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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/anxious-attachment-linked-to-depression-spillover-in-romantic-relationships-study-finds/" style="font-family:Helvetica, sans-serif; letter-spacing:-1px;margin:0;padding:0 0 2px;font-weight: bold;font-size: 19px;line-height: 20px;color:#222;">Anxious attachment linked to depression spillover in romantic relationships, study finds</a>
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<p><p>A study from Germany has found that women with anxious attachment styles face a slightly elevated risk of depressive symptom spillover from their partners. In other words, when their partners reported higher levels of depressive symptoms, anxiously attached women were slightly more likely to experience increases in their own depressive symptoms compared to securely attached women. The study was published in <a href="https://doi.org/10.1111/pere.12584"><em>Personal Relationships</em></a>.</p>
<p>Depressive symptoms include persistent sadness, loss of interest or pleasure, fatigue, changes in appetite or sleep, and difficulty concentrating. While major depressive disorder is a common mental health diagnosis, many individuals experience depressive symptoms that do not meet the criteria for a clinical diagnosis.</p>
<p>These symptoms often do not remain isolated. In romantic relationships, one partner’s depressive symptoms can influence the other partner, potentially leading to increased symptoms over time. This phenomenon is referred to as spillover or crossover of depressive symptoms. However, because both partners are often exposed to similar external stressors, it is sometimes unclear whether such changes are due to true spillover or shared environmental influences affecting both individuals simultaneously.</p>
<p>Study author Preston C. Morgan and his colleagues sought to examine the extent to which a partner’s anxious attachment moderates the spillover of depressive symptoms over time in romantic relationships. Specifically, they wanted to determine whether men’s or women’s initial depressive symptoms could predict changes in their partner’s symptoms over a four-year period—and whether this effect was influenced by anxious attachment. Anxious attachment is a relational style characterized by a heightened fear of abandonment, increased sensitivity to rejection, and a strong need for closeness and reassurance.</p>
<p>The researchers analyzed data from the German Panel Analysis of Intimate Relationships and Family Dynamics (Pairfam), using five waves of data collected between 2010 and 2014.</p>
<p>The final sample included 1,179 heterosexual couples who remained together throughout the study period. At the start of the study, the average age was approximately 36 for men and 33 for women, and 72% of the couples were married. Participants completed validated assessments of depressive symptoms and anxious attachment.</p>
<p>The results showed that participants’ initial levels of depressive symptoms were not significantly associated with changes in their partners’ depressive symptoms across the four-year period. However, women’s anxious attachment significantly moderated this association. In particular, women with higher levels of anxious attachment were slightly more likely to experience increases in their own depressive symptoms when their male partners had elevated depressive symptoms at the start of the study.</p>
<p>“In general, depressive symptoms in one partner seem to be at least partially transferrable to the other romantic partner. This depressive symptom spillover seems to occur at the same time point, but men’s and women’s initial depressive symptoms were not associated with changes in their partner’s depressive symptom trajectories—limiting the spillover effect. However, this long-term spillover effect may occur in one context. Specifically, women who are more anxiously attached—seeking increased reassurance—are at an even higher risk for depressive symptom spillover within their romantic relationships,” the study authors concluded.</p>
<p>The study sheds light on the depressive symptom spillover effect within couples. However, it should be noted that the mean depression level of study participants was low and that these were not clinically depressed individuals. This may have limited the magnitude of detected spillover effects. Furthermore, the study sample was limited to couples who remained together during the study period. Results on other categories of individuals may differ.</p>
<p>The paper, “<a href="https://doi.org/10.1111/pere.12584">Depressive symptoms spillover between romantic partners partially explained by anxious attachment</a>,” was authored by Preston C. Morgan, Jared A. Durtschi, and Salena King.</p></p>
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<td><a href="https://www.psypost.org/scientists-find-16-new-alzheimers-linked-genes-using-multi-ancestry-genome-data/" 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 find 16 new Alzheimer’s-linked genes using multi-ancestry genome data</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">May 23rd 2025, 08:00</div>
