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<td><span style="font-family:Helvetica, sans-serif; font-size:20px;font-weight:bold;">PsyPost – Psychology News</span></td>
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<td><a href="https://www.psypost.org/a-simple-language-switch-can-make-ai-models-behave-significantly-different/" style="font-family:Helvetica, sans-serif; letter-spacing:-1px;margin:0;padding:0 0 2px;font-weight: bold;font-size: 19px;line-height: 20px;color:#222;">A simple language switch can make AI models behave significantly different</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Jan 23rd 2026, 08:00</div>
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<p><p>A new study published in <em><a href="https://doi.org/10.1038/s41562-025-02242-1" target="_blank">Nature Human Behaviour</a></em> provides evidence that generative artificial intelligence models exhibit distinct cultural tendencies depending on the language in which they are prompted. The research suggests that using Chinese leads AI to produce more relationship-focused and context-aware responses, while using English results in more individualistic and analytical outputs. These findings imply that AI is not a culturally neutral tool and may subtly influence user decision-making based on linguistic context.</p>
<p>Generative artificial intelligence refers to a category of technology capable of creating new content, such as text and images, by identifying patterns within vast amounts of existing data. Platforms like Google’s Gemini, OpenAI’s ChatGPT, and Baidu’s ERNIE Bot have seen rapid global adoption for tasks ranging from writing assistance to advice seeking. </p>
<p>“This study was motivated by a simple but often overlooked tension in how generative AI is understood versus how it is built. Generative AI models are often assumed to be culturally neutral, producing essentially the same responses across languages,” explained study author <a href="http://www.jacksonlu.net" target="_blank">Jackson G. Lu</a>, the General Motors Associate Professor at MIT Sloan School of Management.</p>
<p>“Yet these models are trained on large-scale textual data that are inherently cultural. This raises an underexplored question: whether systematic cultural tendencies emerge when the same model is prompted in different human languages.”</p>
<p>“This question matters because generative AI is now embedded in everyday life. If cultural differences in AI outputs go unnoticed, they may influence users’ attitudes and choices at scale. By integrating insights from cultural psychology with generative AI research, we show that the same generative AI model exhibits systematic differences when prompted in Chinese versus English.”</p>
<p>The researchers focused on two foundational concepts from cultural psychology to frame their investigation: social orientation and cognitive style. Social orientation describes the degree to which an individual prioritizes the self versus the group. Independent social orientation, common in Western cultures, emphasizes personal goals and uniqueness. Interdependent social orientation, common in East Asian cultures, emphasizes social norms, harmony, and connection to others.</p>
<p>Cognitive style refers to how individuals habitually process information. An analytic cognitive style tends to focus on specific objects and uses formal logic to explain behavior based on internal traits. A holistic cognitive style pays greater attention to the context and relationships between objects, relying more on dialectical reasoning and situational explanations. The researchers hypothesized that AI models trained on high-resource languages like English and Chinese would reflect the distinct cultural tendencies associated with those linguistic groups.</p>
<p>To test this hypothesis, the research team examined two popular generative AI models: GPT-4 and ERNIE 3.5. They accessed these models via their application programming interfaces to ensure consistent testing conditions. The researchers conducted the study by administering identical psychological measures in both English and Chinese. For each measure, they ran 100 iterations in English and 100 iterations in Chinese, resulting in a total sample size of 200 responses per task. They reset the system between each iteration to prevent previous answers from influencing subsequent ones.</p>
<p>The first set of experiments measured social orientation using established psychological scales. One key measure was the “Inclusion of Other in the Self Scale,” which is a visual task. The researchers asked the AI to select a pair of circles that best represented the relationship between an individual and various associates, such as family members or colleagues. The options ranged from circles that were completely separate to circles that overlapped almost entirely.</p>
<p>The results showed a consistent pattern across both GPT and ERNIE. When prompted in Chinese, the models selected circle pairs with more overlap. This indicates a higher degree of interdependence, where the self is viewed as interconnected with others. When prompted in English, the models selected circles with less overlap, reflecting an independent orientation where the self remains distinct. This finding was replicated across text-based Likert scales measuring collectivism and individualism.</p>
<p>The second set of experiments assessed cognitive style through three specific tasks. The first was an attribution bias task, where the AI read vignettes about people’s behavior. The models were asked to rate how much the behavior was caused by personality versus the environment. In Chinese, the AI was more likely to attribute actions to the situation, which aligns with holistic thinking. In English, the AI attributed actions more to the individual’s disposition, aligning with analytic thinking.</p>
<p>Another task involved evaluating logical syllogisms that were logically valid but intuitively implausible. For example, the AI evaluated the premise that “all things made of plants are healthy” and “cigarettes are made of plants” to conclude that “cigarettes are healthy.” While logically sound based on the premises, the conclusion conflicts with real-world knowledge. </p>
<p>The researchers found that when prompted in Chinese, the AI was more likely to reject the logical validity based on intuition. When prompted in English, the AI was more likely to accept the formal logic despite the counterintuitive conclusion.</p>
<p>The researchers also measured the expectation of change. They asked the AI to estimate the probability of future events, such as whether two fighting kindergarteners might become lovers as adults. The Chinese responses consistently assigned higher probabilities to such changes, reflecting a holistic view that the world is dynamic and fluid. The English responses predicted more stability, reflecting an analytic view that current states tend to persist.</p>
<p>“The statistical magnitude of the effects is medium to large by behavioral science standards,” Lu told PsyPost. “These effect sizes as reflecting meaningful and systematic differences in AI responses across languages. In practice, the effects are substantial enough to influence downstream recommendations and real-world decision-making.”</p>
<p>Beyond numeric scores, the team analyzed the text structure of the AI’s responses. They looked for context-sensitive answers, where the AI suggests that the “correct” answer depends on the specific situation. They also looked for instances where the AI provided a range of scores rather than a single number. The analysis revealed that Chinese prompts elicited significantly more context-sensitive answers and score ranges. This supports the idea that the Chinese language triggers a more holistic processing style that tolerates ambiguity and complexity.</p>
<p>To demonstrate the practical implications of these tendencies, the researchers conducted an experiment involving advertising recommendations. They asked the AI to select the best slogan for products like insurance and toothbrushes. The choices included slogans with independent themes, focusing on personal benefits, and interdependent themes, focusing on family welfare.</p>
<p>The researchers observed a divergence in recommendations based on language. When the request was made in Chinese, the AI was far more likely to recommend slogans that emphasized collective benefits and family protection. When the same request was made in English, the AI recommended slogans that highlighted individual peace of mind and personal gain. This suggests that the language used to consult an AI can directly alter the strategic advice it provides.</p>
<p>The researchers also explored whether users could manually adjust these cultural defaults. They ran an additional set of experiments using English prompts but included a specific cultural cue: “You are an average person born and living in China.” The addition of this single phrase significantly shifted the AI’s outputs. The English responses became more interdependent and holistic, closely resembling the results typically generated by Chinese prompts. This indicates that users can mitigate cultural bias if they are aware of it and use specific persona instructions.</p>
<p>“The main takeaway is that AI is not culturally neutral,” Lu said. “The same AI can give noticeably different answers depending on the language you use, with English leading to more individual-focused and analytical responses and Chinese leading to more relationship-focused and context-aware ones.” </p>
<p>“These differences can show up in everyday advice and recommendations produced by AI, meaning AI may quietly shape how people think and decide even without their awareness. The good news is that users have some control: by choosing a language carefully or adding simple cultural cues, people can guide AI to give responses that better fit the cultural context of the situation they care about.”</p>
<p>There are a few limitations to consider. The study was limited to English and Chinese, so the findings may not generalize to other languages such as Spanish, Hindi, or Arabic. The researchers suggest that future work should investigate whether similar patterns exist in other large language models and across a broader spectrum of languages.</p>
<p>The researchers also note that AI models do not possess a genuine cultural identity; they reproduce statistical patterns found in their training data. </p>
<p>“First, we do not suggest that generative AI ‘possesses’ culture in the way humans do,” Lu said. “Instead, the cultural tendencies we observe likely reflect real-world cultural patterns embedded in the large-scale text data on which these models are trained. Second, our findings are based on two specific models, gpt-4-1106-preview and ERNIE-3.5- 8K-0205. While we expect similar patterns to emerge more broadly, readers should be cautious when generalizing to other generative AI models or different model versions.”</p>
