<table style="border:1px solid #adadad; background-color: #F3F1EC; color: #666666; padding:8px; -webkit-border-radius:4px; border-radius:4px; -moz-border-radius:4px; line-height:16px; margin-bottom:6px;" width="100%">
<tbody>
<tr>
<td><span style="font-family:Helvetica, sans-serif; font-size:20px;font-weight:bold;">PsyPost – Psychology News</span></td>
</tr>
<tr>
<td> </td>
</tr>
</tbody>
</table>
<table style="font:13px Helvetica, sans-serif; border-radius:4px; -moz-border-radius:4px; -webkit-border-radius:4px; background-color:#fff; padding:8px; margin-bottom:6px; border:1px solid #adadad;" width="100%">
<tbody>
<tr>
<td><a href="https://www.psypost.org/childhood-instability-primes-women-for-fast-reproductive-strategies-via-psychopathy-and-impulsivity/" 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;">Childhood instability primes women for “fast” reproductive strategies via psychopathy and impulsivity</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Nov 29th 2025, 08:00</div>
<div style="font-family:Helvetica, sans-serif; color:#494949;text-align:justify;font-size:13px;">
<p><p>New research conducted among female college students provides evidence that difficult childhood environments are associated with the development of specific personality traits that promote riskier sexual behaviors in adulthood. The study was published in the journal <em><a href="https://doi.org/10.1016/j.evolhumbehav.2025.106783" target="_blank">Evolution and Human Behavior</a></em>.</p>
<p>Evolutionary psychologists utilize a framework known as Life History Theory to explain how organisms allocate their energy and resources. This theory proposes that all living things must make trade-offs between investing in their own growth and survival or investing in reproduction. These trade-offs create a spectrum of strategies ranging from “fast” to “slow.”</p>
<p>A fast life history strategy is typically observed in environments that are harsh or unpredictable. In such contexts, the future is uncertain, which makes immediate reproduction an adaptive priority over long-term planning. Conversely, a slow life history strategy is favored in stable, resource-rich environments. This approach prioritizes personal development, delayed gratification, and heavy investment in a smaller number of offspring.</p>
<p>Prior research has established a connection between childhood adversity and accelerated reproductive behaviors. However, the specific psychological mechanisms that drive this relationship have remained less clear. The authors of the new study sought to investigate whether adult personality traits function as the bridge connecting a woman’s early environment to her mating effort.</p>
<p>“There has been debate in the Life History literature about the usefulness of using psychosocial traits to predict mating effort in humans as this cannot be done in animal analogs, which are the bases for Life History Theory,” said study author Lisa M. Bohon, a professor<br>
at California State University, Sacramento.</p>
<p>“In addition, there is dispute over the dimensionality of Life History traits. Some argue that there is one dimension with fast and slow endpoints. We, and others, believe that life history is bidimensional, so that a person can be high or low in both dimensions, or have a mixed strategy. Finally, we were interested in combining the disparate findings in this field by looking at childhood ecology and psychosocial personality traits in one model.”</p>
<p>The researchers focused specifically on the concept of the “microsystem.” This term refers to the immediate environment in which a child develops, including their relationships with parents and exposure to local hazards. </p>
<p>They hypothesized that a disordered microsystem would predict the development of personality traits associated with a fast life history strategy. They further predicted that these traits would mediate the relationship between childhood ecology and adult mating behaviors.</p>
<p>To test these hypotheses, the researchers recruited 875 female undergraduate students from a university in Northern California. The participants ranged in age from 18 to 46 years old, with an average age of approximately 20. The study utilized a comprehensive online survey to gather retrospective data on childhood experiences and current psychological functioning.</p>
<p>Participants completed the Adverse Childhood Experiences Inventory to document history of abuse, neglect, or household dysfunction before the age of 10. They also answered questions regarding parental disengagement, assessing how emotionally connected they felt to their mothers and fathers. Additional measures evaluated the level of crime in their childhood neighborhoods and the presence of adults in the home who were not related to them.</p>
<p>The researchers assessed adult personality using several established psychological scales. To measure traits associated with a fast life history, they utilized the Short Dark Triad Scale. This instrument evaluates psychopathy, narcissism, and Machiavellianism. The researchers also measured impulsivity, neuroticism, and resource control strategies.</p>
<p>Traits associated with a slow life history were assessed using measures of resilience, self-esteem, and secure attachment. The survey also included the Mini-K, a scale designed to measure general slow life history orientation. Finally, the researchers measured mating effort by asking about the age of sexual debut, the total number of lifetime sexual partners, and intentions to engage in risky sexual behaviors in the future.</p>
<p>The data were analyzed using a statistical technique called Structural Equation Modeling. This method allows researchers to test complex relationships between multiple variables simultaneously. The results provided support for the theoretical model linking childhood adversity to adult personality and behavior.</p>
<p>Bohon and her colleagues found that a disordered microsystem was a significant predictor of faster life history traits. Women who reported higher levels of childhood trauma, parental disengagement, or neighborhood crime were more likely to exhibit traits such as psychopathy and impulsivity. They were also more likely to display neuroticism and a desire to control resources and people.</p>
<p>These personality traits explained approximately 22 percent of the relationship between the childhood environment and adult mating effort. Women with these “fast” personality characteristics tended to report a higher number of sexual partners. They also expressed a greater orientation toward short-term mating and a higher willingness to engage in risky sexual acts.</p>
<p>The researchers identified specific environmental factors that appeared particularly influential. Trauma experienced before age 10 emerged as a strong predictor of adult psychopathy. This finding aligns with the idea that traumatic events can fundamentally alter a person’s worldview. It suggests that early adversity may shift an individual’s focus toward immediate survival and dominance.</p>
<p>Parental relationships also played a distinct role in the findings. Disengagement from fathers was strongly associated with adverse outcomes. Women who reported having distant or absent fathers were younger when they first had sex and reported more sexual partners. This supports previous theories suggesting that father absence acts as a cue for an unstable reproductive environment.</p>
<p>The presence of unrelated adults in the childhood home was another significant factor. Participants who lived with a parent’s non-kin partner tended to show signs of a faster life history strategy. This finding is consistent with prior research indicating that the presence of unrelated adults can increase the risk of maltreatment or instability in a household.</p>
<p>“The most important findings were that for women childhood trauma, poor parent-child relationships, parental cohabitation, and neighborhood crime were associated with higher psychopathy (low empathy, high risk- taking and mistreatment of others), lower self-control, and higher anxiety,” Bohon told PsyPost.</p>
<p>“These in turn were associated with accelerated mating effort that includes high risk behaviors. Some of these childhood conditions are under the control of parents and can be addressed in the home regardless of the harshness and unpredictability of the world at large leading to enhanced outcomes for their offspring in adulthood.”</p>
<p>The researchers also examined the influence of the “exosystem,” which refers to broader environmental factors like financial security. While perceived resource insecurity was linked to higher mating effort, it did not strongly predict the development of specific personality traits. This suggests that the immediate social environment has a more direct impact on personality development than broader economic conditions.</p>
<p>“I thought socioeconomic status would be a significant factor in our model, but it was not,” Bohon said. “Only the perception of economic predictability was directly related to mating effort, but not through a faster life history strategy. I found this result hopeful, because it shows that no matter what the economic disadvantage, every child can be nurtured and can thrive, which in adulthood could positively impact her own children and community.”</p>
