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(https://www.psypost.org/birth-control-pill-may-curb-womens-competitive-drive/) Birth control pill may curb women’s competitive drive
Feb 25th 2025, 08:00

A new study has shed light on relationships between hormones, fertility, and competitive behavior in women. Researchers discovered that while natural fluctuations in fertility across the menstrual cycle do not appear to influence a woman’s competitive motivation or actions, the use of hormonal contraceptives is associated with a reduced interest in competition. This finding, published in (https://doi.org/10.1016/j.evolhumbehav.2024.106616) Evolution and Human Behavior, raises important questions about the wider effects of hormonal birth control beyond pregnancy prevention.
Scientists have long been interested in how hormones shape human behavior, particularly in areas related to reproduction and survival. Competition, in its broadest sense, is a fundamental aspect of life, occurring whenever individuals vie for limited resources. From an evolutionary perspective, humans have developed biological and behavioral systems that help them succeed in competitive environments, ultimately increasing their chances of survival and passing on their genes.
Previous research has explored whether the natural hormone changes that occur throughout a woman’s menstrual cycle are linked to shifts in competitive drive. However, the results of these studies have been inconsistent, leaving scientists unsure about the true relationship between ovarian hormones and a woman’s desire to compete and engage in competitive actions. This lack of clarity prompted researchers to conduct a new, more in-depth investigation into this area.
“I’ve always been interested in understanding how signals from the body can shape psychological experiences and behavior,” said study author Lindsie Catherine Arthur, a PhD candidate at The University of Melbourne.
“Competitive motivation is a fundamental aspect of many areas of life, from sports to careers, yet we often overlook the role that hormonal fluctuations might play in these dynamics. Because the menstrual cycle and hormonal contraceptives appear to influence a wide range of psychological processes (such as mood and sexual desire), I wanted to explore how they might impact competitiveness—something that is critical for success in various domains.”
The researchers conducted a detailed study using an online diary method. They recruited 302 women, with an average age of 22, from both a university student pool and the wider community. Participants had to be fluent in English, have regular menstrual cycles, and be confident in their knowledge of their cycle length. Women were excluded if they had certain medical conditions affecting fertility or hormone levels, or if they had recently used emergency contraception, were breastfeeding, or pregnant.
Of the participants, 231 were naturally cycling, meaning they were not using hormonal birth control, while 71 were using hormonal contraceptives, such as birth control pills or hormonal intrauterine devices. The group of hormonal contraceptive users included women using various types, including different generations of oral pills and hormonal intrauterine devices. The demographic makeup of the participants was diverse in terms of sexuality, relationship status, and ethnicity, reflecting a broad range of backgrounds.
The study was designed as a longitudinal diary study, meaning that the same women were tracked over time, providing daily reports. Participants completed a baseline survey to gather information about their demographics and menstrual cycles. Then, for at least 28 days, they filled out a daily survey at 5 pm each day. These surveys asked about their current menstrual cycle status, as well as their feelings and behaviors related to competition, and other psychological factors.
After the initial 28-day period, women could choose to continue participating for another 28 days. To encourage participation, some women received course credit, while others volunteered in exchange for a personalized report about their menstrual cycle patterns. Ten days after their final survey, participants were asked to complete a follow-up survey to record the start date of their next menstrual period.
To measure competitive motivation, the researchers used a well-established questionnaire that assesses different aspects of competitiveness. They focused on two specific aspects: achievement-oriented competitiveness, which reflects a desire to do well and excel, and lack of interest in competition, which indicates a general disinterest in competitive situations. Participants rated their agreement with statements like “Competitive situations allowed me to bring out the best of myself” (for achievement-oriented competitiveness) and “I didn’t care about being the best” (for lack of interest in competition) each day on a five-point scale.
To capture competitive behavior, the researchers developed a new set of questions asking women to report daily on whether they had engaged in certain behaviors that can be related to competition for mates and status. These behaviors included gossiping about someone, spending time on appearance enhancement (like hair, makeup, and clothes), taking selfies, giving advice to others, comparing themselves to others, and looking for negative qualities in others. Participants rated how much they had engaged in each of these behaviors each day on a five-point scale. The researchers found that these six behaviors tended to occur together, suggesting they could be grouped into a single measure of competitive behavior.
Finally, to understand the role of fertility, the researchers estimated each woman’s daily probability of being fertile based on her reported menstrual cycle dates. They used a method called “backward counting,” which calculates fertility probability based on the estimated or reported start date of menstruation. This allowed them to track changes in fertility probability across the menstrual cycle for both naturally cycling women and those using hormonal contraceptives. Importantly, fertility probability was estimated for all participants, even those on hormonal birth control, to allow for a direct comparison between the groups and to see if any fertility-related patterns were unique to naturally cycling women.
Contrary to what they initially expected, the researchers found no link between a woman’s daily fertility probability and her self-reported competitive motivation or competitive behaviors. This means that for naturally cycling women, their reported desire to achieve or their engagement in competitive actions did not change depending on where they were in their menstrual cycle and how likely they were to be fertile.
However, the study did confirm previous research showing a difference between women who use hormonal contraceptives and those who do not. The researchers found that women using hormonal contraceptives consistently reported a greater lack of interest in competition compared to naturally cycling women. This suggests that hormonal birth control might be associated with a general dampening of competitive drive. This effect was not influenced by whether a woman was in a relationship or not.