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<p><p>A new study published in <a href="https://alz-journals.onlinelibrary.wiley.com/doi/full/10.1002/alz.14592"><em>Alzheimer’s & Dementia</em></a> has identified 16 novel genetic regions associated with Alzheimer’s disease by analyzing whole genome sequencing data from a large, ethnically diverse group of individuals. The findings emphasize the importance of including underrepresented populations in genetic research and point to new biological pathways that may play a role in the development of the disease.</p>
<p>Alzheimer’s disease is the most common form of dementia, affecting millions of people around the world. While the disease is known to have a strong genetic component, most studies of its genetic basis have focused on individuals of European ancestry. This limits understanding of the disease’s full genetic architecture and may leave important variants undiscovered. The researchers behind this new study sought to address that gap by analyzing genomic data from over 430,000 individuals of diverse backgrounds, including participants of African, Hispanic/Latino, and other ancestries.</p>
<p>“Alzheimer’s disease is a complex disease with contributions from both genetics and environment,” said Dmitry Prokopenko of the Genetics and Aging Research Unit and the McCance Center for Brain Health at Massachusetts General Hospital. “Most of the previous genome-wide association studies were focused on cohorts with subjects of European descent. Here we have studied diverse cohorts and biobanks with about 50% of non-European descent.”</p>
<p>The team drew on data from four major sources: the National Institute on Aging Genetics of Alzheimer’s Disease Data Storage Site (NIAGADS), the National Institute of Mental Health (NIMH), the UK Biobank, and the All of Us Research Program. The combined dataset included more than 49,000 Alzheimer’s cases—12,074 with a clinical diagnosis and 37,075 defined by a family history of the disease—and over 383,000 controls. Notably, nearly half of the participants from the NIAGADS and All of Us cohorts were of non-European ancestry, providing a much-needed level of diversity.</p>
<p>To identify genetic variants linked to Alzheimer’s, the researchers conducted a genome-wide association study, a method that scans the genome for variants more common in people with the disease than in those without. For clinical Alzheimer’s diagnoses, they uncovered 14 new genetic loci, five of which were common variants and nine of which were rare. These loci were located near or within genes that play roles in brain function, immune response, and cellular signaling.</p>
<p>Among the newly identified common variants were those near the genes FBN2, SLC27A6, DYM, KCNG1, and TIAM1. These genes are involved in processes such as neural connectivity, fatty acid metabolism, and synaptic signaling. For example, FBN2 and SLC27A6 were found to be more active in neurons of individuals with Alzheimer’s pathology, suggesting they may contribute to disease development or progression. KCNG1, a gene related to potassium channels, showed altered expression in both neurons and astrocytes, hinting at a potential role in brain electrical signaling.</p>
<p>The study also identified nine rare variants in genes such as VWA5B1, PDE4D, and NEO1, among others. These rare variants may have strong effects on disease risk, even though they are present in only a small portion of the population. Identifying such variants is especially important for understanding Alzheimer’s in populations that have been historically underrepresented in genetic studies.</p>
<p>In addition to studying people with confirmed clinical diagnoses, the researchers also examined a group of individuals categorized as having “Alzheimer’s disease by proxy”—that is, people with a family history of the disease but no personal diagnosis. Using data from the UK Biobank and All of Us, they conducted a similar genetic analysis and found two new rare loci associated with increased Alzheimer’s risk. These were located near the genes RPL23/LASP1 and CEBPA, both of which are involved in cellular functions that may influence brain health. CEBPA, for instance, plays a role in regulating immune responses in the brain.</p>
<p>The team then compared the findings from the two analyses—clinical Alzheimer’s cases and Alzheimer’s-by-proxy cases. While some overlap existed, most of the new loci identified in one dataset did not replicate in the other. This suggests that the two approaches may capture different aspects of the disease’s genetic architecture, particularly across populations of varying ancestry.</p>
<p>Importantly, the researchers assessed whether the newly discovered genetic regions were located near genes with altered activity in the brains of people with Alzheimer’s. They used previously published single-cell data that mapped gene expression in various brain cell types, such as neurons and astrocytes. Many of the newly identified genes, including DYM, TIAM1, and PDE4D, were indeed differentially expressed in individuals with cognitive impairment and hallmark Alzheimer’s pathology.</p>
<p>These findings offer promising leads for future research into the mechanisms of Alzheimer’s disease. By revealing new genetic associations and linking them to biological changes in the brain, the study helps lay the groundwork for new strategies to predict, prevent, and treat the disease. It also underscores the importance of including participants from a wide range of genetic backgrounds in research.</p>
