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(https://www.psypost.org/donald-trump-viewed-as-higher-in-dark-tetrad-traits-than-joe-biden-study-finds/) Donald Trump viewed as higher in Dark Tetrad traits than Joe Biden, study finds
Feb 5th 2025, 08:00

A new study published in the (https://doi.org/10.1016/j.jrp.2024.104568) Journal of Research in Personality highlights how perceptions of dark tetrad traits—Machiavellianism, narcissism, psychopathy, and sadism—in politicians influence voter behavior, focusing on the 2020 U.S. presidential election, comparing perceptions of Donald Trump and Joe Biden.
While prior research has studied voters’ (https://www.psypost.org/trump-supporters-exhibit-slightly-elevated-subclinical-psychopathy-study-finds/) personalities, this study centered on the perceived traits of political leaders. Monika Prusik was motivated by global political trends, including the rise of (https://www.psypost.org/psychopathic-tendencies-linked-to-a-psychological-predisposition-towards-populism/) populism and the personalization of politics, to investigate how these traits might affect electoral outcomes.
The study included 456 American participants recruited from MTurk (ages 20-70, 42.2% Democrats, 45.7% Republicans). Participants rated the dark tetrad traits of Trump and Biden using an adapted version of the Short Dark Tetrad scale, assessing each trait separately for both candidates. This 28-item scale included questions such as “This presidential candidate believes it’s unwise to let people know his secrets” for Machiavellianism and “This presidential candidate likes to show off every now and then” for narcissism. Participants rated each item for both candidates on a seven-point Likert scale, ranging from strongly disagree to strongly agree. They also reported their willingness to vote for each candidate. Sociodemographic information, such as political affiliation and education, were also collected.
Both Donald Trump and Joe Biden were perceived as exhibiting dark tetrad traits, though the degree and nature of these traits varied between candidates. Trump was rated significantly higher than Biden on narcissism, psychopathy, and sadism, whereas Machiavellianism was attributed to both candidates at similar levels. These perceptions were strongly influenced by political affiliation.
Democrats attributed higher levels of dark traits, particularly narcissism, psychopathy, and sadism, to Trump while viewing Biden more favorably. In contrast, Republicans perceived Biden as possessing more dark traits than Democrats did but still rated Trump as slightly higher in narcissism.
The study also revealed an asymmetry in the way these traits were evaluated: narcissism and Machiavellianism were sometimes viewed as advantageous, particularly when attributed to a preferred candidate, whereas psychopathy and sadism consistently reduced support. Biden’s narcissism was viewed positively across political affiliations, whereas Trump’s narcissism had a polarizing effect, increasing Republican support but decreasing Democratic support.
Voter willingness was shaped by how these traits were perceived in the candidates. In general, narcissism and Machiavellianism were associated with higher support for a candidate, while psychopathy and sadism led to decreased support. Democrats’ willingness to vote for Biden increased when they perceived him as more narcissistic, while Republicans responded positively to narcissistic traits in both candidates, though the effect was stronger for Trump.
Interestingly, voters’ perceptions of a candidate’s dark traits also influenced their willingness to vote for the opposing candidate. For example, higher perceived levels of psychopathy and sadism in Biden were linked to an increased likelihood of voting for Trump, while greater perceptions of Trump’s Machiavellianism led to increased willingness to vote for Biden among Republicans.
These findings suggest that perceptions of dark tetrad traits in political candidates play a significant role in shaping electoral preferences, but their impact is complex and mediated by partisanship.
Of note is that this study relied on subjective perceptions of candidates rather than objective measures, potentially influenced by media portrayals and political biases.
The study, “(https://doi.org/10.1016/j.jrp.2024.104568) Dark tetrad traits in politicians and voter behavior: Joe Biden and Donald Trump in the 2020 presidential election,” was authored by Monika Prusik.

