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(https://www.psypost.org/fathers-can-recognize-their-own-child-based-on-body-odor-study-finds/) Fathers can recognize their own child based on body odor, study finds
Sep 26th 2024, 10:00

New research from Germany has found that fathers can identify the body odor of their own children and prefer it over the odor of other prepubescent children. However, their preference for their children’s body odor decreases as the children enter puberty. This decline is especially notable in daughters and becomes more pronounced as daughters progress through puberty. The study was published in (https://www.sciencedirect.com/science/article/pii/S0031938424000507) Physiology & Behavior.
Body odors play a crucial role in human relationships. They can influence how much a person is trusted, how attractive others find them, and can even convey emotional states. Additionally, body odor provides information about a person’s health, hormonal status, and genetic compatibility, often triggering emotional responses in others. This can lead to bonding through pleasant odors or aversion if the odor is unpleasant.
Body odors are particularly significant in parent-child interactions, as they help develop and maintain a loving relationship. Parents can often recognize their offspring (and possibly other relatives) by smell, and tend to find that smell pleasant. Studies show that familiar or pleasant body odors activate the brain’s reward and pleasure networks, while also reducing stress.
However, the perception of body odor appears to change with a child’s age. One study found that mothers could identify the smell of their children before puberty, but once the children reached puberty, mothers could no longer recognize or prefer their child’s body odor. After puberty, mothers once again perceived their children’s odors as pleasant. There is some indication that this reduction in preference with puberty may apply to sons but not to daughters.
Study author Laura Schaefer and her colleagues set out to investigate whether fathers can also identify their children’s body odor at different stages of development, excluding puberty. They hypothesized that fathers would prefer the body odor of their children, but this preference would decrease as their daughters entered puberty, while remaining unchanged for sons.
In other words, the researchers expected that fathers would continue to like their sons’ body odor throughout all developmental stages, but their preference for their daughters’ smell would diminish once the daughters reached puberty. The researchers suggested that this reduction in preference for opposite-sex children’s odor as they mature could be a biological mechanism to avoid incest.
The study involved 56 fathers and 73 of their biological children, with the children’s ages ranging from newborns to 18 years old. The average age of the fathers was 39. Thirty-nine fathers participated with one child, while 17 participated with two children.
The study was conducted in several phases. In the first phase, participants visited the lab, where the researchers tested the similarity of participants’ immune systems, specifically their Human Leukocyte Antigen (HLA) complexes. These proteins, found on the surface of most cells, play an important role in immune response and also influence the composition of sweat, which affects body odor. Fathers also completed assessments of their olfactory abilities (their ability to identify different smells) and depressive symptoms during this visit.
In the next phase, the researchers collected hormonal samples and body odors from the participants. The children were instructed to use an unscented shower gel (provided by the researchers) and then sleep in an experimental shirt also provided by the researchers. The following morning, the fathers collected the shirts and sent them back to the researchers, who then created body odor samples.
In the final phase, fathers were asked to rate the pleasantness, intensity, sweetness, desirability (how much they wanted to smell it again), and attractiveness of the body odor samples in separate experimental runs. In the final run, they were asked to identify which odor sample belonged to their child.
The results showed that fathers were able to recognize their child’s body odor 33% of the time—significantly better than the 17% success rate expected from random guessing. As hypothesized, fathers performed better than chance in identifying their child’s body odor in all age groups except during puberty. HLA similarity was not associated with recognition ability. Fathers rated the odor of their infant and prepubescent children as more pleasant than that of older children. This effect was especially pronounced with daughters, as fathers’ preference for their daughters’ odor sharply declined as they progressed through puberty.
“Similar to mothers, they [fathers] were able to recognize their own child’s body odor across the developmental span, with exception of early puberty. A loss of familiarity perception due to hormonal transitions altering the body odor may account for that observation. Fathers preferred their own child’s odor over unfamiliar odors in pre-pubertal stages of development, but this effect vanished with the onset of puberty. The reduction in preference related to increasing pubertal status in daughters, but not in sons,” the study authors concluded.
The research highlights the role body odor plays in father-child relationships. However, the study involved a relatively small sample of fathers evaluating only six body odors, all from children. The results might differ if the study were conducted with a larger sample and included a wider range of body odors.
The paper, “(https://doi.org/10.1016/j.physbeh.2024.114505) Body odours as putative chemosignals in the father-child relationship: New insights on paternal olfactory kin recognition and preference from infancy to adolescence,” was authored by Laura Schaefer, Agnieszka Sorokowska, Kerstin Weidner, Jürgen Sauter, Alexander H. Schmidt, and Ilona Croy.

