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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/older-obese-individuals-have-a-lower-risk-of-dementia-but-there-is-a-big-caveat/" 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;">Older obese individuals have a lower risk of dementia, but there is a big caveat</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Sep 2nd 2025, 10:00</div>
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<p><p>A longitudinal study of more than 5,000 older adults revealed that individuals classified as overweight had a 14% lower risk of developing dementia compared to those with normal weight, while obese participants had a 19% lower risk. However, further analysis indicated that individuals who lost weight from midlife to late life had an <em>increased</em> risk of dementia. Those whose weight remained stable had the lowest risk. The paper was published in <a href="https://doi.org/10.1212/wnl.0000000000213534"><em>Neurology</em></a>.</p>
<p>Late-life dementia refers to dementia that develops in older adulthood, typically after the age of 65. It is most often caused by Alzheimer’s disease, but vascular dementia, Lewy body dementia, and mixed forms are also common in later life. Risk increases sharply with age, with prevalence doubling approximately every five years after age 65.</p>
<p>Symptoms typically begin with mild memory loss and confusion and gradually progress to impaired judgment, disorientation, language difficulties, and loss of independence. Age-related changes in blood vessels, the accumulation of abnormal proteins in the brain, and other chronic conditions contribute to its onset. Midlife risk factors such as hypertension, diabetes, and obesity are important predictors of late-life dementia. Lifestyle factors such as physical activity, cognitive engagement, and social connectedness may delay or reduce risk.</p>
<p>Study author Ethan J. Cannon and his colleagues noted that obesity in midlife is a known risk factor for dementia. However, studies indicate that obesity in late life is associated with a lower risk. The researchers sought to better investigate this paradox and identify the factors that might explain it. They particularly focused on changes in body weight between midlife and late life. The authors hypothesized that individuals who lost weight during this period would be at increased risk of dementia, even more so than normal-weight individuals who maintained a stable weight.</p>
<p>They analyzed data from the Atherosclerosis Risk in Communities (ARIC) study, a longitudinal project conducted at four U.S. sites: suburban Minneapolis, MN; Jackson, MS; Forsyth County, NC; and Washington County, MD. From 1987 to 1989, the study enrolled a total of 15,792 participants. Multiple follow-up visits occurred through 2020. For the present analysis, 5,129 participants who remained dementia-free at Visit 5 (2011–2013) were included. At enrollment, participants were 45–64 years old. At Visit 5, the average age was approximately 75 years.</p>
<p>The researchers used data on participants’ body weight and BMI collected at two key timepoints—midlife (1996–1998) and late life (2011–2013)—and linked it to dementia diagnoses through a combination of in-person neuropsychological testing, structured telephone interviews, hospital discharge records, and death certificates.</p>
<p>They first examined whether BMI in late life alone predicted future dementia risk. Consistent with previous studies, individuals classified as overweight or obese in 2011–2013 were less likely to develop dementia in the years that followed. Specifically, those who were overweight had a 14% lower risk, and those who were obese had a 19% lower risk than normal-weight individuals.</p>
<p>However, when the researchers accounted for weight change from midlife to late life, the pattern shifted. Participants who maintained a normal weight over this 15-year period had the lowest risk of dementia. In contrast, individuals who lost at least 2 kg/m² in BMI during this period were at elevated risk—regardless of their BMI category in late life.</p>
<p>Compared to normal-weight individuals who remained weight stable, the hazard of developing dementia was, 2.22 times higher for those who lost weight but remained normal weight in late life, 1.78 times higher for those who lost weight and were overweight in late life, and 1.80 times higher for those who lost weight and were obese in late life.</p>
<p>Even individuals who gained weight and were classified as obese in late life had a slightly increased dementia risk—about 29% higher than the reference group. However, after adjusting for factors such as smoking, alcohol use, depressive symptoms, frailty, hypertension, and diabetes, this association was weakened.</p>
<p>For example, after full statistical adjustment overweight individuals who had lost weight from midlife to late life had a 53% higher risk of dementia, while normal-weight individuals who had lost weight had a 2.05-fold increased risk.</p>