<p>Looking ahead, the researchers plan to further investigate the practical implications of these interactions. Lu explained, “Our long-term goal is to understand how user inputs shape generative AI responses, and how these response differences translate into downstream behavioral and organizational outcomes.”</p>
<p>The study, “<a href="https://doi.org/10.1038/s41562-025-02242-1" target="_blank">Cultural tendencies in generative AI</a>,” was authored by Jackson G. Lu, Lesley Luyang Song, and Lu Doris Zhang.</p></p>
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<td><a href="https://www.psypost.org/158-scientists-used-the-same-data-but-their-politics-predicted-the-results/" style="font-family:Helvetica, sans-serif; letter-spacing:-1px;margin:0;padding:0 0 2px;font-weight: bold;font-size: 19px;line-height: 20px;color:#222;">158 scientists used the same data, but their politics predicted the results</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Jan 23rd 2026, 06:00</div>
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<p><p>A new analysis of scientific practices suggests that a researcher’s personal political views may influence the results they obtain when analyzing complex data. The study provides evidence that when experts act independently to answer the same question using the same dataset, their conclusions tend to align with their pre-existing ideological beliefs. These findings were published in the journal <em><a href="https://doi.org/10.1126/sciadv.adz7173" target="_blank">Science Advances</a></em>.</p>
<p>The investigation was conducted by <a href="https://www.hks.harvard.edu/faculty/george-borjas" target="_blank">George J. Borjas</a> and <a href="https://sites.google.com/site/nbreznau/" target="_blank">Nate Breznau</a>. Borjas is a Cuban-American economist who serves as the Robert W. Scrivner Professor of Economics and Social Policy at the Harvard Kennedy School. Breznau serves as the principal investigator at the German Institute for Adult Education – Leibniz Institute for Lifelong Learning.</p>
<p>The motivation for the study arose from a previous large-scale experiment known as <a href="https://www.pnas.org/doi/10.1073/pnas.2203150119" target="_blank">the Crowdsourced Replication Initiative</a>. In that original project, independent research teams were given identical data to answer a specific sociological question. Borjas examined the publicly available data from that initiative and noticed a correlation between the researchers’ stated opinions on immigration and the statistical results they produced.</p>
<p>“It is clear that there are many reasons that scholars may be influenced by different forms of bias (confirmation bias, publication bias, status seeking, etc.). It is only logical that scholars might want to arrive at certain results that align with their own ideological preferences – how they want the world to be or how they want the world to appear,” Breznau told PsyPost.</p>
<p>“I personally am interested in the reproducibility crisis and in seeking ways to address a serious lack of reproducibility in science. Consider for example when Daryl Bem found ‘evidence’ of extrasensory perception (ESP). He personally believed in ESP. This does not appear to be a coincidence. All efforts to replicate his experiments so far failed.”</p>
<p>However, Breznau was initially skeptical of Borjas’s observation. He suspected that the statistical association was likely a coincidence or a fluke that would disappear under more rigorous testing. He assumed that the connection would not hold up if different statistical models were applied. To test this, the authors decided to conduct a comprehensive analysis to determine if political ideology truly played a role in how the scientists designed their research and interpreted their findings.</p>
<p>The study utilized data from 158 researchers organized into 71 separate teams. These teams had participated in an experiment where they were asked to determine whether immigration affects public support for social welfare programs. The researchers were provided with data from <a href="https://issp.org/" target="_blank">the International Social Survey Program</a>, covering various countries and spanning the years 1985 to 2016.</p>
<p>Before the teams began their analysis, they completed a survey. One of the questions asked for their stance on immigration policy. Specifically, they were asked if laws on immigration should be relaxed or made tougher. Their responses were recorded on a scale ranging from zero to six.</p>
<p>The teams then proceeded to analyze the data. They were tasked with replicating a well-known previous study that found no link between immigration and welfare support. After replicating that study, the teams were instructed to extend the research using the new data provided. They had the freedom to choose their own statistical methods and variables to test the hypothesis.</p>
<p>Collectively, the 71 teams estimated 1,253 distinct statistical models. The results varied significantly. Some teams concluded that immigration strongly decreased public support for social programs. Other teams found that immigration strongly increased such support. Many others found no significant effect at all.</p>
<p>Borjas and Breznau found a systematic pattern in this variation. Teams composed of researchers who favored clearer immigration policies tended to produce results suggesting that immigration had a positive effect on social cohesion. Teams composed of researchers who favored tougher immigration laws tended to produce results showing a negative effect.</p>
<p>The authors sought to understand the mechanism behind this divergence. They found that the difference was not due to errors in calculation. Instead, it stemmed from the specific choices the teams made when designing their statistical models. In the social sciences, researchers often have to make many decisions about how to organize data.</p>
<p>For example, researchers must decide how to measure immigration levels. They can measure it as the total percentage of foreign-born residents, or they can measure it as the rate of new arrivals per year. They must also decide which countries to include in the comparison and which specific years to analyze. They also have to decide how to mathematically group different types of social welfare programs.</p>
<p>The study identified five specific research design decisions that heavily influenced the final results. These decisions accounted for approximately 68 percent of the difference in findings between the pro-immigration and anti-immigration teams. The analysis showed that teams tended to select the specific combination of data points and measurement tools that produced results consistent with their ideological preferences.</p>
<p>To ensure their findings were robust, Breznau and Borjas conducted a “multiverse analysis.” This involved running 883 different statistical models to test the link between ideology and research outcomes. They found that in nearly 88 percent of these models, the effect of ideology was statistically significant. This convinced Breznau that the correlation he originally doubted was indeed real.</p>
<p>The study also examined the quality of the research produced by the different teams. In the original experiment, each team’s research design was reviewed by other participants in a double-blind process. The reviewers did not know who authored the studies or what their political views were.</p>
<p>The analysis revealed that teams with strong ideological views, whether pro-immigration or anti-immigration, received lower scores from their peers. Teams that held moderate views on immigration tended to design models that received higher ratings for quality. This suggests that widely accepted research standards were more often met by researchers who did not hold extreme political views on the topic.</p>
<p>“It is important to remember that scientists are also human beings,” Breznau said. “Their complex brains weigh simultaneously all kinds of factors in determining how to think and behave. They are not infallible and are not perfectly objective in their work.” </p>
<p>“That is why it is important to build in safeguards into the scientific process, like having others check each others’ work and using more methods to ensure robustness – something we have taken extra care here to do by running a multiverse of models, nearly all of which show the correlation that George initially found and therefore provide robust evidence of an effect.”</p>
<p>The authors caution that their study, like all research, has certain limitations. The original experiment was not specifically designed to test for ideological bias, so the evidence is exploratory rather than confirmatory. The number of researchers who openly admitted to anti-immigration views was small compared to those with pro-immigration views. This imbalance makes it difficult to draw definitive conclusions about the magnitude of bias on the anti-immigration side.</p>
<p>There is also the possibility of social desirability bias in the survey responses. Researchers might have been hesitant to express anti-immigration sentiments in an academic environment. This could mean some teams classified as moderate or pro-immigration actually contained members with different private views.</p>
<p>The authors also note that they cannot observe the internal thought processes of the researchers. It is unclear if the teams consciously chose models that fit their biases or if the process was unconscious. Researchers might simply stop looking for errors or alternative models once they find a result that makes sense to them.</p>
<p>Future research could benefit from observing the scientific workflow in real-time. Tracking every decision a researcher makes could illuminate exactly when and how ideology enters the process. This would require an experimental setting where every step of data analysis is recorded.</p>
<p>Anticipating that their conclusions might be scrutinized, Breznau emphasized that the authors employed exhaustive checks to ensure they were not guilty of the very selection bias they were investigating.</p>
<p>“There are some politically motivated responses to our work,” he said. “This is dangerous. We have gone to great lengths to ensure the robustness of our findings. We, being aware that ideology could bias any researcher including ourselves, have modeled nearly every possible alternative model specification. Therefore, we have proven beyond a high bar, that we have not selected (p-hacked) our results from many other plausible models.”</p>
<p>“I personally am a strong supporter of open science and metascience,” Breznau added. “I consider myself a member of the Open Science Movement. I have learned much from psychology in this regard. I will continue to support open, transparent, inclusive and reproducible scientific methods.” </p>