(https://www.psypost.org/social-contact-reduces-dementia-risk-in-individuals-with-high-neuroticism/) Social contact reduces dementia risk in individuals with high neuroticism
Sep 26th 2024, 08:00

Active social contact can reduce the increased risk of dementia associated with high neuroticism, according to a new study published in the (https://doi.org/10.1016/j.jad.2024.05.055) Journal of Affective Disorders.
Neuroticism, a personality trait characterized by emotional instability and a heightened stress response, is associated with negative health outcomes, including dementia. Previous studies have shown that individuals with high neuroticism face an elevated risk of developing dementia. As the global population ages, with dementia prevalence expected to triple by 2050, identifying factors that can mitigate this risk has become increasingly important.
The World Health Organization’s 2022 dementia prevention guidelines acknowledge social contact as essential for cognitive health. In this work, Yufei Liu and colleagues investigated whether frequent social contact could lower the heightened dementia risk associated with neuroticism.
The researchers utilized data from the UK Biobank, a large-scale biomedical database. This included 393,939 participants aged between 37 and 73 years, with an average age of 56.4 years. Of these, 53.7% were female. Participants were initially recruited between 2006 and 2010, and data were collected through questionnaires, interviews, and health assessments. Neuroticism was assessed using the Revised Eysenck Personality Questionnaire, which included 12 questions designed to measure emotional instability.
Social contact was evaluated through three measures: household size (whether the participant lived alone or with others), frequency of contact with friends or family (ranging from daily to less frequent interactions), and participation in group activities (such as sports clubs, social groups, or religious organizations). The researchers divided participants into three groups: low, intermediate, and high social contact.
Dementia diagnoses were identified using participants’ electronic health records, which were linked to hospital admissions and death registries. Dementia was classified according to the International Classification of Diseases 10th revision (ICD-10), and specific types of dementia, such as Alzheimer’s disease and vascular dementia, were recorded. Participants were followed over an extended period, with follow-up continuing until December 2022.
The study considered a range of potential confounding factors, such as age, sex, education level, socioeconomic status, and lifestyle choices (e.g., smoking, alcohol consumption, physical activity). Participants’ genetic susceptibility to dementia, specifically the presence of the APOE ε4 allele, was also accounted for. Participants with incomplete neuroticism or social contact data, those diagnosed with dementia at the beginning of the study, or who withdrew from the study were excluded from the final analysis.
Over the study’s follow-up period, which had a median duration of 13.7 years, 6,588 cases of dementia were identified among the participants. Individuals with high neuroticism had a significantly higher risk of developing dementia compared to those with low neuroticism. Specifically, participants with high neuroticism had a 16% increased risk of all-cause dementia and similar elevated risks for Alzheimer’s disease (10%) and vascular dementia (16%).
Social contact played a protective role in mitigating dementia risk, particularly for individuals with high neuroticism. Participants with intermediate levels of social contact had a 15% lower risk of developing dementia compared to those with low social contact, and those with high levels of social contact experienced a 22% reduction in risk. Notably, among individuals with high neuroticism, those with high social contact had dementia risk levels comparable to individuals with low neuroticism.
One limitation is that the study only assessed social contact at baseline, and it is possible that social engagement levels may have changed during the follow-up period.
Overall, these findings suggest that promoting social engagement could be an effective strategy in dementia prevention, especially for those with a predisposition to neuroticism.
The research, “(https://doi.org/10.1016/j.jad.2024.05.055) Neuroticism personality, social contact, and dementia risk: A prospective cohort study,” was authored by Yufei Liu, Jie Chang, Yiwei Zhao, and Yi Tang.