<p>Meanwhile, overweight and obese individuals whose weight remained stable—or increased—did not differ significantly in dementia risk from the reference group of weight-stable, normal-weight individuals.</p>
<p>“In this study of older adults (mean age 75 years), we found that overweight or obesity in late life was associated with a lower risk of dementia, but that when stratified by weight change history, this result was explained in part by a disproportionate number of low-BMI [body mass index] participants who had experienced weight loss,” the study authors wrote. “Regardless of late-life BMI category, weight loss from midlife to late life was associated with a higher risk of dementia, compared with being normal weight in late life and weight stable from midlife to late life.”</p>
<p>The findings suggest that the so-called obesity paradox may be at least partly explained by unintentional weight loss associated with underlying health decline. The study could not determine whether weight loss was intentional or unintentional. Unintentional weight loss is common in older adults and is often a sign of deteriorating health. In such cases, both weight loss and cognitive deterioration can be the consequence of a decline in physical health.</p>
<p>Additionally, it is important to note that the design of the study does not allow any causal inferences to be derived from the results. That is, higher weight in late life should not be interpreted as protective <em>per se</em>, nor should normal weight be viewed as inherently risky. Rather, trajectories of weight change may serve as an important signal for dementia risk assessment.</p>
<p>The paper, “<a href="https://doi.org/10.1212/wnl.0000000000213534">Association of Body Mass Index in Late Life, and Change from Midlife to Late Life, With Incident Dementia in the ARIC Study Participants</a>,” was authored by Ethan J. Cannon, B. Gwen Windham, Michael Griswold, Priya Palta, David S. Knopman, Sanaz Sedaghat, and Pamela L. Lutsey.</p></p>
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<td><a href="https://www.psypost.org/artificial-intelligence-loses-out-to-humans-in-credibility-during-corporate-crisis-responses/" 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;">Artificial intelligence loses out to humans in credibility during corporate crisis responses</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Sep 2nd 2025, 08:00</div>
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<p><p>As artificial intelligence tools become increasingly integrated into public relations workflows, many organizations are considering whether these technologies can handle high-stakes communication tasks such as crisis response. A new study published in <em><a href="https://www.emerald.com/ccij/article-abstract/doi/10.1108/CCIJ-01-2025-0005/1267886/Credibility-and-organizational-reputation" target="_blank">Corporate Communications: An International Journal</a></em> provides evidence that, at least for now, human-written crisis messages are perceived as more credible and reputationally beneficial than those authored by AI systems.</p>
<p>The rise of generative artificial intelligence has raised questions about its suitability for replacing human labor in communication roles. In public relations, AI tools are already used for media monitoring, message personalization, and social media management. Some advocates even suggest that AI could eventually author press releases or crisis response messages.</p>
<p>However, prior studies have found that people often view AI-generated messages with suspicion. Despite improvements in the sophistication of these systems, the use of AI can still reduce perceptions of warmth, trustworthiness, and competence. Given the importance of credibility and trust in public relations, especially during crises, the study aimed to evaluate how the perceived source of a message—human or AI—affects how people interpret crisis responses.</p>
<p>The researchers also wanted to assess whether the tone or strategy of a message—whether sympathetic, apologetic, or informational—would influence perceptions. Drawing on situational crisis communication theory, they hypothesized that more accommodating responses might boost credibility and protect organizational reputation, regardless of the source.</p>
<p>“Our interest in understanding how people judge the credibility of AI-generated text grew out of a graduate class Ayman Alhammad (the lead author) took with Cameron Piercy (the third author). They talked about research about trust in AI during the class and the questions we posed in our study naturally grew out of that,” said study author Christopher Etheridge, an assistant professor in the William Allen White School of Journalism and Mass Communications at the University of Kansas.</p>
<p>To explore these questions, the researchers designed a controlled experiment using a hypothetical crisis scenario. Participants were told about a fictitious company called the Chunky Chocolate Company, which was facing backlash after a batch of its chocolate bars reportedly caused consumers to become ill. According to the scenario, the company had investigated the incident and determined that the problem was due to product tampering by an employee.</p>