<p>The researchers also looked for evidence of a “slow” life history strategy. As expected, a supportive and predictable childhood environment was associated with positive traits such as resilience, secure attachment, and higher self-esteem. </p>
<p>However, these “slow” traits did not significantly predict lower mating effort in the statistical model. The pathway to increased mating effort appeared to be driven primarily by the presence of “fast” traits rather than the absence of “slow” ones.</p>
<p>There are some limitations to this study that should be considered. The sample consisted entirely of college students, which may limit how well the findings apply to the general population. University students often possess higher levels of conscientiousness and long-term planning skills than the average person.</p>
<p>Additionally, the study was cross-sectional, meaning it captured data at a single point in time. This design makes it difficult to definitively prove that the childhood environment directly caused the adult outcomes, rather than just being associated with them.</p>
<p>Future research could address these issues by utilizing longitudinal designs. Tracking individuals from childhood through adulthood would allow scientists to observe how these traits and behaviors develop in real time. It would also be beneficial to include a more diverse range of participants to ensure the findings hold true across different demographic groups.</p>
<p>“We are currently investigating the components of parenting associated with positive and negative mental and physical health, as well as other behavioral outcomes, in adulthood,” Bohon said.</p>
<p>The researchers emphasized that examining mating effort through an evolutionary lens is not intended to stigmatize sexuality.</p>
<p>“Mating effort is a normal part of human behavior and can have both positive and negative outcomes,” Bohon told PsyPost. “The outcome variables chosen for mating effort (age at sexual debut, number of lifetime partners, openness to short term mating, and openness to sexually risky behaviors such as having sex without a condom or while intoxicated) are associated with unwanted pregnancies, increased sexually transmitted diseases, anxiety, low self-control, and a transactional mindset. We chose these outcome variables because we were interested in understanding how to reduce these negative outcomes.”</p>
<p>The study, “<a href="https://doi.org/10.1016/j.evolhumbehav.2025.106783" target="_blank">Using SEM to test the associations among women’s childhood ecology, adult psychosocial life history traits, and mating effort</a>,” was authored by Lisa M. Bohon, Sophia Sinclair, Raquel R. Medeiros-Tejomaya, Jessica Hamel, and Alexandra H.B. Hock.</p></p>
</div>
<div style="font-family:Helvetica, sans-serif; font-size:13px; text-align: center; color: #666666; padding:4px; margin-bottom:2px;"></div>
</td>
</tr>
</tbody>
</table>
<table style="font:13px Helvetica, sans-serif; border-radius:4px; -moz-border-radius:4px; -webkit-border-radius:4px; background-color:#fff; padding:8px; margin-bottom:6px; border:1px solid #adadad;" width="100%">
<tbody>
<tr>
<td><a href="https://www.psypost.org/scientists-observe-striking-link-between-social-ai-chatbots-and-psychological-distress/" 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;">Scientists observe “striking” link between social AI chatbots and psychological distress</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Nov 29th 2025, 06:00</div>
<div style="font-family:Helvetica, sans-serif; color:#494949;text-align:justify;font-size:13px;">
<p><p>Individuals who interact with social chatbots tend to be younger and experience higher levels of psychological distress than those who do not use such technology. The findings suggest that while these artificial intelligence tools are often designed and marketed to provide companionship, their use is frequently associated with feelings of loneliness and emotional struggle rather than improved well-being. The study was published in the <em><a href="https://doi.org/10.1177/02654075251392956" target="_blank">Journal of Social and Personal Relationships</a></em>.</p>
<p>Automated programs known as social chatbots use natural language processing to mimic human conversation. Some developers market these tools as potential friends or sources of emotional support.</p>
<p>Despite the growing popularity of these applications, scientific understanding regarding who actually uses them remains limited. Previous inquiries often focused on single applications or small groups, leaving a gap in knowledge about broader user characteristics. </p>
<p>It is unclear whether these tools attract individuals who are already socially secure or those seeking to alleviate deficits in their social lives. The authors aimed to identify the demographic and psychological profiles of social chatbot users across different cultural contexts.</p>
<p>“Humans have an innate need to build and maintain meaningful relationships, and in today’s digital world, many of these connections increasingly unfold through technology,” said study author <a href="https://www.tuni.fi/en/people/iina-savolainen" target="_blank">Iina Savolainen</a>, a senior research fellow at Tampere University and vice director of <a href="https://research.tuni.fi/emerging-technologies-lab/" target="_blank">the Emerging Technologies Lab</a>.</p>
<p>“With the rise of AI, more people are using social chatbots to explore new forms of communication or to seek companionship, emotional support, or simply, everyday interaction. Yet despite this growing trend, surprisingly few empirical studies have examined who uses these tools and how such usage relates to well-being. We saw a research gap and wanted to better understand these dynamics through a large, cross-national study.”</p>
<p>To address these questions, the research team analyzed data collected in the autumn of 2023 as part of the longitudinal “Self & Technology” study. The analysis included responses from adults in Finland (1,095 participants), France (1,014), Germany (900), Ireland (588), Italy (1,099), and Poland (967). These nations were selected to represent diverse geographic and cultural regions within Europe, ranging from the Nordic technology hub of Finland to Southern and Eastern European contexts.</p>
<p>Participants completed online surveys assessing their usage of “chatbot friends” such as Replika or My AI. They provided information regarding their frequency of use, ranging from never to multiple times a day. The researchers created a binary category to distinguish between those who used the technology and those who did not.</p>
<p>The team measured psychological distress using the Mental Health Inventory, which asks about feelings of nervousness, depression, and calmness over the past month. Loneliness was assessed using a short version of the University of California Loneliness Scale. The researchers also evaluated self-esteem using a single-item measure.</p>
<p>In addition to mental health metrics, the survey gathered data on the frequency of face-to-face social contact with friends, relatives, or colleagues. Participants reported their general attitudes toward new technologies. They also provided sociodemographic details including age, gender, income, and relationship status.</p>
<p>The researchers found that social chatbot usage varied by country, ranging from a low of 8.67% in Ireland to a high of 17.93% in Italy. Across all six nations, a clear demographic trend emerged regarding age. Individuals who used social chatbots were consistently younger than those who did not.</p>
<p>A primary finding concerning mental health appeared in every country sampled. The analysis showed a positive association between social chatbot usage and psychological distress. This indicates that people engaging with these AI companions were more likely to report symptoms of anxiety, depression, or low mental well-being compared to non-users.</p>
<p>“The cross-cultural consistency was striking; social chatbot use was related to poorer mental well-being in all six countries,” Savolainen told PsyPost. “That broad consistency across different European contexts was quite surprising.” </p>
<p>The relationship between chatbot use and loneliness was significant in four of the six countries: France, Germany, Italy, and Poland. In these nations, users reported higher levels of loneliness than non-users. This pattern was not statistically significant in the data from Finland or Ireland.</p>
<p>Regarding self-esteem, the results were mostly neutral, with one exception found in the French sample. In France, social chatbot usage was associated with higher reported self-esteem. </p>
<p>“An unexpected finding was the positive association between chatbot use and higher self-esteem in France,” Savolainen said. “It is possible that, for some users, chatbots offer a space that feels supportive or affirming and tools designed to be attentive and nonjudgmental can sometimes create a sense of being ‘heard.'” </p>