<p>Despite its strengths, the study has some limitations. The definition of Alzheimer’s-by-proxy relies on self-reported family history, which may introduce errors or inconsistencies, especially in more diverse populations where diagnosis rates may vary due to disparities in healthcare access. Additionally, many of the variants identified in the more diverse All of Us dataset did not replicate in the clinical dataset, raising the possibility of false positives or ancestry-specific effects that require further investigation.</p>
<p>Future research should focus on validating these newly identified loci in independent cohorts and exploring how they influence brain biology. Functional experiments, including laboratory studies of gene expression and protein function, will be necessary to determine the roles these variants play in Alzheimer’s disease.</p>
<p>“Our paper underscores the importance of using diverse cohorts with well-defined phenotypes for discovery and validation,” Prokopenko told PsyPost. “Our study can help discover new precision medicine approaches in diverse populations. Such genetic studies help scientists to discover new biological pathways and disease mechanisms. However, additional studies are needed to fully replicate these novel findings. As next steps, we plan to use gene-based testing to combine signals from multiple rare variants within a gene or functional elements to test for disease association.”</p>
<p>The study, “<a href="https://doi.org/10.1002/alz.14592">Identification of 16 novel Alzheimer’s disease loci using multi-ancestry meta-analyses</a>,” was authored by Julian Daniel Sunday Willett, Mohammad Waqas, Younjung Choi, Tiffany Ngai, Kristina Mullin, Rudolph E. Tanzi, and Dmitry Prokopenko.</p></p>
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<td><a href="https://www.psypost.org/groundbreaking-ai-model-uncovers-hidden-patterns-of-political-bias-in-online-news/" 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;">Groundbreaking AI model uncovers hidden patterns of political bias in online news</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">May 23rd 2025, 06:00</div>
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<p><p>A new study published in <em><a href="https://doi.org/10.1371/journal.pone.0321418" target="_blank" rel="noopener">PLOS One</a></em> introduces a large-scale method for detecting political bias in online news sources using artificial intelligence. By analyzing hundreds of thousands of news articles, the researchers developed a model that predicts political leaning and explains why an outlet is categorized in a particular way.</p>
<p>Bias in media reporting is widely acknowledged, but studying and measuring that bias at scale has been difficult. Traditional approaches often rely on human annotations, which are limited in scope and can themselves be biased. At the same time, most studies focus on narrow expressions of bias—such as loaded wording in headlines—while overlooking broader behavioral patterns like which topics are covered, how frequently, or how much space they are given. The new study addresses these limitations by building a data-driven system that examines a range of bias indicators across a vast number of sources.</p>
<p>“This project actually started as part of my master’s thesis, and I was determined to apply my technical skills toward analyzing a societally important topic,” explained study author <a href="https://rtronnback.github.io/" target="_blank" rel="noopener">Ronja Thelen-Rönnback</a>, a PhD student at Tilburg University and member of the <a href="https://www.tilburguniversity.edu/about/schools/tshd/departments/dca/lab/algorithm-observatory" target="_blank" rel="noopener">Tilburg Algorithm Observatory</a>.</p>
<p>“People are becoming more and more skeptical or disenchanted with news, and a large part of this is (according to surveys) due to perceived political bias. There is a lot of excellent academic work about news bias, but much of it relies on human experts analyzing and labeling bias in articles. This is thorough but, of course, slow—which has inspired some data-driven approaches that can detect bias much faster.”</p>
<p>“However, the data-driven approaches usually do not provide the same level of detail and understanding about news political bias as expert labels, and they also tend to focus on very simple forms of bias. For example, they detect sensational headlines or biased wording in an article. Crucially, experts have long known that there are many more, sometimes subtle, ways that news outlets can be biased. For example, an outlet might entirely avoid reporting on a specific topic, or only report on it very briefly. These kinds of bias are only really visible when one examines an outlet’s overall behavior, not just the text in its articles.”</p>
<p>To conduct a large-scale investigation into political bias in news media, the researchers developed a machine learning-based system capable of classifying the political orientation of web-based news outlets. They grounded their analysis in data from the <a href="https://www.gdeltproject.org/" target="_blank" rel="noopener">Global Database of Events, Language, and Tone (GDELT)</a>, one of the most comprehensive open platforms for monitoring global news coverage. The study focused on English-language articles from 2022, allowing the researchers to examine how thousands of news web domains behaved across a range of topics and how this behavior related to political bias.</p>