<p>Participants were then shown one of six possible press releases responding to the crisis. The releases varied in two key ways. First, they were attributed either to a human spokesperson (“Chris Smith”) or to an AI system explicitly labeled as such. Second, the tone of the message followed one of three common strategies: informational (providing details about the incident), sympathetic (expressing empathy for affected customers), or apologetic (taking responsibility and issuing an apology).</p>
<p>The wording of the messages was carefully controlled to ensure consistency across versions, with only the source and emotional tone changing between conditions. After reading the message, participants were asked to rate the perceived credibility of the author, the credibility of the message, and the overall reputation of the company. These ratings were made using standardized scales based on prior research.</p>
<p>The sample included 447 students enrolled in journalism and communication courses at a public university in the Midwestern United States. These participants were chosen because of their familiarity with media content and their relevance as potential future professionals or informed consumers of public relations material. Their average age was just over 20 years old, and most participants identified as white and either full- or part-time employed.</p>
<p>The results provided clear support for the idea that human authors are still viewed as more credible than AI. Across all three key outcomes—source credibility, message credibility, and organizational reputation—participants rated human-written messages higher than identical messages attributed to AI.</p>
<p>“It’s not surprising, given discussions that are taking place, that people found AI-generated content to be less credible,” Etheridge told PsyPost. “Still, capturing the data and showing it in an experiment like this one is valuable as the landscape of AI is ever-changing.”</p>
<p>Participants who read press releases from a human author gave an average source credibility rating of 4.40 on a 7-point scale, compared to 4.11 for the AI author. Message credibility followed a similar pattern, with human-authored messages receiving an average score of 4.82, compared to 4.38 for AI-authored versions. Finally, organizational reputation was also judged to be higher when the company’s message came from a human, with ratings averaging 4.84 versus 4.49.</p>
<p>These differences, while not massive, were statistically significant and suggest that the mere presence of an AI label can diminish trust in a message. Importantly, the content of the message was identical across the human and AI conditions. The only change was who—or what—was said to have authored it.</p>
<p>In contrast, the tone or strategy of the message (apologetic, sympathetic, or informational) did not significantly influence any of the credibility or reputation ratings. Participants did perceive the tone differences when asked directly, meaning the manipulations were effective. But these differences did not translate into significantly different impressions of the author, message, or company. Even though past research has emphasized the importance of an apologetic or sympathetic tone during a crisis, this study found that source effects had a stronger influence on audience perceptions.</p>
<p>“People are generally still pretty weary of AI-generated messages,” Etheridge explained. “They don’t find them as credible as human-written content. In our case, news releases written by humans are more favorably viewed by readers than those written by AI. For people who are concerned about AI replacing jobs, that could be welcome news. We caution Public Relations agencies against over-use of AI, as it could hurt their reputation with the public when public reputation is a crucial measure of the industry.”</p>
<p>But as with all research, there are some caveats to consider. The study relied on a fictional company and crisis scenario, which might not fully capture real-world reactions, and participants—primarily university students—may not represent broader public attitudes due to their greater familiarity with AI. Additionally, while the study clearly labeled the message as AI-generated, real-world news releases often lack such transparency, raising questions about how audiences interpret content when authorship is ambiguous.</p>
<p>“We measured credibility and organizational reputation but didn’t really look at other important variables like trust or message retention,” Etheridge said. “We also may have been more transparent about our AI-generated content than a professional public relations outlet might be, but that allowed us to clearly measure responses. Dr. Alhammad is leading where our research effort might go from here. We have talked about a few ideas, but nothing solid has formed as of yet.”</p>