<p>“Perhaps French users engage with these tools in a way that encourages such mental boosts. Still, it’s important to acknowledge that chatbots have limitations in truly empathizing or providing sustained emotional support, so these effects should be interpreted with caution.”</p>
<p>The researchers also found that the frequency of face-to-face social contact was not related to chatbot use in any of the countries. This lack of association with in-person contact implies that using AI companions does not necessarily replace human interaction but exists alongside it. </p>
<p>General attitudes toward technology played a role in some regions. A positive view of new technologies was linked to chatbot use in Finland, France, Italy, and Poland. Other demographic factors showed inconsistent patterns. For instance, users in Germany, Ireland, and Poland were less likely to be female.</p>
<p>The consistent link between distress and chatbot usage raises questions about the efficacy of these tools as sources of support. While often intended to help, the data do not support the idea that they successfully alleviate mental health burdens. It is possible that individuals currently struggling are simply more prone to seeking out these tools.</p>
<p>The study highlights that social chatbots might function as “weak ties” in a person’s social network. They can provide conversation and distraction but may lack the depth required to buffer against serious emotional challenges. The user knows the interaction is simulated, which may limit its ability to reduce deep-seated loneliness.</p>
<p>“Across the six countries we studied (Finland, France, Germany, Italy, Ireland, and Poland), we consistently found that younger individuals and those experiencing psychological distress were more likely to use social chatbots,” Savolainen explained. “Loneliness was also a significant factor in several countries.” </p>
<p>“Taken together, these results suggest that social chatbot use may emerge as a response to emotional or social challenges rather than as a tool that inherently improves well-being. This does not mean chatbots can’t be helpful, but it reminds us that the dynamics of use are complex and may reflect underlying needs that technology alone cannot fully address.”</p>
<p>The researchers used robust statistical methods to ensure the reliability of these associations. They controlled for various background variables to isolate the specific relationship between well-being and chatbot use. </p>
<p>However, as with all research, there are some limitations. The study provides a snapshot of associations but cannot prove causality due to its cross-sectional design. It remains unclear whether psychological distress drives people to use chatbots or if the usage itself contributes to distress. </p>
<p>Future investigations could employ longitudinal designs to track changes in well-being over time. This would help determine if chatbots successfully alleviate loneliness or potentially exacerbate it. The authors also suggest that future work should explore the specific motivations behind why individuals turn to these tools.</p>
<p>“We are living a unique time of human–computer interaction, as different technologies are becoming more sophisticated and culturally embedded,” Savolainen said. “Moving forward, we aim to examine social chatbot and user relationships through longitudinal research, which will allow us to trace changes over time and better understand causal dynamics.” </p>
<p>“Social psychological aspects, such as how people form bonds with chatbots, whether they regard them as companions or even “friends,” and how these perceptions influence well-being, is another future direction for us. As chatbots diversify and become more personalized, understanding the evolving nature of these relationships will be crucial.” </p>
<p>“Our study contributes to ongoing conversations about digital well-being and the role of technology in addressing emotional needs,” Savolainen added. “Issues like loneliness, distress, and self-esteem are deeply human challenges that technology often tries to help with -and sometimes succeeds. But our findings highlight the importance of looking critically at these tools, understanding who uses them, and considering how they fit into the bigger picture of human–technology relationships.”</p>
<p>The study, “<a href="https://doi.org/10.1177/02654075251392956" target="_blank">Individual and well-being factors associated with social chatbot usage: A six-country study</a>,” was authored by Rita Latikka, Jenna Bergdahl, Iina Savolainen, Magdalena Celuch, and Atte Oksanen.</p></p>
</div>
<div style="font-family:Helvetica, sans-serif; font-size:13px; text-align: center; color: #666666; padding:4px; margin-bottom:2px;"></div>
</td>
</tr>
</tbody>
</table>
<table style="font:13px Helvetica, sans-serif; border-radius:4px; -moz-border-radius:4px; -webkit-border-radius:4px; background-color:#fff; padding:8px; margin-bottom:6px; border:1px solid #adadad;" width="100%">
<tbody>
<tr>
<td><a href="https://www.psypost.org/how-the-brain-transforms-continuous-sound-into-distinct-words/" 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 the brain transforms continuous sound into distinct words</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Nov 28th 2025, 18:00</div>
<div style="font-family:Helvetica, sans-serif; color:#494949;text-align:justify;font-size:13px;">
<p><p>Two new studies published in the journals <em><a href="https://www.cell.com/neuron/fulltext/S0896-6273(25)00792-5" target="_blank">Neuron</a></em> and <em><a href="https://www.nature.com/articles/s41586-025-09748-8" target="_blank">Nature</a> </em>reveal how the human brain transforms a continuous stream of sound into distinct words. The findings identify a specific neural mechanism that relies on learned experience to detect where one word ends and the next begins. These papers demonstrate that the superior temporal gyrus acts as a critical hub for interpreting spoken language.</p>
<p>Speaking creates a continuous flow of acoustic information without silence between words. A listener must instantly and subconsciously impose boundaries onto this sound wave to comprehend meaning. This process allows a person to hear discrete vocabulary rather than an unending noise.</p>
<p>Edward Chang, a neurosurgeon at the University of California, San Francisco, led the research teams for both projects. The investigators sought to pinpoint exactly where and how this segmentation occurs in the cortex. They focused their attention on the superior temporal gyrus. </p>
<p>This region of the brain sits just above the ear. Neurologists historically considered this area responsible only for low-level sound processing. It was thought to handle basic tasks such as identifying pitch or volume. The new data suggest its role is significantly more advanced.</p>
<p>The researchers utilized electrocorticography to capture brain activity with high precision. This method involves placing a grid of high-density electrodes directly onto the surface of the brain. It provides much greater temporal and spatial resolution than standard non-invasive scanners. The participants were patients undergoing monitoring for epilepsy. They volunteered to listen to various speech samples while the electrodes recorded their neural firing patterns.</p>
<p>The first study focused on the mechanics of how the brain identifies a word. The team analyzed neural activity while participants listened to radio news clips. They discovered that the superior temporal gyrus does not simply react to sound intensity. Instead, the neural activity in this region displays a rhythmic cycle.</p>
<p>The researchers observed a distinct “reset” signal at the end of a spoken word. Neural activity drops sharply at the exact moment a word boundary occurs. This drop serves as a biological marker that punctuates the speech stream.</p>
<p>Between these resets, the neurons engage in a complex process of integration. They encode the phonetic sounds and prosody of the speech. Prosody includes the rhythm and stress patterns of language. The neurons combine these elements to identify the word form.</p>
<p>This processing cycle tracks time in a relative manner rather than absolute seconds. The neural trajectory stretches or compresses to fit the length of the word. A short word like “cat” and a long word like “hippopotamus” trigger the same complete cycle of processing. The brain effectively normalizes the duration of the word to maintain a consistent representation.</p>
<p>The team compared these biological observations to artificial intelligence models. They examined the inner workings of a deep learning algorithm called HuBERT. This model was trained to process speech using self-supervised learning. It figured out patterns in data without being explicitly told what words are.</p>
<p>The deeper layers of the artificial neural network developed a strategy strikingly similar to the human brain. The model spontaneously learned to track word boundaries to make sense of the audio. It also exhibited the same cycle of relative timing found in the cortical recordings. This suggests that the strategy used by the human brain may be a computationally efficient way to solve the problem of speech recognition.</p>
