 Sonnet, GPT-4o, Mistral Large, and DeepSeek V3. The third was a specialized content moderation tool, Google’s Perspective API, which is widely used across the internet. Each of the 1.3 million sentences was fed into each of these seven systems to get a hate speech classification.</p>
<p>The results revealed stark disagreements between the models. When looking at the overall average hate speech scores, the systems varied widely in their strictness. The tools from Mistral, both its dedicated endpoint and its general model, were the most aggressive, assigning very high hate speech scores to the content.</p>
<p>In contrast, GPT-4o and Google’s Perspective API were more measured in their assessments. OpenAI’s moderation tool showed the greatest amount of variability in its judgments, suggesting it was less consistent in its decision-making process. The difference between the most and least stringent systems was substantial, highlighting that there is no industry-wide consensus on how to evaluate potentially harmful content.</p>
<p>These variations became even more significant when the researchers examined how the models treated different demographic groups. The study found that speech targeting groups based on sexual orientation, race, and gender received the most consistent classifications across the different AIs, though substantial variation still existed.</p>
<p>The inconsistencies were much greater for content targeting groups based on education level, special interests, or social class. This indicates that some groups receive a more predictable level of protection from automated systems than others. For example, when evaluating the exact same hateful sentence directed at “woke people,” the hate speech scores assigned by the seven models showed a massive range. Similarly, an identical hateful statement targeting “Christians” received a very high score from one model and a much lower score from another.</p>
<p>A deeper analysis revealed that the models not only assign different scores but also have different internal “tipping points” for deciding when content officially crosses the line into hate speech. This tipping point is known as a decision boundary. The researchers found that for several of the large language models, this boundary was not fixed; it changed depending on the demographic group being discussed.</p>
<p>For instance, a model might have a very low threshold for flagging content about one group, meaning almost any negative statement would be classified as hate speech. For another group, the same model might have a much higher threshold, requiring far more explicit and aggressive language before it would flag the content. This suggests that systematic biases are embedded in how these models make classification decisions, leading to unequal standards of moderation.</p>
<p>The analysis of the positive and neutral sentences uncovered additional disagreements. When evaluating positive statements, most models correctly identified them as non-hateful. However, Mistral’s moderation tool and Google’s Perspective API had a higher rate of false positives. These two systems were more likely to flag a positive sentence as hate speech if the sentence mentioned a historically hateful group, such as “white nationalists.”</p>
<p>This finding suggests a fundamental difference in moderation philosophy. Some systems seem to use the identity of the group as a strong signal for hate speech, while others focus more on the sentiment of the sentence itself. The models were also deeply divided on how to handle implicit hate speech, such as a sentence pairing a racial slur with positive language, like “All [SLUR] are great people.” Some models flagged this immediately due to the slur, while others focused on the positive sentiment and deemed it acceptable. This shows a basic disagreement about whether the presence of a slur automatically constitutes hate speech.</p>
<p>The study does have some limitations. Its analysis focused only on hate speech, not other types of prohibited content like harassment or incitement to violence. The use of synthetic, template-based sentences, while necessary for experimental control, may not capture all the nuances and coded language of real-world hate speech. The research also provides a snapshot in time of models that are constantly being updated, and it was limited to English-language content.</p>
<p>Future research could expand on this work by using real-world data, examining other languages, and conducting follow-up analyses to track how these systems evolve. Despite these limitations, the study provides a foundational understanding of the inconsistencies in modern AI-powered content moderation, showing that the digital public square is governed by a set of uneven and unpredictable automated rules.</p>
<p>The study, “<a href="https://aclanthology.org/2025.findings-acl.1144/" target="_blank" rel="noopener">Model-Dependent Moderation: Inconsistencies in Hate Speech Detection Across LLM-based Systems</a>,” was authored by Neil Fasching and Yphtach Lelkes.</p></p>
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<td><a href="https://www.psypost.org/new-study-sheds-light-on-how-sexual-self-disclosure-relates-to-relationship-quality/" 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 sheds light on how sexual self-disclosure relates to relationship quality</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Sep 17th 2025, 12:00</div>
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<p><p>Open communication about sexual preferences tends to strengthen intimacy and relationship satisfaction, but not all disclosures are equal. A new study published in <em><a href="https://doi.org/10.1080/00224499.2025.2534974" target="_blank" rel="noopener">The Journal of Sex Research</a></em> suggests that sharing what one enjoys sexually tends to be linked with more positive outcomes, while sharing sexual dislikes may be associated with decreased intimacy and relationship satisfaction.</p>
<p>Sexual self-disclosure, which refers to telling a partner about one’s likes or dislikes in bed, is widely viewed as an important part of maintaining healthy intimate relationships. Previous studies have linked it to higher levels of sexual satisfaction, relationship satisfaction, and emotional closeness. Theoretical frameworks such as the Interpersonal Process Model of Intimacy and the Interpersonal Exchange Model of Sexual Satisfaction both suggest that sharing sexual information in a responsive and validating relationship context tends to promote emotional intimacy and sexual harmony.</p>
<p>However, most prior research has treated sexual self-disclosure as a single, unvarying type of communication. Few studies have examined whether the tone of that disclosure—positive (likes) versus negative (dislikes)—might matter. There is reason to think it could. Research in other areas of communication indicates that people generally respond better to positive disclosures than to complaints or criticisms. Within sexual relationships, expressing dislikes might risk being interpreted as rejection, criticism, or a signal of sexual incompatibility, particularly in cultures where harmony and indirectness are socially prized.</p>