Interestingly, when relationship status was considered in the analysis, hormonal contraceptive users also reported engaging in slightly more competitive behaviors overall, although this finding was less robust and requires further investigation. However, the main finding of reduced interest in competition among hormonal contraceptive users remained consistent.
“The main takeaway is that hormones are an important but often invisible factor in motivation and behavior,” Arthur told PsyPost. “We found no evidence that competitive motivation fluctuates across the menstrual cycle, challenging the idea that fertility boosts a woman’s competitiveness. However, we did observe that women using hormonal contraceptives reported generally lower interest in competition compared to non-users.”
“This raises important questions about how hormonal contraceptives influence motivation and behavior beyond their primary function of preventing pregnancy. While more research is needed, these findings highlight the need for research to evaluate the broader psychological and social effects of hormonal contraceptive use.”
The researchers acknowledge some limitations to their study. Diary studies, while useful for tracking changes over time, may not fully capture the complex social context in which competitive behaviors occur. Additionally, the study relied on women’s self-reports.
“While diary studies provide rich longitudinal data, self-reporting can be influenced by factors like social desirability bias or individual differences in interpreting survey questions,” Arthur said. “Additionally, while we accounted for cycle phase using a well-established fertility probability estimation method, we did not measure hormone levels directly.”
Despite these limitations, the study provides valuable insights into the relationship between hormones, fertility, and competition. The lack of connection between fertility probability and competitive motivation in naturally cycling women challenges some existing theories about hormonal influences on female competition. The consistent finding that hormonal contraceptive use is linked to a reduced interest in competition highlights the potential for these medications to have broader psychological effects that warrant further investigation. Future research should explore the underlying mechanisms behind this effect, perhaps by examining how hormonal contraceptives affect specific brain processes related to motivation and reward.
The study, “(https://doi.org/10.1016/j.evolhumbehav.2024.106616) Hormonal contraceptive use, not menstrual cycle phase, is associated with reduced interest in competition,” was authored by Lindsie Catherine Arthur, Brock Bastian, and Khandis Rose Blake.

(https://www.psypost.org/neuroscientists-discover-distinct-brain-circuit-that-drives-risk-preference/) Neuroscientists discover distinct brain circuit that drives risk preference
Feb 25th 2025, 06:00

Navigating uncertain situations is a fundamental part of life for all animals. When faced with a choice between a predictable outcome and a gamble, individuals often consistently lean towards either safety or risk.
A new study published in (https://doi.org/10.1038/s41593-024-01856-4) Nature Neuroscience reveals how a tiny brain region known as the lateral habenula may guide our decisions when facing risk. In a series of experiments using mice, researchers discovered that specific neurons in this area show unique activity patterns that predict whether an animal will choose a safe or a risky option—even before the animal makes its move. The findings suggest that the lateral habenula works together with parts of the hypothalamus to influence decisions based on individual risk preference.
Understanding how the brain makes decisions when faced with uncertainty is a long-standing question in neuroscience. Animals, including humans, are constantly making choices where the outcomes are not guaranteed. For example, an animal might choose to eat a familiar food source that always provides a small amount of food, or it might venture to a new location that could potentially offer a much larger meal, but also carries the risk of finding nothing at all, or even encountering danger.
Scientists have observed that when given such choices between a sure thing and a gamble, most individuals consistently favor one approach over the other. Some are naturally more cautious and prefer the safe option, while others are more adventurous and favor the risky choice. This preference is not random; it is a stable characteristic of an individual.
While we know that brain regions involved in value and motivation are likely involved, the precise brain mechanisms that underpin this consistent risk preference have remained unclear. One brain region that has drawn attention in this context is the lateral habenula. This small but important area is known to be involved in processing value information and has been linked to negative emotions and aversion.
Previous research hinted at its role in economic decision making, as inactivating the lateral habenula in rats disrupted their ability to make advantageous choices in risky situations. However, it was unknown exactly what kind of information the lateral habenula processes during risky decision making, and which brain connections are important for this function. The new study aimed to address these gaps in our knowledge by examining the activity of the lateral habenula and its connections during a risk-based choice task in mice.
“When I started my PhD in 2017, I came across a study which had been published three years earlier investigating risky decision-making in rats. It showed that when rats are faced with making a decision between a safe (1 food pellet) and a risky (4 or 0 food pellet with changing probability ratios) option they adapt their choices based on the maximum reward likelihood,” explained study authors Dominik Groos and Fritjof Helmchen of the University of Zurich.
“But when a tiny brain region called the lateral habenula was pharmacologically inactivated, the rats completely lost the ability to make favorable decisions and chose at random instead. However, what information is encoded in the lateral habenula during risky decision-making, how this information is implemented and what synaptic inputs make relevant contributions have remained unclear. After implementing a similar task for head-fixed mice, I started addressing these questions during my PhD.”
To investigate the brain basis of risk preference, the researchers designed a task where mice could choose between two options: a “safe” option that always provided a medium-sized sugary reward, and a “risky” option that offered either a large sugary reward or a very small reward, each with a certain probability. Importantly, the average reward value was the same for both options, meaning neither option was inherently better in terms of total reward over time.