<p>To confirm that this activity represents perception rather than just acoustics, the researchers used a bistable speech task. They played a looped audio recording that could be heard as two different words depending on where the listener placed the boundary. For example, the sound could be perceived as “turbo” or “boater.”</p>
<p>The acoustic input remained identical throughout the experiment. However, the neural activity shifted based on what the participant reported hearing. When the listener heard “turbo,” the neural reset occurred at a different time than when they heard “boater.” This confirmed that the superior temporal gyrus reflects the internal perceptual experience of the listener.</p>
<p>The second study expanded this inquiry to investigate the role of language experience. The researchers asked whether this segmentation mechanism works for all speech or only for languages the listener understands. They recruited a diverse group of participants. The cohort included native speakers of English, Spanish, and Mandarin.</p>
<p>The volunteers listened to sentences in their native language and in a foreign language they did not speak. The electrodes recorded the brain’s response to both familiar and unfamiliar speech streams. The results highlighted a fundamental difference in how the brain processes these inputs.</p>
<p>The researchers found that the superior temporal gyrus responds to foreign speech with high intensity. The brain successfully processes the basic acoustic ingredients of the unknown language. It identifies vowels and consonants regardless of whether the listener understands them. The auditory machinery remains active and functional.</p>
<p>However, the neural marker for word boundaries disappears when the language is unfamiliar. The sharp drop in activity that signals the end of a word does not occur. The brain fails to segment the continuous stream into discrete units. This explains why foreign languages often sound like a rapid, unbroken blur of noise.</p>
<p>The study included bilingual participants to further test this hypothesis. These individuals listened to both of their spoken languages. The data showed that the boundary detection mechanism worked equally well for both. The same neural populations adjusted their processing to accommodate the specific structures of each language.</p>
<p>The team also examined participants with varying levels of proficiency in a second language. They found that the clarity of the neural boundary signal correlated with the speaker’s skill level. A person with higher proficiency showed a distinct neural signature for word segmentation. A person with lower proficiency showed a weaker or nonexistent signal.</p>
<p>This suggests that the superior temporal gyrus is not a static processor. It is a dynamic system that changes with learning. As a person acquires a language, this brain region tunes itself to the specific statistical patterns of that tongue. It learns to predict where words likely end based on years of exposure.</p>
<p>There are limitations to these studies that contextualize the findings. The primary constraint is the reliance on patients undergoing surgery. This medical necessity dictated the placement of the electrode grids. The recordings capture activity on the surface of the cortex but miss deeper brain structures. Areas buried within the folds of the brain might contribute to this process in ways not yet visible.</p>
<p>The current research focused on the reception of speech. It does not address how this segmentation process interacts with speech production. Future investigations could explore whether the same timing mechanisms govern how we construct sentences before speaking them.</p>
<p>Researchers also hope to understand how this system develops in children. Infants are born without the ability to segment words. They must learn to carve meaningful units out of the noise of conversation. Tracking the emergence of this neural reset signal in the developing brain could offer insights into language acquisition.</p>
<p>These findings provide a new framework for understanding the neurobiology of language. They shift the perspective on the superior temporal gyrus from a simple analyzer to a sophisticated linguistic interface. This region actively constructs the words we hear. It uses a combination of real-time acoustic analysis and learned predictions to structure our auditory world.</p>
<p>The study, “<a href="https://www.cell.com/neuron/fulltext/S0896-6273(25)00792-5" target="_blank">Human cortical dynamics of auditory word form encoding</a>,” was authored by Yizhen Zhang, Matthew K. Leonard, Ilina Bhaya-Grossman, Laura Gwilliams, and Edward F. Chang.</p>
<p>The study, “<a href="https://www.nature.com/articles/s41586-025-09748-8" target="_blank">Shared and language-specific phonological processing in the human temporal lobe</a>,” was authored by Ilina Bhaya-Grossman, Matthew K. Leonard, Yizhen Zhang, Laura Gwilliams, Keith Johnson, Junfeng Lu, and Edward F. Chang.</p></p>
</div>
<div style="font-family:Helvetica, sans-serif; font-size:13px; text-align: center; color: #666666; padding:4px; margin-bottom:2px;"></div>
</td>
</tr>
</tbody>
</table>
<table style="font:13px Helvetica, sans-serif; border-radius:4px; -moz-border-radius:4px; -webkit-border-radius:4px; background-color:#fff; padding:8px; margin-bottom:6px; border:1px solid #adadad;" width="100%">
<tbody>
<tr>
<td><a href="https://www.psypost.org/whom-you-observe-in-your-daily-life-alters-your-willingness-to-tax-the-rich/" 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;">Whom you observe in your daily life alters your willingness to tax the rich</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Nov 28th 2025, 16:00</div>
<div style="font-family:Helvetica, sans-serif; color:#494949;text-align:justify;font-size:13px;">
<p><p>Recent research suggests that the visibility of extreme wealth within a person’s social circle drives their support for economic redistribution but simultaneously fosters political polarization and personal dissatisfaction. A study published in <em><a href="https://doi.org/10.1093/pnasnexus/pgaf339" target="_blank">PNAS Nexus</a></em> combines computational modeling with online experiments to demonstrate how network structure creates specific biases in how individuals perceive inequality.</p>
<p>Economic inequality has expanded significantly across many industrial democracies in recent decades. This trend presents a difficult puzzle for political scientists and economists. Standard political theory posits that in a democracy where wealth is concentrated at the top, the majority of voters—who have below-average wealth—should vote for policies that redistribute resources more evenly.</p>
<p>This concept is known as the median voter theory. It predicts that as the gap between the rich and the middle class widens, the demand for redistribution should rise. However, this theoretical correction often fails to occur in the real world.</p>
<p>One leading explanation for this discrepancy is that individuals lack accurate information about the true state of the economy. Humans do not typically possess a bird’s-eye view of national wealth statistics. Instead, people rely on their immediate social environments to gauge their financial standing. </p>
<p>They look at their neighbors, colleagues, and friends to estimate how they compare to the general population. Because people tend to associate with others who share similar economic backgrounds, their reference samples are often skewed.</p>
<p>When social networks are economically homogeneous, individuals struggle to perceive the true extent of inequality. Poor individuals surrounded by other poor individuals may overestimate their relative standing. Wealthy individuals surrounded by other wealthy individuals may underestimate how advantaged they are. </p>
<p>To investigate this phenomenon, Milena Tsvetkova from the London School of Economics and Political Science collaborated with Henrik Olsson and Mirta Galesic. Olsson and Galesic are affiliated with the Santa Fe Institute and the Complexity Science Hub Vienna. This team of computational social scientists hypothesized that these network structures fundamentally alter voting behavior. The researchers wanted to understand if changing who people see changes how they vote on tax policy.</p>
<p>The research team began their investigation by developing a computational model. This model simulated a population with an unequal distribution of resources, where a small minority held the majority of the wealth.</p>
<p>The researchers programmed digital agents to behave with a mix of self-interest and a distaste for unfairness. These agents wanted to increase their own wealth, but they also experienced a decline in utility when they possessed significantly less or significantly more than their neighbors.</p>
