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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/people-with-anxious-tendencies-are-more-likely-to-support-left-wing-economic-policy/" 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;">People with anxious tendencies are more likely to support left-wing economic policy</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Jan 6th 2026, 08:00</div>
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<p><p>New research provides insight into the psychological underpinnings of political ideology. The findings suggest that individuals who are prone to anxiety are more likely to support left-wing economic policies, particularly when they feel socially excluded. This tendency appears to stem from a deep-seated human need for community support during times of vulnerability. The study was published in the <em><a href="https://doi.org/10.1017/S0007123424000590" target="_blank">British Journal of Political Science</a></em>.</p>
<p>Political scientists and psychologists have long sought to understand the relationship between personality traits and political beliefs. A common perspective in the field has historically suggested that right-wing beliefs serve as a coping mechanism for fearful or anxious people. Theories posited that conservative ideologies provided structure and certainty that appealed to those with sensitive dispositions. </p>
<p>However, recent data has complicated this picture. Surveys frequently show that people who identify as liberals or support left-wing parties report higher levels of distress and negative emotions than their conservative counterparts.</p>
<p>“There’s a very entrenched idea in my subfield (political psychology) that anxiety makes people more conservative/right-wing. The idea is that conservative ideas are more comforting than liberal ideas because they provide simple, neat answers to questions about life and society,” said study author <a href="https://adampanish.com/" target="_blank">Adam R. Panish</a>, a PhD candidate at Stony Brook University.</p>
<p>“So if you’re psychologically predisposed to anxiety or are temporarily anxious, you’ll like conservative ideas more. This idea goes all the way back to the 1940s, when social scientists were trying to make sense of Nazism using Freud’s ideas about repression and anxiety. It’s still one of the first things that most students read about in political psychology courses.” </p>
<p>“But around 2010, political psychologists started publishing modern high quality data that showed the opposite — anxious people were scoring much higher on measures of left-wing attitudes, particularly economic attitudes. So I wanted to try to understand why we were seeing results that are the opposite of what longstanding theories would predict. At the same time, people on social media started talking about the rise of anxiety and depression among young liberals in 2022. So it seemed like a good time to take a closer look.”</p>
<p>The researchers proposed the “social support hypothesis.” This framework looks at political preferences through the lens of evolutionary psychology. For early humans, survival depended entirely on the support of the group. In a foraging environment, injury or illness could be fatal without the care and resource sharing of others. Consequently, the human mind likely evolved to perceive social exclusion as a severe threat to survival. </p>
<p>Modern individuals might interpret state-provided economic support as a contemporary equivalent of tribal care. If this is true, people who are sensitive to threats—specifically those with high anxiety—should gravitate toward policies that ensure material security when they feel their social safety net is lacking.</p>
<p>To test this hypothesis, the researchers utilized data from four large-scale, representative surveys. These included the American National Election Studies, the Cooperative Election Study, and The American Panel Survey from the United States, as well as the Longitudinal Internet Studies for the Social Sciences from the Netherlands. The combined dataset included responses from nearly 18,000 participants. </p>
<p>The researchers employed specific measures to isolate the relevant personality traits. While the broad trait of neuroticism encompasses various negative emotions, the researchers distinguished between two of its primary facets: anxiety and emotional volatility. </p>
<p>Anxiety refers to the tendency to feel vulnerable and worried in response to threats. Volatility refers to irritability and mood swings. The researchers anticipated that only anxiety would predict support for redistribution, as it is the facet connected to feelings of neediness and the desire for protection.</p>
<p>Economic attitudes were measured by asking participants about their support for various government interventions. These included increasing taxes on the wealthy, government spending on healthcare and unemployment, and federal job guarantees. </p>
<p>To measure social exclusion, the surveys asked respondents about the size of their support networks. For example, some questions asked if respondents had people they could rely on during a misfortune. Other measures looked at the number of friends participants had on social media platforms like Facebook.</p>
<p>The results from the survey data offered support for the social support hypothesis. Across all four datasets, higher levels of anxiety consistently predicted stronger support for left-wing economic policies. This relationship held true even when the researchers accounted for demographic factors such as age, sex, and education. </p>
<p>The analysis also confirmed that this effect was specific to anxiety. Emotional volatility did not show a consistent positive relationship with support for redistribution. This suggests that the political leaning is not simply a result of general negative emotionality but is tied specifically to the psychology of vulnerability.</p>
<p>“Our strongest finding is that being a generally anxious person is related to being economically left-wing in both the United States and the Netherlands,” Panish told PsyPost. “This relationship isn’t explained by people’s income, education, health insurance enrollment, homeownership, or employment status. It also isn’t explained by demographic characteristics like gender, race, or age.”</p>
<p>One of the most significant findings from the survey analysis involved the comparison between personality and financial status. Political economists often assume that personal financial circumstances are the primary drivers of views on redistribution. People with less money are expected to want more government help. </p>
<p>However, the researchers found that a person’s level of anxiety was a powerful predictor of their economic views. In fact, anxiety predicted support for redistribution roughly as well as income level did. It was a stronger predictor than other material factors, such as whether a person owned a home, had health insurance, or was currently unemployed.</p>
<p>“Depending on the survey, we found that the most anxious people scored between 0.26 and 0.68 standard deviations higher on scales measuring support for left-wing economic policies than the least anxious people,” Panish explained. “Because these effect sizes can be hard to make sense of in practical terms, we also checked how anxiety measured up against socioeconomic variables like income.” </p>
<p>“We found that anxiety predicted economic attitudes better than health insurance enrollment, homeownership, or employment status, and it was about tied with income. So if you imagine how important a person’s salary is for predicting their economic attitudes, our results show that anxiety is similarly important.”</p>
<p>The data further revealed that social context plays a regulating role in this relationship. Anxiety did not universally lead to left-wing economic preferences. The link was strongest among individuals who felt socially excluded. </p>
<p>In the Dutch sample, for instance, anxiety did not predict economic attitudes among people who felt they had a strong social safety net. However, among those who reported having few people to rely on, high anxiety was associated with a substantial shift toward the left. </p>
<p>A similar pattern emerged in the American data regarding social media connections. Among people with few Facebook friends, those with high anxiety were significantly more supportive of redistribution than those with low anxiety.</p>
<p>To move beyond correlations and test for a causal link, the researchers conducted two experiments using an online platform. These experiments involved 1,291 participants in the United States. The goal was to manipulate feelings of social exclusion in a controlled setting and observe the immediate effect on political attitudes.</p>