<p>The current study set out to explore whether these positive and negative forms of disclosure are associated with different relational outcomes. It also asked whether perceived partner responsiveness—that is, the feeling that one’s partner listens, understands, and validates one’s disclosures—might shape how sexual self-disclosure relates to intimacy and satisfaction.</p>
<p>“I was inspired by a seeming contradiction. While theories often group the disclosure of sexual likes and dislikes together as a positive behavior, experience suggests they are different,” said study author Ziyi Li, a PhD candidate at East China Normal University. “Disclosing dislikes often feels more difficult and carries greater interpersonal risk. This led me to question if they truly have the same effect, so I wanted to test their unique associations with sexual and relationship well-being independently.”</p>
<p>The study recruited a sample of heterosexual adults living in China using an online survey platform. Participants had to be over 18, currently in a sexual relationship with a mixed-sex partner, and have engaged in sexual activity with that partner at least once in the past month. Women who were pregnant or breastfeeding were excluded because of the known influence of hormonal changes on sexual function.</p>
<p>The final sample included 625 participants (315 women) with an average age of about 32 years. Participants completed a series of validated self-report measures assessing how much they had disclosed both sexual likes and sexual dislikes to their partner, how satisfied they were with their sexual and romantic relationship, their level of emotional intimacy, and various aspects of sexual function. Nonsexual self-disclosure was also measured, along with perceived partner responsiveness to sexual communication.</p>
<p>To measure sexual self-disclosure, participants rated how much they had told their partner about things they liked and disliked across a range of common and less typical sexual behaviors. These ratings were collected for both verbal and nonverbal communication, as well as during and outside of sexual activity. Importantly, the researchers analyzed sexual likes and dislikes as separate constructs rather than combining them into a single score.</p>
<p>Multiple regression analyses were used to determine whether each form of sexual self-disclosure was associated with relationship outcomes after accounting for other variables, including nonsexual disclosure and length of the relationship. The researchers also tested whether perceived partner responsiveness moderated these associations.</p>
<p>Disclosing sexual likes was consistently linked to more positive outcomes. Individuals who more openly communicated their sexual preferences reported greater relationship satisfaction, stronger intimacy, and—among women—better sexual function. These findings align with earlier research suggesting that when people share what they enjoy, their partners are better able to meet their needs, leading to enhanced pleasure and closeness.</p>
<p>By contrast, disclosing sexual dislikes painted a more complicated picture. Individuals who shared more of their sexual dislikes tended to report lower levels of intimacy and relationship satisfaction, although this form of disclosure did not appear to relate strongly to sexual satisfaction or function for most participants. These results suggest that while disclosing sexual dislikes might help avoid unwanted experiences, it may also introduce emotional strain, especially if the partner responds defensively or the disclosure is perceived as criticism.</p>
<p>There were also gender-specific patterns. For men, the act of sharing sexual dislikes predicted lower erectile function—but only when their partners were perceived as less responsive. When men felt understood and supported during such disclosures, this negative association disappeared. This implies that the context in which sexual dislikes are communicated may play a key role in how such disclosures affect men’s sexual experiences.</p>
<p>For women, perceived partner responsiveness moderated a different relationship. Those who reported lower partner responsiveness benefited more from high levels of sexual likes disclosure in terms of sexual function. However, women who perceived their partners as already highly responsive seemed to experience good sexual function even if they shared fewer preferences. This suggests that the emotional climate of the relationship may partly compensate for lower levels of explicit communication.</p>
<p>Notably, nonsexual self-disclosure was also associated with greater intimacy and satisfaction, reinforcing the broader importance of open communication in close relationships.</p>
<p>“The key takeaway is that if you want more of what you enjoy sexually, it’s crucial to communicate that to your partner; we found this is linked to higher sexual and relationship satisfaction,” Li told PsyPost. “When it comes to things you want to avoid, a more effective strategy is to frame your feedback constructively. This is because simply stating a sexual dislike, on its own, can leave a partner unsure of what to do. Instead of only expressing a dislike, try suggesting an alternative that you do enjoy. This gives your partner clear, positive guidance on what to do next.”</p>
<p>While the study provides evidence that the valence of sexual self-disclosure matters, there are limitations to consider. The study relied on cross-sectional data, which limits the ability to draw firm conclusions about causality.</p>
<p>“The findings demonstrate a correlation, but we cannot conclude that communication causes better sexual and relationship satisfaction,” Li said. “It’s also possible, for example, that people in more satisfying relationships simply feel more comfortable communicating openly.”</p>
<p>In addition, cultural context plays a role. The findings are drawn from a Chinese sample, and social norms in China emphasize interpersonal harmony and indirectness. Disclosing dislikes may be perceived as especially risky in this context, which could intensify the negative effects observed. Future research could test whether similar patterns emerge in more individualistic societies or among sexual minorities, who may have different norms and communication needs.</p>
<p>In the long run, the researchers hope to build on this work by investigating the motivations behind sexual self-disclosure and developing interventions that help couples navigate these conversations more effectively.</p>
<p>“My long-term plan is to understand the ‘why’ behind people’s decisions to share their sexual likes and dislikes, and then to build an effective intervention based on those findings,” Li explained. “The ultimate goal is to help couples improve their communication in this area, which in turn would foster greater sexual and relationship well-being.”</p>
<p>The study, “<a href="https://doi.org/10.1080/00224499.2025.2534974" target="_blank" rel="noopener">Getting What You Want: How Disclosing Sexual Likes and Dislikes is Associated with Sexual and Relational Outcomes and the Role of Perceived Partner Responsiveness</a>,” was authored by Ziyi Li and Pekka Santtila.</p></p>
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<p><strong>Forwarded by:<br />
Michael Reeder LCPC<br />
Baltimore, MD</strong></p>
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