<p>In the simulation, agents observed a small subset of the population. Based on what they saw, they voted on a tax rate. The model assumed a direct democracy where the median vote determined the policy. </p>
<p>The collected taxes were then redistributed equally among all agents. The researchers manipulated the structure of the social networks to see how different observational paths changed the outcome. They tested conditions where agents mostly saw similar others, different others, or specifically the rich or the poor.</p>
<p>To validate the predictions of their computer model, the researchers recruited 1,440 human participants for a large-scale online experiment. They divided participants into groups of 24. </p>
<p>Within each group, nine participants were randomly assigned a “rich” status with a high initial score. The remaining 15 participants were assigned a “poor” status with a low initial score. The participants were informed that they were part of a larger group, but they could only see the scores of eight other individuals.</p>
<p>The experiment placed these groups into distinct network structures. In “segregated” networks, rich participants only observed other rich people, and poor participants only observed other poor people. </p>
<p>In “homophilous” networks, participants mostly saw others with similar scores. In “heterophilous” networks, participants mostly saw those with different scores. The researchers also created “rich visible” networks, where most connections were directed toward the wealthy members, and “poor visible” networks, where the poor were most prominent.</p>
<p>Participants engaged in three rounds of voting. They used a slider to select their preferred tax rate. After each round, they saw the results and could adjust their vote. The researchers tracked the median tax rate selected by the group and the level of disagreement, or polarization, among the voters. They also asked participants to rate their satisfaction with their scores and their perception of fairness.</p>
<p>The results from the experiment revealed that network structure serves as a powerful lever for collective economic decisions. In the segregated networks, the groups consistently voted for the lowest tax rates. </p>
<p>This occurred because the poor participants, seeing only other poor people, underestimated the potential benefits of redistribution. They did not realize how much wealth was available to be taxed. Simultaneously, the rich participants, seeing only other rich people, felt no pressure to share. This created a consensus for low taxes.</p>
<p>A different dynamic emerged in networks where the wealthy were highly visible. When poor participants were placed in a network where they frequently observed rich neighbors, they voted for significantly higher tax rates. </p>
<p>The visibility of extreme wealth clarified the economic disparity within the group. The poor correctly identified that a high tax rate would redistribute significant resources to them. As a result, the “rich visible” networks produced the highest levels of redistribution.</p>
<p>However, this shift toward higher taxation was accompanied by intense polarization. In the networks where the rich were visible, the voting gap between the rich and the poor was the widest. The poor participants radicalized, often demanding a 100 percent tax rate. </p>
<p>The rich participants generally refused to budge from their preference for low taxes. While the segregated groups achieved a peaceful consensus on low redistribution, the groups with visible wealth experienced deep political conflict.</p>
<p>The study also uncovered a paradox regarding individual happiness. The researchers found that poor participants in the segregated networks were the most satisfied with their outcomes. Even though they remained the poorest in absolute terms because they voted for low redistribution, they felt content. They were essentially shielded from the knowledge of their relative disadvantage.</p>
<p>On the other hand, poor participants in the “rich visible” networks reported the lowest levels of satisfaction. This dissatisfaction persisted even though they ended up with more money due to the higher tax rates they voted for. </p>
<p>The act of constantly comparing themselves to the visible wealthy induced a sense of relative deprivation. They were objectively better off financially, yet they felt worse about their situation and viewed the game as unfair.</p>
<p>The experiment also yielded unexpected demographic findings. Women who were assigned to the “poor” condition tended to vote for lower taxes than men in the same position. This finding contradicts general survey data, which usually indicates that women favor more redistribution than men. </p>
<p>The authors suggest this behavior might stem from a stronger tendency among the female participants to conform to external social norms. In the United States, where the participants were recruited, there is a strong cultural norm against high taxation.</p>
<p>The researchers note several limitations to their study. The experiment utilized a simplified economic model with a flat tax and equal redistribution. Real-world economies involve progressive taxation, complex welfare systems, and potential negative impacts on productivity if taxes are too high. </p>
<p>Additionally, the participants were US residents who likely brought their own strong political beliefs into the experiment. The data showed that many participants voted for tax rates close to the actual US effective tax rate, rather than the rate that would mathematically maximize their earnings in the game.</p>
<p>The study suggests that breaking down information silos can indeed increase political support for redistribution. When the poor see the rich, they want to tax them. </p>
<p>However, the authors highlight that this is not a cost-free solution. Strategies that increase the visibility of excessive wealth may successfully shift policy, but they risk exacerbating political polarization and reducing the subjective well-being of the less advantaged.</p>
<p>Future research could investigate how these dynamics play out in cultures with different baseline attitudes toward inequality. It would also be useful to explore how voluntary network formation influences these outcomes. </p>
<p>In the real world, people actively choose their social circles, potentially reinforcing the segregation that the researchers found leads to apathy. The study implies that while ignorance may be bliss for the disadvantaged, it also cements their economic status.</p>
<p>The study, “<a href="https://doi.org/10.1093/pnasnexus/pgaf339" target="_blank">Social networks affect redistribution decisions and polarization</a>,” was authored by Milena Tsvetkova, Henrik Olsson and Mirta Galesic.</p></p>
</div>
<div style="font-family:Helvetica, sans-serif; font-size:13px; text-align: center; color: #666666; padding:4px; margin-bottom:2px;"></div>
</td>
</tr>
</tbody>
</table>
<table style="font:13px Helvetica, sans-serif; border-radius:4px; -moz-border-radius:4px; -webkit-border-radius:4px; background-color:#fff; padding:8px; margin-bottom:6px; border:1px solid #adadad;" width="100%">
<tbody>
<tr>
<td><a href="https://www.psypost.org/artificial-intelligence-helps-decode-the-neuroscience-of-dance/" 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 helps decode the neuroscience of dance</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Nov 28th 2025, 14:00</div>
<div style="font-family:Helvetica, sans-serif; color:#494949;text-align:justify;font-size:13px;">
<p><p>Dance engages the human mind in a way few other activities can, merging the rhythmic perception of sound with the visual appreciation of movement. A <a href="https://doi.org/10.1038/s41467-025-65039-w" target="_blank">new study</a> uses advanced artificial intelligence to decode how the brain processes this complex art form, revealing that computer models can accurately predict neural activity in human observers. The research suggests that expert dancers process these performances differently than novices, displaying a surprisingly high level of diversity in their neural responses.</p>
<p>Scientists have attempted to understand how the brain interprets sensory information for decades. Traditional experiments often rely on simple, isolated stimuli to maintain control over the data. A researcher might expose a participant to a single tone or a flashing light to see which neurons fire. While this approach offers precision, it often fails to capture the complexity of real-world experiences where senses blend continuously. The human brain rarely processes sound without context or sight without accompanying noise.</p>
<p>To address this limitation, a team of researchers turned their attention to dance. This medium naturally fuses dynamic body movement with music, requiring the observer to integrate visual and auditory signals simultaneously. Previous neuroscience studies on dance often separated these elements. They might show participants silent videos of movement or play music without visual accompaniment. Such methods sever the intricate connection between a beat and a step, which is the defining characteristic of dance.</p>