<p>Participants in the experiment were asked to create a personal profile for a supposedly interactive online discussion group. They uploaded a first name and wrote a short biography. They were then shown a series of other profiles, which were actually generated by the researchers. The participants could “like” the other profiles and see how many likes they received in return. </p>
<p>The researchers randomly assigned participants to one of two conditions. In the inclusion condition, participants received an average number of likes from the group. In the exclusion condition, participants received almost no likes, simulating social rejection.</p>
<p>Following this manipulation, participants answered questions about their political preferences. The policies in question mirrored those in the surveys, such as government-provided healthcare and job guarantees. The researchers also measured the participants’ baseline levels of anxiety before the manipulation took place.</p>
<p>The experimental results mirrored the survey findings. Among participants who were predisposed to anxiety, those who experienced rejection in the online task expressed higher support for social welfare policies. This shift occurred even though the rejection came from anonymous strangers on the internet who could not provide actual material support. </p>
<p>This supports the idea that the reaction is driven by an evolved psychological system that equates exclusion with a need for provisioning. The effect was not observed among anxious participants who were included, nor was it observed among participants with low levels of anxiety.</p>
<p>The researchers point out certain limitations to the study. While the link between anxiety and economic views was robust, the relationship between anxiety and social policies was less consistent. Anxious people did not uniformly lean left on issues like abortion or immigration.</p>
<p>“We make an attempt in the paper to explain why anxious people might be more economically left-wing,” Panish said. “We theorize that anxious people like the idea of government providing care and resources because they tend to feel vulnerable, and we test this indirectly by manipulating a stimulus that humans have evolved to associate with vulnerability — being socially excluded.”</p>
<p>“As predicted, we find that people with anxious personalities are especially likely to become more economically left-wing when they feel excluded. However, it is important to note that this dynamic may not explain all or even most of the relationship between anxiety and ideology. There is much more work to be done in this area.”</p>
<p>Future research could investigate how these personality dynamics play out in different political contexts or with different types of social threats. The researchers also suggest that understanding the evolutionary roots of personality can help explain other political behaviors. </p>
<p>“I am working on several follow-up studies to understand how anxiety shapes people’s political attitudes, some in collaboration with my co-author on this study, Andy Delton,” Panish said.</p>
<p>The study, “<a href="https://doi.org/10.1017/S0007123424000590" target="_blank">Why Anxious People Lean to the Left on Economic Policy: Personality, Social Exclusion, and Redistribution</a>,” was authored by Adam R. Panish and Andrew W. Delton.</p></p>
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<td><a href="https://www.psypost.org/language-learning-rates-in-autistic-children-decline-exponentially-after-age-two/" 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;">Language learning rates in autistic children decline exponentially after age two</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Jan 6th 2026, 06:00</div>
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<p><p>A new study published in the <em><a href="https://doi.org/10.1007/s10803-025-07132-z" target="_blank">Journal of Autism and Developmental Disorders</a></em> suggests that the ability to comprehend complex language relies on a developmental window that may close significantly earlier for autistic children than for their neurotypical peers. The findings suggest that while autistic children initially acquire the cognitive skills necessary for language comprehension at a typical rate, their learning pace tends to slow down exponentially starting as early as two years of age.</p>
<p>Complex language comprehension involves more than just understanding individual words. It requires a cognitive function known as Prefrontal Synthesis. This is the ability to mentally combine separate objects or concepts into a novel image or scene. This skill allows a person to understand the difference between “the dog bit the boy” and “the boy bit the dog.”</p>
<p>Without Prefrontal Synthesis, individuals may struggle to understand sentences that rely on word order, spatial prepositions, or recursive structures. This specific deficit is common among individuals with autism spectrum disorder. Estimates suggest that between 30 and 40 percent of individuals with autism experience significant challenges with this type of mental synthesis.</p>
<p>“A long-standing question in autism research concerns why some individuals never fully acquire syntactic comprehension, a limitation that often leads to lifelong difficulties with independent living and employment,” explained study author Andrey Vyshedskiy, a lecturer at Boston, University and author of <em><a href="https://amzn.to/4pj6d8P" target="_blank">The Evolution of Language: How the Brain Evolved Syntactic Language from Early Mammals to Homo sapiens</a></em>.</p>
<p>“Two competing explanations have been proposed. One suggests a persistent, lifelong barrier that consistently slows learning at every age. The other proposes that there is a critical period during which the brain is especially capable of acquiring the neurocognitive mechanisms needed for syntactic comprehension, and that this critical period may be shorter in autism.”</p>
<p>“Despite decades of discussion, there had been little large-scale longitudinal evidence capable of distinguishing between these two possibilities. Most prior studies were small, cross-sectional, or focused on older children, thereby missing the earliest developmental window.” </p>
<p>“Our motivation was to use a very large, real-world longitudinal dataset to directly test which developmental trajectory better fits observed learning patterns,” Vyshedskiy continued. “Understanding this distinction has important practical implications, because it speaks directly to when intervention should occur and how urgently early support may be needed.”</p>
<p>The study utilized data collected through a language therapy application available on major app stores. Parents used the app to track their child’s progress over time. The final sample consisted of 15,183 autistic individuals and 138 neurotypical individuals. The participants ranged in age from 2 to 22 years. Inclusion in the study required that caregivers completed at least three separate evaluations over a period of six months or longer.</p>
<p>The primary measure used was the Mental Synthesis Evaluation Checklist. This is a parent-reported assessment designed to evaluate a child’s Prefrontal Synthesis ability. The checklist includes 20 items that ask about the child’s ability to understand stories, engage in pretend play, and comprehend spatial prepositions.</p>
<p>The researchers used these repeated assessments to calculate a “learning rate” for each participant. This rate represented the change in their score over time. By looking at how this rate changed as the children grew older, the team could model the developmental trajectory for both groups.</p>
<p>The analysis revealed distinct patterns between the autistic and neurotypical groups. At two years of age, both groups exhibited similar learning rates. Autistic children showed a learning rate of approximately 5.9 points per year, while neurotypical children showed a rate of 6.1 points per year.</p>
<p>This finding indicates that at the earliest age measured, autistic children were acquiring these cognitive skills at a pace comparable to typically developing children. However, the trajectories diverged significantly as age increased. For the neurotypical group, the learning rate remained high and relatively constant until about seven years of age.</p>
<p>In contrast, the learning rate for the autistic group began to decline much earlier. The data showed an exponential decrease in the rate of learning that started shortly after age two. As the children grew older, their annual gains in Prefrontal Synthesis became progressively smaller.</p>
<p>“The main takeaway is simple but important: timing matters enormously for syntactic comprehension development in autism,” Vyshedskiy told PsyPost. “We found that autistic children initially learn key comprehension-related skills at roughly the same rate as neurotypical children. However, for many autistic children, this learning rate begins to decline much earlier—sometimes as early as 1 to 3 years of age. After this critical-slowing-down-age, gains become progressively harder.”</p>
<p>The researchers further broke down the autistic cohort by severity levels. The researchers found that the timing of the decline correlated strongly with the severity of the autism diagnosis. The point at which the learning rate began to drop—referred to as the critical inflection point—occurred earliest in those with the most severe symptoms.</p>
<p>For children with severe autism, the learning rate began to slow down significantly around 1.4 years of age. For those with moderate autism, this inflection point occurred around 2.0 years of age. Children with mild autism maintained their initial learning rate for longer, with the decline beginning around 3.2 years of age.</p>
<p>“The effects are substantial, not subtle,” Vyshedskiy explained. “The difference between developmental trajectories is large enough to explain why some individuals eventually acquire syntactic comprehension abilities while others do not.”</p>
<p>“In our modeling, neurotypical children maintain a high syntactic learning rate until at least age 7. Autistic children show an earlier decline, with the timing depending on severity. Children with more severe autism exhibit this learning rate decline earliest (that is, they have the shortest critical-slowing-down-age).”</p>
<p>“Practically speaking, this means that the same amount of instruction delivered at ages 2 and 4 can have very different effects,” Vyshedskiy said. “Early intervention is not just helpful—it may be developmentally decisive.”</p>
<p>“Parents are often dismissive of their toddler’s delay in language comprehension, assuming the child will ‘catch up’ on his/her own. This assumption can be risky. A lack of syntactic comprehension can often be corrected at age two through additional syntactic conversations, reading fairy tales aloud, and engaging in imaginative play. The same interventions, when introduced at age four, may no longer support the acquisition of syntactic comprehension.”</p>
<p>A potential misinterpretation of these findings is that learning becomes impossible after these ages. The study identifies a slowing of the rate of acquisition, not necessarily a total cessation of learning. However, it implies that the effort required to make gains increases substantially after the critical period begins to close.</p>
<p>As with all research, there are some limitations to consider. The data relies on parent reports rather than direct clinician observation. While the assessment tool has demonstrated high internal reliability, parent reports can be subject to bias or wishful thinking.</p>
<p>Further investigation is also needed to explore the specific neural mechanisms involved. Understanding exactly why the critical period might close earlier could open new avenues for medical or therapeutic interventions. Researchers might also investigate whether specific types of intensive therapy can extend this window.</p>
<p>The implications of these findings are significant for clinical practice. Currently, many children are not diagnosed with autism until nearly four years of age. By this time, according to this data, the learning rate for Prefrontal Synthesis may have already begun to decline significantly.</p>
<p>This suggests that early intervention is a matter of urgency. Waiting for a formal diagnosis before beginning language therapy may result in missing the most fertile window for developing syntactic comprehension. </p>
<p>“Many readers are surprised by the age-dependent nature of syntactic comprehension acquisition, although this should not be unexpected,” Vyshedskiy told PsyPost. “Consider pronunciation as an example: unless a person is raised in a French-speaking environment, acquiring native-like French pronunciation after approximately five years of age is exceedingly difficult. Early exposure and practice are essential for accent-free speech.”</p>
<p>“Similarly, maturation of the networks underlying syntactic comprehension requires sustained engagement in syntactic dialogue, imaginative play, and storytelling. As a result, the critical-period constraints governing the development of syntactic comprehension are likely as strong as those governing accent learning. In most children, the most sensitive period for acquiring syntactic comprehension occurs before age five, although its timing varies across individuals, much like height, weight, and other physiological traits.”</p>
<p>“Autistic children are often disadvantaged on two fronts: first, by reduced spontaneous engagement in conversations, joint attention, and imaginative play; and second, by an abbreviated critical period for syntactic comprehension acquisition,” Vyshedskiy said. “Together, these factors constrain the natural maturation of syntactic networks and help explain why autistic children often derive the greatest benefit from early, intensive language intervention.”</p>
<p>Overall, this research offers a detailed look at the developmental timing of a crucial cognitive skill. It shifts the focus from simply looking at what skills are missing to understanding when the opportunity to learn them is most active. This temporal perspective could help refine how and when therapies are delivered to autistic children.</p>
<p>“About 20 years ago, I posed a critical question: given that many autistic children show limited interest in conversations, imaginative play, and story listening, could a structured set of syntactic exercises effectively complement naturalistic syntactic exposure?” Vyshedskiy added. “Furthermore, if such an exercise set could be developed, would it be possible to deliver this form of brain training during the peak sensitive period for syntactic comprehension development, between the ages of two and four years?”</p>
<p>“To enable early access to therapy, we have gamified language therapy and made it easy for parents to administer by packaging all syntactic exercises in one app: Mental Imagery Therapy for Autism (MITA). In a 3-year observational clinical study of 6,454 children with ASD, children who engaged with the MITA syntactic comprehension intervention showed 2.2-fold greater language improvement than children matched by initial evaluations. This difference was statistically significant (p<0.0001).” </p>
<p>“The study results have been published in the journal <em><a href="ttps://www.mdpi.com/2227-9032/8/4/566" target="_blank">Healthcare</a></em>. Based on this study results, the Food and Drug Administration (FDA) has granted the MITA language therapy intervention Breakthrough Device designation status,” Vyshedskiy said.</p>
<p>“One important point is that this work was only possible because thousands of families contributed longitudinal data over many years. The large number of participants—over 15,000— allowed us to identify developmental patterns that are invisible in traditional small studies.”</p>
<p>“More broadly, the study reframes language development not just as vocabulary learning, but as the maturation of syntactic comprehension. Understanding how and when this ability develops may help bridge neuroscience, linguistics, and clinical practice in a more unified way.”</p>
<p>The study, “<a href="https://doi.org/10.1007/s10803-025-07132-z" target="_blank">Age-Dependent Process Governs Executive Function Disability in Autistic Children</a>,” was authored by Andrey Vyshedskiy, Allegra Marsiglio, Sahil Batham, Alessandro Tagliavia, Rohan Venkatesh, Anel Tarakbay, Sagar Mundhia, Samarth Urs, Edward Khokhlovich, and Eugene Pinsky.</p></p>
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<td><a href="https://www.psypost.org/fascinating-new-neuroscience-model-predicts-intelligence-by-mapping-the-brains-internal-clocks/" 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;">Fascinating new neuroscience model predicts intelligence by mapping the brain’s internal clocks</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Jan 5th 2026, 18:00</div>
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<p><p>A new study suggests that the brain processes information with high efficiency by synchronizing the physical wiring of neural networks with the varying speeds of local brain activity. Published in <em><a href="https://www.nature.com/articles/s41467-025-66542-w" target="_blank">Nature Communications</a></em>, this research offers a mathematical framework that aligns the brain’s structural connections with the timing of its electrical pulses. The findings indicate that models accounting for these varied internal speeds can better predict individual cognitive abilities compared to traditional approaches.</p>
<p>To understand the study, it is necessary to first grasp the concept of the connectome. The human brain consists of billions of neurons connected by a dense web of white matter fibers. Neuroscientists refer to this comprehensive map of neural connections as the connectome. This physical structure serves as the highway system upon which brain activity travels. While the structure remains relatively static, the activity within it is dynamic and constantly changing.</p>