<p>The study, “<a href="https://doi.org/10.1108/CCIJ-01-2025-0005" target="_blank">Credibility and organizational reputation perceptions of news releases produced by artificial intelligence</a>,” was authored by Ayman Alhammad, Christopher Etheridge, and Cameron W. Piercy.</p></p>
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<td><a href="https://www.psypost.org/simple-rhythmic-sounds-can-reshape-the-brains-entire-network-landscape-study-finds/" style="font-family:Helvetica, sans-serif; letter-spacing:-1px;margin:0;padding:0 0 2px;font-weight: bold;font-size: 19px;line-height: 20px;color:#222;">Simple rhythmic sounds can reshape the brain’s entire network landscape, study finds</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Sep 2nd 2025, 06:00</div>
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<p><p>Even a basic auditory rhythm can reconfigure how the brain organizes itself, according to a new study published in <em>Advanced Science</em>. The researchers introduced a new analytical tool called FREQ-NESS, which maps how brain networks operate simultaneously across different frequencies. The results suggest that hearing rhythmic tones engages the auditory cortex while also reorganizing the brain’s broader network configuration, shifting dominant oscillations and enhancing communication between slower and faster brain rhythms.</p>
<p>Cognitive neuroscience aims to understand how the brain functions as a dynamic system that processes ongoing information from the environment. A key challenge has been accurately capturing how different brain networks—each with their own frequency and spatial characteristics—operate at the same time, particularly when the brain is responding to external stimuli like music or speech.</p>
<p>Previous research has tended to focus either on specific anatomical regions or on broad, canonical frequency bands such as alpha or gamma. These approaches have produced useful but fragmented insights. Another complication is that multiple brain processes overlap in time and space, which makes it hard to isolate individual networks using traditional methods.</p>
<p>To address this, the authors developed a method that could simultaneously capture frequency-specific brain networks with fine spatial and temporal resolution. Their approach also aimed to avoid the limitations of prior methods that depend on pre-defined anatomical regions or that group oscillations into broad frequency categories. The result was FREQ-NESS, short for FREQuency-resolved Network Estimation via Source Separation.</p>
<p>“We wanted a simple, transparent way to see how multiple brain networks operate at specific frequencies at the same time, and how this organization changes when the brain engages with sound and rhythm,” said study author <a href="https://scholar.google.com/citations?user=SbYDcxEAAAAJ&hl=it" target="_blank">Mattia Rosso</a> (<a href="https://bsky.app/profile/mattiarosso.bsky.social" target="_blank">@mattiarosso</a>), a postdoctoral researcher at the Center for Music in the Brain at Aarhus University.</p>
<p>“Existing tools either predefine regions/bands or blur overlapping processes, so we built FREQ-NESS as an analytical pipeline to estimate frequency-resolved networks directly at the level of the brain’s anatomical sources, facilitating the analysis of the temporal and spatial dynamics within the networks as well as the interactions across networks.”</p>
<p>The researchers recruited 29 participants, mostly non-musicians, and recorded their brain activity using magnetoencephalography (MEG), which offers high temporal precision. Participants underwent two five-minute sessions: one where they sat in a resting state while watching a silent movie, and another where they passively listened to rhythmic tones while watching the same movie. The tones were presented at a steady rate of 2.4 Hz—about two beats per second.</p>
<p>The MEG data were source-reconstructed to estimate the activity of over 3,500 brain voxels, representing fine-grained locations across the brain. These voxel time series were analyzed using generalized eigendecomposition, or GED, to separate narrowband (frequency-specific) activity from broader background signals. This allowed the researchers to isolate networks that were most active at specific frequencies.</p>
<p>They applied this analysis to 86 frequencies ranging from 0.2 Hz to nearly 100 Hz. Each frequency’s network structure was characterized by both how much variance it explained (a proxy for prominence) and by its spatial activation pattern. The researchers also examined how low-frequency activity modulated the amplitude of higher-frequency networks, a phenomenon known as cross-frequency coupling.</p>
<p>During the resting condition, the brain showed a typical 1/f pattern, with low frequencies dominating. Familiar networks emerged, including the default mode network (DMN) at low frequencies, a parieto-occipital alpha network peaking around 10.9 Hz, and a motor-related beta network around 22.9 Hz. These findings are consistent with past work showing that different brain areas tend to operate preferentially in certain frequency bands.</p>