<p>The research team, led by Yu Takagi and Hiroshi Imamizu at the University of Tokyo, sought to bridge this gap using modern technology. They aimed to determine if advanced computer programs could mimic the way the human brain perceives these combined sensations. They also wanted to see if the brain activity of a professional dancer differed significantly from that of someone with no dance training.</p>
<p>The investigators recruited fourteen participants for the experiment. Half of the group consisted of expert dancers with more than five years of training in various genres. The other half included novices who had no formal dance background. These individuals watched five hours of video clips featuring various styles of street dance, such as hip-hop, lock, and pop. They viewed these clips while inside a functional magnetic resonance imaging scanner. This machine tracks blood flow in the brain to measure activity in real time.</p>
<p>To analyze the massive amount of data collected, the team utilized a deep generative artificial intelligence model called EDGE. This computer program is designed to create realistic dance choreography. It works by analyzing a music track and predicting the physical movements that should accompany it. The model effectively hallucinates a dance routine based on the audio input it receives.</p>
<p>The researchers extracted mathematical features from the artificial intelligence model. These features represented motion, audio, and the combined cross-modal information. The team then built encoding models to see which of these features best predicted the actual brain activity recorded in the participants. They found that the cross-modal features explained the brain activity more accurately than the motion or audio features alone.</p>
<p>This predictive success was most evident in the high-level association areas of the brain. These are the regions responsible for combining different types of sensory information. The results indicate that the brain does not just see a moving body and hear a song as separate events. Instead, it processes the interaction between the two as a distinct, unified phenomenon. The artificial intelligence model operates by predicting the next movement in a sequence, and its success in predicting brain activity suggests that the human brain may function similarly.</p>
<p>The study also highlighted distinct differences between the two groups of participants. The brain activity of expert dancers was more accurately predicted by the dance features than that of the novices. This implies that the experts possess a neural framework that is more finely tuned to the nuances of choreography. However, the experts also exhibited greater variability among themselves.</p>
<p>While the brain patterns of the novices looked relatively similar to one another, each expert processed the performance in a unique way. This finding challenges the assumption that expertise leads to a uniform way of seeing the world. Instead, deep knowledge appears to allow for a more personalized interpretation of the art. </p>
<p>Professor Hiroshi Imamizu commented on this unexpected result. “Surprisingly, compared to nonexpert audiences, our brain-activity simulator was able to more precisely predict responses in experts. Even more interesting was the fact that while nonexperts exhibited individual differences in response patterns, the videos elicited a more diverse number of patterns in experts.”</p>
<p>Beyond the mechanics of movement, the team explored how the brain encodes the emotional content of dance. They asked a large separate group of people to rate the video clips on qualities such as aesthetics, dynamics, and boredom. The researchers then mapped these subjective ratings onto the brain data using their computational model. They found that specific emotional concepts correlated with activity in distinct brain networks.</p>
<p>Feelings of boredom were associated with reduced activity in the default mode network. This is a set of brain regions that is typically active when the mind is wandering or at rest. Conversely, the perception of dynamic movement triggered increased activity in these same areas. Aesthetic appreciation was linked to activity in both the visual cortex and higher-level processing areas. This suggests that enjoying a dance involves a conversation between basic visual perception and complex evaluative thought.</p>
<p>The team tested their model further by creating artificial dance clips. They took the original dance motions and paired them with music from different genres. They fed these mismatched clips into their simulation to estimate how the brain would respond. The simulation predicted that matching music and motion activates sensory regions of the brain more strongly. Mismatched pairings appeared to engage frontal areas of the brain, possibly reflecting the detection of an error or incongruence.</p>
<p>There are limitations to the study that frame how these results should be interpreted. The stimuli focused primarily on street dance, a genre where movement and music are tightly coupled. It is not yet clear if these findings would apply to contemporary dance or other forms with looser connections between sound and choreography. Additionally, the participants watched the videos while lying motionless in a scanner. This passive observation differs from the experience of watching a live performance in a theater.</p>
<p>The researchers also note that the participants were observing dance rather than performing it. Understanding the neural activity of dancers while they are actually moving remains a goal for future science, though it presents significant technical challenges. Despite these hurdles, the study represents a significant step forward in linking computational models with human artistic experience.</p>
<p>The team hopes to use these findings to bridge the gap between neuroscience and the arts. They envision a future where these tools could assist choreographers or help explain the universal appeal of dance. “We would love nothing more than to see our developed brain-activity simulator be used as a tool to create new dance styles which move people,” Imamizu said.</p>
<p>The study, “<a href="https://doi.org/10.1038/s41467-025-65039-w" target="_blank">Cross-modal deep generative models reveal the cortical representation of dancing</a>,” was authored by Yu Takagi, Daichi Shimizu, Mina Wakabayashi, Ryu Ohata, and Hiroshi Imamizu.</p></p>
</div>
<div style="font-family:Helvetica, sans-serif; font-size:13px; text-align: center; color: #666666; padding:4px; margin-bottom:2px;"></div>
</td>
</tr>
</tbody>
</table>
<table style="font:13px Helvetica, sans-serif; border-radius:4px; -moz-border-radius:4px; -webkit-border-radius:4px; background-color:#fff; padding:8px; margin-bottom:6px; border:1px solid #adadad;" width="100%">
<tbody>
<tr>
<td><a href="https://www.psypost.org/psychologists-say-climate-anxiety-is-a-form-of-pre-traumatic-stress/" 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;">Psychologists say climate anxiety is a form of pre-traumatic stress</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Nov 28th 2025, 12:00</div>
<div style="font-family:Helvetica, sans-serif; color:#494949;text-align:justify;font-size:13px;">
<p><p>We are living in an age of anxiety. People face multiple existential crises such as climate change and <a href="https://theconversation.com/why-some-countries-are-more-likely-to-believe-nuclear-war-wont-happen-to-them-244322">conflicts</a> that could potentially escalate into nuclear war.</p>
<p>So how do people cope with competing threats like this? And what happens to <a href="https://www.routledge.com/Understanding-Climate-Anxiety/Beattie/p/book/9781032616766?srsltid=AfmBOopWK0vZF2uxUmRF1ry4uLCNaqel-azlzlN0AtWa9mmkq64eMjRv">climate</a> anxiety when wars suddenly erupt and compete for our attention?</p>
<p>Climate change affects our physical and <a href="https://www.apa.org/news/press/releases/2017/03/mental-health-climate.pdf">mental health</a>, directly through extreme climate-related droughts, wildfires and intense storms. It also affects some people indirectly through so-called “climate anxiety”. This term <a href="https://www.sciencedirect.com/science/article/pii/S0887618520300773?casa_token=PewJiAvteP8AAAAA:hV_PGMoTOrmQGZs5c6xILzRAPfty9YVgmEDeln4YHWOFyyqpZVVKEH3YRBQjBYu705vLSndp">covers</a> a range of negative emotions and states, including not just anxiety, but worry and concern, hopelessness, anger, fear, grief and sadness.</p>
<p>A team of researchers led by Caroline Hickman from the University of Bath surveyed 10,000 children and young people (aged 16 to 25 years) in ten countries (Australia, Brazil, Finland, France, India, Nigeria, Philippines, Portugal, the UK and the US). They <a href="https://www.thelancet.com/journals/lanplh/article/PIIS2542-5196(21)00278-3/fulltext?ref=gerardmazza.com">found</a> that 45% of respondents said their feelings about climate change negatively affected their daily lives. It was worse for respondents from developing countries.</p>