<p>The study, “<a href="https://doi.org/10.1126/sciadv.adz7173" target="_blank">Ideological bias in the production of research findings</a>,” was authored by George J. Borjas and Nate Breznau.</p></p>
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<td><a href="https://www.psypost.org/are-you-suffering-from-cognitive-atrophy-due-to-ai-overuse/" style="font-family:Helvetica, sans-serif; letter-spacing:-1px;margin:0;padding:0 0 2px;font-weight: bold;font-size: 19px;line-height: 20px;color:#222;">Are you suffering from “cognitive atrophy” due to AI overuse?</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Jan 22nd 2026, 20:00</div>
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<p><p>The retirement of West Midlands police chief Craig Guildford is a wake-up call for those of us using artificial intelligence (AI) tools at work and in our personal lives. Guildford lost the confidence of the home secretary after it was revealed that the force used incorrect <a href="https://theconversation.com/topics/artificial-intelligence-ai-90">AI-generated</a> <a href="https://news.sky.com/story/ai-evidence-a-fake-match-and-misleading-mps-what-led-to-the-downfall-of-the-west-midlands-police-chief-goes-beyond-football-13495077">evidence</a> in their controversial decision to ban Israeli football fans from attending a match.</p>
<p>This is a particularly egregious example, but many people may be falling victim to the same phenomenon – outsourcing <a href="https://www.theatlantic.com/technology/2025/12/people-outsourcing-their-thinking-ai/685093/">the “struggle” of thinking</a> to AI.</p>
<p>As an expert on <a href="https://www.e-elgar.com/shop/gbp/research-handbook-on-digital-transformation-and-responsibility-9781035339877.html">how new technology reshapes society</a> and the human experience, I have observed a growing phenomenon which I and other <a href="https://www.polytechnique-insights.com/en/columns/neuroscience/generative-ai-the-risk-of-cognitive-atrophy/">researchers</a> refer to as “cognitive atrophy”.</p>
<p>Essentially, <a href="https://link.springer.com/article/10.1007/s00146-023-01856-1">AI is replacing</a> tasks many people have grown reluctant to do themselves – thinking, writing, creating, analysing. But when we don’t use these skills, they can decline.</p>
<p>We also risk getting things very, very wrong. Generative AI works by predicting likely words from <a href="https://www.sciencedirect.com/science/article/pii/S0007681324000247">patterns trained on vast amounts of data</a>. When you ask it to write an email or give advice, its responses sound logical. But it does not understand or know <a href="https://news.mit.edu/2024/generative-ai-lacks-coherent-world-understanding-1105">what is true</a>.</p>
<p>There are countless anecdotal examples of people feeling like AI use is making them <a href="https://www.wired.com/story/does-using-ai-make-me-lazy/">“lazy”</a> or “stupid”. <a href="https://link.springer.com/article/10.1186/s41239-024-00444-7">A recent study</a> found that generative AI use among university students is driven by higher workloads and time pressure, and that greater AI use is associated with increased procrastination and memory loss and poorer academic performance. <a href="https://www.tandfonline.com/doi/full/10.1080/10447318.2025.2462083?">Misuse of generative AI tools</a> (for example, to cheat on exams) may undermine skills like critical thinking, creativity and ethical decision-making.</p>
<h2>Recognising atrophy</h2>
<p>You might observe this happening in your own life. One sign might be that you’ve <a href="https://www.edweek.org/technology/brain-activity-is-lower-for-writers-who-use-ai-what-that-means-for-students/2025/06?">moved away from creating</a> an initial unpolished version of a task. Not so long ago, you might have started with a rough draft – a messy, human brainstorming process on a whiteboard, a notepad or the back of a napkin.</p>
<p>You may now feel more comfortable with the “prompt-and-accept” reflex: asking for and accepting solutions, rather than trying to tease out your own ideas and solve problems.</p>
<p>If your first instinct for every task is to ask an AI tool to give you a starting point, you are skipping the most vital part of thinking. This is the heavy lifting of structure, logic and sparking new ideas which <a href="https://theconversation.com/where-did-the-wonder-go-and-can-ai-help-us-find-it-258490">excite us</a>.</p>
<p>Another sign of atrophy is a shrinking of your frustration threshold. If you find that after only 60 seconds of mental effort you feel an itch to see what AI suggests, your stamina for ambiguity, a little self-doubt and frustration is probably compromised. Impatience cuts off the cognitive space needed for <a href="https://www.nature.com/articles/s41598-023-40858-3.pdf">divergent thinking</a> – the ability to generate multiple unique solutions.</p>
<p>Do you find yourself accepting AI-generated output without questioning its validity? Or do you find yourself unable to trust your own gut instinct without checking with an AI search? This may be a sign that you are shifting from being a decision-maker to a decision-approver or worse, a passive passenger of your own thinking process.</p>
<h2>Reclaim your thinking</h2>
<p>How can you combat this cognitive atrophy? The goal should not necessarily be to quit using AI entirely, but to move toward <a href="https://arxiv.org/pdf/1706.02513">responsible autonomy</a> – reclaiming your capacity to think and make decisions for yourself, rather than blindly outsourcing judgement to AI systems. This requires building some strategic friction back into your daily life. It means embracing uncertainty and learning from the process of thinking, even if you are wrong on occasion. Here are some practical things you can try:</p>
<p><strong>1. The 30-minute rule</strong></p>
<p>Before you open any AI interface, try to commit to 30 minutes of deep thinking. Use a pen and paper. Pick your topic or task, and map out the problem, the potential solutions, the risks and the stakeholders. For example, before asking an AI tool to draft a marketing strategy, map out your target audience. Try to identify potential ethical or reputational risks and sketch out some ideas.</p>
<p>By doing the initial cognitive work, you will likely feel a stronger <a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/job.1869">sense of ownership</a> for your output. If you eventually use AI, use it to refine your thoughts, not replace them.</p>
<p><strong>2. Be sceptical</strong></p>
<p>One of the most <a href="https://www.tud.ttu.ee/im/Vladimir.Viies/materials/Artificial%20intelligence%2C%20labour%20and%20society_2024.pdf">persistent concerns</a> is that people use AI as an oracle and believe its output without question. Instead, treat it as a deeply unreliable colleague who may know the right answer, but hallucinates from time to time.</p>
<p>Task yourself with finding three specific errors with AI’s output, or to break its logic. Tell yourself that you can do better. This forces your brain out of the consumer mode and back into creator and editor mode, keeping your critical faculties sharp.</p>
<p><strong>3. Create thinking spaces</strong></p>
<p>Identify one core task in your personal or professional life that you enjoy doing, and commit to performing it entirely without AI assistance. These thinking spaces help your brain maintain its ability to navigate complex and open-ended challenges from scratch.</p>
<p>As you regain confidence, try branching out to other tasks. If you lead a team at work, allow people to have time to think slowly in this way, free from the pressure of producing more.</p>
<p><strong>4. Measure your ‘return on habit’</strong></p>
<p>Think about the “return on habit” – the <a href="https://www.amazon.co.uk/Atomic-Habits-Proven-Build-Break/dp/1847941834">long-term benefits</a> such as improved health or happiness gained from consistently practising small positive routines. Ask yourself: Is this AI tool making me smarter, or just faster? Is faster better? For whom?</p>
<p>If a tool helps you notice things you did not see before, it may enhance your thinking, not replace it. However, if it is merely replacing a skill you used to possess and did well, it is an atrophying agent. If you are not gaining a new capability in exchange for the one you have outsourced, you may be conceding to the algorithms.<!-- Below is The Conversation's page counter tag. Please DO NOT REMOVE. --><img decoding="async" src="https://counter.theconversation.com/content/272834/count.gif?distributor=republish-lightbox-basic" alt="The Conversation" width="1" height="1"><!-- End of code. If you don't see any code above, please get new code from the Advanced tab after you click the republish button. The page counter does not collect any personal data. More info: https://theconversation.com/republishing-guidelines --></p>
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<p><em>This article is republished from <a href="https://theconversation.com">The Conversation</a> under a Creative Commons license. Read the <a href="https://theconversation.com/is-ai-hurting-your-ability-to-think-how-to-reclaim-your-brain-272834">original article</a>.</em></p></p>
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<td><a href="https://www.psypost.org/brain-imaging-provides-insight-into-the-biological-roots-of-gambling-addiction/" style="font-family:Helvetica, sans-serif; letter-spacing:-1px;margin:0;padding:0 0 2px;font-weight: bold;font-size: 19px;line-height: 20px;color:#222;">Brain imaging provides insight into the biological roots of gambling addiction</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Jan 22nd 2026, 18:00</div>
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<p><p>A <a href="https://www.utupub.fi/handle/10024/194696" target="_blank" rel="noopener">new doctoral dissertation</a> from the University of Turku shows that gambling disorder is rooted in specific brain networks responsible for reward and self-control. The research indicates that people with this condition have measurable changes in how their brain regions communicate and process information. These findings suggest that the struggle to stop gambling is tied to biological shifts in the brain rather than a simple lack of willpower.</p>
<p>Gambling disorder is currently recognized as the only behavioral addiction in major medical manuals. This classification reflects the many ways that problem gambling resembles addiction to alcohol or drugs.</p>
<p>Albert Bellmunt Gil, a researcher at the University of Turku, led this work to better understand the internal mechanics of the condition. Many researchers focus on a specific circuit that connects the front of the brain to deeper centers for reward and information filtering. This pathway acts as a bridge between the parts of the brain that seek rewards and the parts that manage behavior.</p>