<p>Brain regions do not all operate at the same tempo. Some areas, such as those responsible for processing sight and sound, must react almost instantly to incoming stimuli. Other areas, particularly those involved in complex thought and decision-making, integrate information over longer periods. These characteristic speeds are known as intrinsic neural timescales.</p>
<p>Engineers and mathematicians often use a framework called Network Control Theory to study complex systems. This approach models how a system moves from one state to another based on its connectivity and inputs. Neuroscientists have adapted this theory to model how the brain switches between different patterns of activity. A persistent limitation in this field has been the assumption that all brain regions function at the same speed.</p>
<p>Standard models typically assign a uniform time constant to every node in the network. This simplification makes the mathematics easier to handle but fails to reflect biological reality. Jason Z. Kim from Cornell University and Linden Parkes from Rutgers University sought to correct this discrepancy. Along with their colleagues, they developed a new model that infers the specific timescale of each brain region based on its behavior.</p>
<p>The researchers hypothesized that a model allowing for variable timescales would provide a more accurate representation of brain function. They used data from the Human Connectome Project, which includes brain scans from hundreds of young adults. The team utilized functional magnetic resonance imaging to observe how brain activity patterns shift over time. They also used diffusion-weighted imaging to map the structural white matter connections for the same individuals.</p>
<p>The team designed an algorithm to “learn” the internal decay rates of different brain regions. In this context, the decay rate represents how quickly a burst of activity fades away in a specific area. A faster decay corresponds to a shorter timescale, while a slower decay indicates a longer window for processing information. The algorithm adjusted these rates until the model could accurately simulate the transition from one brain state to another.</p>
<p>One of the primary measures the researchers looked at was “control energy.” In control theory, energy represents the magnitude of the input required to drive a system from a starting point to a desired end point. A highly efficient system requires less control energy to achieve a transition. The researchers found that their optimized model required consistently less energy than the standard uniform model.</p>
<p>This reduction in energy suggests that the brain is naturally wired to leverage these diverse timescales. By aligning the speed of local processing with the global network structure, the brain minimizes the metabolic cost of thinking and reacting. The researchers validated this finding by comparing their results against random null models. They found that the energy savings were specific to the actual anatomy of the human brain.</p>
<p>The study also demonstrated that this optimization allows the brain to be controlled by fewer inputs. In the uniform model, a simulation might require inputs to almost every region to successfully guide the brain’s state. The optimized model achieved the same transitions by stimulating a much smaller subset of regions. This finding has potential implications for understanding how localized neural signals can influence global brain states.</p>
<p>To confirm that their mathematical values corresponded to biological reality, the authors compared their model’s timescales with gene expression maps. They utilized the Allen Human Brain Atlas, a detailed dataset showing which genes are active in different parts of the cortex. The researchers looked specifically at genes related to inhibitory interneurons, which are cells that regulate the timing of neural firing.</p>
<p>Two specific markers of inhibitory cells, somatostatin and parvalbumin, show distinct patterns across the brain. Parvalbumin-expressing cells are typically associated with fast signaling and sensory processing. Somatostatin-expressing cells are linked to slower regulatory processes. The researchers found a strong correlation between their model-based timescales and the density of these molecular markers.</p>
<p>Regions that the model identified as having fast timescales showed higher expression of genes associated with parvalbumin. Conversely, regions with slow timescales in the model were rich in genes related to somatostatin. This biological validation indicates that the mathematical optimization successfully captured the underlying cellular architecture of the cortex. The model derived these values solely from imaging data, without prior knowledge of the gene maps.</p>
<p>The team also examined whether these findings held true across different species. They applied the same modeling approach to high-resolution connectivity data from mice. The results mirrored those found in humans. The mouse model showed similar improvements in energy efficiency and exhibited the same correlations with inhibitory cell markers.</p>
<p>This cross-species consistency suggests that the alignment of structural connectivity and neural timescales is a fundamental principle of brain organization. Evolution appears to have conserved this efficient architecture. The findings imply that the coordination between macroscale wiring and microscale cellular properties is essential for mammalian brain function.</p>
<p>Beyond general biological principles, the researchers investigated whether their model could explain individual differences in humans. They fit their optimized model to the specific brain scans of each participant in the study. This generated a unique set of timescales for every individual. The team then checked how well these personalized models tracked with the participants’ actual brain activity during rest.</p>
<p>Participants whose intrinsic timescales were better aligned with their structural connections tended to transition more frequently between different brain states. This suggests that a well-tuned brain is more dynamic and flexible. The researchers then engaged in a predictive modeling exercise. They attempted to forecast participants’ scores on various cognitive tests based on the properties of their brain models.</p>
<p>The optimized model outperformed the standard uniform model in predicting cognitive behavior. Features derived from the variable-timescale model showed stronger associations with performance on tasks involving fluid intelligence and spatial orientation. This indicates that the subtle variations in how fast different brain regions operate are relevant for higher-order cognition.</p>
<p>The authors noted several caveats to their work. The study relied on magnetic resonance imaging, which has limitations in temporal resolution. Neural activity happens on the order of milliseconds, while the imaging data captures changes over seconds. Consequently, the model likely captures a smoothed approximation of the true neural dynamics.</p>
<p>Additionally, the structural maps used in the study cannot distinguish the direction of information flow along nerve fibers. The researchers had to assume bidirectional connections for the human data, which is a simplification of the actual biology. However, the successful replication in the mouse dataset, which used directed connectivity data, mitigates this concern to some degree.</p>
<p>Future research will likely focus on how these timescales change during development and aging. The brain undergoes massive structural reorganization during childhood and adolescence. Tracking how intrinsic timescales evolve alongside these structural changes could provide insights into the maturation of cognitive abilities.</p>
<p>There is also potential for applying this framework to the study of psychiatric and neurological disorders. Conditions such as schizophrenia and autism are often described as network disorders involving disruptions in brain connectivity. It is possible that these conditions also involve a mismatch between the brain’s physical wiring and its temporal processing speeds.</p>
<p>The study, “<a href="https://www.nature.com/articles/s41467-025-66542-w" target="_blank">Inferring intrinsic neural timescales using optimal control theory</a>,” was authored by Jason Z. Kim, Richard F. Betzel, Ahmad Beyh, Amber Howell, Amy Kuceyeski, Bart Larsen, Caio Seguin, Xi-Han Zhang, Avram Holmes and Linden Parkes.</p></p>
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<td><a href="https://www.psypost.org/liberal-state-policies-during-adolescence-linked-to-lower-dementia-risk-in-later-life/" 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;">Liberal state policies during adolescence linked to lower dementia risk in later life</a>
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<p><p>A new study suggests that the political environment in which a person grows up may influence their brain health decades later. Researchers found that older adults who resided in U.S. states with more liberal policies during their adolescence were less likely to develop dementia than those raised in conservative states. These findings point to the potential long-term health consequences of local government decisions made early in a resident’s life. The research was published in the <em><a href="https://doi.org/10.1177/00221465251371065" target="_blank">Journal of Health and Social Behavior</a></em>.</p>
<p>Dementia affects millions of Americans, but the condition is not distributed evenly across the country. Rates of cognitive decline vary substantially depending on geography. Higher prevalence is generally observed in the southern United States compared to the Northeast and West Coast. This geographic variation suggests that environmental factors play a role alongside individual genetics and lifestyle choices. Sociologists refer to these environmental factors as macrosocial determinants. These are the broad political and economic structures that shape daily life.</p>
<p>State governments in the United States hold considerable power over these structures. They control funding for public schools, set minimum wages, and regulate environmental standards. Prior investigations have connected state policies to physical health outcomes such as life expectancy and cardiovascular disease. However, the relationship between these broad policy environments and cognitive aging has remained largely unexplored. Additionally, most previous analysis focused on the policies in place while a person is an adult rather than the environment they experienced as a child.</p>
<p>Meghan Zacher, a researcher at Brown University, led the investigation into this connection. Zacher and her colleagues sought to understand if the political context of adolescence leaves a lasting mark on the brain. They hypothesized that early life conditions might initiate trajectories of health that persist into old age. This concept is known as life course theory. It suggests that exposures during sensitive developmental periods can become biologically embedded. This influences how the body ages long after the exposure has ended.</p>
<p>To test their hypothesis, the research team analyzed data from the Health and Retirement Study. This is a long-running survey that tracks a representative sample of older adults in the United States. The team focused on a group of 6,410 participants who were between the ages of 65 and 80 in the year 2000. None of these individuals had dementia at the start of the analysis. The researchers tracked these individuals for sixteen years to observe who developed the condition.</p>
<p>The team linked each participant to historical data regarding the political orientation of the state where they lived as a teenager. They used a specific metric known as state policy liberalism. This score summarizes the general orientation of state laws on a spectrum from conservative to liberal. The measure aggregates nearly 150 different types of policies enacted between 1936 and 2014.</p>
<p>Liberal policies in this index generally include regulations and wealth redistribution. Examples include higher minimum wages, progressive tax structures, and stricter environmental protections. Conservative policies tend to emphasize economic freedom and cultural traditionalism. These might include right-to-work laws or restrictions on abortion access. By using this aggregate score, the researchers avoided focusing on any single law. Instead, they captured the overall political climate of the state.</p>
<p>The analysis revealed a consistent association between the policy environment of adolescence and cognitive health in old age. Individuals who grew up in states with higher policy liberalism scores faced a lower risk of developing dementia. This relationship held true even when the researchers accounted for the participant’s race, gender, and the education level of their parents.</p>
<p>The team also checked to see if the policy environment where the adults currently lived was the real driver of the results. They found that while current policies matter for other health outcomes, the effect of the adolescent environment on dementia remained distinct. </p>
<p>Living in a liberal state during teenage years provided a protective benefit regardless of whether the person lived in a liberal or conservative state in their older years. The researchers tested for an interaction between past and present environments but found that the benefits of an early liberal context persisted across different adult environments.</p>
<p>A statistical review of the data quantified the reduction in risk. For every standard deviation increase in the liberalism of the adolescent state policy, the risk of developing dementia dropped by approximately 17 percent. This pattern was observed across different demographic groups. The protective association was present for both men and women. It was also present for both non-Hispanic Black and White participants.</p>
<p>The authors then investigated why this connection might exist. They looked at intermediate factors that link early life to later health. The most prominent pathway identified was educational attainment. Liberal policy environments in the past often involved greater investment in public resources, including schools. Participants raised in these environments tended to complete more years of schooling.</p>
<p>Education is believed to create cognitive reserve. This is the brain’s ability to improvise and find alternate ways of getting a job done. This reserve helps the brain cope with the damage caused by diseases like Alzheimer’s. By fostering better educational opportunities, liberal state policies may have helped residents build this neurological resilience.</p>
<p>Beyond education, the researchers looked at adult income and health behaviors. Factors such as smoking, obesity, and cardiovascular conditions like hypertension are known risk factors for dementia. The study found that these elements explained a small portion of the link between state policies and dementia. For instance, states with higher tobacco taxes might reduce smoking rates among young people. This prevents long-term vascular damage that harms the brain.</p>
<p>However, the combined influence of education, economic status, and health behaviors only accounted for about one-third of the total association. This implies that other mechanisms are likely at work. It suggests that the sociopolitical environment impacts biological development in ways that standard health metrics do not fully capture. It is possible that unmeasured factors such as air quality or stress levels also play a role.</p>
<p>These results align with broader research linking state governance to population well-being. A recent analysis by Nancy Krieger and colleagues found that states with conservative political leadership between 2012 and 2024 <a href="https://www.psypost.org/conservative-political-leadership-associated-with-higher-premature-mortality-rates/" target="_blank">experienced higher rates of premature death</a> and infant mortality compared to more liberal states. </p>
<p>While that research focused on immediate outcomes like vaccine uptake and food security, the new findings on dementia suggest that these political contexts also exert a delayed influence that manifests decades later. Both studies underscore the idea that the ideological orientation of state governments plays a central role in shaping the physical and cognitive health of residents.</p>
<p>While the results of the new research identify a strong pattern, the study design has limitations. Because the research is observational, it cannot definitively prove that liberal policies caused the reduction in dementia cases. It is possible that unmeasured factors distinguish states with different political orientations. </p>
<p>The researchers attempted to mitigate this by using statistical techniques that compare individuals born in the same state. These sensitivity analyses supported the main conclusion. Even among people born in the same location, exposure to different policy eras mattered.</p>
<p>Future research is needed to explore the specific types of policies that drive might these benefits. Understanding whether education funding, environmental regulations, or social safety nets are the primary drivers could help guide public health interventions. The authors suggest that policymakers should consider the long-term cognitive implications of state-level legislation. As the population ages, understanding the upstream causes of dementia becomes increasingly necessary for public health planning.</p>
<p>The study, “<a href="https://doi.org/10.1177/00221465251371065" target="_blank">State Policy Liberalism in Adolescence and Risk for Dementia from 2000 to 2016 among Older U.S. Adults</a>,” was authored by Meghan Zacher, Samantha Brady, and Susan E. Short.</p></p>
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<td><a href="https://www.psypost.org/mental-health-ratings-in-the-u-s-hit-historic-lows-new-data-shows/" 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;">Mental health ratings in the U.S. hit historic lows, new data shows</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Jan 5th 2026, 14:00</div>
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<p><p>American adults are feeling less positive about their psychological well-being than at any point in the last two decades. A new report indicates that the percentage of United States adults who consider their mental health to be top-tier has fallen below a specific threshold for the first time on record. These figures come from <a href="https://news.gallup.com/poll/700079/mental-health-ratings-continue-worsen.aspx">a recent survey conducted and released by Gallup</a>.</p>
<p><strong>Background and Context</strong></p>
<p>Public health researchers monitor self-reported health data to identify shifts in societal well-being that clinical data alone might miss. Subjective assessments provide early indicators of broader changes in healthcare demand, economic productivity, and social cohesion. For nearly twenty years, these specific metrics regarding how Americans view their own minds remained notably consistent.</p>
<p>From 2001 through 2019, the national mood appeared stable in aggregate. During that extended period, at least 42 percent of respondents consistently rated their mental health as “excellent.” This baseline suggested a certain resilience or steadiness in the American public’s psychological state, regardless of economic cycles or political shifts.</p>
<p>The arrival of the COVID-19 pandemic introduced a massive variable into this stability. Researchers aimed to determine if the initial shock of the global health crisis created a temporary dip or a lasting alteration in the national psyche. The study seeks to measure the persistence of these effects five years after the onset of the pandemic. It also aims to pinpoint which specific demographic groups bear the heaviest burden of this psychological shift.</p>
<p><strong>Study Methodology</strong></p>
<p>The researchers gathered data through telephone interviews conducted in November 2025. The sample consisted of over 1,300 adults aged 18 and older, living across all 50 U.S. states and the District of Columbia. To ensure the findings accurately reflected the national population, the team used random digit dial methods to reach both landline and cellphone users.</p>
<p>The analysts then weighted the collected samples to correct for potential selection biases. They matched the data to national demographics including gender, age, race, Hispanic ethnicity, education, region, and population density. This process ensures that the views of specific groups are not overrepresented or underrepresented in the final statistics.</p>
<p><strong>Findings on Mental Well-Being</strong></p>
<p>The primary discovery is a continuing erosion of mental well-being across the country. Only 29 percent of adults now describe their mental health as “excellent.” This figure sits below the 30 percent mark for the first time in the survey’s history. Just six years prior, before the pandemic began, 43 percent of respondents gave themselves this top rating.</p>
<p>When combining those who feel “excellent” with those who feel “good,” the total is 72 percent. This combined number also represents a new low point, dropping by three percentage points from previous measurements. The data suggests that the pandemic acted as a distinct pivot point. By November 2020, roughly eight months into the health crisis, the “excellent” rating had already tumbled nine points to 34 percent.</p>
<p>The decline appears across almost all major demographic subgroups. However, the intensity of this drop varies by age and education. Younger generations serve as the primary drivers of this downward trend. Over the past six years, adults belonging to Generation Z and the millennial generation saw their “excellent” ratings drop by approximately 15 percentage points each.</p>
<p>Currently, only about 23 percent of Gen Z adults view their mental health as excellent. Millennials track closely behind at 28 percent. This stands in distinct contrast to older cohorts. No less than 34 percent of adults in Generation X, the baby boomer generation, or the Silent Generation rate their mental health as excellent.</p>
<p>Education levels also play a substantial role in these changing perceptions, though perhaps not in the way one might expect. Historically, college graduates reported substantially better mental health than non-graduates.</p>
<p>That gap has largely vanished in recent years. Graduates saw a 17-point drop in excellent ratings since the 2014-2019 period. This steep decline has brought their self-assessments much closer to the levels reported by those without degrees, who experienced a smaller 10-point decline.</p>
<p>The study found that men and women experienced similar declines in their mental health assessments. However, racial groups did not share this trajectory equally. White and Black Americans reported steeper drops in well-being compared to Hispanic Americans. Political affiliation also correlated with the results, with Democrats reporting a smaller decrease in mental health ratings than Republicans or independents.</p>
<p><strong>Trends in Seeking Treatment</strong></p>
<p>As self-assessment worsens, reliance on professional help has risen. The survey indicates that 24 percent of U.S. adults visited a mental health professional in the past year. This includes 8 percent who attended more than ten sessions. On average, adults now report 3.2 visits per year. This represents a steep increase from the 1.1 visits average recorded in 2001 and 1.5 in 2004.</p>
<p>Younger generations are utilizing these services at much higher rates. Thirty-six percent of Gen Z adults and 29 percent of millennials visited a professional in the past year. This rate is significantly higher than that of older groups; only 14 percent of baby boomers and 7 percent of the Silent Generation reported similar visits. Gen Z and millennial adults now average over four mental health visits annually, more than double the rate of their older counterparts.</p>
<p>This increase in treatment seeking may reflect a cultural shift as well as a rise in distress. Younger adults have matured in an era where mental health struggles are acknowledged more openly. Medical communities and employers place greater emphasis on emotional well-being than in previous decades. This environment may make younger Americans more comfortable admitting their mental health is subpar and seeking assistance.</p>
<p><strong>Physical Health Remains Stable</strong></p>
<p>The survey highlights a sharp divergence between physical and mental health trends. While mental well-being metrics plummeted, physical health ratings remained fairly consistent. About 25 percent of adults describe their physical health as excellent. This is only a slight dip from the 29 percent average seen during the 2014-2019 period.</p>
<p>When looking at the combined “excellent” and “good” ratings for physical health, the number sits at 77 percent. This is largely similar to where the metric has hovered for the past 25 years. This stability suggests that the current crisis in well-being is primarily psychological rather than physiological.</p>
<p>An inverse relationship exists regarding age. Older Americans generally rate their mental health higher than younger groups. However, the pattern flips for physical health. Younger adults consistently rate their physical bodies better than older generations do. Thirty-six percent of Gen Z adults describe their physical health as excellent, compared to just 16 percent of the Silent Generation.</p>
<p><strong>Medical Visits and Trust</strong></p>
<p>Americans continue to visit medical doctors for physical ailments at steady rates. The average adult visited a medical doctor 5.3 times in the past year. This frequency is similar to the 4.7 visits measured in 2001. Routine preventative care also remains common, with 76 percent of adults reporting they had a routine checkup in the past year.</p>
<p>While attendance is stable, trust in medical advice shows signs of erosion. Sixty-three percent of adults feel confident in the accuracy of their doctor’s advice without needing a second opinion. This is lower than the 70 percent trust level seen in 2010. Roughly one-third of the population now feels it is necessary to check for second opinions or conduct their own research.</p>
<p>Age influences this trust level heavily. Older adults represent the most trusting cohort, with 77 percent expressing confidence in their physicians. In contrast, trust drops to 67 percent among those aged 50 to 64. It falls further to below 60 percent for adults under age 50.</p>
<p>Political divides persist in the medical realm as well. Democrats express confidence in their doctors’ advice at a rate of 74 percent. This is notably higher than Republicans at 62 percent and independents at 57 percent. These partisan gaps have remained relatively consistent over the last few years.</p>
<p><strong>Financial Anxieties</strong></p>
<p>The data tables accompanying the report reveal that economic concerns related to healthcare remain prevalent. Forty-seven percent of respondents classify not having enough money to pay for medical care as a major concern. This anxiety is not distributed evenly. Lower-income households express significantly higher levels of worry regarding healthcare affordability.</p>
<p>Additionally, fear regarding insurance coverage persists. Forty-one percent of adults worry that they or a family member will be denied coverage for a pre-existing condition. Another 43 percent worry about going without health insurance entirely at some point. These financial stressors provide a backdrop to the broader findings on health and well-being.</p>
<p>The report concludes that while Americans are maintaining their physical health routines, their psychological state has undergone a profound shift. The stability that characterized the early 21st century has dissolved. It has been replaced by a landscape where younger, educated adults report struggling the most, even as they engage more frequently with mental healthcare providers.</p></p>
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<td><a href="https://www.psypost.org/new-research-challenges-western-assumptions-about-autistic-social-cognition/" style="font-family:Helvetica, sans-serif; letter-spacing:-1px;margin:0;padding:0 0 2px;font-weight: bold;font-size: 19px;line-height: 20px;color:#222;">New research challenges Western assumptions about autistic social cognition</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Jan 5th 2026, 12:00</div>
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<p><p>A new study found that non-autistic U.K. adults are less able to understand animations (representing specific words) generated by autistic individuals compared to animations generated by non-autistic individuals. In contrast, in the Japanese group, there were no differences between autistic and non-autistic individuals in their understanding of animations generated by autistic individuals. The research was published in <a href="https://doi.org/10.1186/s13229-025-00659-z"><em>Molecular Autism</em></a>.</p>
<p>Autism is a neurodevelopmental condition characterized by differences in social communication, sensory processing, and patterns of interests and behavior. Autistic people tend to perceive, process, and respond to information in ways that differ from how non-autistic individuals do. Their brains process and interpret information in a different way than non-autistic individuals. This often creates problems in communication between autistic and non-autistic individuals.</p>
<p>An important aspect of these communication problems is that non-autistic people tend to assume their own social intuitions are universal. This leads them to misinterpret autistic communication styles, such as direct speech, reduced eye contact, or atypical prosody (rhythm, stress, pitch, intonation patterns, and the overall way one speaks). As a result, non-autistic individuals may misunderstand the intentions of autistic people even when autistic people communicate clearly from their own perspective.</p>
<p>Differences in sensory experience can also cause behaviors in autistic individuals that are misread as disinterest, rudeness, or withdrawal. Social norms that are unspoken or context-dependent are particularly challenging for autistic individuals to understand. Research suggests this problem in understanding goes in both directions (non-autistic people not understanding autistic individuals and vice versa). This is sometimes called the double empathy problem.</p>
<p>Study author Bianca A. Schuster and her colleagues wanted to explore in more detail the issues autistic and non-autistic individuals have in understanding each other. These authors refer to this as bi-directional mentalizing difficulties. They wanted to know whether such difficulties are universal across different cultures. In particular, they wanted to know whether these difficulties in understanding are present in non-Western cultures as well. They chose to compare the Japanese culture with the U.K. culture.</p>
<p>Study participants were 48 Japanese (25 autistic, 23 non-autistic) and 49 U.K.-based adults (25 autistic, 24 non-autistic). To be enrolled in the study, participants were required to have lived in the respective country for a minimum of 10 years.</p>
<p>All autistic participants had a clinical diagnosis of autism or autism spectrum disorder. In the U.K. sample, the autistic group was significantly older than the non-autistic group (average age of 32 years vs. 24 years). In the Japanese sample, the age gap was smaller and not statistically significant (29 years vs. 27 years), but the groups were not matched on IQ.</p>
<p>First, participants were asked to complete a version of the animations mentalizing task. Participants created short videos in which they used interacting triangles to depict specific mental state and non-mental state words. The mental state words to be depicted were “arguing”, “surprising”, and “teasing”. The non-mental state words were “following”, “searching”, and “dancing”. For each of these words, participants generated one animation by moving two triangles around the touchscreen of a tablet.</p>
<p>After this, they viewed animations created by other participants. For each animation, they had to indicate to what extent, on a scale from 0 to 100, they thought it represented each of the 6 possible words.</p>
<p>Results showed that non-autistic participants from the U.K. were worse at interpreting animations created by autistic individuals than animations created by non-autistic individuals. In contrast, autistic individuals were similarly accurate in interpreting both animations created by autistic and those created by non-autistic individuals. The study found that this difficulty in understanding autistic-generated animations applied to both mental state and non-mental state words.</p>
<p>In Japan, there were no differences between autistic and non-autistic individuals in their accuracy in understanding animations. Additionally, Japanese participants showed better accuracy than U.K. participants, and all autistic participants showed higher accuracy for animations generated by Japanese autistic participants (compared to those generated by U.K. autistic participants).</p>
<p>“The present study provides new evidence to support a perspective shift in social cognition research, away from individual impairments towards the dynamic interplay between participants of social exchanges. Our results thus support a reframing of autism from a social communication disorder to a ‘description encompassing a broad range of developmental differences and experiences’,” study authors concluded.</p>
<p>The study contributes to the scientific understanding of autism. However, study authors note that the two U.K. groups were not matched on age, while the two Japanese groups differed in cognitive abilities (IQ). Also, the animations task used has important methodological limitations which may disadvantage autistic individuals. It might not have been a very good indicator of the mentalizing abilities of the compared groups.</p>
<p>The paper, “<a href="https://doi.org/10.1186/s13229-025-00659-z">A cross‑cultural examination of bi‑directional mentalising in autistic and non‑autistic adults,</a>” was authored by Bianca A. Schuster, Y. Okamoto, T. Takahashi, Y. Kurihara, C. T. Keating, J. L. Cook, H. Kosaka, M. Ide, H. Naruse, C. Kraaijkamp, and R. Osu.</p></p>
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<td><a href="https://www.psypost.org/simple-anthropomorphism-can-make-an-ai-advisor-as-trusted-as-a-romantic-partner/" 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;">Simple anthropomorphism can make an AI advisor as trusted as a romantic partner</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Jan 5th 2026, 10:00</div>
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<p><p>A new study published in <em><a href="https://doi.org/10.1016/j.chb.2025.108877" target="_blank" rel="noopener">Computers in Human Behavior</a></em> has found that people are far more willing to accept financial advice from a romantic partner than from an artificial intelligence program. The findings suggest that this preference is driven primarily by emotional connections and the feeling that a partner cares about one’s well-being. However, the data also indicates that giving AI human-like names or using it as a supportive tool for a human advisor can significantly increase a person’s willingness to trust it.</p>
<p>Erik Hermann from the European University Viadrina and Max Alberhasky from California State University Long Beach conducted this research. They sought to understand the intersection of financial technology and close relationships.</p>
<p>Financial robo-advisors have become a common tool for investment, offering automated advice based on algorithms. Despite the efficiency of these tools, financial decisions are often made within the context of a romantic partnership. The researchers noted that while previous studies have looked at trust in AI for impersonal tasks, little was known about how AI compares to a significant other when money is on the line.</p>
<p>“Although AI tools and devices are regularly used within relationship contexts (e.g., smart home devices, intelligence personal assistants like Alexa), prior research has mainly focused on individual use and responses,” explained Hermann, an interim professor of marketing. “The question arises how consumers react when they are provided with advice by their romantic partners versus AI advisors. Financial decision making as a rather high-stakes decision-making context appears particularly interesting to answer this question.”</p>
<p>The researchers hypothesized that trust is not a single concept but is composed of two distinct parts. These are cognitive trust and affective trust. Cognitive trust refers to the belief in an advisor’s skills, competence, and reliability.</p>
<p>Affective trust is rooted in emotional bonds, feelings of security, and the belief that the advisor genuinely cares about the decision-maker. The researchers aimed to see which type of trust plays a larger role in financial advice acceptance.</p>
<p>In the first study, the researchers recruited 301 participants through the online platform Prolific. All participants were screened to ensure they were currently in a romantic relationship.</p>
<p>The study presented a hypothetical scenario in which the participant had inherited $10,000 from a distant relative. They were asked to choose between two investment funds for this money. One option was a riskier fund with higher potential returns, while the other was a safer fund with lower average returns.</p>
<p>After the participants indicated their initial preference, they received a recommendation to switch to the other fund. This recommendation came from either their romantic partner or an AI robo-advisor. The participants then rated how likely they were to follow this advice. They also completed surveys to measure their levels of cognitive and affective trust in the source of the advice.</p>
<p>The results showed a clear preference for human advice. Participants were significantly more likely to switch their investment choice when the advice came from their romantic partner compared to the AI. The analysis revealed that this decision was mediated by both types of trust.</p>
<p>However, affective trust was a much stronger predictor than cognitive trust. This suggests that the emotional assurance provided by a partner is more influential than the perceived technical competence of a machine in this context.</p>
<p>The second study involved 298 participants who were also in relationships. The researchers wanted to see if they could reduce the resistance to AI by making the technology appear more human. This process is known as anthropomorphism. Participants were randomly assigned to one of three groups.</p>
<p>One group received advice from a romantic partner. Another group received advice from a standard robo-advisor. The third group received advice from a robo-advisor that was given a gender-neutral human name, “Alex.”</p>
<p>The researchers found that simply giving the AI a name changed how people reacted to it. Participants were just as likely to accept advice from “Alex” as they were from their romantic partner. Both the romantic partner and the anthropomorphized AI were trusted significantly more than the standard, unnamed AI.</p>
<p>The data once again showed that affective trust was the primary driver of this effect. By giving the AI a name, the researchers successfully increased the participants’ sense of emotional connection to the technology.</p>
<p>Study 3A included 445 participants and introduced a new concept called human-AI collaboration. The researchers wanted to see what would happen if the romantic partner used AI to formulate their advice. Participants were divided into three conditions. They received advice from a romantic partner, a standalone AI, or a romantic partner who was assisted by AI.</p>
<p>The findings indicated that using AI did not hurt the partner’s credibility. Participants were equally likely to follow advice from a partner and a partner assisted by AI. Both human-involved options were preferred over the standalone AI. This suggests that people are comfortable with AI being used as a tool to enhance human decision-making. The presence of a human intermediary appears to maintain the necessary levels of affective trust.</p>
<p>The final experiment, Study 3B, was designed to replicate the previous findings with greater realism. The researchers recruited 376 participants. Instead of generic descriptions of funds, they used real investment options from Fidelity. The “safe” option was described as a US bond index fund, while the “risky” option was an emerging markets fund. The researchers also improved the dialogue used in the scenario to make the partner’s advice sound more natural.</p>
<p>The results of Study 3B mirrored the previous experiments. Participants were significantly more likely to listen to their partner or an AI-assisted partner than to a standalone AI. The statistical analysis confirmed that affective trust was the dominant factor influencing these decisions.</p>
<p>The feeling that the advisor cared about the participant’s financial well-being was the most critical element. Cognitive trust regarding the advisor’s ability to analyze data was less important for the final decision.</p>
<p>“Generally, people prefer investment advice from their romantic partners over AI advice—which is called algorithm or AI aversion, because financial advice from one’s romantic partner is associated with stronger feelings of cognitive and, particularly, affective trust,” Hermann told PsyPost.</p>
<p>“Interestingly, a quite simply human-like cue—i.e., giving the AI advisor a name—reduces AI aversion. Similarly, when AI assists romantic partners, this advice is accepted at similarly high rates as romantic partner advice alone.”</p>
<p>There are limitations to this research that should be considered. The studies relied on hypothetical scenarios rather than real-world financial losses or gains. It is possible that people might act differently if their own actual savings were at risk. Additionally, trust in real relationships is complex and built over years. A brief experimental scenario can only approximate these deep psychological bonds.</p>
<p>“The evidence of AI aversion is robust across four experimental studies,” Hermann said. “However, the studies rely on hypothetical investment scenarios. This allows for experimental control but people’s willingness to follow financial advice may differ when the consequences are not hypothetical.”</p>
<p>The researchers also warn against the potential for creating misplaced trust. Designing AI to seem more human effectively increases acceptance, but this could lead to over-reliance on flawed systems.</p>
<p>“AI design should not aim at maximizing consumer trust but to foster so-called calibrated trust,” Hermann explained. “That is, AI and services providers should encourage confidence in AI advisors’ competence and capabilities while simultaneously helping consumers understand its limitations. Otherwise, miscalibrated trust can have negative real-world consequences like over-reliance, misuse, and/or blind trust.”</p>
<p>Future research could explore these dynamics in longitudinal studies. It would be valuable to see if trust in an AI advisor grows naturally over time as a user becomes familiar with it. Researchers could also investigate if these findings hold true for other types of decisions, such as choosing a medical treatment or buying a home. For now, the evidence indicates that while AI has superior data processing power, the human touch remains an essential component of trusted advice.</p>
<p>The study, “<a href="https://doi.org/10.1016/j.chb.2025.108877">The Trusted Partner for financial decision making: Romantic partner or AI?</a>,” was authored by Erik Hermann and Max Alberhasky.</p></p>
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<p><strong>Forwarded by:<br />
Michael Reeder LCPC<br />
Baltimore, MD</strong></p>
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