<p>The first step involved transforming raw article-level data into a structured, outlet-level dataset. GDELT tags news articles with thematic labels—ranging from crime and immigration to climate change and economic topics—and provides metadata such as tone, word count, and whether the article included visual content like images or videos. These data points served as proxies for different types of media bias, including tone bias (how emotionally charged or neutral a story is), selection bias (which topics are covered or ignored), and size bias (how much space is given to different topics).</p>
<p>To build a representative sample and reduce noise, Thelen-Rönnback and her colleagues filtered out themes that were either too obscure or appeared too infrequently, ending up with over 500 themes and nearly 7,000 features per web domain.</p>
<p>In addition to the GDELT data, the researchers supplemented their dataset with information from Media Bias Fact Check, an independent organization that evaluates media outlets based on factors like political leaning, factual accuracy, web traffic, and press freedom in the outlet’s country of origin. These outlet-level features—such as whether a domain was a newspaper or TV station, or how credible it was rated—were incorporated into one version of the experiment to assess whether they could improve classification accuracy.</p>
<p>The researchers then created two sets of ground truth political bias labels. One came from Media Bias Fact Check, where human experts assigned each outlet a political classification on a five-point scale: left, left-center, least biased, right-center, and right. The other came from a study by Robertson and colleagues, which inferred political leaning based on Twitter user behavior. In that method, websites frequently shared by registered Democrats were assumed to lean left, and those shared by Republicans were assumed to lean right. These continuous scores were grouped into the same five political categories for comparison.</p>
<p>Using these ground truth labels, the researchers trained and tested multiple machine learning models, including a feed-forward neural network, support vector machines (SVM), AdaBoost, and XGBoost classifiers. They also included two large language models (GPT-4o-mini and LLaMA 3.1) as baseline comparisons, asking them to classify political bias without any fine-tuning or additional training.</p>
<p>Across all experiments, model performance was evaluated based on classification accuracy and the area under the receiver operating characteristic curve (AUC), which measures how well a model can distinguish between classes.</p>
<p>The neural network consistently outperformed other models. When trained on the full set of features—including both traditional tone-based and alternative bias indicators like article counts and image presence—it achieved an accuracy of 76% and an AUC score of 81% when using the Media Bias Fact Check labels. This marked a significant improvement over the majority baseline model, which simply predicted the most common class and achieved only 45% accuracy. The language model baselines, surprisingly, performed no better than the majority baseline, typically defaulting to the “least biased” label for most outlets.</p>
<p>“We used large language models (GPT-4o-mini and LLaMA 3.1) to see how they would perform compared to smaller, traditional machine learning models,” Thelen-Rönnback told PsyPost. “They did not do well at all, though it is worth noting our implementation of them was very straightforward. Nevertheless, given that there is currently a lot of hype surrounding large language models, we show that they do not always perform the best, and that smaller models can be more than sufficient for many tasks.”</p>
<p>The researchers also examined whether different types of features influenced model performance. When models were trained only on traditional bias features such as tone and sentiment, performance was lower. When trained only on alternative features like topic coverage and media presence, performance improved. But the best results came from using all features together, suggesting that a multifaceted approach to detecting bias—one that includes what topics are covered, how much space is given to them, and whether visuals are used—yields a more accurate picture of a news outlet’s political slant.</p>
<p>“Our work uses an existing database that tracks news worldwide (GDELT) to automatically label political bias of news outlets with machine learning,” Thelen-Rönnback explained. “We account for multiple forms of bias, which is, as of now, somewhat rare in the field. We show that (unsurprisingly) this makes it much easier to detect political bias compared to just looking at a single, narrow expression of bias.”</p>
<p>To make the results interpretable, the researchers used a model-agnostic explainability technique called SHAP (Shapley Additive Explanations). SHAP assigns importance values to each feature used in the model, showing which variables had the most influence on a specific prediction. These explanations revealed that features related to article counts on politically charged themes—such as gun ownership, environmental regulation, and election fraud—were often among the most informative. In some cases, more surprising themes like natural disasters or sanitation also played a role, though the reasons for their relevance were less clear.</p>