The mice were trained to perform this task while their heads were gently held still, allowing for precise monitoring of brain activity. Over several days of training, the researchers observed that individual mice developed stable preferences. Some mice consistently chose the safe option, becoming classified as “risk-averse,” while others predominantly selected the risky option, becoming “risk-prone.” A small group of mice showed no strong preference, categorized as “risk-neutral.”
These preferences remained consistent even when the locations of the safe and risky options were swapped, indicating that the mice had truly learned to associate each option with its respective reward profile, rather than just preferring a specific location.
“Mice engage in risky decision-making and show distinct risk preferences,” Groos and Helmchen told PsyPost. “The majority (about 2/3) of animals is risk averse, meaning that they strongly prefer the safe over the risky option, while fewer animals (less than 1/3) are risk prone, preferring the risky option, and very few frequently switch between options across testing sessions (risk neutral individuals). This individual preference was stable across the entire testing period (multiple weeks). Stable risk preferences and a prominent risk aversion in the majority of individuals is also observed in humans.”
To understand what was happening in the brain during these choices, the researchers used a variety of advanced techniques. First, they used a method called two-photon calcium imaging to monitor the activity of individual neurons in the lateral habenula over extended periods. This allowed them to track how the activity of specific brain cells changed as the mice were making their decisions. They found that the activity of many neurons in the lateral habenula changed just before the mice made a choice, during what the researchers called the “deliberation period.”
These activity patterns were different depending on whether the mouse was about to choose its preferred option (safe for risk-averse mice, risky for risk-prone mice) or its less preferred option. Some neurons showed higher activity when the mouse was about to choose its preferred option, while others showed lower activity. These neurons were termed “risk-preference selective cells,” indicating that their activity reflected the individual risk preference of the mouse even before the action was taken.
To understand where this risk-preference information in the lateral habenula might be coming from, the researchers investigated the brain regions that connect to it. They used a technique called anatomical tracing to map the inputs to the lateral habenula across the entire brain. This revealed that several brain regions project to the lateral habenula, including areas involved in motivation and decision-making like the orbitofrontal cortex, prefrontal cortex, ventral pallidum, and hypothalamus. However, when they examined which of these connections were functionally relevant during the risk-taking task, they found a particularly strong link to the hypothalamus, specifically the medial hypothalamus.
Using multi-fiber photometry, a technique that measures the combined activity of a population of neurons, the researchers simultaneously recorded activity in the lateral habenula and several of its input regions as the mice performed the risk-choice task. They found that the activity in the medial and lateral hypothalamus showed the strongest correlation with activity in the lateral habenula during the deliberation period. Interestingly, while both hypothalamic regions showed a similar degree of overall connection to the lateral habenula, the nature of this connection appeared to differ. The link between the medial hypothalamus and lateral habenula strengthened over the course of the deliberation period, suggesting a dynamic influence.
To directly test the role of hypothalamic inputs to the lateral habenula in risk preference, the researchers used optogenetics. This technique allows for the precise control of neuronal activity using light. They selectively inhibited the activity of neurons in the lateral hypothalamus or medial hypothalamus that project to the lateral habenula.
When they inactivated the projections from the medial hypothalamus to the lateral habenula, the mice became less decisive in their choices and their preference for their usual option was disrupted. However, inactivating the projections from the lateral hypothalamus to the lateral habenula had no such effect on behavior. This indicated that the medial hypothalamus, but not the lateral hypothalamus, is specifically needed for stable risk-preference based decision-making in this task.
Further investigation revealed a surprising difference in how the medial and lateral hypothalamus communicate with the lateral habenula. Using electrophysiological recordings in brain slices, they found that projections from the lateral hypothalamus to the lateral habenula primarily released the excitatory neurotransmitter glutamate. However, projections from the medial hypothalamus released both glutamate and GABA, an inhibitory neurotransmitter.
This suggests that the medial hypothalamus can exert a more complex, fine-tuned control over lateral habenula activity by simultaneously exciting and inhibiting its neurons. This dual signaling might be critical for setting the risk-preference bias observed in the lateral habenula and for ensuring confident decision-making.
“Based on our results, the lateral habenula encodes a general risk preference bias since its activity is augmented for the individually preferred option be it risky (in risk-prone individuals) or safe (in risk-averse individuals) right before a decision is reported,” Groos and Helmchen explained. “During risky decision-making lateral habenula activity is modulated by direct long-range inputs from the medial hypothalamus which can co-release excitatory neurotransmitter glutamate and inhibitory GABA onto lateral habenula neurons.”
“There were three main findings that surprised us. First, augmented lateral habenula activity activity was previously associated with negative motivational value and aversion. Here, increased activity in risk-preference selective lateral habenula neurons seem to have an appetitive, positive value. Second, we tested several long-range synaptic inputs to the lateral habenula but did not expect that the medial hypothalamus but not higher brain areas would have an influence on lateral habenula activity and risky decision-making behavior. Third, we were surprised to learn that medial hypothalamus axons can co-release excitatory neurotransmitter glutamate and inhibitory GABA onto lateral habenula neurons.”
This study has some limitations. It focused on a relatively simple form of economic decision-making in mice. It remains to be seen whether the same brain circuits are involved in more complex forms of decision-making. Future research should explore the role of the lateral habenula and hypothalamus in more intricate decision scenarios and in human risk preference.