<p>When participants listened to the rhythmic tones, the brain’s network landscape changed in three key ways.</p>
<p>First, the auditory stimulation caused the emergence of new networks that were sharply attuned to the stimulation frequency of 2.4 Hz and its harmonic at 4.8 Hz. These networks were concentrated in the auditory cortex, including Heschl’s gyrus, and extended to medial temporal areas such as the hippocampus and insula. These regions are known to play roles in both early and higher-level auditory processing.</p>
<p>Second, existing brain networks shifted their frequency preferences and spatial configurations. For example, the peak alpha activity moved from 10.9 Hz to 12.1 Hz, and the spatial focus of alpha activity shifted from parieto-occipital regions to sensorimotor areas. This suggests that the alpha network, commonly involved in attention and inhibition, reorganized itself in response to rhythmic input—perhaps to support movement preparation or sensory prediction.</p>
<p>Third, some networks remained largely unchanged. The motor-related beta network, centered around 22.9 Hz and focused in the precentral gyrus, showed similar prominence and topography in both resting and listening conditions. This stability suggests that not all networks are influenced equally by external stimuli.</p>
<p>Beyond these shifts, the researchers also found that listening to rhythmic tones strengthened the coordination between slower and faster rhythms. Specifically, the phase of the auditory-attuned network at 2.4 Hz modulated the amplitude of gamma-band activity—fast oscillations above 60 Hz—in brain areas such as the insula and frontal operculum. This cross-frequency coupling was stronger during listening than at rest, suggesting that the brain ramps up coordination across timescales to process rhythmic stimuli.</p>
<p>Importantly, these gamma-band networks were not located in the auditory cortex itself, but appeared in broader associative regions. This indicates that the brain’s response to rhythm is not limited to processing sounds per se—it may also involve integrating sensory input with memory, attention, or predictive processes.</p>
<p>“While based on our simulations we expected the primary auditory network to attune to the auditory stimulus, we were surprised to find that the rest of the network landscape underwent a global reorganization,” Rosso told PsyPost. “In particular, we found that the alpha network not only sped up (from about 10.9 Hz to 12.1 Hz) but also shifted from occipital to sensorimotor regions, suggesting a preparatory state for action in response to rhythmic input.” </p>
<p>“We were also struck that the gamma effects weren’t centered in primary auditory cortex but in a broader network (insula, inferior temporal, hippocampal, frontal operculum/IFG), suggesting that fast activity is mediated by interactions with low-frequency auditory networks rather than originating locally. Finally, the robustness of the method with very short recordings (down to ~30 seconds) was a pleasant surprise, confirming that the method is both sensitive and feasible.”</p>
<p>The researchers confirmed the robustness of their method through several replication tests. They showed that similar network landscapes could be detected in independent datasets, that shorter recording durations still produced reliable patterns, and that using different sensor types (gradiometers vs. magnetometers) had little effect on the results.</p>
<p>To test the importance of the method’s spatial and temporal structure, they also introduced randomization procedures. When the spatial arrangement of voxels was scrambled, the frequency content of the data was preserved but the resulting networks were meaningless. When both space and time structure were disrupted, the method failed to isolate any coherent networks. This provided further evidence that the detected patterns were not artifacts but reflected genuine brain dynamics.</p>
<p>“Even during very simple listening, the brain reconfigures, attuning its internal dynamics to the external world in order to process information,” Rosso summarized. “It does so in three main ways: 1) it attunes its primary sensory networks to the stimulation frequency, 2) adapts the frequency and spatial arrangement of intrinsic oscillations, and 3) boosts communication between slow and faster internal rhythms. In short, rhythm doesn’t just ‘light up the auditory area’—it retunes the brain’s network landscape as a whole.”</p>
<p>While FREQ-NESS provides a powerful tool for mapping frequency-specific brain networks, the study had some limitations, including a modest sample size, the absence of baseline noise data from “empty-room” MEG recordings, and a minimalistic task design. The researchers suggest that future work could involve more ecologically valid stimuli and expand the method to support broadband and multimodal applications.</p>