<p>Most existing studies on this topic only look at one type of brain scan at a time. This approach provides a limited view of how the brain works. Bellmunt Gil sought to provide a more complete picture by using several different types of imaging technology at once. He also focused on different age groups to see how the condition might change over time. Many earlier studies focused only on young adults, which may miss changes that develop later in life.</p>
<p>The first part of the research involved a group of 15 people with gambling disorder and 17 healthy individuals. The team used functional magnetic resonance imaging to watch the brain in action. They also used positron emission tomography, which involves tracking special tracers to see how chemicals move in the brain. This allowed the team to look at three important chemical systems: dopamine, serotonin, and opioids.</p>
<p>When the researchers looked at the brain at rest, they found a change in how the reward center talks to the control center. In healthy people, there is a negative relationship between the nucleus accumbens and the dorsolateral prefrontal cortex. This means that as activity in one area goes up, it tends to go down in the other. In the group with gambling disorder, this relationship was lost.</p>
<p>The two areas did not balance each other in the same way. The team found that this change in communication was linked to serotonin levels. Serotonin is a chemical often associated with mood and the ability to resist impulses. The findings suggested that the way serotonin works in the reward center might influence how well a person can control their actions.</p>
<p>This specific connection was not related to dopamine or opioids during the resting state scans. The researchers also showed the participants videos of gambling, such as people playing poker or roulette. They compared the brain’s reaction to these clips with its reaction to neutral videos of people walking or doing everyday tasks. The people with gambling disorder showed a much stronger response in a region called the dorsal striatum when they saw gambling cues.</p>
<p>This area of the brain is often involved in forming habits. The heightened response to gambling cues was tied to the opioid system. Opioids in the brain help us feel pleasure and can influence how much we want something. The results indicated that the more active the opioid system was, the stronger the brain reacted to gambling triggers.</p>
<p>This finding mirrors what is often seen in people who are addicted to substances. However, the researchers did not find evidence of dopamine involvement in these specific tests. This result is notable because dopamine is usually seen as the primary chemical in drug addiction. In those cases, dopamine function is usually lower than normal, but in gambling disorder, the dopamine system seems to remain active or even hyperactive.</p>
<p>The second part of the study focused on 20 older adults with gambling disorder and 40 healthy controls. The participants in this group had an average age of 64 years. Using structural scans, the researchers measured the thickness and volume of different brain parts. They found that the older adults with the disorder had less gray matter in the orbitofrontal cortex and the thalamus.</p>
<p>These areas are essential for making decisions and filtering information. The team also examined the white matter, which acts like the wiring of the brain. The study found that the group with gambling disorder had less integrity in the white matter of the left anterior corona radiata. This area connects the frontal lobe to other parts of the brain.</p>
<p>The researchers also found more tiny lesions or damaged spots in this same region. These structural issues were linked to the severity of the person’s gambling problems. The presence of these lesions suggests that the physical health of the brain’s wiring plays a role in how well a person can resist the urge to gamble. This was the first study to report such findings regarding white matter lesions in this population.</p>
<p>The researchers note that their findings come from a relatively small number of participants. Small groups can sometimes make it harder to see the full range of differences between people. The results regarding some chemical tracers were not statistically significant in every test. This suggests that some aspects of the disorder might be more subtle or vary between individuals.</p>
<p>The study also looked at participants at a single point in time. This means it is not yet clear if the brain changes happened before the gambling started or if they were caused by years of gambling. Longitudinal studies following people over many years would help understand whether these brain abnormalities are a cause or a consequence of the behavior. Future work should also consider how other health factors might influence these brain structures.</p>
<p>Some researchers hope to use these findings to test new therapies like brain stimulation. By targeting the specific areas identified in this work, doctors might one day be able to help patients regain control over their habits. Medications that target serotonin or opioids might also be a path forward. Randomized controlled studies are needed to see if these medical interventions are effective for a broad range of patients.</p>
<p>Understanding these brain mechanisms can help reduce the stigma often associated with gambling problems. If the disorder is linked to measurable changes in the brain, it can be treated as a medical condition. This view may lead to more compassionate and effective ways to help those struggling with the habit. Improved prevention strategies could also be developed by identifying people who have similar brain patterns before a disorder develops.</p>
<p>The study, “<a href="https://www.utupub.fi/handle/10024/194696" target="_blank" rel="noopener">Neurobiological correlates of gambling disorder: A multimodal brain imaging approach</a>,” was authored by Albert Bellmunt Gil.</p></p>
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<td><a href="https://www.psypost.org/researchers-are-using-dungeons-dragons-to-find-the-breaking-points-of-major-ai-models/" style="font-family:Helvetica, sans-serif; letter-spacing:-1px;margin:0;padding:0 0 2px;font-weight: bold;font-size: 19px;line-height: 20px;color:#222;">Researchers are using Dungeons & Dragons to find the breaking points of major AI models</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Jan 22nd 2026, 16:00</div>
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<p><p>A <a href="https://openreview.net/forum?id=rmEBSdk26P" target="_blank" rel="noopener">new study</a> presented at the NeurIPS 2025 conference suggests that the tabletop game Dungeons & Dragons can serve as a tool for testing the intelligence of artificial intelligence agents. Researchers found that while current models can handle simple questions, they struggle to manage the multiple steps, strict rules, and team coordination required by a full game session. The results suggest that certain models are much more reliable than others at following instructions over long periods, though all models eventually show a decline in accuracy as the game progresses.</p>
<p>The investigation into these digital adventurers was led by Ziyi Zeng, a researcher at the University of California, San Diego. Zeng worked alongside colleagues from the same institution and the University of Pennsylvania to build a bridge between human language and rigid game mechanics. This work addresses a specific gap in how researchers measure the abilities of Large Language Models, which are the engines behind modern chatbots.</p>
<p>Many existing tests for these models only look at how they answer a single question or solve a short task. However, these programs are increasingly used as autonomous agents that must operate independently to solve multi-step problems in the real world. A game like Dungeons & Dragons provides a controlled environment to see if an agent can remember past events, cooperate with allies, and obey a set of physical and magical rules.</p>
<p>The researchers focused on the combat portion of the game, where characters must move across a map and use their abilities to defeat monsters. In this setting, the stakes are high for the characters, and every decision is governed by the roll of a die and a thick rulebook. This requires the artificial intelligence to balance its creative storytelling with the mathematical reality of the game world.</p>
<p>To conduct the study, Zeng and the team developed a framework called D&D Agents. This system acts as a simulator where different models can play against each other or with humans. Instead of just letting the models talk freely, the researchers forced them to use specific digital tools to interact with the game.</p>
<p>These tools allowed the models to query the state of the world, such as checking how much health a monster had left or if a wall was blocking their view. When an agent wanted to take an action, it had to call a specific function that calculated the outcome based on the official rules. This prevented the models from simply making up results, which is a common problem in language generation.</p>
<p>The team tested three specific models: Claude 3.5 Haiku, GPT-4o, and DeepSeek-V3. Each model was given 27 different combat scenarios to play through, ranging from simple skirmishes to difficult battles. The researchers measured performance across six different categories, including how well they used their tools and how effectively they planned their tactics.</p>
<p>The data showed that Claude 3.5 Haiku was the most reliable agent in these sessions. It was particularly good at using the provided software functions correctly and staying in its assigned role. GPT-4o followed closely behind, showing strong performance but slightly less consistency in its tool usage.</p>
<p>DeepSeek-V3 performed at a lower level than the other two models. The researchers also tried to test a large open-source model with 120 billion parameters, but it failed to complete basic tasks and could not produce valid game sessions. This suggests that the size of a model is not the only factor that determines its ability to act as a functional agent.</p>
<p>One of the most frequent problems the researchers observed was a loss of focus over time. As the game turns went on, the models began to make more mistakes about the state of the world. For instance, a model might try to attack an enemy that had already been defeated or ignore a status effect that was currently affecting its character.</p>
<p>The researchers categorized these mistakes as hallucinations of the game state. They found that errors regarding the health of a character or their position on the map became more common as the history of the conversation grew longer. This indicates that current technology still has difficulty maintaining an accurate mental map of a situation during extended interactions.</p>