“While the lateral habenula and the hypothalamus are evolutionarily highly conserved brain regions it remains to be tested whether they are also involved in human risky decision-making,” Groos and Helmchen noted.
The discovery that the medial hypothalamus and lateral habenula circuit is important for decision confidence opens exciting avenues for research into conditions like depression and addiction. The lateral habenula is known to be hyperactive in depression, and understanding how hypothalamic inputs regulate its activity in decision-making could provide valuable insights into the brain mechanisms underlying these disorders and potentially lead to new therapeutic strategies.
“The long-term goals are to better understand how these regions are involved in value-based decision-making in general and whether this can be used to ameliorate human suffering as the lateral habenula is a brain region prominently involved in major depressive disorder and substance abuse disorders,” the researchers said.
The study, “(https://doi.org/10.1038/s41593-024-01856-4) A distinct hypothalamus–habenula circuit governs risk preference,” was authored by Dominik Groos, Anna Maria Reus, Peter Rupprecht, Tevye Stachniak, Christopher Lewis, Shuting Han, Adrian Roggenbach, Oliver Sturman, Yaroslav Sych, Martin Wieckhorst, Johannes Bohacek, Theofanis Karayannis, Adriano Aguzzi, and Fritjof Helmchen.

(https://www.psypost.org/harsh-parenting-in-childhood-linked-to-dark-personality-traits-in-adulthood-study-finds/) Harsh parenting in childhood linked to dark personality traits in adulthood, study finds
Feb 24th 2025, 16:00

A recent study published in the journal (https://doi.org/10.1016/j.paid.2025.113089) Personality and Individual Differences has a connection between the way parents discipline their children and the development of undesirable personality traits later in life. The findings indicate that experiencing harsh parenting, particularly psychological aggression and severe physical assault, during childhood is associated with a higher likelihood of exhibiting traits from the Dark Tetrad – a group of personality characteristics that include narcissism, Machiavellianism, psychopathy, and sadism – in adulthood.
The Dark Tetrad is a concept in psychology that brings together four distinct but related personality traits that are considered socially aversive. Narcissism is characterized by an inflated sense of self-importance and a need for admiration. Machiavellianism involves manipulation and exploitation of others for personal gain. Psychopathy is marked by a lack of empathy, impulsivity, and antisocial behavior. Sadism is the tendency to derive pleasure from inflicting pain or suffering on others. These traits, while existing on a spectrum in the general population, are often associated with negative interpersonal outcomes and can be detrimental to both individuals exhibiting them and those around them.
Prior research has established a connection between negative experiences in childhood and antisocial tendencies in adulthood. Scientists are working to understand the specific mechanisms that explain this link. One area of focus is the role of personality development. It is thought that early life experiences, particularly those within the family, can shape an individual’s personality in ways that either increase or decrease the likelihood of developing Dark Tetrad traits. 
Some theories suggest that individuals growing up in harsh or unpredictable environments may develop certain personality traits as a way to adapt and survive. These adaptive strategies, while potentially helpful in challenging childhood contexts, might manifest as Dark Tetrad traits in adulthood. For example, manipulation and a focus on self-interest (Machiavellianism) could be seen as ways to navigate an unstable home life. Similarly, a lack of empathy and impulsivity (psychopathy) might develop as a response to consistent maltreatment. 
While genetic factors are known to play a role in personality, environmental influences, especially parenting styles, are considered significant contributors to the development of these traits. The new study aimed to explore specifically how different types of parental discipline, ranging from non-violent methods to severe aggression, relate to the Dark Tetrad traits in adulthood. 
“Our interest in this topic stems from the extensive literature linking childhood adversity to antisocial behaviors in adulthood,” said study author David Pineda, an assistant professor and director of the Forensic Psychology Unit at Miguel Hernández University of Elche.
“While many studies have explored how adverse experiences impact mental health and emotional regulation, fewer have focused on their role in shaping dark personality traits such as Machiavellianism, psychopathy, narcissism, and sadism. Understanding the early environmental factors that contribute to these traits provides valuable insight into personality development and may help inform interventions aimed at mitigating negative long-term outcomes.”
For their study, the researchers recruited 370 adult participants from Spain. The participants, whose ages ranged from 18 to 80, were primarily recruited through social media platforms like Instagram, Facebook, and LinkedIn. The majority of participants were women (nearly 74%), and a large portion were university students or graduates. Participants were asked to complete an online survey that included several questionnaires. To measure narcissism, Machiavellianism, and psychopathy, the researchers used a shortened version of the Dark Triad scale. 
This questionnaire contains statements like “People see me as a natural leader” (narcissism), “Most people can be manipulated” (Machiavellianism), and “People often say I’m out of control” (psychopathy). Participants rated how much they agreed with these statements on a scale from “totally disagree” to “totally agree.” Sadism was assessed using a separate questionnaire specifically designed to measure everyday sadism. This scale included items such as “I think about hurting people who irritate me,” which participants also rated on a similar agreement scale.
To assess childhood experiences with parental discipline, the researchers used the Parent-Child Conflict Tactics Scales. This questionnaire asks participants to recall how often their parents used different discipline tactics when they were children. The questionnaire was slightly modified to focus on the frequency of these tactics. The discipline tactics were categorized into four types: nonviolent discipline (like explaining why something was wrong), psychological aggression (including shouting, yelling, or screaming), corporal punishment or minor assault (such as spanking with a hand), and severe assault (like grabbing the neck and choking). 