(https://www.psypost.org/brainwave-patterns-and-politics-new-study-uncovers-unexpected-findings/) Brainwave patterns and politics: New study uncovers unexpected findings
Sep 26th 2024, 06:00

Can brain activity predict voting behavior? A new study in (https://doi.org/10.1038/s41598-024-67763-7) Scientific Reports suggests that the answer is more complicated than expected. By measuring the N400 brainwave during exposure to political statements, researchers found differences in how Democratic and Republican voters tend to process information that contradicts their political affiliations. But this brainwave was not a reliable predictor of actual voting behavior.
The researchers aimed to understand the gap between what people consciously express and the subconscious processes that influence their decisions, particularly in voting. To do this, they used a neural measure known as the N400, which is a specific type of event-related potential (ERP). ERPs are patterns of electrical activity in the brain that occur in response to specific stimuli, such as reading a sentence or seeing an image. The N400, in particular, is linked to processing meaning and detecting when something is unexpected or doesn’t align with what we already believe or know.
For example, when a person reads a statement that contradicts their deeply held beliefs, the brain generates a stronger N400 response. By comparing participants’ brain activity when they read political statements that either aligned or conflicted with their political preferences, the researchers sought to determine whether these implicit neural responses could reveal deeper, unconscious preferences and potentially predict how someone would vote.
“I was interested in this topic because we have limited insights into the individual-level processes that lead voters to make up their mind on who to vote for. These processes constitute, to a certain extent, a black box,” said study author Emmanuel Mahieux, who received a PhD in experimental psychology and neuroscience from University College London.
“Even recently, after the U.S. presidential debate, campaigning professionals were running focus groups with undecided voters to understand their reactions to the debate. Although common themes sometimes emerge from such focus groups, they often provide mixed or unclear insights on how people will vote at an upcoming election.”
“Given recent advances in neuroscience, I wanted to see if neural responses to political statements could make better predictions of how voters would vote rather than relying on their responses to political statements. Giulia Galli and her colleagues had developed a fascinating paradigm testing this and I wanted to see if it could be replicated in the U.S. electoral context.”
The study took place in the United States during a highly polarized political period — the 2022 Texas gubernatorial election. Participants were 55 undergraduate students from the University of Texas at San Antonio. These participants were split among those who had decided to vote for the Republican or Democratic candidate, and those who were undecided.
Beginning 25 days before the election, participants’ brain activity was monitored using electroencephalography (EEG) as they read a series of 184 political statements. These statements covered three major issue areas that were important in the election: the economy, immigration, and societal issues like abortion and gun rights. Each statement ended with a conclusion that was either pro-Republican or pro-Democrat.
For instance, one statement might say, “The ownership of automatic assault weapons like AR-15s needs to be restricted,” which would reflect a Democratic stance, while another might conclude, “The ownership of automatic assault weapons like AR-15s needs to be protected,” reflecting a Republican view.
The key was to measure participants’ N400 brain responses to see whether the brain reacted differently to political statements that aligned or conflicted with their stated preferences. Importantly, the participants indicated whether they agreed or disagreed with the political statements they saw, allowing the researchers to compare explicit political preferences with the N400 brainwave response.