<p>“The brain is an extremely complex system: frequency is only one criterion for its organization,” Rosso noted. “FREQ-NESS is explicitly designed with the limited scope of separating brain networks and analyzing their interactions based on their frequency-specificity. We are very much aware of this, and so should be whoever is going to use the method for their investigation of the brain. Frequency alone does not ‘solve the brain.'”</p>
<p>“Our study presents the frequency-resolved variant of the broader NESS framework, which stands for ‘Network Estimation via Source Separation’. In a research landscape moving toward overly complex or AI-based ‘black box’ approaches, our aim is to extract insights from simple and interpretable linear decomposition methods, with very few degrees of freedom.” </p>
<p>“The two major directions now are: making the method multimodal (e.g., applicable to data of different nature), and developing a broadband variant, which is already in press in a high-impact journal. In the meantime, we and collaborators across European centers are applying the pipeline to study how the brain’s network landscape changes across states of consciousness, during psychedelic intake, with aging, in musicians, and other experimental conditions and populations.”</p>
<p>“The FREQ-NESS toolbox and full pipeline are openly available on our GitHub repository (<a href="https://github.com/mattiaRosso92/Frequency-resolved_brain_network_estimation_via_source_separation_FREQ-NESS/tree/main/FREQNESS_Toolbox" target="_blank">https://github.com/mattiaRosso92/Frequency-resolved_brain_network_estimation_via_source_separation_FREQ-NESS/tree/main/FREQNESS_Toolbox</a>), and the paper is open access,” he added. “We hope others will use it to probe how ongoing brain rhythms shape perception and action. We are also very open to providing guidance and setting up collaborations, so please do not hesitate to reach out.”</p>
<p>The study, “<a href="https://doi.org/10.1002/advs.202413195" target="_blank">FREQ-NESS Reveals the Dynamic Reconfiguration of Frequency-Resolved Brain Networks During Auditory Stimulation</a>,” was authored by Mattia Rosso, Gemma Fernández-Rubio, Peter Erik Keller, Elvira Brattico, Peter Vuust, Morten L. Kringelbach, and Leonardo Bonetti.</p></p>
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<td><a href="https://www.psypost.org/teenager-with-hyperthymesia-exhibits-extraordinary-mental-time-travel-abilities/" 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;">Teenager with hyperthymesia exhibits extraordinary mental time travel abilities</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Sep 1st 2025, 14:00</div>
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<p><p>A new case study describes a teenage girl with an extraordinary ability to recall personal memories in vivid detail and mentally revisit specific moments in her life at will. Her experiences reflect a rare condition known as hyperthymesia, or highly superior autobiographical memory. The report, published in <em><a href="https://doi.org/10.1080/13554794.2025.2537950" target="_blank" rel="noopener">Neurocase</a></em>, highlights her unusual capacity for mental time travel—not only into the past, but also into imagined futures—offering researchers new insight into how autobiographical memory may be organized and accessed in the brain.</p>
<p>Autobiographical memory refers to the ability to recall personal experiences and events situated in time and space. It includes both general life knowledge and episodic details that help people construct a sense of self and continuity over time. Some individuals possess an exceptionally rich form of this memory. Known as hyperthymesia or autobiographical hypermnesia, this condition is marked by an unusual ability to recall past personal events with extraordinary clarity and accuracy.</p>
<p>People with this ability tend to describe their memories as highly vivid, emotionally intense, and embedded in a strong sense of personal re-experiencing. This subjective feeling—often referred to as autonoetic consciousness—is closely linked to what scientists call mental time travel, or the capacity to mentally revisit the past and pre-experience the future.</p>
<p>Although a handful of such cases have been documented in the literature, hyperthymesia remains rare and poorly understood. In this new case report, researchers Valentina La Corte, Pascale Piolino, and Laurent Cohen—based at institutions including the Paris Brain Institute and the Université Paris Cité—explore how one teenage girl’s memory system operates, and how it may contribute to future research on personal memory and cognition.</p>
<p>The subject of the study, referred to as TL, was a 17-year-old high school student in France when she came to the researchers’ attention. She had long known her memory was different. As a child, she would casually mention her ability to mentally revisit past events to check for details, only to be accused of lying by her peers. Eventually, she disclosed this ability to her family at age 16.</p>