Participants indicated how often these things happened to them on a scale from “This never happened” to “More than 20 times.” After collecting the data, the researchers used statistical analyses to examine the relationships between the different types of parental discipline and the Dark Tetrad traits. They looked at correlations to see if there were general associations and regression analyses to determine if specific discipline tactics uniquely predicted each Dark Tetrad trait, even when considering factors like age and gender.
The study’s findings revealed significant positive correlations between all types of parental discipline tactics and each of the Dark Tetrad traits. This means that individuals who reported experiencing any of these discipline methods as children tended to score higher on measures of narcissism, Machiavellianism, psychopathy, and sadism in adulthood. 
However, the regression analyses provided a more nuanced picture. When considering the unique contribution of each discipline tactic, psychological aggression emerged as a significant and unique predictor of both psychopathy and sadism. This suggests that experiencing parental psychological aggression in childhood is particularly linked to the development of psychopathic and sadistic traits in adulthood. 
Severe assault, on the other hand, was found to be a unique and positive predictor of Machiavellianism, narcissism, and psychopathy. This indicates that experiencing severe physical violence from parents is specifically associated with higher levels of Machiavellianism, narcissism, and, to a lesser extent, psychopathy. 
“One surprising aspect of our findings was the differential impact of various types of parental discipline tactics on dark personality traits,” Pineda told PsyPost. “While we expected psychological aggression to be associated with Machiavellianism, our results showed that severe physical aggression played a more significant role. This suggests that extreme parental behaviors may contribute differently to personality development, reinforcing the need for nuanced research on how specific adverse experiences shape different aspects of dark personality traits.”
Interestingly, nonviolent discipline and corporal punishment or minor assault did not significantly predict any of the Dark Tetrad traits when considered alongside the more severe forms of aggression. In essence, while all forms of harsh parenting showed some association with Dark Tetrad traits, psychological aggression and severe assault were the most impactful and uniquely related to specific traits within the Dark Tetrad.
“Our study highlights that childhood experiences, particularly exposure to severe parental discipline tactics, can play a role in shaping dark personality traits,” Pineda explained. “Psychological aggression was found to be particularly linked to psychopathy and sadism, while severe physical assault was associated with Machiavellianism, narcissism, and psychopathy. These findings emphasize the importance of fostering supportive and non-violent parenting environments, as early-life adversities can leave lasting imprints on personality development and social behavior.”
But the study, like all research, has some limitations.
“First, our study is cross-sectional, meaning that we cannot establish causality between childhood experiences and dark personality traits—longitudinal studies would be necessary to confirm these relationships over time,” Pineda noted. “Additionally, we relied on retrospective self-reports, which may be subject to memory biases. Finally, while our sample was relatively diverse, future research should include broader demographic and cultural representations to ensure generalizability.”
Looking ahead, future research could explore how genetic predispositions, cultural factors, and broader social environments interact with parental discipline to shape personality development. Researchers could also investigate how factors like social support, personal resilience, or therapeutic interventions might buffer the negative effects of adverse childhood experiences and prevent the development of these undesirable personality traits. 
“We aim to further investigate the mechanisms linking childhood experiences to dark personality traits, particularly through longitudinal designs that track personality development over time,” Pineda said. “Additionally, we are interested in exploring potential protective factors—such as social support, resilience, or therapeutic interventions—that might buffer the negative effects of adverse childhood environments. Ultimately, our goal is to contribute to the development of prevention and intervention strategies that promote healthier personality development.”
“Our study underscores the need for early interventions in families where psychological or physical aggression is present. Addressing negative parenting practices at an early stage may help prevent the development of maladaptive personality traits that could later manifest in antisocial behaviors. We hope that this research encourages further studies on the role of early-life experiences in shaping personality and social functioning.”
The study, “(https://doi.org/10.1016/j.paid.2025.113089) Dark childhood, dark personality: Relations between experiences of child abuse and dark tetrad traits,” was authored by Manuel Galán, David Pineda, Pilar Rico-Bordera, Jose A. Piqueras, and Peter Muris.

(https://www.psypost.org/can-your-brainwaves-reveal-if-youre-falling-for-fake-news-ai-is-trying-to-find-out/) Can your brainwaves reveal if you’re falling for fake news? AI is trying to find out
Feb 24th 2025, 14:00

In the ambitious pursuit to tackle the harms from false content on (https://link.springer.com/content/pdf/10.1007/s13278-023-01028-5.pdf) social media and (https://www.sciencedirect.com/science/article/pii/S266682702100013X) news websites, data scientists are getting creative.
While still in their training wheels, the (https://doi.org/10.1038/s42256-024-00881-z) large language models (LLMs) used to create chatbots like ChatGPT are being recruited to spot (https://doi.org/10.3390/fi16080298) fake news. With better detection, AI fake news checking systems may be able to warn of, and ultimately counteract, serious harms from (https://arxiv.org/pdf/2102.04458) deepfakes, (https://dl.acm.org/doi/full/10.1145/3613904.3642805) propaganda, (https://ieeexplore.ieee.org/abstract/document/9750122) conspiracy theories and (https://doi.org/10.1007/s11042-023-17470-8) misinformation.