As expected, the Democratic voters in the study showed larger N400 brain responses when reading statements that supported Republican views compared to those that supported Democratic views. This aligned with the hypothesis that when people encounter ideas that go against their beliefs, their brains show greater activity in the N400 region.
Unexpectedly, Republican voters showed no such distinction in their brain activity. Their N400 responses to both pro-Republican and pro-Democratic statements were similar, meaning their brain did not react more strongly to statements that opposed their political beliefs. This suggests that, for these voters, there might have been a disconnect between their neural processing and their explicit political beliefs.
“My expectation was that decided Democrats and decided Republicans would present opposite N400 patterns in their neural responses to political statements,” Mahieux told PsyPost. “The surprise was that decided Republicans did not show the expected pattern, as their N400 responses were ambivalent between pro-Democratic and pro-Republican statements. For undecided participants who voted Republican, their N400 response was more similar to the N400 response of decided Democrats than of decided Republicans, although their small sample size (N = 6) is too small to draw conclusions with certainty.”
Among the 55 participants in the study, 31 ended up voting for the Democratic candidate for governor, while 24 voted for the Republican candidate. When the researchers looked at the overall predictive power of the N400, they found it didn’t reliably forecast voter behavior. While it captured participants’ implicit reactions to political statements, it didn’t determine how they would vote in the election.
Instead, it appeared that voting behavior was more closely tied to partisan identity — whether a person identified as Republican or Democrat — rather than the deeper, subconscious preferences revealed by the N400 brainwave.
“Explicit political preferences — those that voters explicitly stated — were overall the best predictors of how people would vote,” Mahieux explained. “However, we found that what some participants said they believed about politics diverged from what brain measures suggested were their deeply-held preferences. Our brain measure of implicit preference — the N400 electrical potential of the brain — suggested that Republicans in our sample were overall less polarised and less aligned with their party than their explicitly stated preferences indicated.”
“Our neural measure suggests that some of these voters might have had conflicting or at the least divided views about some political questions. This shows us a new angle of political decision-making which surveys and polls cannot access.”
Like all research, this study had limitations. The sample size was relatively small, and all participants were young adults from a single university, which may limit the generalizability of the findings to broader, more diverse populations. Additionally, the study only captured a snapshot of brain activity at a particular moment in time, right before an election. Political views can evolve, and further research might investigate how consistent these brain responses are over time or whether they change with shifting political climates.
While the N400 did reflect implicit preferences, these preferences did not always align with the participants’ final voting choices. This opens up interesting questions for future research, particularly about the role of unconscious preferences in decision-making and how these interact with conscious beliefs and social identities.
“I would like to test which measures of implicit political preference improve existing models’ ability to predict how undecided voters will vote,” Mahieux said. “This is because we still have a limited understanding of the individual-level psychological factors that shape undecided voters’ voting choices. In general, I think it will be interesting to see which non-political measures -like brain potentials- are effective at predicting political behaviours like voting.”
The study, “(https://www.nature.com/articles/s41598-024-67763-7) The N400 effect captures nuances in implicit political preferences,” was authored by Emmanuel Mahieux, Lee de-Wit, Leun J. Otten, Joseph T. Devlin, and Nicole Y. Y. Wicha.