<p>In one illustrative example, the model accurately classified <em>Breitbart</em> as a right-wing outlet due to its high frequency of negatively toned articles about crime-related themes such as cartels, kidnapping, and robbery. Similarly, <em>The Guardian</em> was correctly identified as left-leaning due to a strong emphasis on inequality and social movements. These insights provided a window into not just what label the model assigned, but why it reached that conclusion—addressing a major critique of previous machine learning approaches that treated models as “black boxes.”</p>
<p>“We use an explainability tool to provide ‘reasonings’ for each classification—so our models do not just say ‘<em>Breitbart</em> is right biased,’ but they actually show that Breitbart discusses a lot of themes related to crime, and that this is what pushed the model to classify it as right-wing,” Thelen-Rönnback told PsyPost.</p>
<p>To test the reliability of the two labeling systems, the researchers compared how often Media Bias Fact Check and the Twitter-based method agreed. They found that only 46% of web domains shared the same label across both systems, indicating a significant level of disagreement. However, this isn’t entirely surprising given the subjective nature of bias detection. Even two human-annotated sources—Media Bias Fact Check and another platform called AllSides—agreed on only 57% of domains. This suggests that while human labels remain the gold standard, automatically derived labels are not dramatically worse and could be useful when manual evaluations are unavailable.</p>
<p>“We try to use machines to characterize how news sources are biased, rather than humans,” Thelen-Rönnback said. “Machine learning allows us to determine if a news outlet is showing bias at a much larger scale, much faster than what humans can—and potentially in ways that humans might not consider. For example, we see that some unexpected topics like climate disasters are informative to the models, but these are quite often not considered by human experts.”</p>
<p>“News outlets can be biased in many different ways, and some of them are currently not being considered sufficiently. We hope that changing this might help the field advance. We also try to provide some transparency into why a specific outlet is deemed to be biased, which is going to become increasingly important to ensure that the public can understand and trust the news they consume. Hopefully, this can, down the line, lead to a better-informed society and a healthier political environment.”</p>
<p>But there are some caveats to consider. “Right now, getting the output requires some technical expertise with programming and AI models,” Thelen-Rönnback noted. “We also relied on the left-right political split, but this might not be the most appropriate one worldwide. Nevertheless, our approach does easily allow for a different set of political labels, if need be.”</p>
<p>Looking ahead, the researchers hope to expand their system to classify other dimensions of media content. “Ideally, we would be able to expand this from political bias to other relevant aspects like disinformation or unreliability,” Thelen-Rönnback explained. “We are currently also looking at not just online news, but the internet more broadly. Specifically, we’re looking at how search engines react to polarizing topics, which is very exciting since search engines are nowadays the most trusted source of news.”</p>
<p>The study, “<a href="https://doi.org/10.1371/journal.pone.0321418" target="_blank" rel="noopener">Automatic large-scale political bias detection of news outlets</a>,” was authored by Ronja Rönnback, Chris Emmery, and Henry Brighton.</p></p>
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<td><a href="https://www.psypost.org/gut-bacteria-can-influence-how-brain-proteins-are-modified-by-carbohydrates/" 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;">Gut bacteria can influence how brain proteins are modified by carbohydrates</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">May 22nd 2025, 14:00</div>
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<p><p>A new study published in <em><a href="https://www.nature.com/articles/s41594-025-01485-w" target="_blank">Nature Structural and Molecular Biology</a></em> offers groundbreaking evidence that gut bacteria can influence how proteins in the brain are chemically modified by sugars, a process known as glycosylation. Using a new method called DQGlyco, the researchers were able to analyze brain glycosylation with unprecedented resolution, revealing that different microbial populations in the gut can alter these patterns in ways that may affect brain function.</p>
<p>Protein glycosylation is a biological process in which sugar molecules are added to proteins, helping regulate how proteins function, interact, and communicate within and between cells. While this process plays important roles in many systems of the body, its involvement in the brain has been difficult to study due to the complexity and diversity of sugar modifications. Past studies have also been limited by technical constraints in identifying and quantifying the many forms of glycosylation that can occur on a single protein.</p>
<p>To overcome these challenges, researchers developed a method called deep quantitative glycoprofiling, or DQGlyco. This new technique allows scientists to measure protein glycosylation at a scale and resolution not previously possible. The team then used DQGlyco to examine how glycosylation patterns in the mouse brain are affected by different gut microbiomes.</p>
<p>The study began by validating the DQGlyco method in both human cells and mouse brain tissue. This method combines improved sample preparation, high-sensitivity detection, and precise labeling techniques to isolate and analyze glycopeptides—protein fragments with attached sugar chains. The researchers applied their method to brain tissue from mice, identifying over 177,000 unique glycopeptides. This is more than 25 times the number identified in previous studies, offering an unprecedented view of the brain glycoproteome.</p>
<p>With this enhanced dataset, the researchers discovered widespread “microheterogeneity”—meaning that individual sites on a protein often carried many different sugar forms, known as glycoforms. On average, they found about 17 glycoforms per site, and in some cases, more than 600 different glycoforms at a single location. This variation was not random. Sites with greater microheterogeneity were more likely to be located in structured and accessible parts of the protein, suggesting that structural features help determine how extensively proteins are glycosylated.</p>
<p>The study also revealed that certain types of glycosylation—such as those involving specific sugar types like fucose or sialic acid—were more common on particular protein domains, such as those involved in immune signaling or cell adhesion. These findings suggest that the chemical diversity of sugar modifications may be functionally relevant, influencing how proteins interact and perform their roles.</p>
<p>To explore the influence of the gut microbiome on brain glycosylation, the team conducted experiments using germ-free mice. These mice were either kept sterile or colonized with different bacterial populations, including a single common gut species or a defined community of eight bacteria. Two weeks later, the researchers analyzed the mice’s brain tissue.</p>
<p>The results showed substantial differences in glycosylation patterns between the groups. Over 2,500 glycopeptides in the brain were affected by the presence or absence of gut microbes. These changes were especially prominent on proteins known to be involved in neurotransmission, such as glutamate receptors and ion channels. Many of the altered glycoforms were located at sites with high microheterogeneity, indicating that microbial signals might be influencing the most dynamically glycosylated regions of brain proteins.</p>
<p>Interestingly, only a small number of changes in protein abundance were observed, suggesting that the differences were not due to changes in how much protein was produced, but rather in how those proteins were modified after being made. Additional experiments showed that glycosylation changes were also associated with differences in protein solubility and thermal stability, two indicators of changes in protein function or localization.</p>
<p>The study also found that certain glycoforms were more likely to be surface-exposed on cells, using enzymes to selectively cleave sugars or proteins from intact cells. Surface-exposed glycoforms tended to be more complex and processed, whereas high-mannose forms—often considered immature—were less accessible. This helped distinguish between proteins that were still being processed inside cells versus those functioning at the cell surface.</p>
<p>By combining deep profiling with structural predictions from AlphaFold, the researchers created a model that could predict whether a given glycosylation site was likely to be highly variable or not. This model, based on structural exposure and folding, successfully distinguished between high and low microheterogeneity sites and suggested that glycosylation patterns are influenced by the 3D structure of proteins.</p>
<p>While the findings open new directions for understanding the gut-brain axis, the study has several limitations. The experiments were conducted in mice, so it is not yet clear how directly the results apply to humans. The study also measured glycosylation patterns at a specific time point, meaning that the dynamic nature of these changes over longer periods remains unknown. Additionally, although changes in glycosylation were linked to structural and biophysical changes in proteins, the functional consequences for behavior or brain activity were not directly tested.</p>
<p>Future research may explore how these glycosylation changes influence neural development, cognition, or disease risk. The authors also suggest that their DQGlyco method could be applied to human tissue or disease models to identify glycosylation signatures linked to neurological or psychiatric disorders.</p>
<p>The study, “<a href="https://doi.org/10.1038/s41594-025-01485-w" target="_blank">Uncovering protein glycosylation dynamics and heterogeneity using deep quantitative glycoprofiling (DQGlyco)</a>,” was authored by Clément M. Potel, Mira Lea Burtscher, Martin Garrido-Rodriguez, Amber Brauer-Nikonow, Isabelle Becher, Cecile Le Sueur, Athanasios Typas, Michael Zimmermann, and Mikhail M. Savitski.</p></p>
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<td><a href="https://www.psypost.org/tiger-mom-parenting-boosts-teens-cognitive-skills-but-undermines-emotional-development-study-suggests/" 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;">“Tiger mom” parenting boosts teens’ cognitive skills but undermines emotional development, study suggests</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">May 22nd 2025, 12:00</div>
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<p><p>Recent research published in <a href="https://doi.org/10.1016/j.ehb.2024.101463"><em>Economics and Human Biology</em></a> found that when mothers hold dominant positions in their children’s educational decisions, they are more likely to adopt a “tiger mom” approach. This parenting style is associated with enhanced cognitive performance and academic achievement in adolescents but may hinder the development of non-cognitive skills and reduce investment in leisure time.</p>
<p>“Tiger moms” are mothers who practice strict, high-expectation parenting styles. The term became widely known through Amy Chua’s 2011 book <em>Battle Hymn of the Tiger Mother</em>. This approach is often associated with East Asian parenting, particularly in Chinese or Chinese-American households.</p>
<p>Tiger moms emphasize academic excellence, discipline, and obedience. They enforce rigorous study schedules and often restrict recreational activities. Supporters argue that this style fosters work ethic, perseverance, and achievement, while critics warn it may contribute to anxiety, low self-esteem, and strained parent-child relationships. Research on its effects has produced mixed results, with some children thriving under such pressure and others experiencing emotional or behavioral difficulties.</p>
<p>Study author Chengkui Liu and colleagues sought to examine how maternal dominance in educational decision-making affects adolescent human capital—a term that encompasses the knowledge, skills, and attributes that contribute to a person’s productivity and life outcomes.</p>
<p>The researchers analyzed data from the 2014 wave of the China Family Panel Studies (CFPS), a nationally representative longitudinal survey covering 25 provinces, municipalities, and autonomous regions in China. While the CFPS has been conducted regularly since 2010, the 2014 wave was used because it includes detailed data on household decision-making.</p>
<p>Cognitive ability was assessed using standardized word memory and math tests. Non-cognitive ability was measured using indicators related to the Big Five personality traits: conscientiousness, agreeableness, extraversion, openness, and emotional stability. Maternal dominance was defined based on whether the mother reported having the final say in decisions about the child’s education.</p>
<p>The results showed that approximately 49% of mothers reported having decision-making authority over their child’s education. Children of these mothers, on average, scored higher on cognitive ability tests but lower on measures of non-cognitive traits compared to those whose mothers did not have such authority.</p>
<p>Girls tended to outperform boys on cognitive measures, although there were no significant gender differences in non-cognitive outcomes. The researchers found that maternal dominance was linked to increased parental time spent on daily care and homework supervision, greater provision of academic resources, and reduced time spent on leisure activities. However, there was no corresponding increase in emotionally responsive parenting or activities aimed at social or emotional development.</p>
<p>“It is clear that as mothers have the decision-making authority in children’s education, parents allocate more time to daily care and homework tutoring and provide additional extracurricular learning resources for adolescents, ultimately enhancing their cognitive abilities,” the authors wrote. “Meanwhile, parents tend to be more demanding. Conversely, the mothers’ dominance in the children’s educational decisions results in reduced investment in leisure time, with no statistically significant effects on parents’ responsiveness and activities related to talent development, mental growth, and parent-child bonding. The above three mechanisms indicate that when mothers hold dominant positions in their children’s educational decisions, they are more likely to adopt a ‘tiger mom’ approach to fostering their children’s human capital development.”</p>
<p>While the study offers important insights into parenting and developmental outcomes in China, the correlational nature of the analysis limits the ability to draw firm conclusions about causality. Additionally, the use of Big Five personality traits as a proxy for non-cognitive ability, though common in psychological research, remains subject to debate regarding its comprehensiveness and validity.</p>
<p>The paper, “<a href="https://doi.org/10.1016/j.ehb.2024.101463">Cognitive or non-cognitive? The effect of maternal dominance on adolescent human capital: Evidence from adolescents’ educational decisions</a>,” was authored by Chengkui Liu, Feirong Ren, Liuyi Yang, Wei Fan, and Xiongcai Huang.</p></p>
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<p><strong>Forwarded by:<br />
Michael Reeder LCPC<br />
Baltimore, MD</strong></p>
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