<p>Climate anxiety can potentially serve a <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0074708">positive</a> function. Anger, for example, can push people to act to help mitigate the <a href="https://www.taylorfrancis.com/chapters/edit/10.4324/9780203728215-35/dimensions-anxiety-disorders-david-barlow">effects of climate change</a>.</p>
<p>But it can also lead to <a href="https://www.mdpi.com/1660-4601/20/4/3085">“eco-paralysis”</a>, a feeling of being overwhelmed, inhibiting people from taking any effective action, affecting their sleep, work and study, as a result of them <a href="https://www.mdpi.com/2071-1050/12/19/7836?ref=troisiemebaobab.com">dwelling</a> endlessly on the problem.</p>
<p>Climate anxiety is not included in the <a href="https://www.psychiatry.org/psychiatrists/practice/dsm">American Psychiatric Association’s</a> authoritative guide to the diagnosis of mental disorders. In other words, it is not officially recognised as a mental disorder.</p>
<p>Some say this is a good thing. The author and Stanford academic <a href="https://www.penguinrandomhouse.ca/books/647141/generation-dread-by-britt-wray/9780735280724">Britt Wray wrote:</a> “The last thing we want is to pathologise this moral emotion, which stems from an accurate understanding of the severity of our planetary health crisis.”</p>
<p>But if it is not officially recognised, will people take it seriously enough? Will they just dismiss people who suffer from it as <a href="https://www.independent.co.uk/life-style/snowflake-meaning-definition-gammon-piers-morgan-trump-b737499.html">“snowflakes”</a> – too sensitive and too easily hurt by the hard realities of life. This is a major dilemma.</p>
<p>I explore how climate anxiety relates to other types of clinical anxiety in my recent book, <a href="https://www.routledge.com/Understanding-Climate-Anxiety/Beattie/p/book/9781032616766?srsltid=AfmBOoqA1Mdvzw4yi8CFT__DAc9z6MslCe1_7thMUDtWgOXOPaXV1JL6">Understanding Climate Anxiety</a>, recognising that there are adaptive and non-adaptive forms of anxiety.</p>
<p>According to Steven Taylor, a clinical psychologist from the University of British Columbia, adaptive anxiety can “motivate climate activism, such as efforts to reduce one’s carbon footprint”. <a href="https://www.sciencedirect.com/science/article/pii/S0887618520301274?casa_token=GyWYMZ1l-OYAAAAA:CDR6FS8TYlzgT5xQMCuuxNkXboV0W0InSMtJSEZOAjdauBT1a8VvHxonTTCZCpb5TlRzfHG7">Maladaptive</a> anxiety, however, may “take the form of anxious passivity”, he warned, where the person feels anxious but utterly helpless.</p>
<p>Identifying different types of climate anxiety, understanding their precursors and how they interact with personality is a major psychological challenge. Identifying ways of alleviating climate anxiety and making it more adaptive, and focused on possible climate mitigation, is a major societal challenge.</p>
<p>But there’s another important issue. Some <a href="https://www.taylorfrancis.com/books/mono/10.4324/9781351051828/psychology-climate-change-geoffrey-beattie-laura-mcguire">global</a> leaders, including Donald Trump, don’t believe in human-induced climate change, claiming it’s <a href="https://www.independent.co.uk/news/world/americas/us-politics/trump-climate-change-scam-hurricane-helene-georgia-b2621271.html">“one of the great scams”</a>. He seems to view climate anxiety as an overblown <a href="https://www.washingtonpost.com/climate-environment/2025/04/10/trump-princeton-funding-cuts-climate-anxiety/">reaction</a> to propaganda pumped out by a biased media.</p>
<p>This can make the experience much worse for those who feel anxious but then having their feelings dismissed.</p>
<p>Some psychologists argue that climate anxiety can be a form of <a href="https://muse.jhu.edu/pub/1/article/753062/summary?casa_token=ndPxcFS7bcAAAAAA:AHX9l6lFWQu7iHdQpzI7NA8bUiTtmBszgP5IRKnQChW7J42Ha9Fi7Tx-qpaijaOIJLprx6ys">pre-traumatic stress disorder</a>. This hypothesis arose from <a href="https://books.google.co.uk/books?hl=en&lr=&id=9sTiDwAAQBAJ&oi=fnd&pg=PT15&dq=grose+2020+pre+traumatic+&ots=W4BdCVCIRt&sig=lpaaPRyKWg3LZUT4fk6r5Yx7kkg&redir_esc=y#v=onepage&q=grose%202020%20pre%20traumatic&f=false">observations of climate scientists</a> and their growing feelings of anger, distress, helplessness and depression as the climate situation has worsened.</p>
<p>In 2015, <a href="https://journals.sagepub.com/doi/full/10.1177/2167702614551766?casa_token=fWBmcVchiKcAAAAA%3AtqesM8YN_Fy2dSIl9iuAEASwA3WXcEFuvsPtjqsMyxOJfgaUp5_jz1vMGSRn2AKvLNUVnq0QwCs">researchers</a> devised a new clinical measure to assess pre-traumatic stress reactions using items found in the diagnostic and statistical manual for post-traumatic stress disorder, but now focused on the future rather than the past, asking about “repeated, disturbing dreams of a possible future stressful experience”, for example.</p>
<p>They tested Danish soldiers before their deployment in Afghanistan and found that “involuntary intrusive images and thoughts of possible future events … were experienced at the same level as post-traumatic stress reactions to past events before and during deployment”.</p>
<p>They also found that soldiers who experienced higher levels of pre-traumatic stress before deployment had an increased risk of post-traumatic stress disorder after their return from the war zone. Their hypervigilance primed their nervous system to react more strongly when anything untoward occurred.</p>
<p>This would suggest that we need to take stress reactions to future anticipated events such as climate change very seriously.</p>
<h2>The crisis response</h2>
<p>But how important is climate anxiety in the context of these other threats? <a href="https://www.thelancet.com/journals/lanplh/article/PIIS2542-5196(24)00097-4/fulltext">Researchers</a> assessed the emotional state and mental health of people aged 18 to 29 years in five countries (China, Portugal, South Africa, the US and UK) focusing on three global issues: climate change, an environmental disaster (the Fukushima nuclear accident in Japan), and the wars in Ukraine and the Middle East.</p>
<p>They found the strongest emotional engagement was with the ongoing wars, with climate change a close second, and the radiation leak third. The strongest emotional responses to the wars were concern, sadness, helplessness, disgust, outrage and anger. For climate change, the strongest responses were concern, sadness, helplessness, disappointment and anxiety.</p>
<p>All three crises made young people feel concerned, sad, and very importantly helpless, but climate change has this burning level of anxiety added into the bubbling mix.</p>
<p>It seems that climate anxiety still has this undiminished power regardless of all the other awful things that are currently happening in the world, and I suspect the stigma of being dismissed as “snowflakes” makes this particular fear response all the more unbearable.</p>
<p> </p>
<p><em>This article is republished from <a href="https://theconversation.com">The Conversation</a> under a Creative Commons license. Read the <a href="https://theconversation.com/could-climate-anxiety-be-a-form-of-pre-traumatic-stress-disorder-a-psychologist-explains-the-research-260849">original article</a>.</em></p></p>
</div>
<div style="font-family:Helvetica, sans-serif; font-size:13px; text-align: center; color: #666666; padding:4px; margin-bottom:2px;"></div>
</td>
</tr>
</tbody>
</table>
<table style="font:13px Helvetica, sans-serif; border-radius:4px; -moz-border-radius:4px; -webkit-border-radius:4px; background-color:#fff; padding:8px; margin-bottom:6px; border:1px solid #adadad;" width="100%">
<tbody>
<tr>
<td><a href="https://www.psypost.org/specific-depression-symptoms-linked-to-distinct-patterns-of-inflammation-and-cognitive-deficit/" 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;">Specific depression symptoms linked to distinct patterns of inflammation and cognitive deficit</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Nov 28th 2025, 10:00</div>
<div style="font-family:Helvetica, sans-serif; color:#494949;text-align:justify;font-size:13px;">
<p><p>Recent research published in the <em><a href="https://doi.org/10.1016/j.jpsychires.2025.08.023" target="_blank">Journal of Psychiatric Research</a></em> indicates that specific clusters of depressive symptoms are linked to distinct biological inflammation markers and cognitive deficits. By breaking depression down into different categories of symptoms, the study reveals that feelings of sadness are associated with bodily inflammation, while physical slowing is linked to delayed reaction times. This investigation offers a more nuanced understanding of how major affective disorders affect both the body and the brain.</p>
<p>Major depressive disorder and bipolar disorder are complex mental health conditions. They are characterized by what clinicians call major depressive episodes. These episodes are not identical across every patient. Some individuals experience profound sadness, while others suffer primarily from physical lethargy or sleep disturbances.</p>
<p>For many years, the medical community focused on the “monoamine hypothesis” to explain these conditions. This theory suggests that depression arises from an imbalance of neurotransmitters like serotonin, norepinephrine, and dopamine. While this remains a significant part of psychiatric understanding, newer evidence points to other biological systems.</p>
<p>One area of intense study is the immune system. Previous research has established a connection between depression and inflammation. Specifically, depressed patients often show elevated levels of proinflammatory cytokines. These are signaling proteins that help control the body’s immune response.</p>
<p>Two specific proteins, C-reactive protein (CRP) and tumor necrosis factor-alpha (TNF-α), are frequently found at higher levels in people with mood disorders. However, most past studies looked at depression as a single, monolithic diagnosis. They rarely examined whether specific types of symptoms correlated with these inflammatory markers.</p>
<p>In addition to biological changes, depression also affects cognitive performance. A key area of cognitive function is inhibitory control. This is the brain’s ability to stop an automatic response or suppress an impulse. Deficits in this area can lead to difficulty in regulating behavior and making decisions.</p>
<p>Researchers from Taipei Veterans General Hospital and National Yang Ming Chiao Tung University in Taiwan sought to clarify these relationships. They hypothesized that different “domains” of depression would have unique footprints in the body and mind. The team was led by Ju-Wei Hsu, Mu-Hong Chen, and their colleagues.</p>
<p>The researchers recruited 327 participants for the study. This group included adolescents and adults between the ages of 16 and 64. The study population comprised 94 patients with bipolar disorder and 233 patients with major depressive disorder.</p>
<p>The team also recruited a control group of healthy individuals. These controls had no history of mental or major physical illnesses. This allowed the researchers to establish a baseline for cognitive performance and inflammation levels.</p>
<p>To assess the participants’ mental state, the researchers used the Montgomery–Åsberg Depression Rating Scale (MADRS). This is a standard clinical tool used to measure the severity of depressive episodes. The researchers did not just look at the total score from this scale.</p>
<p>Instead, they utilized a “three-domain model” to categorize symptoms. The first domain is dysphoria. This category includes reported sadness, pessimistic thoughts, and suicidal ideation. It represents the emotional core of depression.</p>
<p>The second domain is retardation. This refers to psychomotor dysfunction. It includes symptoms such as difficulty concentrating, lassitude, an inability to feel emotion, and apparent sadness in one’s demeanor. This domain captures the “slowing down” often seen in severe depression.</p>
<p>The third domain is vegetative symptoms. This category covers physiological changes. It includes inner tension, reduced sleep, and reduced appetite. These symptoms reflect how depression disrupts basic bodily functions.</p>
<p>For the biological assessment, the research team collected blood samples from the participants. They analyzed these samples to measure the concentrations of CRP and TNF-α. The participants provided these samples after fasting for several hours to ensure accuracy.</p>
<p>To measure cognitive function, the participants completed a computerized test known as the ” go/no-go task.” This is a standard psychological experiment used to evaluate inhibitory control. During the test, a symbol appears on a screen.</p>
<p>Participants were instructed to press a button as quickly as possible when they saw the “go” signal, which was an “X.” However, they had to refrain from pressing the button when the “no-go” signal, a “+,” appeared. This requires the brain to inhibit the impulse to press the button.</p>
<p>The researchers measured three specific outcomes from this task. They recorded the number of correct responses. They counted the number of errors, which indicates a failure of inhibition. Finally, they measured the reaction time, or how long it took participants to press the button.</p>
<p>The results highlighted significant differences between the patient groups and the healthy controls. Patients with bipolar disorder exhibited the highest levels of TNF-α compared to both the depressive disorder group and the healthy controls. Both patient groups performed worse on the cognitive task than the healthy participants.</p>
<p>When the researchers analyzed the specific symptom domains, they found distinct patterns. The dysphoria domain showed a positive association with inflammation. Patients with higher levels of sadness and pessimistic thoughts had significantly higher levels of CRP and TNF-α.</p>
<p>Dysphoria was also linked to specific cognitive errors. Patients scoring high in this domain made more errors in the go/no-go task. This suggests that intense emotional sadness may be linked to impulsive responses or a failure to withhold action when necessary.</p>
<p>The retardation domain showed a different pattern. High scores in psychomotor retardation were associated with longer reaction times. These patients were slower to respond to the “go” signal. This aligns with the clinical observation that these patients experience a general slowing of physical and mental processes.</p>
<p>The vegetative domain presented a third distinct outcome. Patients with severe sleep and appetite disturbances had fewer correct responses overall. This indicates that physiological disruptions may impair the ability to sustain attention and accuracy during tasks.</p>
<p>The study also explored how age influenced these associations. The researchers performed a stratified analysis comparing adolescents to adults. They found that the link between dysphoria and inflammation was present in adults but not in adolescents.</p>
<p>The authors suggest this may be due to the developmental stage of the immune system. The immune responses in adolescents might not yet interact with depressive symptoms in the same way they do in adulthood. This finding highlights the potential biological differences in early-onset depression.</p>
<p>In adolescents, the cognitive impacts also differed slightly. Dysphoria in this younger group was associated with both more errors and faster reaction times. This combination points to a high level of impulsivity.</p>
<p>The researchers noted that the retardation symptoms in adolescents were linked to slower reaction times, similar to adults. This suggests that the “slowing” aspect of depression is consistent across age groups. However, the emotional symptoms may drive more impulsive behavior in youth.</p>
<p>The study did have several limitations that affect how the results should be interpreted. The patients continued to take their prescribed psychotropic medications during the study. Ethical guidelines prevented the researchers from asking patients to stop medication.</p>
<p>It is possible that these medications influenced cytokine levels or cognitive performance. The researchers adjusted their statistical models to account for various factors, but the drug effects remain a variable. Future research using a drug-free design would help verify these results.</p>
<p>Another limitation was the sample size of the adolescent group. The study included only 37 adolescents, compared to a much larger number of adults. This makes the findings regarding age differences preliminary rather than definitive.</p>
<p>The authors also noted that they only measured two specific inflammatory markers. The immune system involves a vast network of signals. Future studies should investigate other cytokines, such as interleukin-6 or interferon-gamma, to build a complete picture.</p>
<p>Despite these caveats, the study provides evidence that depression is heterogeneous. It suggests that treating “depression” as a single entity may miss important biological and cognitive nuances. Recognizing that sadness correlates with inflammation while lethargy correlates with slow reaction times could help tailor future treatments.</p>
<p>The authors conclude that their work supports a domain-based view of mental illness. As they state in their report, “Our findings suggest that different symptom domains of depression exert different effects on proinflammatory cytokine profiles and inhibitory control function, which may reflect the heterogeneity of depressive episodes.”</p>
<p>The study, “<a href="https://doi.org/10.1016/j.jpsychires.2025.08.023" target="_blank">Depressive symptom domains exert different effects on proinflammatory cytokines and inhibitory control function among patients with major affective disorders</a>,” was authored by Ju-Wei Hsu, Wei-Chen Lin, Ya-Mei Bai, Hsiang-Hsuan Huang, Jia-Shyun Jeng, Shih-Jen Tsai, and Mu-Hong Chen.</p></p>
</div>
<div style="font-family:Helvetica, sans-serif; font-size:13px; text-align: center; color: #666666; padding:4px; margin-bottom:2px;"></div>
</td>
</tr>
</tbody>
</table>
<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>
<p> </p>
<p><s><small><a href="#" style="color:#ffffff;"><a href='https://blogtrottr.com/unsubscribe/565/DY9DKf'>unsubscribe from this feed</a></a></small></s></p>