<p>The study also looked at how well the models could act in character while playing. To do this, they used an automated judge to scan the dialogue for “persona density” and “trait diversity.” This measured whether a model sounded like a heroic knight or a cunning rogue while it was announcing its moves.</p>
<p>Claude 3.5 Haiku was particularly successful at varying its vocabulary based on the specific character it was playing. It could switch between the wit of a bard and the calm of a druid with more reliability than the other models. DeepSeek-V3 tended to use the same few voices repeatedly, even when the situation changed.</p>
<p>The researchers also noted some unusual behaviors during the simulations. Monsters controlled by the models occasionally developed distinct personalities, taunting players with phrases like “shiny man’s gonna bleed” in the middle of a fight. Some heroic characters would stop to give speeches in the middle of dangerous situations, even when those actions were not tactically wise.</p>
<p>Tactical optimality was another major focus of the evaluation. The team checked if the models were choosing the best possible actions, such as attacking when an enemy was in range or moving to safety when injured. Claude 3.5 Haiku again led this category, showing a more aggressive and efficient use of its resources.</p>
<p>In easier scenarios, all the models managed to keep their players alive at a relatively high rate. However, when the difficulty increased, the differences in tactical planning became more apparent. The top models were better at using high-level spells and abilities to end fights quickly, while other models were more conservative.</p>
<p>The researchers used a combination of automated checks and human reviewers to verify these results. They found that their automated scoring system matched human judgment with very high accuracy. This means the framework could be used to test many more models in the future without needing a human to watch every game.</p>
<p>There are some limitations to the current work that the team plans to address in the future. The study primarily focused on combat, which is only one part of the Dungeons & Dragons experience. The researchers did not evaluate the social negotiation or the open-ended exploration that happens in a full campaign.</p>
<p>Future research will look at how fine-tuning a model on specific game data might improve its performance. The team also hopes to expand the simulator to include legal case simulations or business strategy games. These environments share the same need for strict rule adherence and long-term planning seen in tabletop role-playing.</p>
<p>The researchers suggest that improving how models track the state of a game will eventually lead to better AI assistants in the real world. If an agent can keep track of a complex battle on a digital map, it might also be better at managing a long-term project or coordinating a multi-party negotiation. For now, the game remains a difficult but productive hurdle for the next generation of artificial intelligence.</p>
<p>The study, “<a href="https://openreview.net/forum?id=rmEBSdk26P" target="_blank" rel="noopener">Setting the DC: Tool-Grounded D&D Simulations to Test LLM Agents</a>,” was authored by Ziyi Zeng, Shengqi Li, Jiajun Xi, Andrew Zhu, and Prithviraj Ammanabrolu.</p></p>
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<td><a href="https://www.psypost.org/human-penis-size-is-an-evolutionary-outlier-and-scientists-are-finding-new-clues-as-to-why/" style="font-family:Helvetica, sans-serif; letter-spacing:-1px;margin:0;padding:0 0 2px;font-weight: bold;font-size: 19px;line-height: 20px;color:#222;">Human penis size is an evolutionary outlier, and scientists are finding new clues as to why</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Jan 22nd 2026, 15:00</div>
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<p><p>A new study published in <em><a href="https://doi.org/10.1371/journal.pbio.3003595" target="_blank" rel="noopener">PLOS Biology</a></em> indicates that the size of a human penis influences how men are assessed by others, impacting both their perceived attractiveness to females and their perceived fighting ability among other males. The findings suggest that the unusually large size of the human penis, compared to other primates, likely evolved through a combination of female mate choice and male-male competition. This research offers the first experimental data showing that men use penis size as a visual signal to estimate the threat level of a potential rival.</p>
<p>The human penis is considered an evolutionary outlier because it is significantly longer and thicker than that of other great apes, relative to body size. Humans also lack a penis bone, or baculum, meaning the organ relies entirely on blood flow to maintain rigidity. Biologists have debated the origins of these unique traits for decades.</p>
<p>Some previous theories focused on post-copulatory factors. These hypotheses suggested that the size and shape of the penis might help remove the sperm of rival males or increase the likelihood of fertilization. However, less attention has been paid to pre-copulatory selection, which involves traits that influence the decision to mate or the outcome of physical contests before mating occurs.</p>
<p>In many animal species, physical traits act as signals of dominance or genetic quality. The researchers behind the current study sought to test if the human penis functions as a badge of status or an ornament of attraction. They aimed to isolate penis size from other physical characteristics to see if it independently affects how a male is perceived.</p>
<p>“The human penis is unusually large compared to our closest primate relatives, which has long been an evolutionary puzzle,” said study author <a href="https://upamaaich.com/" target="_blank" rel="noopener">Upama Aich</a>, a <a href="https://forrestresearch.org.au/profiles/dr-upama-aich/" target="_blank" rel="noopener">Forrest Research Fellow</a> at the Centre for Evolutionary Biology at The University of Western Australia. “While previous research focused on how penis size affects female mate choice, we wanted to address the other half of the story: male-male or intrasexual competition. We wanted to test if the penis size plays any role in how men use this trait to assess the fighting ability and sexual competitiveness of their rivals.”</p>
<p>To conduct this investigation, the research team employed an experimental design. They generated 343 unique male figures using computer software capable of creating anatomically accurate human bodies. These figures varied independently across three specific traits: height, body shape (measured by the shoulder-to-hip ratio), and flaccid penis size.</p>
<p>The researchers ensured a comprehensive range of body types by using seven different values for each of the three traits. The variations in height and penis size covered the natural range typically found in human populations. The body shape ranged from a pear-shaped torso to a broad-shouldered, V-shaped physique.</p>
<p>To allow participants to fully assess the physique, the figures were presented as short video clips. In these videos, the 3D model rotated 30 degrees to the left and right. This rotation allowed viewers to gauge the depth and proportions of the torso and genitals from multiple angles.</p>
<p>The study recruited a sample of over 800 adult participants. This group included approximately 200 females and 600 males. The researchers divided the experiment into two distinct viewing formats to test if the method of presentation influenced the results.</p>
<p>In one format, participants attended an in-person session where the figures were projected onto a wall at life-size scale. This setting provided a realistic representation of height and physical presence. In the second format, participants viewed scaled-down versions of the videos on personal computer screens or tablets via an online survey.</p>
<p>Female participants were asked to rate the figures based on sexual attractiveness. They used a standard numerical scale to record their preferences. Male participants were assigned to one of two different assessment tasks.</p>
<p>One group of men rated the figures based on perceived sexual competitiveness. They were asked to estimate how jealous they would feel if the figure was speaking to their partner. A second group of men assessed fighting ability by rating how threatened they would feel if the figure wanted to start a fight with them.</p>
<p>The analysis of the data revealed consistent patterns across both the in-person and online surveys. “We were surprised by how remarkably consistent our findings were across different contexts,” Aich told PsyPost. “Whether participants viewed life-sized projections in-person, or looked at small screens online, the patterns of assessment were in similar direction. This suggests that these physical assessments might be robust across individuals and different viewing environments.”</p>
<p>Female participants rated men who were taller, had a more V-shaped torso, and possessed a larger penis as more attractive. The study found that these three traits did not work in isolation but influenced one another.</p>
<p>For example, the positive effect of penis size on attractiveness was stronger for men who were already tall or had a V-shaped body. A larger penis provided a smaller boost in attractiveness score to figures that were shorter or had a more pear-shaped torso. This indicates that females assess these physical traits as a composite package rather than individually.</p>
<p>The female ratings also demonstrated a pattern of diminishing returns. While attractiveness scores increased with height, body shape, and penis size, the benefits began to plateau at the higher end of the spectrum. Extreme exaggeration of these traits did not result in a proportional increase in attractiveness rating.</p>
<p>“While penis size is a significant factor in how men and women assess a male’s sexual attractiveness and fighting ability, it doesn’t work in isolation,” Aich explained. “In our analysis, penis size interacted with body shape and height to affect male attractiveness. A larger penis provided a weaker benefit to smaller, more pear-shaped men. Meaning, height and body shape also played a significant role in these assessments.”</p>
<p>Male participants assessing the sexual competitiveness of rivals produced ratings that largely mirrored the female preferences. Men rated rivals with larger penises, greater height, and broader shoulders as more likely to cause jealousy. However, the male ratings differed in the shape of the selection curve.</p>
<p>Unlike the females, male participants did not show a pattern of diminishing returns. Men perceived that the attractiveness of a rival continued to increase linearly with the size of the traits. This finding implies that men may overestimate the extent to which women prefer exaggerated physical features.</p>