The next level AI tools will personalise detection of false content as well as protecting us against it. For this ultimate leap into user-centered AI, data science needs to look to behavioural and neuroscience.
Recent work suggests we might (https://doi.org/10.1016/j.chb.2020.106633) not always consciously know that we are encountering fake news. Neuroscience is helping to discover what is going on unconsciously. Biomarkers such as (https://ieeexplore.ieee.org/abstract/document/9304909) heart rate, (https://dl.acm.org/doi/abs/10.1145/3382507.3418857) eye movements and (https://ieeexplore.ieee.org/abstract/document/9277701) brain activity) appear to subtly change in response to fake and real content. In other words, these biomarkers may be “tells” that indicate if we have been taken in or not.
For instance, when humans look at faces, eye-tracking data shows that we scan for rates of blinking and (https://doi.org/10.1016/j.jvcir.2024.104263) changes in skin colour caused by blood flow. If such elements seem unnatural, it can help us decide that we’re looking at a deepfake. This knowledge can give AI an edge – we can train it to mimic what humans look for, among other things.
The personalisation of an AI fake news checker takes shape by using findings from (https://dl.acm.org/doi/abs/10.1145/3382507.3418857) human eye movement data and (https://ieeexplore.ieee.org/abstract/document/9277701) electrical brain activity that shows what types of false content has the greatest impact neurally, psychologically and emotionally, (https://doi.org/10.1016/j.chb.2022.107307) and for whom.
Knowing our specific interests, personality and (https://doi.org/10.1080/0960085X.2023.2224973) emotional reactions, an AI fact-checking system could detect and anticipate which content would trigger the most severe reaction in us. This could help establish when people are taken in and what sort of material fools people the easiest.
Counteracting harms
What comes next is customising the safeguards. Protecting us from the harms of fake news also requires building systems that could intervene – some sort of (https://doi.org/10.1027/1864-1105/a000407) digital countermeasure to fake news. There are several ways to do this such as warning labels, links to expert-validated credible content and even asking people to try to consider different perspectives when they read something.
Our own personalised AI fake news checker could be designed to give each of us one of these countermeasures (https://journals.sagepub.com/doi/full/10.1177/1529100620946707) to cancel out the harms from false content online.
Such technology is already being trialled. Researchers in the US have studied how people interact with (https://dl.acm.org/doi/pdf/10.1145/3544548.3581219) a personalised AI fake news checker of social media posts. It learned to reduce the number of posts in a news feed to those it deemed true. (https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2019.00011/full) As a proof of concept, another study using social media posts tailored additional news content to each media post to encourage users to view alternative perspectives.
Accurate detection of fake news
But whether this all sounds impressive or dystopian, before we get carried away it might be worth asking some basic questions.
Much, if not all, of the work on (https://journals.sagepub.com/doi/pdf/10.1177/20563051221150412) fake news, deepfakes, disinformation and (https://journals.sagepub.com/doi/pdf/10.1177/17456916221141344) misinformation  highlights the same problem that any lie detector would face.
There are many types of lie detectors, not just the polygraph test. Some exclusively depend on linguistic analysis. Others are systems designed to read people’s faces to detect if they are leaking micro-emotions that give away that they are lying. By the same token, there are AI systems that are designed to detect if a face is genuine or a deep fake.
Before the detection begins, we all need to agree on what a lie looks like if we are to spot it. In fact, in (https://doi.org/10.1177/09637214231173095) deception research shows it can be easier because you can instruct people when to lie and when tell the truth. And so you have some way of knowing the ground truth before you (https://doi.org/10.1080/00909880305377) train a human or a (https://doi.org/10.1016/j.actpsy.2020.103250) machine to tell the difference, because they are provided with examples on which to base their judgements.
Knowing how good an expert lie detector is depends on how often they call out a lie when there was one (hit). But also, that they don’t frequently mistake someone as telling the truth when they were in fact lying (miss). This means they need to know what the truth is when they see it (correct rejection) and don’t accuse someone of lying when they were telling the truth (false alarm). What this refers to is signal detection, and the same logic applies to (https://doi.org/10.1177/1745691620986135) fake news detection which you can see in the diagram below.
For an AI system detecting fake news, to be super accurate, the hits need to be really high (say 90%) and so the misses will be very low (say 10%), and the false alarms need to stay low (say 10%) which means real news isn’t called fake. If an AI fact-checking system, or a human one is recommended to us, based on signal detection, we can better understand how good it is.
There are likely to be cases, as has been reported in a recent (https://www.mdpi.com/2673-5172/5/2/50/pdf) survey, where the news content may not be completely false or completely true, but partially accurate. We know this because the speed of news cycles means that what is considered accurate at one time, may later (https://doi.org/10.1080/13669877.2022.2049623) be found to be inaccurate, or vice versa. So, a fake news checking system has its work cut out.
If we knew in advance what was faked and what was real news, how accurate are biomarkers at indicating unconsciously which is which? The answer is not very. Neural activity (https://ieeexplore.ieee.org/iel7/9851848/9851959/09851990.pdf?casa_token=M5v1Y02PojMAAAAA:vcoUqhoCXi8F9R0cyq49HEAvMpWjFw6UND5vMTrR2TQ8NSgRobKeUT-7GvUZlVo4r_DHSFmYzA) is most often the same when we come across real and fake news articles.
When it comes to eye-tracking studies, it is worth knowing that there are different types of data collected from eye-tracking techniques (for example the length of time our eye fix on an object, the frequency that our eye moves across a visual scene).
So depending on what is analysed, some studies show that (https://dl.acm.org/doi/pdf/10.1145/3517031.3529619?casa_token=H_djGz0jSMUAAAAA:qOJuvnWT1ER05kzEYreuK1YC2hDzsF0SdyHtDdeS3pRxOA4L5vReqXHpLBSfRO2_v1JYWpBIBnWUBw) we direct more attention when viewing false content, while others show the (https://dl.acm.org/doi/pdf/10.1145/3397271.3401221?casa_token=yuYm20sEGgEAAAAA:LxvBqml_pS0hi8ojlM7vLdITFGJvSrOwsOm56_zyudAll89DKUGzmLA4y1lrQW7GD1yWOUF_7US5TQ) opposite.
Are we there yet?
AI fake news detection systems on the market are already using insights from behavioural science to help (https://doi.org/10.1111/jasp.12959) flag and warn us against fake news  content. So it won’t be a stretch for the same AI systems to start appearing in our news feeds with customised protections for our unique user profile. The problem with all this is we still have a lot of basic ground to cover in knowing what is working, but also checking (https://doi.org/10.48550/arXiv.2308.10800) whether we want this.
In the worst case scenario, we only see fake news as a problem online as an excuse to solve it using (https://books.google.co.uk/books/about/Smart_Until_It_s_Dumb.html?id=rfuizwEACAAJ&redir_esc=y) AI. But false and inaccurate content is everywhere, and gets discussed (https://www.csap.cam.ac.uk/media/uploads/files/1/offline-vs-online-sharing.pdf) offline. Not only that, we don’t by default believe all fake news, some times we use it in discussions to (https://doi.org/10.3390/journalmedia5020050) illustrate bad ideas.
In an imagined best case scenario, data science and behavioural science is confident about the scale of the various harms fake news might cause. But, even here, AI applications combined with scientific wizardry might still be very poor substitutes for less sophisticated but more effective solutions.
 
This article is republished from (https://theconversation.com) The Conversation under a Creative Commons license. Read the (https://theconversation.com/how-close-are-we-to-an-accurate-ai-fake-news-detector-242309) original article.

(https://www.psypost.org/the-ups-and-downs-of-open-relationships-new-research-on-consensual-non-monogamy/) The ups and downs of open relationships: New research on consensual non-monogamy
Feb 24th 2025, 12:00

A study published in the (https://link.springer.com/article/10.1007/s10508-024-02823-7) Archives of Sexual Behavior has discovered that people in consensually nonmonogamous relationships experience both challenges and rewards from their partners’ other romantic relationships. While some struggle with jealousy and time constraints, many report emotional growth, deeper connections, and even friendships with their partners’ other partners.
Unlike traditional monogamy, where exclusivity is expected, consensual nonmonogamy allows for multiple romantic or sexual relationships with everyone’s consent. While this structure challenges societal norms, it has been growing in visibility.
Researchers Jennifer Arter and Sacha S. Bunge aimed to better understand both the costs and benefits of being in a consensual nonmonogamy relationship.
The study involved 51 adults, with an average age of 37 years old, who had experience in consensual nonmonogamy relationships (3 to 50 years). Participants were recruited through word-of-mouth and snowball sampling within consensual nonmonogamy communities. They took part in interviews that lasted between 48 and 109 minutes, in which they discussed their experiences, feelings, and interactions with metamours.
The researchers analyzed the interviews using a qualitative method called reflexive thematic analysis, which allowed them to identify patterns in the responses. Overall, nearly all participants were found to have experienced both positive and negative effects from their partners’ other relationships.
Many participants reported struggling with difficult emotions such as jealousy and insecurity. They often worried that their partner’s other relationship was more fulfilling, or that their metamour was more attractive or interesting than them.
Several participants expressed frustration over having less time with their partner due to their other relationships. Some felt left out when their partner and metamour engaged in activities they wanted to be part of.
In some cases, a partner’s other relationship led to tension or even changes in the primary relationship. Some participants found themselves offering emotional support when their partner experienced difficulties with a metamour, which could become emotionally exhausting over time.
Difficulties related to metamour interactions were also reported, where several participants had negative experiences with metamours, describing difficulties in communication, differences in values, or even outright hostility.
However, many participants reported experiencing “compersion”- a feeling of happiness when seeing their partner enjoy another relationship. For some, this joy was general, coming from their partner’s overall well-being. Others described a more personal sense of pleasure when hearing about or witnessing their partner’s interactions with a metamour.
Several participants noted that their partners’ other relationships had unexpected benefits for their own relationships. When a partner was fulfilled by another relationship, they often brought that positive energy back into their existing relationship.
Other participants found that consensual non-monogamy allowed them to focus more on their personal interests. When their partner spent time with someone else, they had opportunities for solitude, hobbies, or other social connections.
Finally, many participants also reported forming meaningful friendships or support networks with their metamours.
The authors noted one striking conclusion from their results: “the costs … and benefits can be broken down into pairs that appear to be, in a general sense, mirror images. Thus, feelings about sharing partners could be painful (e.g., jealousy) or pleasurable (i.e., compersion); one could “miss out” on time/activities with a partner or could benefit from partners getting needs met in other relationships [etc.]”.
While the study provides valuable insights, the researchers note some limitations. For instance, the participants were mostly individuals who actively interact with their metamours, so their experiences may not reflect those of people who prefer to keep their relationships more separate.
The study, “(https://doi.org/10.1007/s10508-024-02823-7) Perceived Impacts of Partners’ Other Relationships on Oneself in Consensual Nonmonogamy,” was authored by Jennifer Arter and Sacha S. Bunge.

(https://www.psypost.org/in-group-bias-speeds-up-happy-face-detection/) In-group bias speeds up happy face detection
Feb 24th 2025, 10:00

People tend to recognize happy faces faster when the person in the image belongs to their own social group or a majority group. This research was published in (https://doi.org/10.1177/01461672241310917) Personality & Social Psychology Bulletin.
Douglas Martin and colleagues investigated whether the happy face advantage—the tendency to recognize happy faces more quickly than other expressions—varies based on the perceiver’s and target’s social categories. Previous research has consistently observed this effect, though its strength appears to differ across racial and gender groups.
Some studies report a strong happy face advantage for White faces, whereas findings for Black and male faces have been mixed. These inconsistencies suggest that the effect is not universal but shaped by intergroup relationships.
Building on social categorization theories, which propose that people process emotions differently depending on whether a face belongs to their ingroup or outgroup, the researchers tested whether the happy face advantage is a flexible, context-dependent phenomenon influenced by racial and gender dynamics.
The researchers conducted five experiments to examine how social category dynamics influence the happy face advantage—the tendency for happy faces to be categorized more quickly than angry ones. Participants from diverse racial backgrounds (White, Black, and Chinese) were recruited via Prolific Academic and completed a speeded emotion categorization task. They identified the emotional expression (happy or angry) of faces from different racial and gender groups as quickly and accurately as possible. The target faces, drawn from the Chicago Face Database and the Tsinghua Facial Expression Database, ensured standardized representation of race and gender.
In Experiment 1, 197 Chinese and White participants categorized happy and angry faces from both racial groups, equally split between male and female targets. This study tested whether people process happy faces more quickly when the target belongs to their racial or gender ingroup.
In Experiment 2, the researchers examined how the absence of a racial majority group (White) would influence the happy face advantage. They recruited 204 Black and Chinese participants and presented them only with Black and Chinese faces, allowing the study to isolate ingroup favoritism without the influence of a dominant racial category.
Experiments 3a-3c tested whether, in the absence of ingroup faces, participants would still recognize happy expressions more quickly in majority outgroup members. In Experiment 3a, 95 Chinese participants categorized happy and angry faces of White and Black individuals. In Experiment 3b, 103 Black participants categorized White and Chinese faces. In Experiment 3c, 103 White participants categorized Black and Chinese faces. These studies explored whether the happy face advantage would be stronger for majority outgroup members (i.e., those encountered more frequently in society) compared to minority outgroups.
Each trial began with a fixation cross, followed by a brief presentation of a face (300 milliseconds). Participants categorized the emotion using keyboard keys, and reaction times and accuracy rates were recorded.
The results of Experiment 1 showed that White participants exhibited a strong happy face advantage for White faces but not for Chinese faces. In contrast, Chinese participants showed a happy face advantage for both their ingroup and White faces, suggesting that exposure to majority-group members (White individuals in the UK, where the study was conducted) influences emotion recognition. Female perceivers demonstrated a stronger happy face advantage for female faces, while male perceivers recognized happy faces of both genders equally.
Experiment 2 revealed a clearer pattern of ingroup bias. Both Black and Chinese participants showed a significantly stronger happy face advantage for ingroup members than for outgroup members. Unlike in Experiment 1, where Chinese perceivers recognized happy White faces as easily as happy ingroup faces, they now showed a distinct preference for their ingroup. This suggests that in the absence of a dominant racial group, people rely more on ingroup favoritism when recognizing emotions.
Experiments 3a-3c provided further evidence that exposure to majority groups influences the happy face advantage. In Experiment 3a, Chinese perceivers categorized happy White faces more quickly than happy Black faces, reinforcing the idea that people are more efficient at processing happy expressions from majority-group members. A similar pattern emerged in Experiment 3b, where Black perceivers recognized happy White faces faster than happy Chinese faces. In Experiment 3c, White perceivers exhibited a greater happy face advantage for Black faces than for Chinese faces, likely reflecting greater exposure to Black individuals in the UK compared to Chinese individuals.
Overall, the research demonstrated that the happy face advantage is not a fixed cognitive bias but a flexible, socially driven process.
This research focused on happy and angry expressions, meaning that findings may not generalize to other emotions.
The research “(https://doi.org/10.1177/01461672241310917) Social Category Modulation of the Happy Face Advantage,” was authored by Douglas Martin, Ewan Bottomley, Jacqui Hutchison, Agnieszka E. Konopka, Gillian Williamson, and Rachel Swainson.

Forwarded by:
Michael Reeder LCPC
Baltimore, MD

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