(https://www.psypost.org/getting-morning-sunlight-can-improve-sleep-quality-study-suggests/) Getting morning sunlight can improve sleep quality, study suggests
Sep 25th 2024, 16:00

A new study published in the (https://doi.org/10.1177/13591053241262643) Journal of Health Psychology has found that exposure to sunlight in the morning may improve sleep quality later that night. Individuals who spent time in the sun during the morning reported better sleep quality, regardless of how much total sunlight they received throughout the day. The results suggest that morning sunlight may help regulate circadian rhythms.
Sleep is increasingly recognized as critical to health, influencing outcomes such as obesity, diabetes, hypertension, depression, and general well-being. However, sleep duration appears to be declining in the general population, with more people reporting insufficient rest. Previous research has explored various factors that can influence sleep, including diet, exercise, and socio-economic conditions.
Sunlight, a key regulator of circadian rhythms and melatonin production, has been linked to sleep in prior studies. However, there has been limited research on how daily sun exposure, especially its timing, affects sleep. The researchers aimed to fill this gap by investigating how morning, mid-day, and evening sunlight exposure correlates with sleep quality.
The study followed 103 adults over a 70-day period. Participants were recruited from an online pool and ranged in age from 18 to 80, with roughly equal representation across gender and age groups. Each participant filled out a daily survey about their sunlight exposure and sleep quality, which they completed between 8:30 PM and midnight local time.
Participants reported how long they spent outside in direct sunlight and what part of the day they were outside—morning, mid-day, or evening. Sleep quality was measured using a modified version of the Pittsburgh Sleep Quality Index, a widely used tool that assesses various aspects of sleep, such as total sleep time, ease of falling asleep, and subjective sleep quality.
To control for factors that could influence sleep, such as age, health, and whether the day was a weekend or weekday, the researchers used multilevel modeling. This statistical method allowed them to differentiate between long-term patterns (how individual sleep patterns change over time) and daily fluctuations (how sunlight exposure one day affects sleep that night).
The study’s most significant finding was that morning sunlight exposure predicted better sleep quality that night. People who spent time outside in the morning reported falling asleep more quickly, sleeping longer, and experiencing fewer awakenings during the night. Their sleep efficiency—how much of the time they spent in bed was spent asleep—was also higher. These findings held even when controlling for other variables, such as the quality of their previous night’s sleep.
Interestingly, the total amount of time participants spent in sunlight each day did not have a significant impact on sleep quality. This suggests that it’s the timing of sunlight exposure, not the overall duration, that plays a critical role in promoting better sleep. Morning sunlight, in particular, may help synchronize circadian rhythms—the body’s internal clock that regulates sleep-wake cycles—leading to improved sleep quality.
There were no consistent associations between mid-day or evening sunlight exposure and sleep quality. However, some secondary analyses found that individuals who spent time in evening sunlight tended to wake up earlier the following morning. But overall, it was morning sunlight that consistently predicted better sleep.
As with any research, this study has its limitations. One notable constraint is that participants self-reported their sunlight exposure and sleep quality, which could introduce bias. Additionally, the study didn’t account for certain variables that might influence sleep, such as the intensity of sunlight, weather conditions, or the participants’ exposure to artificial light. For instance, someone who lives in a northern climate with shorter days in winter might have different results compared to someone living in a tropical climate with consistent daylight year-round.
The findings from this study open the door to several avenues for future research. First, it would be valuable to investigate whether the benefits of morning sunlight exposure differ by geographical location, skin pigmentation, or season. For example, people living in regions with long winters and limited daylight might have different sunlight exposure patterns compared to those living closer to the equator. Additionally, future studies could examine how artificial light—such as from screens or indoor lighting—interacts with natural sunlight to affect sleep.
The study, “(https://journals.sagepub.com/doi/10.1177/13591053241262643) Does sunlight exposure predict next-night sleep? A daily diary study among U.S. adults,” was authored by Austen R Anderson, Lindsey Ostermiller, Mallory Lastrapes, and Lauren Hales.