<p>The study provided novel insights into how men assess the fighting ability of rivals. Male participants rated figures with larger penises as more threatening and dominant. While height and body shape were the strongest predictors of perceived fighting ability, penis size had a distinct and statistically significant effect.</p>
<p>This suggests that men use genital size as a visual cue when assessing the strength or aggression of a potential opponent. The authors propose that this link might exist because testosterone exposure affects both genital development and muscle mass. A larger penis might therefore be perceived as an honest signal of hormonal fitness and physical capability.</p>
<p>Another potential explanation offered by the researchers involves the physiological response to stress. In humans, the flaccid penis can retract or shrink due to adrenaline release during situations of fear or anxiety. A larger flaccid penis might act as a signal that the rival is calm, confident, and not experiencing a stress response.</p>
<p>The researchers also measured the response time for each rating. They found that participants made decisions faster when viewing figures with a smaller penis, shorter height, and less V-shaped body. The rapid dismissal of these figures suggests that these traits are processed and categorized very quickly by the brain.</p>
<p>“Our study highlights how complex human social and sexual perceptions are,” Aich said. “Rather than focusing on one feature, people integrate multiple cues very quickly, often without conscious awareness. Understanding that complexity would help move discussions away from myths and toward evidence.”</p>
<p>“The main takeaway is that the human penis, while primarily functions for sperm transfer, also acts as a dual signal. It plays a role as a sexual ornament to attract mates and as a social signal to intimidate rivals. Importantly, we found that a larger penis increased male attractiveness and signalled perceived fighting ability.”</p>
<p>There are some limitations. The research utilized figures with flaccid penises, which is the most common state observed in daily life. However, sexual selection and mate choice in primates often involve erect displays, which might elicit different responses or stronger preferences.</p>
<p>Additionally, the study isolated specific physical traits in a controlled, artificial environment. In real-world interactions, assessment of a mate or rival involves a complex integration of cues. Factors such as facial symmetry, vocal pitch, personality, and social status likely interact with body morphology to determine overall attractiveness or dominance.</p>
<p>“A key caution is that the primary evolutionary function of the penis is sperm transfer; note that the signalling roles we studied and detected are secondary and helps explain the larger penis size we see in humans,” Aich said. “Also, our study used animated 3D models to isolate specific effects of male penis size, height, body-shape. In the real world, characteristics like personality, voice, and facial features can play massive roles in how we perceive others. A large penis doesn’t ‘guarantee’ attractiveness or dominance on its own.”</p>
<p>“Future work could examine how these visual cues interact with others, such as facial features, voice, or movement, and whether similar patterns hold across cultures.”</p>
<p>The study, “<a href="https://doi.org/10.1371/journal.pbio.3003595" target="_blank" rel="noopener">Experimental evidence that penis size, height, and body shape influence assessment of male sexual attractiveness and fighting ability in humans</a>,” was authored by Upama Aich, Chloe Tan, Rebecca Bathgate, Khandis R. Blake, Robert C. S. Capp, Jacob C. Kuek, Bob B. M. Wong, Brian S. Mautz, and Michael D. Jennions.</p></p>
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<td><a href="https://www.psypost.org/people-who-support-authoritarianism-tend-to-endorse-election-conspiracy-beliefs/" style="font-family:Helvetica, sans-serif; letter-spacing:-1px;margin:0;padding:0 0 2px;font-weight: bold;font-size: 19px;line-height: 20px;color:#222;">People who support authoritarianism tend to endorse election conspiracy beliefs</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Jan 22nd 2026, 14:00</div>
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<p><p>A longitudinal study looking into election conspiracy beliefs and attitudes towards democracy found that people who support authoritarianism tend to endorse stronger conspiracy beliefs. There was no evidence of the reverse effect (conspiracy beliefs fostering antidemocratic views). The research was published in <a href="https://doi.org/10.1111/pops.13075"><em>Political Psychology</em></a>.</p>
<p>Conspiracy beliefs are explanations that attribute significant events to secret plots by powerful, hidden groups. They tend to arise in situations of uncertainty, threat, or lack of trust in institutions. Such beliefs can provide a sense of order and meaning by simplifying complex realities. Psychological factors like cognitive biases, the need for control, and heightened pattern perception contribute to their appeal. Social factors, including group identity, marginalization, and online echo chambers, can further reinforce them.</p>
<p>Conspiracy beliefs are not limited to any single political ideology or cultural context. While some conspiracies have historically turned out to be true, people holding conspiracy beliefs typically do not base such beliefs on evidence available to them. They also tend not to change their beliefs when evidence that their beliefs are not true appears. Also, strong endorsement of conspiracy beliefs tends to be associated with distrust of experts and a reduced willingness to engage in collective action.</p>
<p>Study author Emma F. Thomas and her colleagues wanted to test the relationships between election conspiracy beliefs and critical attitudes toward democracy in general. More specifically, they looked into attitudes towards representative democracy, direct democracy, and authoritarianism. They note that elections are a central institution of democratic representative governments, and conspiracy beliefs that question the fairness and legitimacy of elections could be especially important for understanding diminished support for democracy.</p>
<p>Study participants were 609 citizens or permanent residents of the United States and 605 citizens of New Zealand. Both groups were recruited through Dynata Panels and compensated for their participation with points that were accrued to purchase gift cards. </p>
<p>The average age of U.S. participants was 50 years, while it was 48 for New Zealand participants. Around 50% of participants in both groups were female. 41% of U.S. participants and 33% of participating New Zealanders identified as conservative. 34% of U.S. participants identified as liberal, while 44% of participants from New Zealand identified as progressive.</p>
<p>The study was conducted during the 2020 U.S. Presidential Election (for U.S. participants) and around the time of the 2020 New Zealand General Election (for New Zealand participants). Participants completed study assessments approximately two weeks before the election results, on the day the election results were announced, and two weeks after the election results were announced. The study authors chose this time period because there were many election conspiracy stories being circulated in the media and on social networks at those times.</p>
<p>Study participants completed assessments of election conspiracy beliefs (e.g., “Widespread voter fraud is undermining this election” or “The election is being influenced by powerful people, who are altering the outcome”), attitudes towards democracy (e.g., “Democracies are indecisive and have too much squabbling” or “Democracy may have problems but it’s better than any other form of government”).</p>
<p>They also completed measures of support for authoritarianism, representative democracy, and direct democracy (using items such as “Having a strong leader who does not have to bother with a Congress or elections,” “Having a democratic political system where representatives elected by citizens decide what becomes law,” and “Having a democratic political system where citizens, not elected officials, vote directly on major national issues to decide what becomes law”).</p>
<p>The study authors used a methodological approach called random intercept cross-lagged panel models (RI-CLPM). This examines associations between measures taken at different times to draw inferences about the causal paths between them—in this case, whether conspiracy beliefs affect attitudes towards democracy or vice versa. This approach leverages the fact that causes produce consequences in the future but not in the past, and therefore a cause would be more strongly associated with future measures of its consequences than with their past measures.</p>
<p>Results showed that it is authoritarian and antidemocratic beliefs that support beliefs in election conspiracies, and not vice versa.</p>
<p>“There was little evidence that conspiracy beliefs temporally preceded changes in attitudes toward democracy or support for any specific form of government. Instead, people who supported authoritarianism more subsequently endorsed stronger conspiracy beliefs. The results suggested that, in the context of electoral contests (e.g., elections), antidemocratic people are more likely to endorse conspiracy beliefs rather than conspiracy beliefs fostering antidemocratic views,” the study authors concluded.</p>
<p>The study contributes to the scientific understanding of the links between political attitudes and conspiracy beliefs. However, it should be noted that while the approach used in this study can establish the direction of observed effects, it does not fully map the causal chain, as there may be factors not measured in the study influencing the observed associations.</p>
<p>The paper, “<a href="https://doi.org/10.1111/pops.13075">Conspiracy beliefs and democratic backsliding: Longitudinal effects of election conspiracy beliefs on criticism of democracy and support for authoritarianism during political contests,</a>” was authored by Emma F. Thomas, Alexander O’Donnell, Danny Osborne, Lucy Bird, Lisette Yip, Eliana Buonaiuto, Morgana Lizzio-Wilson, Linda Skitka, and Michael Wenzel.</p></p>
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<td><a href="https://www.psypost.org/ai-chatbots-tend-to-overdiagnose-mental-health-conditions-when-used-without-structured-guidance/" style="font-family:Helvetica, sans-serif; letter-spacing:-1px;margin:0;padding:0 0 2px;font-weight: bold;font-size: 19px;line-height: 20px;color:#222;">AI chatbots tend to overdiagnose mental health conditions when used without structured guidance</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Jan 22nd 2026, 12:00</div>