<p>TL’s recollections were not merely accurate—they were structured. She described a highly organized internal world where memories were stored in a large, rectangular “white room” with a low ceiling. Within this mental space, personal memories were arranged thematically. Sections were dedicated to family life, vacations, friends, and even her collection of soft toys. Each toy had its own memory tag, including information about when and from whom it was received.</p>
<p>Importantly, these recollections were not purely factual. They carried emotional weight and vivid perceptual details. TL could mentally relive events from both her original perspective and from an outside observer’s view. She described, for instance, her first day of school in striking detail: what she wore, the weather, and the precise visual memory of her mother watching her through the fence. These experiences were accompanied by a strong sense of re-experiencing.</p>
<p>In contrast to this rich “white room” of personal events, TL referred to her semantic and academic knowledge as “black memory.” This separate system lacked emotional content and spatial organization, and it was acquired through deliberate effort rather than spontaneous recollection.</p>
<p>Beyond memory storage, TL described three additional rooms in her internal world, each associated with specific emotional functions. A cold “pack ice” room helped her cool down when angry. A “problems room” was empty but served as a space for pacing and thinking. A more uncomfortable “military room,” associated with her father’s absence due to military service, was linked to guilt. These features suggest a broader internal architecture shaped by emotional needs and reflective processes, not just memory content.</p>
<p>To objectively assess TL’s autobiographical memory, the researchers administered two standardized tasks. The first, the TEMPau task, evaluates how people recall personal events from different life stages. TL was assessed only for childhood and adolescence, given her age. Her scores were well above the norm. She produced highly specific, richly detailed memories and consistently reported a sense of “remembering” rather than simply “knowing” facts. She also demonstrated an ability to shift between field and observer perspectives when describing events.</p>
<p>The second task, the TEAAM, measured her ability to recall or imagine brief, detailed episodes from both the past and the future. TL again performed at the top of the expected range. Her imagined future events were not only plausible and contextually rich, but also carried a strong feeling of “pre-experience”—a subjective sensation akin to remembering, but applied to anticipated personal events.</p>
<p>The researchers noted that TL’s performance declined slightly when imagining more distant future events. This tendency aligns with what is typically seen in the general population: the further away an event is in time, the less vivid and detailed it tends to be. Still, TL’s ability to mentally travel through time—to revisit the past and pre-live the future—was striking.</p>
<p>Although TL’s case provides compelling insights, it is important to acknowledge the limits of case studies in general. Single cases do not allow for broad generalizations. There are no control groups for comparison, and findings cannot speak to prevalence or typical development.</p>
<p>In TL’s case, the researchers did not use some of the traditional diagnostic tools for hyperthymesia, such as verifying public event recall or testing calendar accuracy over many years. Her superior memory was based on self-report, corroborated by performance on specific tasks, but not benchmarked against the same criteria used in some earlier studies.</p>
<p>There are also limits to verifying the accuracy of detailed childhood memories. Some events may be reconstructed, influenced by photos, family stories, or even dreams, which TL herself acknowledged as occasional sources of memory content.</p>
<p>Despite these constraints, case reports like this one remain an important tool in the scientific study of the mind. When researchers encounter rare or unusual cognitive profiles, documenting them in detail can generate new hypotheses, refine existing theories, and identify directions for future research.</p>
<p>In TL’s case, her vivid and structured autobiographical memory, along with her conscious and emotionally guided use of mental time travel, points to a potentially unique form of memory organization. Unlike previous cases where hyperthymesia seemed involuntary or overwhelming, TL appeared to have some degree of control over how she accessed and navigated her memories. Her experiences also invite questions about the role of emotion, spatial imagery, and subjective perspective in the construction of memory.</p>
<p>Additionally, her use of imagined rooms tied to emotional regulation adds a novel dimension to how memory might interface with other cognitive processes. It hints at the possibility that some individuals may develop internal mechanisms to manage the emotional weight of their memories or structure them in ways that aid retrieval.</p>
<p>“It is difficult to generalize findings about hyperthymesia, since they rely on only a few cases. