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<td><a href="https://www.psypost.org/pregnancy-reshapes-the-brain-study-reveals-gray-matter-changes-linked-to-hormones-and-bonding/" 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;">Pregnancy reshapes the brain: Study reveals gray matter changes linked to hormones and bonding</a>
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<p><p>Pregnancy and the postpartum period bring about profound changes in a woman’s body, and new research shows that these transformations extend to the brain. A groundbreaking longitudinal study published in <em><a href="https://doi.org/10.1038/s41467-025-55830-0" target="_blank" rel="noopener">Nature Communications</a></em> has mapped the changes in brain structure that occur from pre-pregnancy through the postpartum period. The findings reveal a U-shaped pattern of gray matter volume, with reductions during late pregnancy followed by partial recovery postpartum. These changes, which are linked to hormonal fluctuations, have significant implications for maternal psychological well-being and attachment to the newborn.</p>
<p>While previous studies had observed structural changes in the brain during pregnancy, these were often limited by small sample sizes, incomplete timeframes, or a lack of hormonal and psychological data. Additionally, much of the research had focused on animal models, leaving questions about how these findings translated to humans.</p>
<p>The team of researchers from the Universitat Autònoma de Barcelona (UAB), the Gregorio Marañón Health Research Institute, and the Hospital del Mar Research Institute, along with other international institutions, sought to build a comprehensive picture of the maternal brain by examining its changes across the full perinatal period, from before conception to six months postpartum. They aimed to investigate how these brain changes correlate with the hormonal fluctuations that are a hallmark of pregnancy and postpartum, as well as how these changes impact maternal mental health and attachment to the infant.</p>
<p>“Motherhood triggers profound neurobiological and psychological changes, yet these remain surprisingly understudied. This gap exists partly due to historical biases in research, ethical constraints on studying pregnant women, and the challenges of integrating multidisciplinary methodologies. Our study aims to address these limitations and advance understanding of maternal brain adaptations,” explained senior author Oscar Vilarroya, a research director at UAB.</p>
<p>The researchers conducted a longitudinal study involving 127 first-time mothers from the Barcelona area, tracking them from pre-conception through six months postpartum. Participants underwent five Magnetic Resonance Imaging sessions: before pregnancy, at 18 and 34 weeks of gestation, and one and six months after giving birth. The design allowed the researchers to use each participant’s pre-pregnancy brain structure as a baseline for comparison.</p>
<p>In addition to imaging, participants provided urine samples to measure hormone levels and completed questionnaires to assess psychological well-being and maternal attachment. To isolate the effects of pregnancy from parenting or unrelated factors, the study also included two control groups: 20 non-gestational mothers (female partners of the gestational mothers) and 32 women without children.</p>
<p>The imaging data focused on gray matter volume, a key indicator of structural brain changes. The researchers observed a distinct U-shaped trajectory in gray matter volume among gestational mothers. Gray matter volume decreased as early as the second trimester, peaking in late pregnancy, and partially recovered by six months postpartum. These changes were most prominent in brain regions associated with the Default Mode and Frontoparietal networks, areas linked to social cognition and decision-making.</p>
<p>Importantly, the changes were unique to gestational mothers and were not seen in the non-gestational mothers or women without children, suggesting that pregnancy-specific factors, rather than general parenting experiences, drive these brain adaptations.</p>
<p>The study also revealed a strong link between gray matter changes and hormonal fluctuations. Two estrogens, estriol sulfate and estrone sulfate, followed an inverse U-shaped trajectory, increasing during pregnancy and plummeting after childbirth. The degree of gray matter volume reduction and subsequent recovery closely mirrored these hormonal patterns, indicating that estrogens likely play a critical role in driving the observed brain remodeling.</p>
<p>“Pregnancy transforms the brain, with 94% of gray matter experiencing up to a 5% reduction in volume, followed by partial recovery postpartum,” Vilarroya told PsyPost. “These changes occur in regions linked to social cognition, influenced by hormonal shifts.”</p>
<p>Beyond structural changes, the researchers explored the psychological implications of these adaptations. They found that greater recovery of gray matter volume during the postpartum period was associated with stronger maternal attachment at six months postpartum, particularly lower levels of hostility toward the infant.</p>
<p>“Intriguingly, a stronger recovery of gray matter postpartum is associated with greater maternal-infant bonding, underscoring the interplay between brain structure, hormones, and behavior,” Vilarroya said.</p>
<p>Maternal well-being mediated this relationship, suggesting that the recovery process not only supports attachment but also improves overall mental health. These findings emphasize that the brain’s remodeling during pregnancy and postpartum is an adaptive process, preparing mothers for the psychological and behavioral demands of caregiving.</p>
<p>The research represents a major step forward in understanding how pregnancy reshapes the brain. By linking structural brain changes to hormonal dynamics and maternal attachment, it sheds light on the complex interplay between biology and behavior during the transition to motherhood. Future studies will build on these findings to explore how these changes influence parenting, cognitive function, and long-term mental health, offering a more comprehensive picture of the maternal brain.</p>
<p>“Our aim is to uncover the complex interplay of neurobiological, hormonal, immunological, and psychological factors during the transition to motherhood, contributing to both scientific understanding and maternal health care,” Vilarroya said. “This study emphasizes the need to recognize pregnancy as a critical period for brain plasticity and maternal well-being, with implications for parenting and family dynamics.”</p>
<p>The study, “<a href="https://www.nature.com/articles/s41467-025-55830-0" target="_blank" rel="noopener">Pregnancy entails a U-shaped trajectory in human brain structure linked to hormones and maternal attachment</a>,” was authored by Camila Servin-Barthet, Magdalena Martínez-García, María Paternina-Die, Luis Marcos-Vidal, Daniel Martín de Blas, Anna Soler, Olha Khymenets, Daniel Bergé, Gemma Casals, Pilar Prats, Oscar J. Pozo, Clara Pretus, Susana Carmona, and Oscar Vilarroya.</p></p>
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<td><a href="https://www.psypost.org/adolescents-with-authoritarian-leanings-exhibit-weaker-cognitive-ability-and-emotional-intelligence/" 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;">Adolescents with authoritarian leanings exhibit weaker cognitive ability and emotional intelligence</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Jan 23rd 2025, 06:00</div>
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<p><p>A recent study published in the <em><a href="https://onlinelibrary.wiley.com/doi/10.1111/jopy.13007" target="_blank" rel="noopener">Journal of Personality</a></em> has found that adolescents with lower levels of both cognitive and emotional abilities are more likely to hold authoritarian attitudes, whether on the left or right of the political spectrum. The findings highlight how limitations in reasoning and emotional regulation are tied to authoritarianism, shedding light on the shared psychological traits that underpin these ideological attitudes.</p>
<p>Adolescence is a critical developmental period when political beliefs and ideological attitudes begin to take shape, yet studies examining these processes among adolescents are sparse. Researchers at Ghent University in Belgium aimed to determine whether the relationships observed in adults—such as the link between <a href="https://www.psypost.org/genetic-variations-help-explain-the-link-between-cognitive-ability-and-liberalism/" target="_blank" rel="noopener">lower cognitive abilities</a> and <a href="https://www.psypost.org/higher-levels-of-cognitive-ability-linked-to-stronger-support-for-freedom-of-speech/" target="_blank" rel="noopener">more authoritarian attitudes</a>—also apply to adolescents.</p>
<p>One of the primary motivations for the study was to address the assumption that ideological development primarily occurs during adulthood, particularly following exposure to higher education. This assumption has been widely discussed in political psychology, with theories suggesting that adolescence is too early for meaningful political engagement due to limited cognitive capacities.</p>
<p>However, recent research has challenged this view, arguing that proto-ideological beliefs emerge even in childhood and that understanding these early beliefs can shed light on how ideological attitudes develop. By focusing on adolescents, the researchers hoped to capture a critical stage in this developmental trajectory.</p>
<p>Another key goal was to examine the role of emotional abilities, which had received less attention than cognitive abilities in prior research. Emotional abilities—such as understanding and regulating emotions—are crucial for navigating social situations and making judgments about societal issues. Given that previous studies on adults found <a href="https://www.psypost.org/people-with-lower-emotional-intelligence-are-more-likely-to-hold-right-wing-views-study-finds/" target="_blank" rel="noopener">emotional abilities to be strong predictors of ideological attitudes</a>, the researchers hypothesized that these abilities might play an even more significant role during adolescence.</p>
<p>To examine how cognitive and emotional abilities relate to ideological attitudes, the researchers recruited 507 participants aged 15 to 22 years. The participants were primarily high school and college students, ensuring a sample representative of middle and late adolescence.</p>
<p>The researchers used standardized intelligence tests to measure cognitive abilities. For participants aged 16 or younger, the WISC-V test was administered, assessing skills such as verbal comprehension, fluid reasoning, and processing speed. For those 17 and older, the Kaufman Adolescent and Adult Intelligence Test was used, which measures fluid and crystallized intelligence through tasks like reasoning, symbol learning, and comprehension.</p>
<p>Emotional abilities were assessed using four abridged performance-based tests, including measures of emotional understanding and recognition. The results from these tests were combined into a composite score to represent each participant’s overall emotional abilities.</p>
<p>To measure ideological attitudes, the researchers used three well-established scales. <a href="https://www.psypost.org/right-wing-authoritarianism-appears-to-have-a-genetic-foundation/">Right-wing authoritarianism</a> was assessed using items measuring conformity, respect for authority, and aggression toward out-groups. Social dominance orientation was measured by assessing preferences for hierarchical versus egalitarian social structures. Finally, <a href="https://www.psypost.org/large-study-indicates-left-wing-authoritarianism-exists-and-is-a-key-predictor-of-psychological-and-behavioral-outcomes/">left-wing authoritarianism</a> was evaluated using a scale adapted to capture opposition to traditional authority and advocacy for redistributive policies.</p>
<p>The results showed significant relationships between both cognitive and emotional abilities and ideological attitudes, though the strength and nature of these relationships varied. In line with prior studies in adult populations, adolescents with lower cognitive abilities were more likely to endorse authoritarian attitudes, both on the right and left of the political spectrum.</p>
<p>Interestingly, the relationship between cognitive abilities and social dominance orientation was weaker than for right-wing authoritarianism, suggesting that hierarchical preferences might be less cognitively driven.</p>
<p>Emotional abilities, however, emerged as even stronger predictors of ideological attitudes than cognitive abilities. Adolescents with lower emotional abilities were more likely to hold authoritarian beliefs across all three dimensions: right-wing authoritarianism, social dominance orientation, and left-wing authoritarianism. Furthermore, when emotional abilities were included in the models, the influence of cognitive abilities on ideological attitudes became nonsignificant, suggesting that emotional abilities may play a mediating role.</p>
<p>Another key finding was the striking similarity between right-wing and left-wing authoritarian attitudes. Both shared common psychological underpinnings, such as a preference for conformity and authority, which were negatively related to emotional and cognitive abilities. This overlap supports the idea that authoritarian attitudes, regardless of political orientation, stem from similar cognitive and emotional processes.</p>
<p>Interestingly, the study found no significant differences between middle and late adolescents in the strength or nature of these relationships. This consistency suggests that the links between abilities and ideological attitudes are stable across this developmental period, challenging the notion that these relationships emerge only in adulthood.</p>
<p>“In summary, the present results mirror the results obtained in adult samples, with similar relationships of more or less equal magnitude between cognitive abilities and ideological attitudes, and with even stronger relationships for emotional abilities,” the researchers wrote. “The very concept of ‘ideological attitudes’ almost necessitates that its developmental roots go back in time. Many people are committed to politics, but in reflecting on ‘the political’ they do not always think in terms of specific policy issues. Ideological attitudes, after all, have a different compass, one that aligns with values and norms . Such values and norms likely develop at younger ages and are amenable to socialization, making people responsive to specific ideological attitudes.”</p>
<p>The findings shed light on how both cognitive and emotional abilities are related to adolescents’ ideological attitudes. However, because the data were cross-sectional, the researchers could not establish causal relationships or observe how these abilities and attitudes evolve over time. This limitation leaves unanswered questions about whether deficits in cognitive and emotional abilities lead to authoritarian attitudes or if certain ideological beliefs influence the development of these abilities. Future longitudinal studies are necessary to explore these dynamics in greater depth, tracking changes across adolescence and into adulthood to better understand the developmental pathways that connect abilities to ideological preferences.</p>
<p>The study, “<a href="https://doi.org/10.1111/jopy.13007" target="_blank" rel="noopener">The Relationship Between Cognitive and Emotional Abilities and Ideological Attitudes Among Adolescents</a>,” was authored Alain Van Hiel, Kim Dierckx, Hilde Depauw, Tassilo Tissot, Ruben Van Severen, Johnny Fontaine, and Arne Roets.</p></p>
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<td><a href="https://www.psypost.org/ai-models-struggle-with-expert-level-global-history-knowledge/" style="font-family:Helvetica, sans-serif; letter-spacing:-1px;margin:0;padding:0 0 2px;font-weight: bold;font-size: 19px;line-height: 20px;color:#222;">AI models struggle with expert-level global history knowledge</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Jan 22nd 2025, 16:00</div>
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<p><p>Researchers recently evaluated the ability of advanced artificial intelligence (AI) models to answer questions about global history using a benchmark derived from the Seshat Global History Databank. The study, <a href="https://nips.cc/virtual/2024/poster/97439" target="_blank">presented at the Neural Information Processing Systems conference</a> in Vancouver, revealed that the best-performing model, GPT-4 Turbo, achieved a score of 46% on a multiple-choice test, a marked improvement over random guessing but far from expert comprehension. The findings highlight significant limitations in current AI tools’ ability to process and understand historical knowledge, particularly outside well-documented regions like North America and Western Europe.</p>
<p>The motivation for the study stemmed from a desire to explore the potential of artificial intelligence (AI) tools in aiding historical and archaeological research. History and archaeology often involve analyzing vast amounts of complex and unevenly distributed data, making these fields particularly challenging for researchers. </p>
<p>Advances in AI, particularly in large language models (LLMs), have demonstrated their utility in fields like law and data labeling, raising the question of whether these tools could similarly assist historians by processing and synthesizing historical knowledge. Researchers hoped that AI could augment human efforts, providing insights that might otherwise be missed or speeding up labor-intensive tasks like data organization.</p>
<p>Peter Turchin, a project leader at the Complexity Science Hub, and his collaborators developed the <a href="https://seshatdatabank.info/" target="_blank">Seshat Global History Databank</a>, a comprehensive repository of historical knowledge. They recognized the need for a systematic evaluation of AI’s understanding of history. The researchers hoped the study would not only reveal the strengths and weaknesses of current AI but also guide future efforts to refine these tools for academic use.</p>
<p>The Seshat Global History Databank includes 36,000 data points about 600 historical societies, covering all major world regions and spanning 10,000 years of history. Data points are drawn from over 2,700 scholarly sources and coded by expert historians and graduate research assistants. The dataset is unique in its systematic approach to recording both well-supported evidence and inferred conclusions.</p>
<p>To evaluate AI performance, the researchers converted the dataset into multiple-choice questions that asked whether a historical variable (e.g., the presence of writing or a specific governance structure) was “present,” “absent,” “inferred present,” or “inferred absent” during a given society’s time frame. Seven AI models were tested, including GPT-3.5, GPT-4 Turbo, Llama, and Gemini. Models were provided with examples to help them understand the task and were instructed to act as expert historians in their responses.</p>
<p>The researchers assessed the models using a balanced accuracy metric, which accounts for the uneven distribution of answers across the dataset. Random guessing would result in a score of 25%, while perfect accuracy would yield 100%. The models were also tested on their ability to distinguish between “evidenced” and “inferred” facts, a critical skill for historical analysis.</p>
<p>“We wanted to set a benchmark for assessing the ability of these LLMs to handle expert-level history knowledge,” explained first author Jakob Hauser, a resident scientist at the Complexity Science Hub. “The Seshat Databank allows us to go beyond ‘general knowledge’ questions. A key component of our benchmark is that we not only test whether these LLMs can identify correct facts, but also explicitly ask whether a fact can be proven or inferred from indirect evidence.”</p>
<p>GPT-4 Turbo outperformed the other models, achieving a balanced accuracy of 43.8% on the four-choice test. While this score exceeded random guessing, it still fell well short of expert-level performance. In a simplified two-choice format (“present” versus “absent”), GPT-4 Turbo performed better, with an accuracy of 63.2%. These results suggest that while the models can identify straightforward facts, they struggle with more nuanced historical questions.</p>
<p>“One surprising finding, which emerged from this study, was just how bad these models were. This result shows that artificial ‘intelligence’ is quite domain-specific. LLMs do well in some contexts, but very poorly, compared to humans, in others,” Turchin remarked.</p>
<p>The study also revealed patterns in the models’ performance across regions, time periods, and types of historical data. Models generally performed better on earlier historical periods (e.g., before 3000 BCE) and struggled with more recent data, likely due to the increasing complexity of societies and historical records over time. Regionally, performance was highest for societies in the Americas and lowest for Sub-Saharan Africa and Oceania, highlighting potential biases in the models’ training data.</p>
<p>“LLMs, such as ChatGPT, have been enormously successful in some fields—for example, they have largely succeeded by replacing paralegals. But when it comes to making judgments about the characteristics of past societies, especially those located outside North America and Western Europe, their ability to do so is much more limited,” explained Turchin, who leads the Complexity Science Hub’s research group on social complexity and collapse. </p>
<p>Interestingly, the models exhibited relative consistency across different types of historical data, such as military organization, religious practices, and legal systems. However, performance varied significantly between models. GPT-4 Turbo consistently outperformed others in most categories, while smaller models like Llama-3.1-8B struggled to achieve comparable results.</p>
<p>The researchers acknowledged several limitations in their study. The Seshat Databank, while comprehensive, reflects the biases of its sources, which are predominantly in English and focused on well-documented societies. This linguistic and regional bias likely influenced the models’ performance. Additionally, the study only tested a limited number of AI models, leaving room for future evaluations of newer or more specialized tools.</p>
<p>The study also highlighted challenges in interpreting historical data. Unlike fields with clear-cut answers, history often involves ambiguity and debate, making it difficult to design objective benchmarks for AI evaluation. Furthermore, the models’ underperformance in regions like Sub-Saharan Africa underscores the need for more diverse training data that accurately represents global history.</p>
<p>Looking ahead, the researchers plan to expand the Seshat dataset to include more data from underrepresented regions and to incorporate additional types of historical questions. They also aim to test newer AI models to assess whether advancements in AI technology can address the limitations identified in this study.</p>
<p>“The main takeaway from this study is that LLMs, while impressive, still lack the depth of understanding required for advanced history. They’re great for basic facts, but when it comes to more nuanced, PhD-level historical inquiry, they’re not yet up to the task,” said Maria del Rio-Chanona, the study’s corresponding author and an assistant professor at University College London.</p>
<p>The paper, “<a href="https://nips.cc/virtual/2024/poster/97439" target="_blank">Large Language Models’ Expert-level Global History Knowledge Benchmark (HiST-LLM)</a>,” wa authored by Jakob Hauser, Daniel Kondor, Jenny Reddish, Majid Benam, Enrico Cioni, Federica Villa, James S. Bennett, Daniel Hoyer, Pieter François, Peter Turchin, and R. Maria del Rio-Chanona.</p></p>
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<td><a href="https://www.psypost.org/wealth-inequality-reaches-new-heights-what-the-data-says-about-americas-future/" 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;">Wealth inequality reaches new heights: What the data says about America’s future</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Jan 22nd 2025, 14:00</div>
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<p><p>One need only glance at headlines about <a href="https://www.forbes.com/real-time-billionaires/#61e399543d78">Jeff Bezos, Elon Musk</a> and other super-wealthy individuals to understand that <a href="https://doi.org/10.1257/jep.34.4.3">wealth in America</a> is increasingly concentrated in fewer and fewer hands. Inequality is sharply on the rise.</p>
<p>Until now, however, little has been known about where the richest households are located, which cities are the most unequal and how these trends have evolved.</p>
<p>In a <a href="https://doi.org/10.1038/s41597-024-03059-9">new analysis</a> <a href="https://munkschool.utoronto.ca/person/tom-kemeny">I conducted</a> with my colleagues, we reveal where wealth is most concentrated within and between communities, cities and states. The result is <a href="https://doi.org/10.3886/E192306">GEOWEALTH-US</a> – the first data that tracks the geography of wealth in the United States and how it has changed since 1960.</p>
<p>The overall picture is worrying. The wealthiest cities in the U.S. are now almost seven times richer than the poorest regions, a disparity that has almost doubled since 1960. Meanwhile, especially in urban coastal areas, wealth has become highly concentrated in the hands of a few. The picture from the geography of wealth suggests we are even more divided than we thought.</p>
<h2>Mapping inequality</h2>
<p>To measure wealth locally, we built precise models of household wealth, applying sophisticated machine learning techniques to data from the Federal Reserve’s <a href="https://www.federalreserve.gov/econres/scfindex.htm">survey of consumer finances</a>.</p>
<p>We then used the models to estimate wealth among households in the <a href="https://www.census.gov/programs-surveys/decennial-census.html">decennial census</a> and <a href="https://www.census.gov/programs-surveys/acs">American community survey</a>, where we can identify where people live.</p>
<p>Experts define wealth as the difference between the value of a household’s assets – cash, real estate and stocks, for example – and its liabilities, including mortgages, student loans and credit card debt. Wealth is also called “<a href="https://www.census.gov/topics/income-poverty/wealth.html">net worth</a>.”</p>
<p>Using <a href="https://doi.org/10.3886/E192306">GEOWEALTH-US</a>, we show that the wealth distribution across the U.S. has transformed since 1960. Inequality between the nation’s flourishing urban centers and other areas of the country, especially in parts of the South and Midwest, is higher than it has ever been over the previous 60 years.</p>
<p>The expansion of wealth inequality is a challenge to <a href="https://www.britannica.com/topic/American-Dream">the American Dream</a>: the notion that, with hard work, opportunity and prosperity are accessible to all.</p>
<p>Wealth enables choice and stability. Poorer households have more trouble providing the best <a href="https://doi.org/10.7758/rsf.2021.7.3.01">nutrition</a> and <a href="https://doi.org/10.7758/RSF.2016.2.6.01">education for their children</a>. Additionally, people growing up in lower-wealth households are less likely to <a href="https://doi.org/10.1093/qje/qjy028">spur innovation in a field</a> or start <a href="https://doi.org/10.1007/s11187-016-9742-9">successful new businesses</a>. Wealth also profoundly affects one’s <a href="https://doi.org/10.1056/nejmms2209521">health</a>, leaving the least wealthy in our society significantly more vulnerable to <a href="https://doi.org/10.1001/jamainternmed.2017.3903">premature death and disability</a>.</p>
<h2>Large wealth gaps between places</h2>
<p>We analyzed average household wealth across the U.S. between 1960 and 2022, using <a href="https://www.census.gov/programs-surveys/geography/guidance/geo-areas/pumas.html">census-defined</a> communities of about 100,000 residents.</p>
<p>At the community level, the lack of wealth can make a major difference in how well cities work for their residents.</p>
<p>People who grow up in wealthier places can reap benefits that <a href="https://doi.org/10.1093/qje/qju022">span generations</a>. As a result of property taxes and philanthropy, wealthier communities have greater resources for schools, health care, transportation and other infrastructure.</p>
<p>Good schools are one benefit of wealthy communities that may improve social mobility even for <a href="https://academic.oup.com/cjres/article-abstract/17/1/37/7286403">children born into poverty</a>, studies suggest.</p>
<p>The map for 2022 reveals major disparities in typical (median) net worth across communities. Many of the least wealthy locations are in poor neighborhoods in some of America’s biggest cities – for instance, parts of the Bronx and East Harlem in New York, and areas of Houston and Milwaukee. A typical household in the five poorest communities had assets worth about $18,000. Many households in these locations held more debt than assets. Other wealth-poor areas of the country included parts of Baton Rouge, Louisiana, and Cincinnati, Ohio.</p>
<p>The wealthiest communities today tend to be found in urban coastal areas.</p>
<p>Palo Alto, California, and Nassau County, New York, are two of the nation’s five wealthiest places. The top five areas had median household net worth of nearly $1.7 million. That’s almost 90 times wealthier than the poorest five places.</p>
<p></p>
<p>These wealth divides help explain why, between 2019 and 2021, according to the <a href="https://www.schoolfinancedata.org/">school finance indicators database</a>, the Palo Alto Unified School District in California spent about $7,000 more per student than the minimum required to achieve <a href="https://nces.ed.gov/nationsreportcard/">national benchmark test scores</a>. Meanwhile, the East Baton Rouge school district spent almost $4,000 less per student than is required to meet those same national standards. Cincinnati Public Schools underspent by more than $9,000 per pupil.</p>
<h2>Large wealth gaps within places</h2>
<p>We also looked at wealth divides in cities and communities. Average wealth levels in a community matter, but so does their unequal distribution.</p>
<p>Inequality, especially when a community is <a href="https://doi.org/10.1017/S0022381608090105">racially diverse</a> and <a href="https://onlinelibrary.wiley.com/doi/10.1111/ajps.12227">spatially segregated</a>, has been linked to <a href="https://doi.org/10.1073/pnas.1102493108">underinvestment in public goods such as schools, roads and hospitals</a>.</p>
<p>Our research identified large gaps in wealth within communities.</p>
<p>For example, in certain parts of California such as San Jose and Santa Monica, we found that the richest 10% of residents are about seven times wealthier than the median household. In contrast, in many parts of Utah and Minnesota, the wealthiest 10% of households are only about three times wealthier than the median household.</p>
<p>Coastal areas, then, are not simply wealthier than the rest of the country; wealth in these places is also less equally shared.</p>
<p></p>
<p>We also found that wealth is unequally distributed across many parts of the South. This reflects <a href="https://www.hoover.org/research/long-shadows-slavery-and-jim-crow-uncovering-economic-impact-black-americans">the legacy of slavery</a>, discrimination and uneven economic development over generations.</p>
<p>Regardless of geography, across America we found that the most unequal places were likely to have larger populations of African Americans, Hispanics and other people of color. In these locations, white households were overrepresented among the wealthiest. Households of color, meanwhile, generally had much lower net worth.</p>
<h2>The map of wealth is changing</h2>
<p><a href="https://doi.org/10.1038/s41597-024-03059-9">Extensive testing</a> shows that our model estimates wealth with a high level of accuracy. And by mapping household wealth rather than household income, which is what researchers more commonly use to assess economic well-being, we found that place-based divides are much worse than previously believed.</p>
<p>Our data shows that wealth gaps between places have grown much more than income gaps since 1960. By 2020, gaps in average wealth levels were about 60% higher than equivalent income gaps.</p>
<p></p>
<p>This appears to be driven by the changing economic fortunes of cities.</p>
<p>Average wealth levels in the San Francisco Bay Area, Seattle, New York and Boston have risen dramatically as these areas have cemented their leadership in high-technology sectors and finance.</p>
<p><a href="https://www.bls.gov/opub/mlr/2018/beyond-bls/the-fall-of-employment-in-the-manufacturing-sector.htm">The loss of manufacturing jobs</a>, meanwhile, destroyed wealth in many American communities. In 1960, the industrial hub of Cleveland, Ohio, had among the highest levels of average household wealth in the country, according to our data. In 2020, Cleveland ranked 466th out of the 722 areas in our study.</p>
<p>Within cities, we also observed a rise in wealth concentration. In the Minneapolis metropolitan area, for instance, the share of total wealth held by the richest 0.1% of households has almost tripled, from about 3% in 1960 to almost 9% by 2020. This means that, compared with the past, just a few families there now own a much larger piece of the pie.</p>
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<h2>Ladder to success becoming harder to climb</h2>
<p>Multiple factors may explain the growing pooling of wealth. They include the rising concentration of <a href="https://doi.org/10.1080/00130095.2023.2244111">high-paying jobs</a> in major metro areas and the explosive growth in housing values in these <a href="https://www.aeaweb.org/articles?id=10.1257%2Fpol.5.4.167&ref=akusion-ci-shi-dai-bizinesumedeia">high-performing cities</a>.</p>
<p>Changing federal <a href="https://books.google.com/books/about/The_Triumph_of_Injustice_How_the_Rich_Do.html?id=_iSfDwAAQBAJ">tax policies</a> have also favored the affluent at the expense of regular Americans.</p>
<p>If such policies continue under the next Trump administration, the divided geography of wealth may well grow worse – with significant consequences for <a href="https://doi.org/10.1093/cjres/rsx024">U.S. democracy</a>.<!-- Below is The Conversation's page counter tag. Please DO NOT REMOVE. --><img decoding="async" src="https://counter.theconversation.com/content/228377/count.gif?distributor=republish-lightbox-basic" alt="The Conversation" width="1" height="1"><!-- End of code. If you don't see any code above, please get new code from the Advanced tab after you click the republish button. The page counter does not collect any personal data. More info: https://theconversation.com/republishing-guidelines --></p>
<p> </p>
<p>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/soaring-wealth-inequality-has-remade-the-map-of-american-prosperity-228377">original article</a>.</p></p>
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<td><a href="https://www.psypost.org/new-neuroscience-research-sheds-light-on-distinct-patterns-of-learning-and-generalization-in-autistic-adults/" style="font-family:Helvetica, sans-serif; letter-spacing:-1px;margin:0;padding:0 0 2px;font-weight: bold;font-size: 19px;line-height: 20px;color:#222;">New neuroscience research sheds light on distinct patterns of learning and generalization in autistic adults</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Jan 22nd 2025, 12:00</div>
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<p><p>A study recently published in <a href="https://doi.org/10.1002/aur.3286"><em>Autism Research</em></a> found that adults with autism process visual categories and generalize learned information differently compared to neurotypical adults. While individuals with autism can learn and apply visual categories, they do so more slowly, exhibit unique brain activity during learning, and face challenges when generalizing to new shapes. These findings suggest that differences in neural processing, particularly in handling prediction errors, underlie these variations and may relate to symptoms such as difficulty with uncertainty.</p>
<p>Categorization and generalization are critical for navigating complex environments. These cognitive processes allow individuals to group similar stimuli and apply learned information to new contexts. Previous research indicates that individuals with autism experience challenges in these areas, potentially due to an atypical ability to balance incoming sensory input with predictions. This mismatch may contribute to their difficulties in managing uncertainty and adapting to variability. The current study aimed to investigate how adults with autism learn and generalize visual categories and to uncover the neural mechanisms underlying these processes.</p>
<p>“I am interested in the potential differences in visual information processing in individuals with and without autism,” said study author <a href="https://orcid.org/0000-0002-0799-5464">Jaana Van Overwalle</a> of KU Leuven. “In general, autism is still mainly seen as differences in social behavior. However, differences in information processing could explain why people with autism differently process (visual) input and therefore might also react in an atypical way.”</p>
<p>The researchers recruited 38 adults with autism and 38 neurotypical adults matched for age, gender, and intelligence. Participants with autism had a formal diagnosis and no additional neurological or psychiatric conditions. To explore how these groups learned and generalized visual categories, the researchers designed a task involving abstract shapes that varied along two dimensions: aspect ratio (width-to-height) and curvature.</p>
<p>During the experiment, participants first completed a training phase. They were shown shapes on a computer screen and asked to categorize them into one of two groups, using feedback to improve their accuracy. Electroencephalography (EEG) was used to record participants’ brain activity during this phase, focusing on electrical brain patterns known as event-related potentials that reflect responses to stimuli and feedback. Following training, participants were tested on their ability to generalize what they had learned to new, slightly different shapes and to an entirely new set of shapes.</p>
<p>“The main takeaway from the study is that adults with autism can process visual information (i.e., visual categories and generalize) just as neurotypical individuals do,” Van Overwalle told PsyPost. “However, we found some key differences in the way individuals with autism do so.”</p>
<p>In particular, the researchers found that adults with autism were slower to learn the visual categories during the training phase. This delay was not due to differences in the strategies they reported using but instead reflected differences in neural activity during learning. Their brains showed altered responses to errors, suggesting that prediction error processing—the ability to use feedback to update understanding—was less efficient.</p>
<p>“Individuals with autism require more time to learn new categories and apply them in the correct way,” Van Overwalle said. “This altered behavior is reflected in atypical neural signals.”</p>
<p>When it came to generalization, individuals with autism could apply their learning to shapes that were similar to the training examples but struggled more than neurotypical participants when faced with novel shapes. This reduced ability to generalize was especially pronounced for shapes that required a more holistic interpretation of visual features, such as aspect ratio.</p>
<p>“While individuals with autism were able to generalize the categories they learned earlier to similar shapes as well as neurotypicals, they had more difficulties when generalizing to novel shapes compared to neurotypicals,” Van Overwalle explained.</p>
<p>Neural data supported these behavioral findings. Adults with autism exhibited atypical patterns of brain activity, particularly in regions associated with visual processing and feedback evaluation. For example, the N1 component, linked to early visual categorization, was weaker and less lateralized in participants with autism. Additionally, the P300 component, which reflects attention and evaluation of feedback, was delayed and exhibited increased frontal activation in autistic individuals, suggesting a need for greater cognitive resources to process errors.</p>
<p>“We found a distinct difference in neural processing of feedback in autistic individuals during category learning,” Van Overwalle said. “I was particularly surprised about the differences in the neural processing of feedback (i.e., more frontal activity) in individuals with autism.”</p>
<p>But as with all research, there are some limitations. The EEG data was collected from a subset of participants, which may reduce the generalizability of the neural findings. Additionally, the experiment focused on rule-based learning, which may not fully capture other types of categorization processes, such as those involving intuition or integration of complex information.</p>
<p>“Because of practical issues during the COVID pandemic, we were only able to collect neural data on a subgroup of the recruited participants,” Van Overwalle noted. “A new study should increase the number of the participants during neuroimaging and maybe functional magnetic resonance imaging (fMRI) would also be an interesting step to further investigate the neural differences during learning and feedback processing between individuals with and without autism.”</p>
<p>“Autism is a neurodevelopmental condition. When information processing occurs differently from a young age, individuals with autism will show an altered cognitive and social development. This is why, we are currently investigating category learning in toddlers (3-5 years) with and without autism. This would enable us to pinpoint the precise challenges during a critical developmental period. A next step would be to investigate under which circumstances children with autism excel during information processing and learning.”</p>
<p>The study, “<a href="https://doi.org/10.1002/aur.3286">Altered category learning and reduced generalization in autistic adults</a>,” was authored by Jaana Van Overwalle, Stephanie Van der Donck, Birte Geusens, Bart Boets, and Johan Wagemans.</p></p>
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<td><a href="https://www.psypost.org/adverse-childhood-experiences-linked-to-increased-defensive-gun-use-through-heightened-threat-sensitivity/" 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;">Adverse childhood experiences linked to increased defensive gun use through heightened threat sensitivity</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Jan 22nd 2025, 10:00</div>
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<p><p>A new study published in the <em><a href="https://www.sciencedirect.com/science/article/abs/pii/S0022395624007325" target="_blank" rel="noopener">Journal of Psychiatric Research</a></em> explores how adverse childhood experiences (ACEs) influence defensive gun use among adults with firearm access. The researchers found that individuals exposed to ACEs, such as abuse, neglect, or household dysfunction, are more likely to engage in defensive gun use due to heightened sensitivity to perceived threats. This suggests that for some people, early traumatic experiences can shape a worldview where danger feels ever-present, potentially prompting the use of firearms.</p>
<p>Adverse childhood experiences have long been recognized as risk factors for various negative outcomes, including mental health challenges, risky behaviors, and interpersonal violence. However, little research has focused on how these early experiences might affect patterns of firearm use in adulthood, particularly defensive gun use. Defensive gun use refers to using or displaying a firearm to protect oneself, others, or property, whether or not the situation involves an actual threat.</p>
<p>While proponents argue that DGU is a legitimate and necessary form of self-defense, critics highlight its potential to escalate conflicts and increase harm. The United States, with its high rates of firearm ownership and prevalence of ACEs, presents a context where these issues intersect.</p>
<p>“My research interests stem from a curiosity about how experiences years prior to an outcome come to influence that outcome. I am especially interested in how changes in how people think may explain the link between an individual’s experiences and their behavior,” said study author Sultan Altikriti, a postdoctoral fellow at the New Jersey Gun Violence Research Center in the<br>
School of Public Health at Rutgers University.</p>
<p>“Research has done a great job of identifying early-life risk factors and their associated future harms. My research deals with identifying the specific cognitive links between these risk factors and the increased likelihood of negative life outcomes. Adverse childhood experiences (ACEs) and gun use are examples of salient early risk factors and serious, potentially deadly behavior later in life.”</p>
<p>The study analyzed data from 3,130 adults with firearm access, drawn from a larger nationally representative sample of 8,009 U.S. adults recruited through an online research panel. Participants answered questions about their exposure to ACEs, engagement in defensive gun use, and psychological factors such as threat sensitivity and depressive symptoms.</p>
<p>ACEs were measured using a composite score based on self-reported experiences of physical and emotional abuse, sexual abuse, and household dysfunction. Threat sensitivity was assessed using participants’ agreement with statements reflecting distrust and hypervigilance, such as “people can’t be trusted” or “people are not what they seem.” Depressive symptoms were evaluated through a standardized questionnaire measuring issues like low mood, fatigue, and difficulty concentrating.</p>
<p>Defensive gun use was measured by asking participants whether they had ever used a firearm in specific defensive scenarios, such as telling someone they were armed, showing a firearm, or firing it either near or at a perceived threat.</p>
<p>The researchers found that individuals with higher ACE scores were more likely to report engaging in defensive gun use. Each additional ACE was linked to a 17% increase in the likelihood of defensive gun use.</p>
<p>People with higher levels of threat sensitivity were more likely to engage in defensive gun use. Threat sensitivity partially explained the relationship between ACEs and DGU, accounting for about 11% of the total association.</p>
<p>“ACEs have been linked to a wide range of negative life outcomes, including poor mental health, criminal behavior, and even early death,” Altikriti told PsyPost. “Early intervention to prevent ACEs is an efficient strategy to reduce harm broadly. Into adulthood, however, ACEs may have already impacted how individuals experience and interpret the world around them. Interventions can target these downstream effects of ACEs to protect against future harm. This study was focused on identifying some of those intervention targets to reduce gun violence among adults. Increased threat sensitivity seems to be one promising avenue for that.”</p>
<p>In contrast, while depressive symptoms were associated with ACEs, they did not significantly predict engagement in defensive gun use. This suggests that internalizing behaviors like depression may not drive defensive firearm use in the same way that externalizing behaviors like heightened threat sensitivity do.</p>
<p>“The relationships between ACEs and threat sensitivity and ACEs and depressive symptoms were very similar in magnitude, but depressive symptoms were unrelated to defensive gun use,” Altikriti said. “This suggested that although threat sensitivity and depressive symptoms may both result from ACEs, only threat sensitivity appears to then increase the odds that someone will have engaged in defensive gun use. This highlights that, for some people, ACEs may make them see the world as a more threatening place and leave them predisposed to using a firearm to defend themselves, whether or not they are actually in danger.”</p>
<p>Men and individuals exposed to direct or indirect firearm victimization were also more likely to report defensive gun use. However, other factors like income, education, and marital status were not significant predictors in this study.</p>
<p>The study focused on threat sensitivity and depressive symptoms as potential mediators but these factors explain only a small portion of the relationship between ACEs and defensive gun use. Other mechanisms, such as anxiety, impulsivity, or substance use, may also play a role and warrant further investigation.</p>
<p>“Threat sensitivity accounted for a small portion of the association between ACEs and defensive gun use,” Altikriti noted. “This was not unexpected, as the relationships between early life risk factors and outcomes years later often have complex causal pathways. This complexity cannot be distilled into a few factors without diminishing our ability to account for the outcome of interest. A good balance between simplicity and complexity helps make research informative yet still policy-relevant.”</p>
<p>“My long-term goal for this project is to continue looking for and identifying factors that link ACEs with adverse outcomes in adulthood. My hope is that this research can be used to implement effective intervention strategies to mitigate the harms associated with ACEs.”</p>
<p>The measure of defensive gun use relied on participants’ self-reports, which may not distinguish between legitimate and illegitimate defensive actions. Future studies could benefit from collecting more contextual information about defensive gun use incidents to better understand the circumstances and rationale behind firearm use.</p>
<p>“I want to emphasize that defensive gun use is based on the perception of threat,” Altikriti explained. “So, although defensive gun use can be a justified and sometimes necessary response to the behavior of others, there are likely to be some incidents that are not justified and may themselves be an instigation of violence rather than a reaction to it. These incidents are more likely to be the ones associated with threat sensitivity.”</p>
<p>The study, “<a href="https://doi.org/10.1016/j.jpsychires.2024.12.030" target="_blank" rel="noopener">Adverse childhood experiences and defensive gun use: The indirect role of threat sensitivity and depressive symptoms</a>,” was authored by Sultan Altikriti, Daniel C. Semenza, Alexander Testa, Dylan B. Jackson, and Michael D. Anestis.</p></p>
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<p><strong>Forwarded by:<br />
Michael Reeder LCPC<br />
Baltimore, MD</strong></p>
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