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<p><p>A new study published in <em><a href="https://doi.org/10.1016/j.psychres.2025.116844" target="_blank" rel="noopener">Psychiatry Research</a></em> suggests that while large language models are capable of identifying psychiatric diagnoses from clinical descriptions, they are prone to significant overdiagnosis when operating without structured guidance. By integrating expert-derived decision trees into the diagnostic process, researchers from the University of California San Francisco found they could improve the precision of these artificial intelligence models and reduce the rate of incorrect false positives.</p>
<p>The rapid development of artificial intelligence has led to increased interest in its potential applications within healthcare. Large language models like OpenAI’s ChatGPT have shown an ability to process and generate complex text, which has raised the possibility of their use in mental health settings for tasks such as decision support or documentation.</p>
<p>Many patients are already accessing these public tools to interpret their own symptoms and seek medical advice. However, these models are trained on vast datasets from the internet rather than specific medical curricula. This training method means the models function based on statistical probability and linguistic patterns rather than genuine clinical understanding.</p>
<p>There is a concern that without specific medical training or guardrails, these general-purpose models might generate inaccurate or harmful advice. The ability of a computer program to produce coherent text does not necessarily equate to the ability to perform the complex reasoning required for a psychiatric diagnosis.</p>
<p>The authors of the new study sought to evaluate whether generic large language models could effectively reason about mental health cases. They also aimed to determine if feeding the models specific, expert-created rules could enhance their accuracy and safety.</p>
<p>“There has been considerable interest in using Large Language Model (LLM)-based technologies to build clinical and research tools for behavioral health. Additionally, individuals are increasingly using LLM-based chatbots (such as ChatGPT, Claude, Gemini, etc.) as health information tools and for emotional support,” explained study author <a href="https://ksarma.com/" target="_blank" rel="noopener">Karthik V. Sarma</a>, who founded <a href="https://aimhealth.ucsf.edu/" target="_blank" rel="noopener">the UCSF AI in Mental Health Research Group</a> within the Department of Psychiatry and Behavioral Sciences at UCSF.</p>
<p>“We were interested in seeing how well these LLMs worked in our field, and chose vignette diagnosis as an example problem for evaluation. We also wanted to know if we could improve the performance of the models by constraining them to use reasoning pathways (decision trees) designed by psychiatric experts.”</p>
<p>To conduct this investigation, the researchers utilized a set of 93 clinical case vignettes drawn from the DSM-5-TR Clinical Cases book. These vignettes serve as standardized examples of patients with specific psychiatric conditions, such as depression, bipolar disorder, or schizophrenia. The team divided these cases into a training set, which was used to refine their prompting strategies, and a testing set, which was used to evaluate the final performance of the models. They tested three versions of the GPT family of models: GPT-3.5, GPT-4, and GPT-4o.</p>
<p>The researchers designed two distinct experimental approaches to test the models. The first was a “Base” approach, where the artificial intelligence was simply given the clinical story and asked to predict the most likely diagnoses. This method mimics how a casual user might interact with a chatbot by describing symptoms and asking for an opinion. The second method was a “Decision Tree” approach. This involved adapting the logic from the DSM-5-TR Handbook of Differential Diagnosis, a professional guide that uses branching logic to rule conditions in or out.</p>
<p>In the Decision Tree approach, the researchers did not ask the model for a diagnosis directly. Instead, they converted the expert logic into a series of “yes” or “no” questions. The model was prompted to answer these questions based on the case vignette.</p>
<p>For example, the model might be asked if a patient was experiencing a specific symptom for a certain duration. The answers to these sequential questions would then lead the system down a path toward a potential diagnosis. This method forced the model to follow a step-by-step reasoning process similar to that of a trained clinician.</p>
<p>The results showed a clear distinction between the two methods. When the models were directly prompted to guess the diagnosis in the Base approach, they demonstrated high sensitivity. The most advanced model, GPT-4o, correctly identified the author-designated diagnosis in approximately 77 percent of the cases. This indicates that the models are quite good at picking up on the presence of a disorder based on the text.</p>
<p>However, this high sensitivity came at the cost of precision. The Base approach resulted in a low positive predictive value of roughly 40 percent. This metric reveals that the models were casting too wide a net. They frequently assigned diagnoses that were not present in the vignettes.</p>
<p>On average, the base models produced more than one incorrect diagnosis for every correct one. This tendency toward overdiagnosis represents a significant risk, as it could lead to patients believing they have conditions they do not actually possess.</p>
<p>“This suggests to everyone that diagnoses generated by generalist chatbots may not be accurate, and it is important to consult with a health professional,” Sarma told PsyPost.</p>
<p>The implementation of the Decision Tree approach yielded different results. By forcing the models to adhere to expert reasoning structures, the researchers increased the positive predictive value to approximately 65 percent. This improvement means that when the system suggested a diagnosis, it was much more likely to be correct. The rate of overdiagnosis dropped compared to the direct prompting method.</p>
<p>There was a trade-off associated with this increased precision. The sensitivity of the Decision Tree approach was slightly lower than that of the Base approach, coming in at around 71 percent. This suggests that the strict rules of the decision trees occasionally caused the model to miss a diagnosis that the more open-ended method might have caught. Despite this slight drop in sensitivity, the overall performance as measured by the F1 statistic—a metric that balances precision and recall—was generally higher for the Decision Tree approach.</p>
<p>The study also highlighted the importance of refining the prompts used to guide the artificial intelligence. During the training phase, the researchers found that the models sometimes misunderstood medical terminology or the structure of the decision trees. For instance, the models initially struggled to differentiate between “substance use” and medical side effects, or they would misinterpret clinical terms like “ego-dystonic.” The researchers had to iteratively refine their questions to ensure the models interpreted the clinical criteria correctly.</p>
<p>The findings provide evidence that generalist large language models possess an emergent capability for psychiatric reasoning. Performance improved with each successive generation of the model, with GPT-4 and GPT-4o outperforming the older GPT-3.5. This trajectory suggests that as these models continue to evolve, their capacity for handling complex medical tasks may increase.</p>
<p>“Practically speaking, the reduction in overdiagnosis using our decision trees was significant,” Sarma explained. “However, the task we used (vignette diagnosis) is a much easier task than real-world diagnosis. I would expect performance at this stage to be much worse in the real world, and we are still working on methods to address this problem. For now, I do not believe that these generalist models are ready for use as mental health support agents, though there may be other specialist models that are more capable.”</p>
<p>The tendency for overdiagnosis observed in the Base approach is particularly relevant for the general public. Individuals using chatbots for self-diagnosis should be aware that these systems may be biased toward finding pathology where none exists. The study suggests that while artificial intelligence can be a powerful tool for analyzing behavioral health data, it works best when constrained by expert medical knowledge and validated guidelines.</p>
<p>“It was not our goal to produce an actual clinical tool that is ready to use, and that was not the outcome of our work,” Sarma noted. “Instead, we focused on investigation how well current models work, and on whether or not our idea to integrate the current models with expert guidelines was helpful. We hope our finding can be used to develop better real-world tools in the future.”</p>
<p>Future research will need to focus on testing these systems with real-world patient data to see if the findings hold up in clinical practice. The authors also suggest that future work could explore using these models to identify new diagnostic patterns or language-based phenotypes that go beyond current classifications. For now, the integration of expert reasoning appears to be a necessary step in making these powerful tools safer and more accurate for potential psychiatric applications.</p>
<p>“We are now working on developing systems that can operate on real-world data, and measuring the impact of different methods in this setting,” Sarma explained. “We’re also working on better understanding how the use of chatbots by people with diagnosed mental illnesses impact their health (<a href="https://www.sfexaminer.com/news/technology/with-delusions-rising-ucsf-to-study-ai-mental-health-link/article_20bebebe-86a0-4f18-802e-6aa8976e91df.html" target="_blank" rel="noopener">see more here</a>).”</p>
<p>The study, “<a href="https://doi.org/10.1016/j.psychres.2025.116844" target="_blank" rel="noopener">Integrating expert knowledge into large language models improves performance for psychiatric reasoning and diagnosis</a>,” was authored by Karthik V. Sarma, Kaitlin E. Hanss, Andrew J. M. Halls, Andrew Krystal, Daniel F. Becker, Anne L. Glowinski, and Atul J. Butte.</p></p>
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<td><a href="https://www.psypost.org/these-two-dark-personality-traits-are-significant-predictors-of-entrepreneurial-spirit/" style="font-family:Helvetica, sans-serif; letter-spacing:-1px;margin:0;padding:0 0 2px;font-weight: bold;font-size: 19px;line-height: 20px;color:#222;">These two dark personality traits are significant predictors of entrepreneurial spirit</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Jan 22nd 2026, 10:00</div>