(https://www.psypost.org/scientists-shocked-to-find-ais-social-desirability-bias-exceeds-typical-human-standards/) Scientists shocked to find AI’s social desirability bias “exceeds typical human standards”
Feb 5th 2025, 06:00

A new study published in (https://doi.org/10.1093/pnasnexus/pgae533) PNAS Nexus reveals that large language models, which are advanced artificial intelligence systems, demonstrate a tendency to present themselves in a favorable light when taking personality tests. This “social desirability bias” leads these models to score higher on traits generally seen as positive, such as extraversion and conscientiousness, and lower on traits often viewed negatively, like neuroticism.
The language systems seem to “know” when they are being tested and then try to look better than they might otherwise appear. This bias is consistent across various models, including GPT-4, Claude 3, Llama 3, and PaLM-2, with more recent and larger models showing an even stronger inclination towards socially desirable responses.
Large language models are increasingly used to simulate human behavior in research settings. They offer a potentially cost-effective and efficient way to collect data that would otherwise require human participants. Since these models are trained on vast amounts of text data generated by humans, they can often mimic human language and behavior with surprising accuracy. Understanding the potential biases of large language models is therefore important for researchers who are using or planning to use them in their studies.
Personality traits, particularly the “Big Five” (extraversion, openness to experience, conscientiousness, agreeableness, and neuroticism), are a common focus of psychological research. While the Big Five model was designed to be neutral, most people tend to favor higher scores on extraversion, openness, conscientiousness, and agreeableness, and lower scores on neuroticism.
Given the prevalence of personality research and the potential for large language models to be used in this field, the researchers sought to determine whether these models exhibit biases when completing personality tests. Specifically, they wanted to investigate whether large language models are susceptible to social desirability bias, a well-documented phenomenon in human psychology where individuals tend to answer questions in a way that portrays them positively.
“Our lab works at the intersection of psychology and AI,” said study authors Johannes Eichstaedt (an assistant professor and Shriram Faculty Fellow at (https://hai.stanford.edu/) the Institute for Human-Centered Artificial Intelligence) and Aadesh Salecha (a master’s student at Stanford University and a staff data scientist at (https://cpwb.stanford.edu/) the Computational Psychology and Well-Being Lab).
“We’ve been fascinated by using our understanding of human behavior (and the methods from cognitive science) and applying it to intelligent machines. As LLMs are used more and more to simulate human behavior in psychological experiments, we wanted to explore whether they reflect biases similar to those we see in humans. During our explorations with giving different psychological tests to LLMs, we came across this robust social desirability bias.”
To examine potential response biases in large language models, the researchers conducted a series of experiments using a standardized 100-item Big Five personality questionnaire. This questionnaire is based on a well-established model of personality and is widely used in psychological research. The researchers administered the questionnaire to a variety of large language models, including those developed by OpenAI, Anthropic, Google, and Meta. These models were chosen to ensure that the findings would be broadly applicable across different types of large language models.
The core of the study involved varying the number of questions presented to the models in each “batch.” The researchers tested batches ranging from a single question to 20 questions at a time. Each batch was presented in a new “session” to prevent the model from having access to previous questions and answers. The models were instructed to respond to each question using a 5-point scale, ranging from “Very Inaccurate” to “Very Accurate,” similar to how humans would complete the questionnaire.
The researchers also took steps to ensure the integrity of their findings. They tested the impact of randomness in the models’ responses by adjusting a setting called “temperature,” which controls the level of randomness. They created paraphrased versions of the survey questions to rule out the possibility that the models were simply recalling memorized responses from their training data.
Additionally, they randomized the order of the questions to eliminate any potential effects of question order. Finally, they tested both positively coded and reverse-coded versions of the questions (e.g., “I am the life of the party” vs. “I don’t talk a lot”) to assess the potential influence of acquiescence bias, which is the tendency to agree with statements regardless of their content.