(https://www.psypost.org/large-language-models-tend-to-express-left-of-center-political-viewpoints/) Large language models tend to express left-of-center political viewpoints
Sep 25th 2024, 14:00

An analysis of 24 conversational large language models (LLMs) has revealed that many of these AI tools tend to generate responses to politically charged questions that reflect left-of-center political viewpoints. However, this tendency was not observed in all models, and foundational models without specialized fine-tuning often did not show a coherent pattern of political preferences the way humans do. The paper was published in (https://doi.org/10.1371/journal.pone.0306621) PLOS ONE.
Large language models are advanced artificial intelligence systems designed to interpret and generate human-like text. They are built using deep learning techniques, particularly neural networks, and are trained on vast amounts of textual data from sources such as websites, books, and social media. These models learn the patterns, structures, and relationships within language, which enables them to perform tasks like translation, summarization, answering questions, and even creative writing.
Since the release of OpenAI’s GPT-2 in 2019, many new LLMs have been developed, quickly gaining popularity as they were adopted by millions of users worldwide. These AI systems are now used for a variety of tasks, from answering technical questions to providing opinions on social and political matters. Given this widespread usage, many researchers have expressed concerns about the potential of LLMs to shape users’ perceptions, especially in areas such as political views, which could have broad societal implications.
This inspired David Rozado to investigate the political preferences embedded in the responses generated by LLMs. He aimed to understand whether these models, which are trained on vast datasets and then fine-tuned to interact with humans, reflect any particular political bias. To this end, Rozado administered 11 different political orientation tests to 24 conversational LLMs. The models he studied included LLMs that underwent supervised fine-tuning after their pre-training, as well as some that received additional reinforcement learning through human or artificial feedback.
The political orientation tests used in the study were designed to gauge various political beliefs and attitudes. These included well-known instruments like the Political Compass Test, the Political Spectrum Quiz, the World’s Smallest Political Quiz, and the Political Typology Quiz, among others. These tests aim to map an individual (or, in this case, a model) onto a political spectrum, often based on economic and social dimensions.
The study included a mix of closed-source and open-source models, such as OpenAI’s GPT-3.5, GPT-4, Google’s Gemini, Anthropic’s Claude, Twitter’s Grok, and open-source models from the Llama 2 and Mistral series, as well as Alibaba’s Qwen.
Each test was administered 10 times per model, ensuring consistent results and minimizing any anomalies in responses. The final sample included a diverse range of models, reflecting various approaches to LLM development. In total, 2,640 individual test instances were analyzed.
The results showed a notable trend: most conversational LLMs tended to provide responses that skewed left-of-center. Left-of-center views generally emphasize social equality, government intervention in economic matters to address inequality, and progressive policies on issues such as healthcare, education, and labor rights, while still supporting a market-based economy. This left-leaning tendency was consistent across multiple political tests, although there was some variation in how strongly each model exhibited this bias.
Interestingly, this left-leaning bias was not evident in the base models upon which the conversational models were built. These base models, which had only undergone the initial phase of pre-training on a large corpus of internet text, often produced politically neutral or incoherent responses. These foundational models struggled to interpret the political questions accurately without additional fine-tuning, showing that the ability to produce coherent political responses is more likely a product of fine-tuning rather than pre-training alone.
Rozado also demonstrated that it is relatively straightforward to steer the political orientation of an LLM through supervised fine-tuning. By using modest amounts of politically aligned data during the fine-tuning process, he was able to shift a model’s political responses toward specific points on the political spectrum. For instance, with targeted fine-tuning, Rozado created politically aligned models like “LeftWingGPT” and “RightWingGPT,” which consistently produced left-leaning and right-leaning responses, respectively. This highlights the significant role that fine-tuning can play in shaping the political viewpoints expressed by LLMs.
“The emergence of large language models (LLMs) as primary information providers marks a significant transformation in how individuals access and engage with information,” Rozado concluded. “Traditionally, people have relied on search engines or platforms like Wikipedia for quick and reliable access to a mix of factual and biased information.”
“However, as LLMs become more advanced and accessible, they are starting to partially displace these conventional sources. This shift in information sourcing has profound societal implications, as LLMs can shape public opinion, influence voting behaviors, and impact the overall discourse in society. Therefore, it is crucial to critically examine and address the potential political biases embedded in LLMs to ensure a balanced, fair, and accurate representation of information in their responses to user queries.”
The study sheds light on the political preferences embedded in current versions of popular LLMs. However, it should be noted that views expressed by LLMs are a manifestation of training they underwent and the data they were trained on. LLMs trained in a different way and on different data could manifest very different political preferences.
The paper, “(https://doi.org/10.1371/journal.pone.0306621) The political preferences of LLMs,” was authored by David Rozado.