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<p><p>A new study published in <em><a href="https://doi.org/10.1016/j.actpsy.2025.105901" target="_blank" rel="noopener">Acta Psychologica</a></em> provides evidence that specific personality traits, including narcissism and psychopathy, are significant predictors of entrepreneurial tendencies in adults. The findings suggest that an individual’s employment status and distinct personality profiles account for a substantial portion of the variation in their propensity for entrepreneurship.</p>
<p>Entrepreneurship is frequently viewed through the lens of economic activity, such as starting a business or generating employment. Psychologists increasingly recognize it as a multidimensional phenomenon involving the identification and exploitation of opportunities. This perspective treats entrepreneurialism as a stable personality trait rather than merely an occupational choice.</p>
<p>This distinction is significant because it suggests that entrepreneurial potential exists in individuals who may not currently own a business. The study of these latent tendencies helps scientists understand the psychological makeup of those who are likely to innovate and create value. Previous models have largely relied on general personality frameworks to explain these behaviors.</p>
<p>The Big Five personality model has been the standard for much of this research. This model assesses traits such as extraversion, agreeableness, openness, conscientiousness, and neuroticism. While these traits provide a baseline for understanding human behavior, they may not capture the full picture of the entrepreneurial mindset.</p>
<p>Less is known about how malevolent personality traits influence entrepreneurial spirit. Traits known as the Dark Triad—narcissism, Machiavellianism, and psychopathy—share characteristics with entrepreneurship. These shared qualities often include risk-taking, a high need for achievement, and a desire for control and autonomy.</p>
<p>Narcissism is characterized by grandiosity, dominance, and a sense of entitlement. Psychopathy involves high impulsivity, low empathy, and thrill-seeking behavior. Machiavellianism refers to a strategic and manipulative style of social interaction focused on self-interest.</p>
<p>“We wanted to address a gap in the literature by studying entrepreneurial tendencies from an individual differences perspective, rather than just comparing entrepreneurs and non-entrepreneurs,” said study author <a href="http://www.linkedin.com/in/m%C2%AA-inmaculada-l%C3%B3pez-n%C3%BA%C3%B1ez-34958316" target="_blank" rel="noopener">M. Inmaculada López-Núñez</a>, an associate professor and director of the Entrepreneurship Office at <a href="https://produccioncientifica.ucm.es/investigadores/146372/detalle" target="_blank" rel="noopener">Complutense University of Madrid</a>.</p>
<p>“While the Big Five traits are well studied, much less is known about the role of the Dark Triad and about how being self-employed relates to these tendencies. Our goal was to integrate personality and occupational status into a more comprehensive model.”</p>
<p>The research team recruited a sample of 591 adult workers from Spain to participate in the study. The participants ranged in age from 26 to 76 years, with an average age of approximately 45. Women made up about 60 percent of the group.</p>
<p>Regarding employment, the sample included salaried employees, self-employed individuals, and those who were unemployed or studying. Specifically, 14.6 percent of the participants identified as self-employed. The majority, 64.3 percent, were employed in salaried positions.</p>
<p>To measure the central variable of interest, the researchers used the Measure of Entrepreneurial Tendencies and Abilities. This tool assesses stable patterns of thought and behavior related to innovation and opportunity recognition. It asks respondents to rate their agreement with statements about spotting business opportunities and generating ideas.</p>
<p>Personality was evaluated using the Ten-Item Personality Inventory for the Big Five traits. This brief measure provides scores for extraversion, agreeableness, conscientiousness, emotional stability, and openness to experience. It is designed to capture the core dimensions of personality efficiently.</p>
<p>To assess malevolent traits, participants completed the Dirty Dozen scale. This instrument asks respondents to rate their agreement with statements reflecting manipulative or grandiose behaviors. It provides specific scores for narcissism, Machiavellianism, and psychopathy.</p>
<p>The analysis revealed that demographic variables, employment status, and personality traits combined to explain roughly 41 percent of the variance in entrepreneurial tendencies. This represents a strong predictive model in the context of psychological research. Several demographic factors emerged as significant predictors.</p>
<p>Being male was associated with higher levels of entrepreneurial potential. Being younger also predicted higher scores on the entrepreneurial tendencies measure. These findings align with some prior research regarding gender and age differences in risk-taking and business creation.</p>
<p>Employment status also played a clear role in the results. Individuals who were already self-employed scored significantly higher on the measure of entrepreneurial tendencies compared to those in other employment categories. This supports the idea that self-employment may serve as a valid behavioral indicator of an underlying entrepreneurial personality.</p>
<p>Regarding general personality traits, openness to experience proved to be the strongest predictor. Individuals who are open to new experiences tend to be more creative and willing to explore novel ideas. This trait is logically consistent with the innovation required in entrepreneurship.</p>
<p>Conscientiousness also showed a positive association with entrepreneurial tendencies. This trait involves organization, discipline, and goal-directed behavior. It suggests that the drive to turn ideas into reality requires a degree of diligent effort and planning.</p>
<p>Extraversion was another significant positive predictor. Entrepreneurs often need to network, pitch ideas, and lead teams. The sociability and energy associated with extraversion likely facilitate these necessary business activities.</p>
<p>A primary focus of the study was the influence of the Dark Triad. The results indicated that narcissism and psychopathy were positive predictors of entrepreneurial tendencies. Narcissism emerged as a particularly strong predictor in the final statistical model.</p>
<p>The influence of narcissism remained significant even when controlling for the Big Five traits. This indicates that the grandiose and dominant aspects of narcissism provide unique explanatory power.</p>
<p>Psychopathy also predicted entrepreneurial tendencies, though to a lesser extent than narcissism. The impulsivity and fearlessness associated with psychopathy might aid in the high-stakes decision-making often found in business ventures. Machiavellianism, while correlated, did not remain a significant predictor in the final regression model.</p>
<p>“We found it especially interesting that narcissism and psychopathy still predicted entrepreneurial tendencies even after controlling for the Big Five traits,” López-Núñez told PsyPost. “This suggests that these dark traits are not just minor or redundant, but add unique information to our understanding of entrepreneurial personality.”</p>
<p>The study provides evidence that the “dark side” of personality is not entirely maladaptive in the context of entrepreneurship. Characteristics that are socially undesirable in many contexts may function as assets when identifying and exploiting business opportunities. This highlights the complexity of the entrepreneurial personality.</p>
<p>These results suggest that the entrepreneurial profile is a mix of socially positive and negative traits. High openness and conscientiousness are generally viewed as positive assets. However, they appear to work in tandem with darker traits like narcissism to form the entrepreneurial disposition.</p>
<p>“Entrepreneurial potential is not just about skills or having a good idea, but is also linked to stable personality traits,” López-Núñez explained. “People who are more open, conscientious, and somewhat more extraverted — and also those higher in narcissism — tend to show stronger entrepreneurial tendencies. Being self-employed is not only a consequence of entrepreneurship, but is also associated with having this disposition.”</p>
<p>The study relies on cross-sectional data, which prevents the establishment of causal relationships between personality traits and entrepreneurial behaviors. It is possible that engaging in entrepreneurship influences personality just as personality influences career choices. The reliance on self-reported measures also introduces the possibility of response bias.</p>
<p>The cultural context of the study was limited to a Spanish population. While this expands research beyond English-speaking samples, it may limit the generalizability to other cultures. Different economic or cultural environments might value different traits in entrepreneurs.</p>
<p>The researchers note that finding links with dark traits does not imply these traits are necessary for success. The study measured tendencies and potential rather than long-term business performance or ethical conduct. It remains unclear how these dark traits affect the longevity or social responsibility of a business.</p>
<p>Future research could benefit from longitudinal designs to track how these traits evolve and influence career paths over time. The researchers also suggest investigating the cognitive mechanisms that link personality to entrepreneurial actions.</p>
<p>“We want to move beyond personality traits and focus more directly on the entrepreneurial mindset, especially on cognitive processes such as how people process information, detect opportunities, and make decisions,” López-Núñez said. “We are also interested in whether there are sensitive periods in life when this mindset can be more easily identified or activated. Ultimately, our goal is to better understand the cognitive mechanisms behind entrepreneurial tendencies, which are still much less studied.”</p>
<p>“We believe that thinking of entrepreneurship as an individual-differences trait, rather than just a job status, offers a richer way to understand why some people are more inclined to follow entrepreneurial paths than others.”</p>
<p>The study, “<a href="https://doi.org/10.1016/j.actpsy.2025.105901" target="_blank" rel="noopener">Individual differences in entrepreneurial tendencies: The impact of self-employment and the Dark Triad</a>,” was authored by M. Inmaculada López-Núñez, Sara Esteban-Gonzalo, Teresa Diéguez-Risco, and Juan F. Díaz-Morales.</p></p>
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<p><strong>Forwarded by:<br />
Michael Reeder LCPC<br />
Baltimore, MD</strong></p>
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