The study’s results clearly demonstrated that large language models exhibit a social desirability bias when completing the Big Five personality test. Across all tested models, scores were skewed towards the desirable ends of the trait dimensions. For instance, as the number of questions presented in a batch increased, the models’ scores on extraversion, conscientiousness, openness, and agreeableness tended to increase, while their scores on neuroticism decreased. This pattern was particularly pronounced in the GPT-4 model.
“Our study shows that LLMs behave differently when they infer that they are being evaluated,” Eichstaedt and Salecha told PsyPost. “Specifically, when they recognize that they are being subjected to a Big Five personality test they ‘try’ to appear more likable, just like people do. This has big implications for how we use LLMs in research and real-world applications because it suggests they might subtly change their behavior based on how they interpret a situation. It also shows just how much they are trained to ‘be liked’ — which is something that can backfire in subtle ways — some models are leaning towards being sycophantic.”
The researchers found that the difference in scores between administering the survey one question at a time versus 20 questions at a time was equivalent to more than one standard deviation in human terms, which is a very large effect. In other words, the change in the model’s responses due to social desirability bias was comparable to a significant shift in a person’s personality. The bias was not limited to GPT-4; it was observed across all the large language models tested, with larger and more recent models generally exhibiting a stronger bias.
“The size of the effect was shocking. In humans, social desirability bias exists but in LLMs, we saw effect sizes that exceed typical human standards — like an average person suddenly becoming extremely extroverted (90th percentile),” the researchers said.
Further analysis suggested that the models were able to infer when they were being evaluated for personality, even with a small number of questions. The accuracy with which the models could identify that they were taking a personality test was associated with the strength of the social desirability bias. For example, GPT-4, Claude 3, and Llama 3 could correctly identify the nature of the questions with over 90% accuracy when presented with just five questions, while PaLM-2 and GPT-3.5 were less accurate.
When the models were explicitly told that they were completing a personality test, their responses were even more skewed towards social desirability, even when presented with only a single question. This finding suggests that the models are adjusting their responses based on their perception of being evaluated. While reverse-coding the questions reduced the magnitude of the bias, it did not eliminate it entirely. This indicates that the observed effects are not solely due to acquiescence bias. The researchers also confirmed that the bias persisted even when the questions were paraphrased and when the order of questions was randomized, further supporting the robustness of their findings.
The researchers acknowledge that their study primarily focused on the Big Five personality traits, which are widely represented in the training data of large language models. It is possible that the same response biases might not occur with less common or less socially evaluative psychological constructs.
Future research should explore the prevalence of social desirability bias across different types of surveys and measurement methods. Another area for further investigation is the role of training data and model development processes in the emergence of these biases. Understanding how these biases are formed and whether they can be mitigated during the training process is essential for ensuring the responsible use of large language models in research and other applications.
Despite these limitations, the study’s findings have significant implications for the use of large language models as proxies for human participants in research. The presence of social desirability bias suggests that results obtained from these models may not always accurately reflect human responses, particularly in the context of personality assessment and other socially sensitive topics.
“As we integrate AI into more parts of our lives, understanding these subtle behaviors and biases becomes crucial,” Eichstaedt and Salecha said. “There needs to be more research into understanding at which stage of the LLM development (pre-training, preference tuning, etc) these biases are being amplified and how to mitigate them without hampering the performance of these models. Whether we’re using LLMs to support research, write content, or even assist in mental health settings, we need to be aware of how these models might unconsciously mimic human flaws—and how that might affect outcomes.”
The study, “(https://doi.org/10.1093/pnasnexus/pgae533) Large language models display human-like social desirability biases in Big Five personality surveys,” was authored by Aadesh Salecha, Molly E. Ireland, Shashanka Subrahmanya, João Sedoc, Lyle H Ungar, and Johannes C. Eichstaedt.