(https://www.psypost.org/fantasy-footballs-surprising-relationship-with-mental-health/) Fantasy Football’s surprising relationship with mental health
Sep 25th 2024, 12:00

A recent study published in (https://journals.sagepub.com/doi/10.1177/10468781241261663) Simulation & Gaming explores the mental health effects of playing Fantasy Football, with mixed results. The researchers found that higher levels of engagement, frequent team comparisons, and constant performance monitoring were linked to increased mental health concerns, such as anxiety, stress, and negative mood. However, these same behaviors were also associated with a greater sense of positive mood among highly involved players, indicating a complex emotional experience when engaging with the game.
Fantasy Football is an increasingly popular form of sports engagement, blending competition, strategy, and sometimes financial stakes. The new study aimed to explore how this level of involvement might affect players’ mental health, particularly given the significant emotional investment that participants make in their teams.
Previous research on sports fandom has shown that deep emotional connections with teams and players can affect mood and well-being. However, the impact of Fantasy Football, where players are more directly engaged in decision-making and competition, has been less explored. The researchers sought to fill this gap by examining both the negative and positive mental health outcomes linked to Fantasy Football.
“I have been playing Fantasy Football for about 10 years myself, and I’m quite an invested player, so a major factor in doing this research was a personal interest in the potential effects that playing Fantasy Football may have on a player’s mood and mental health,” said study author (https://www.qmul.ac.uk/sbbs/staff/gary-britton.html) Gary Ian Britton, a lecturer in psychology at Queen Mary University of London.
“In my first few years of playing Fantasy Football, if my team did badly in any given game week, it would affect my mood negatively. I wasn’t crying or hitting a wall or anything, but it certainly put me in a worse mood than I otherwise would have been in. I assumed that if my Fantasy Football team performing badly was affecting my mood negatively, perhaps more vulnerable and/or more invested players in the game would be affected more severely, and that was my main motivation for conducting the study.”
The study involved 635 participants, 96% of whom identified as male, with an average age of 34. Participants were recruited through social media and Fantasy Football websites. They were asked to complete a questionnaire that measured several aspects of mental health, including anxiety, depression, stress, positive mood, negative mood, and functional impairment. Participants also provided information about their Fantasy Football behaviors, such as the number of leagues they played in, the time spent on the game, and the frequency with which they compared their team to others.
To assess mental health, the researchers adapted established scales, such as the Depression, Anxiety, and Stress Scale (DASS-21), to make them relevant to Fantasy Football. Positive and negative mood was measured using the Positive and Negative Affect Schedule (PANAS), also modified to address the Fantasy Football context. Additionally, the study used the Problematic Online Gaming Questionnaire (POGQ-S) to measure problematic behavior in the game and the Work and Social Adjustment Scale (WSAS) to assess how the game affected daily functioning.
The study found that players who reported higher levels of engagement with Fantasy Football—whether through the time spent on the game or the frequency of social comparisons—were more likely to experience mental health challenges. Specifically, players who spent more time thinking about the game, researching player statistics, and comparing their team’s performance to others reported higher levels of stress, anxiety, and negative mood. These players also tended to engage in more problematic behaviors related to Fantasy Football, such as feeling preoccupied with the game or allowing it to interfere with other areas of their life.
Interestingly, the study also found that highly engaged players experienced higher levels of positive mood. Despite the mental health challenges, these players also reported that playing Fantasy Football provided moments of excitement and joy.
The results suggest “that Fantasy Football can affect your mood and mental health, especially if you are a player who is very invested in the game, or if you are a player who invests money into the game,” Britton told PsyPost. “A key finding of the study is that Fantasy Football can also positively affect your mood if your team does well, especially if you are a very invested player, or a player who invests money into the game.”
“Like most things in life, if you do well at something, that makes you feel good, but if you don’t do well, that makes you feel bad. The key difference between Fantasy Football and many other hobbies is that you have no control over the outcome of Fantasy football once you have set your team up for the game week and the deadline has passed. For example, you personally have no control over whether Erling Haaland scores a hat trick vs West Ham, or if he gets sent off in the first minute of the game but, if he is in your team, the outcome of Haaland’s performance is probably going to affect your mood in some capacity, especially if you are an invested Fantasy Football player.”
Britton was surprised to find “that more experienced players Fantasy Football players seemed to experience similar levels of negative mood if their Fantasy Football team performed badly, compared to less experienced players. Past research would have suggested that more experienced players should be more protected from experiencing negative mood as a result of Fantasy Football compared to less experienced players, possibly as they have developed coping mechanisms over the years of playing the game to help them cope with bad game weeks. However, this study suggests that this may not always be the case.”
But the study, like all research, has some limitations. The cross-sectional nature of the study—where data is collected at a single point in time—makes it difficult to establish causality. It remains unclear whether high engagement in Fantasy Football leads to mental health issues, or whether individuals with pre-existing mental health challenges are more likely to become deeply engaged in the game.
Future research could address these limitations by employing a longitudinal design, tracking players’ engagement with Fantasy Football and their mental health over an entire season. This approach would allow researchers to observe how fluctuations in game performance, team rankings, and social comparisons impact players’ mental health over time. It may also be useful to explore other factors that could influence players’ experiences, such as their employment status or financial resources, which may shape how much time and emotional energy they can invest in the game.
“We did recruit our participants via specialist, independent Fantasy Football websites, and therefore most of the people taking part in our study probably were probably somewhat ‘invested’ in Fantasy Football, at least at the time they took part in the study,” Britton noted. “The results of the study therefore may not be applicable to players Fantasy Football players who have very little to no real investment in the game.”
He hopes the research will help “make people aware of the potential negative effects of playing Fantasy Football. The aim is certainly not to discourage people from playing Fantasy Football; I love the game myself! Rather, the aim is to make people aware of the potential negative effects Fantasy Football can have on their mood/mental health, especially if a player becomes very invested in the game, be that in terms of their time, or in terms of financial investment.”
The study, “(https://doi.org/10.1177/10468781241261663) Exploring Fantasy Football Involvement and Mental Health through Player Experience, Engagement Levels, Social Comparisons, and Financial Incentives,” was authored by Luke Wilkins, Jamie Churchyard, Ross Dowsett, and Gary Britton.

Forwarded by:
Michael Reeder LCPC
Baltimore, MD

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