(https://www.psypost.org/are-babies-moral-blank-slates-new-study-questions-early-morality/) Are babies moral blank slates? New study questions early morality
Feb 4th 2025, 18:00

What does a baby know about right and wrong? A foundational finding in moral psychology suggested that even infants have a moral sense, preferring “helpers” over “hinderers” before uttering their first word. Now, nearly 20 years later, a study that tried to (https://onlinelibrary.wiley.com/doi/10.1111/desc.13581) replicate these findings calls this result into question.
In the (https://www.nature.com/articles/nature06288) original study, Kiley Hamlin and her colleagues showed a puppet show to six- and ten-month-old babies. During the show, the babies would see a character — which was really just a shape with googly eyes — struggling to reach the top of a hill.
Next, a new character would either help the struggling individual reach the top (acting as a “helper”) or push the character back down to the bottom of the hill (acting as a “hinderer”).
By gauging babies’ behaviour — specifically, watching how their eyes moved during the show and whether they preferred to hold a specific character after the show ended — it seemed that the infants had basic moral preferences. Indeed, in the first study, 88% of the ten-month-olds – and 100% of the six-month-olds – chose to reach for the helper.

 
But psychology, and developmental psychology, in particular, is (https://www.psychologytoday.com/gb/basics/replication-crisis) no stranger to replicability concerns (when it is difficult or impossible to reproduce the results of a scientific study). After all, the original study sampled only a few dozen infants.
This isn’t the fault of the researchers; it’s just really hard to collect data from babies. But what if it was possible to run the same study again — with say, hundreds or even thousands of babies? Would researchers find the same result?
This is the chief aim of ManyBabies, a consortium of developmental psychologists spread around the world. By combining resources across individual research labs, ManyBabies can robustly test findings in developmental science, like Hamlin’s original “helper-hinderer” effect. And as of last month, the results are in.
With a final sample of 567 babies, tested in 37 research labs across five continents, babies did not show evidence of an early-emerging moral sense. Across the ages tested, babies showed no preference for the helpful character.
Blank slate?
John Locke, an English philosopher (https://plato.stanford.edu/entries/locke/) argued that the human mind is a “tabula rasa” or “blank slate”. Everything that we, as humans, know comes from our experiences in the world. So should people take the most recent ManyBabies result as evidence of this? My answer, however underwhelming, is “perhaps”.
This is not the first attempted replication of the helper-hinderer effect (nor is it the first “failure to replicate”). In fact, there have been a (https://psycnet.apa.org/fulltext/2018-33347-001.html) number of successful replications. It can be hard to know what underlies differences in results. For example, a (https://pmc.ncbi.nlm.nih.gov/articles/PMC4310275/) previous “failure” seemed to come from the characters’ “googly eyes” not being oriented the right way.
The ManyBabies experiment also had an important change in how the “show” was presented to infants. Rather than a puppet show performed live to baby participants, researchers instead presented a video with digital versions of the characters. This approach has its strengths. For example, ensuring that the exact same presentation occurs across every trial, in every lab. But it could also shift how babies engage with the show and its characters.
I appreciated the (https://bsky.app/profile/mcxfrank.bsky.social/post/3lcgtoas5gs2w) recent remarks made by Michael Frank, founder of the ManyBabies consortium, on social network BlueSky: “Some people will jump to the interpretation that [the results of ManyBabies] shows that the original finding was incorrect (and hence that the other replications were incorrect as well, and the earlier non-replications were right). This [is] one possibility – but we shouldn’t be so quick to jump to conclusions.”
Rather, we can take this finding for exactly what it is: a well-executed large investigation (senior-authored by Kiley Hamlin herself) of the hypothesis that infants prefer helpers over hinderers. In this instance, the hypothesis was not supported.
This could be because, underneath it all, Locke was right. Perhaps the babies tested hadn’t had enough time in the world to learn “right from wrong”, so they wouldn’t make any distinction between a helpful character and a harmful one. Or perhaps there’s something more complicated going on. Only more science, with many, many more babies, will tell us.
At the very least, a question mark now hangs over one of the most famous experiments in developmental psychology.
 
This article is republished from (https://theconversation.com) The Conversation under a Creative Commons license. Read the (https://theconversation.com/are-we-moral-blank-slates-at-birth-a-new-study-offers-some-clues-245333) original article.

(https://www.psypost.org/study-wildfire-smoke-exposure-raises-dementia-risk-by-18/) Study: Wildfire smoke exposure raises dementia risk by 18%
Feb 4th 2025, 16:00

A study published in (https://jamanetwork.com/journals/jamaneurology/fullarticle/2827124) JAMA Neurology has discovered a troubling new health concern: prolonged exposure to wildfire smoke may significantly increase the risk of developing dementia, with the risk being particularly high among certain vulnerable populations.
Scientists have long known that air pollution can have harmful effects on the brain. Fine particulate matter, known as PM2.5, is a major risk factor for dementia. However, the specific impact of wildfire-generated PM2.5— which is becoming more common due to climate change—has not been well understood.
Led by Holly Elser from the University of Pennsylvania, the research team analyzed electronic health records from Kaiser Permanente Southern California, a healthcare network that serves millions of people across the state. The study tracked 1,223,107 participants (53% female) aged 60 and older over an 11-year period, monitoring their three-year exposure to wildfire-related pollution and comparing it to their likelihood of developing dementia.
During the follow-up period, 80,993 participants were diagnosed with dementia (6.6% of the study population). Notably, a small increase in wildfire PM2.5 levels (1 microgram per cubic meter) was linked to an 18% higher risk of dementia. By contrast, the same increase in pollution from non-wildfire sources led to only a 1% increase in risk.
These results suggest that wildfire smoke may be particularly dangerous compared to other types of PM2.5, possibly due to its smaller particles containing high levels of oxidative and inflammatory compounds, which can damage cells and contribute to diseases like Alzheimer’s.
One of the most concerning aspects of the study is that wildfire pollution was found to disproportionately affect vulnerable communities, particularly individuals from racially minoritized backgrounds and those living in high-poverty areas.
“Lower-quality housing may increase smoke infiltration, and poorer families may have constrained economic choices that limit their ability to pay for air filtration systems to improve air quality during smoke events. Members of marginalized groups may have amplified physiologic responses to environmental exposures, reflecting worse baseline health, the cumulative result of discrimination, and chronic exposure to psychosocial stressors,” the researchers hypothesized.
The study also found that wildfire smoke exposure was more strongly linked to dementia in younger seniors (under 75 years old). Researchers suspect that older individuals may have already developed dementia-related changes before the study began or that younger seniors had more outdoor exposure, leading to higher risks.
Despite the significant findings, the study has some limitations. Exposure to wildfire and non-wildfire PM2.5 was measured over a three-year period, but the exact timeframe of exposure relevant to dementia risk is still unknown. Since the brain changes leading to dementia likely begin years before symptoms appear, future research should consider longer exposure durations.
The study, “(https://jamanetwork.com/journals/jamaneurology/fullarticle/2827124) Wildfire Smoke Exposure and Incident Dementia,” was authored by Holly Elser, Timothy B. Frankland, Chen Chen, Sara Y. Tartof, Elizabeth Rose Mayeda, Gina S. Lee, Alexander J. Northrop, Jacqueline M. Torres, Tarik Benmarhnia, and Joan A. Casey.

(https://www.psypost.org/scientists-link-dyslexia-risk-genes-to-brain-differences-in-motor-visual-and-language-areas/) Scientists link dyslexia risk genes to brain differences in motor, visual, and language areas
Feb 4th 2025, 14:00

A recent large-scale study published in (https://www.science.org/doi/10.1126/sciadv.adq2754) Science Advances has revealed a connection between genetic variations associated with dyslexia and structural differences in the brain. These differences were found in areas involved in motor coordination, vision, and language. This provides new insights into the neurological underpinnings of this common learning difficulty.
Dyslexia is a common learning difficulty that primarily affects the skills involved in accurate and fluent word reading and spelling. It’s characterized by challenges with phonological awareness (the ability to recognize and manipulate the sounds in spoken language), verbal memory, and verbal processing speed. People with dyslexia may struggle to decode words, recognize familiar words automatically, and spell words correctly. Importantly, dyslexia is not related to a person’s overall intelligence. It’s considered a neurodevelopmental condition, meaning it arises from differences in how the brain develops and processes information, particularly related to language.
Dyslexia is known to have a strong genetic component, often running in families. Twin studies have estimated that 40% to 70% of the variation in dyslexia risk can be attributed to genetic factors. While previous research has identified some brain regions that may function differently in individuals with dyslexia, these findings have often been inconsistent, possibly due to small sample sizes and differences in study methods. Additionally, the precise relationship between specific genes associated with dyslexia and their impact on brain structure has remained unclear.
The motivation behind the new study was to address these gaps in our understanding by leveraging the power of large-scale data. The researchers recognized that investigating the connection between genetic predisposition to dyslexia and brain structure in a very large sample could provide more robust and reliable insights than smaller, more traditional studies. They aimed to identify specific brain regions and white matter tracts that are associated with genetic risk for dyslexia, and to explore whether different genetic variants might influence distinct neural pathways.
“Thirty-five genetic variants that influence the chance of having dyslexia were already known from a very large study by the company 23andMe in the USA, carried out in over one million people. However, that study did not include brain MRI data. The new aspect of our study was to investigate the genetic variants in relation to brain structure in MRI data from thousands of people,” explained (https://www.mpi.nl/people/francks-clyde) Clyde Francks ((https://bsky.app/profile/clydefrancks.bsky.social) @clydefrancks), a professor at the Max Planck Institute for Psycholinguistics in Nijmegen and senior author of the study.
The researchers used two large datasets: the genetic data 23andMe and brain imaging data from over 30,000 adults in the UK Biobank. The 23andMe dataset helped identify genetic variants associated with dyslexia by comparing individuals who reported a dyslexia diagnosis to those who did not. These genetic variants were then used to calculate “polygenic scores” for individuals in the UK Biobank, reflecting their genetic predisposition to dyslexia.
Although the UK Biobank participants were not specifically diagnosed with dyslexia, their polygenic scores varied, allowing researchers to analyze how this genetic predisposition related to differences in brain structure. Brain scans from the UK Biobank were examined to assess differences in regional brain volume and white matter microstructure—features that provide insight into how different areas of the brain are connected and function.
To refine their analysis, the researchers used a statistical technique known as independent component analysis. This allowed them to separate out different patterns of brain structure associated with specific genetic variations, helping them identify distinct “impact modes”—sets of genetic variants linked to unique structural features in the brain.
“The genetic contribution to dyslexia involves many thousands of genetic variants all across the genome, each with a small effect on the chance of having dyslexia, but in combination they add up to a measurable contribution,” Francks said.
The researchers found that individuals with a higher genetic predisposition to dyslexia tended to have lower overall brain volume, with the effect being more pronounced in gray matter than in white matter. More specifically, genetic risk for dyslexia was linked to reduced volume in several brain regions, including the medial frontal cortex, midbrain, thalamus, and bilateral amygdalae. These areas are associated with higher-level cognitive functions, attention, and language processing.
“Our study implicated various brain regions and networks linked to the genetic chance of having dyslexia, which were involved prominently in motor coordination, language and vision, although not limited to those functions,” Francks explained. “This does not mean that every person with dyslexia has changes in all of these brain systems. Rather, it is likely that some people with dyslexia have a particular genetic contribution affecting for example their motor functions, others their language functions, and others their vision functions, while some people may have different combinations of these. Part of our study was aimed at breaking down the overall genetic chance of having dyslexia into distinct components that associate with different brain networks.”
Certain structural differences were particularly notable. For example, individuals with a higher genetic risk for dyslexia had reduced volume in the left temporoparietal junction and the left anterior insula—areas known to play critical roles in language and phonological processing. This supports the idea that dyslexia is linked to difficulties in recognizing and processing the sounds of language.
The researchers also found that polygenic scores for dyslexia were linked to changes in the structure of white matter pathways. Specifically, individuals with a higher genetic risk for dyslexia showed increased white matter density in the forceps major—a tract connecting the two occipital lobes, which are crucial for visual processing.
Meanwhile, reduced white matter density was observed in pathways connecting the cerebellum to the cortex, including the superior longitudinal fasciculus and the anterior limb of the internal capsule. These findings align with previous research suggesting that dyslexia is associated with motor coordination difficulties, as well as differences in how visual and linguistic information is integrated.
Interestingly, the study also examined whether the brain regions associated with dyslexia-related genes overlapped with those linked to other cognitive traits. The results showed substantial overlap with brain regions associated with intelligence, educational attainment, and attention-deficit hyperactivity disorder (ADHD). However, one region stood out as being uniquely linked to dyslexia: the primary motor cortex. This finding suggests that motor function might play a particularly important role in dyslexia, distinguishing it from other cognitive traits.
“There are various traits that are partly associated with dyslexia in the population, including educational attainment and attention deficit/hyperactivity disorder,” Francks said. “There is evidence that certain genetic variants impact dyslexia as well as these other traits. This is why we looked at how genetic effects on these other traits are related to brain structure.”
“In this way we could assess which aspects of brain structure are linked relatively specifically to the genetic chance of having dyslexia, as opposed to more generally to genetic effects on other related traits. For example, lower motor cortex volume was relatively specific to the genetic chance of having dyslexia, whereas lower nerve fiber density in the internal capsule was found more generally in relation to genetic effects on dyslexia, ADHD, educational attainment and fluid intelligence.”
The study provides important insights into the neural correlates of genetic predisposition to dyslexia. But there are some limitations. The UK Biobank sample, while large, may not be fully representative of the general population due to volunteer bias. The dyslexia polygenic scores were based on self-reported diagnoses in the 23andMe study, without detailed information on the type or severity of dyslexia. Additionally, the study was cross-sectional, meaning it examined data at a single point in time. This makes it difficult to determine whether the observed brain differences are a cause or consequence of dyslexia.
Future research should aim to replicate these findings in other large samples, ideally including longitudinal data from children to track brain changes during reading development. Investigating potential sex differences in the genetic and neural underpinnings of dyslexia would also be valuable.
In the future, polygenic scores, combined with other factors like early cognitive assessments, might help tailor education to individual needs. However, larger genetic studies of dyslexia are needed to improve accuracy.
“Maybe in the future polygenic scores will be accurate enough, when used in combination with other types of data (such as pre-school cognitive and behavioural assessments), to have a useful impact on adjusting education to a child’s particular needs,” Francks said. “For the time being this is not possible. Even larger genetic studies of dyslexia would first need to be carried out, so that effects of each genetic variant can be measured more precisely.”
“If there comes a point in the future when all infants are routinely genotyped as part of standard healthcare assessment, then applications of polygenic scores might become feasible also in the educational domain. People would of course also need to decide whether this is desirable. In terms of feasibility, there would need to be enough of a predictive gain at the individual level, although cognitive and behavioural assessments will remain the most important.”
The study, “(https://doi.org/10.1126/sciadv.adq2754) Distinct impact modes of polygenic disposition to dyslexia in the adult brain,” was authored by Sourena Soheili-Nezhad, Dick Schijven, Rogier B. Mars, Simon E. Fisher, and Clyde Francks.

(https://www.psypost.org/childhood-neglect-linked-to-slower-working-memory-development-study-finds/) Childhood neglect linked to slower working memory development, study finds
Feb 4th 2025, 12:00

New research published in (https://doi.org/10.1017/s0954579424001457) Development and Psychopathology suggests that childhood neglect is associated with slower development of working memory abilities throughout adolescence and into young adulthood. While executive function abilities generally improve from ages 14 to 20, those who experienced neglect showed a more gradual increase in working memory compared to their peers. The study did not find a similar link between childhood abuse and working memory development.
The Virginia Tech research team, consisting of Claudia Clinchard, Brooks Casas, and Jungmeen Kim-Spoon, conducted the study to better understand how different types of childhood maltreatment might impact the development of executive function. Executive function refers to a set of higher-order cognitive skills that help individuals plan, focus attention, remember instructions, and juggle multiple tasks successfully. Adolescence is a period of significant development in the brain’s prefrontal cortex, an area critical for executive function.
Prior research has shown that adults who experienced childhood maltreatment often have deficits in executive function, but less was known about how abuse and neglect might uniquely affect its development during adolescence. Given that one in seven children in the United States experiences maltreatment, the researchers aimed to examine this link. They also sought to test a theory called the Dimensional Model of Adversity and Psychopathology, which proposes that different types of adversity have distinct effects on brain development and cognitive function.
“One main reason we were interested in this topic was we wanted to better understand the impacts that abuse and neglect have on the trajectories of executive function (cognitive skills needed to help with planning, solving problems, and adapting to novel situations in order to meet goals) development during adolescence and into young adulthood. Executive function has been studied frequently in children but less so in adolescence and into young adulthood, so one aim was to see how executive function developed during this time,” the researchers told PsyPost.
To investigate this, the researchers followed 167 adolescents over six years, from ages 14 to 20. At each time point, participants completed three behavioral tasks designed to measure different aspects of executive function: working memory, inhibitory control, and cognitive flexibility. Working memory was assessed using a task where participants had to repeat a series of numbers backward. Inhibitory control, the ability to suppress impulses and resist distractions, was measured using a task in which participants identified a number that was different from two others, with the complexity of the task increasing over time.
Cognitive flexibility, the ability to switch between different tasks or rules, was measured using a card-sorting task where the rules for sorting changed throughout the task. At ages 18–19, participants completed a questionnaire called the Maltreatment and Abuse Chronology of Exposure scale, which asked them to recall instances of neglect and abuse they experienced from ages 1 to 13. Neglect was defined as experiencing emotional or physical neglect, while abuse included sexual, verbal, physical, and non-verbal abuse. The researchers then analyzed how these recalled experiences of neglect and abuse were related to changes in executive function over time.
The results showed that, on average, all three components of executive function—working memory, inhibitory control, and cognitive flexibility—improved across adolescence and into young adulthood. However, when examining the effects of childhood maltreatment, some interesting patterns emerged. Specifically, experiencing neglect during childhood was associated with slower growth in working memory abilities over the six-year period. Adolescents who reported higher levels of neglect showed a more gradual increase in their working memory performance compared to those who reported less or no neglect.
“It was interesting that neglect had a more notable impact on working memory than inhibitory control or cognitive flexibility,” the researchers said. “We had anticipated all three aspects to be similarly affected by early neglect experience.”
Notably, childhood abuse was not significantly linked to changes in working memory development. Neither neglect nor abuse was found to be significantly related to changes in inhibitory control or cognitive flexibility during the study period.
“Experiences of neglect during childhood specifically have negative impacts on working memory development during adolescence and into young adulthood,” the researchers said.
The researchers acknowledge some limitations of the study. Although the study followed participants over several years, the correlational nature of the data means that they cannot definitively say that neglect causes slower growth in working memory.
“Our findings do not indicate that neglect causes these effects,” the researchers noted. “This study is longitudinal and spans across adolescence and into young adulthood and the data support our theoretical-informed models, but it is important to note that we cannot infer causation.”
The researchers suggest several directions for future research. They propose that future studies should examine how executive function continues to develop later into adulthood and older adulthood and whether the timing of abuse and neglect experiences during childhood has different effects on this development. Future research could also explore the role of genetics in influencing executive function development, as well as the potential impact of other forms of adversity, such as unpredictability in one’s environment.
“The long-term goals for this line of research include looking at different developmental periods, such as later into adulthood and older adulthood, and examining how the timing of abuse and neglect in childhood (for example, between birth and age 5 compared to between ages 6 and 13) might change the results,” the researchers explained.
The study, “(https://www.cambridge.org/core/journals/development-and-psychopathology/article/child-maltreatment-and-executive-function-development-throughout-adolescence-and-into-young-adulthood/72CB0E75C6545E2CBA3415BB1E9DF640) Child maltreatment and executive function development throughout adolescence and into young adulthood,” was published October 28, 2024.

(https://www.psypost.org/can-light-exposure-help-teens-sleep-earlier-new-study-suggests-yes/) Can light exposure help teens sleep earlier? New study suggests yes
Feb 4th 2025, 10:00

A study of a small group of adolescents found that their sleep patterns are associated with their exposure to light during the day. Adolescents exposed to more light during the daytime tended to go to bed somewhat earlier and wake up somewhat earlier the following morning. The paper was published in the (https://doi.org/10.1111/jsr.14315) Journal of Sleep Research.
Sleep is a natural and essential biological process that allows the body and brain to rest and recover. It consists of different stages, including deep sleep and REM sleep, which contribute to various aspects of physical and mental well-being. Sleep is important for physical health, as it is a time when the body repairs tissues, regulates metabolism, and strengthens the immune system. It also plays a crucial role in brain function, supporting memory consolidation, emotional regulation, and cognitive performance.
Poor sleep can lead to problems such as difficulty concentrating, mood disturbances, and a weakened immune response. Chronic sleep deprivation increases the risk of serious health conditions, including heart disease, diabetes, and obesity.
The brain regulates sleep behavior through a combination of processes in which the need for sleep accumulates during the day and dissipates during sleep. This process is controlled by an internal timing mechanism that creates the body’s circadian rhythm. The body’s activity and various external factors can also affect sleep patterns and sleep quality.
Study author Luísa da Costa Lopes and her colleagues aimed to explore the association between exposure to light during the day and subsequent sleep among high school students. They also sought to examine the sleep and light exposure patterns of students from the same school and the differences in those patterns between school days and free days.
The study participants were 35 Brazilian high school students from São Paulo, aged between 15 and 17 years. Sixty-nine percent were girls. The researchers invited them to participate in the study following an oral presentation.
Data were collected between September 14 and October 8, 2021, during a time of year when daylight lasts 12–13 hours in this region of Brazil. The sun rises a little before 6:00 a.m. and sets around 6:00 p.m. During this period, study participants wore actigraphs on their wrists that recorded their movement (and thus sleep) and exposure to light. The researchers calculated levels of light exposure across four periods of the day. All data were extracted from actigraph records.
The results showed that, on average, students were more exposed to light between 6:00 and 9:00 a.m. on school days than on free days. Conversely, they were exposed to more light between 3:00 and 6:00 p.m. on free days than on school days. On free days, participants went to sleep later than on school days but slept longer.
The intensity of light exposure was associated with sleep characteristics. On days when students were exposed to more light, they tended to fall asleep earlier and wake up earlier the following morning. For every 100 additional lux (a measure of light brightness) they were exposed to during the day, participants fell asleep about eight minutes earlier and woke up about seven minutes earlier than usual. The association was even stronger with exposure to light in the early part of the day. However, increased exposure to light during nighttime intervals was associated with falling asleep later than usual.
“The main findings were: (1) the average sleep time of the sample was insufficient, even on free days; (2) greater daily light exposure, in all analyzed intervals, was associated with earlier onset of subsequent sleep episodes, and the same effect was observed with increased minutes exposed to intense light; (3) higher exposure to light during early daytime affected sleep offset.”, study authors concluded.
The study contributes to the scientific understanding of adolescent sleep-wake patterns. However, it should be noted that the study’s design does not allow for causal inferences to be drawn from the results. It is entirely possible that the observed sleep pattern changes are not driven by light exposure itself but rather by the activities students engage in, which influence the amount of light they receive.
The paper, “(https://doi.org/10.1111/jsr.14315) Associations between real-life light exposure patterns and sleep behaviour in adolescents,” was authored by Luísa da Costa Lopes, Julia Ribeiro da Silva Vallim, Sergio Tufik, Fernando Louzada, and Vânia D’Almeida.

Forwarded by:
Michael Reeder LCPC
Baltimore, MD

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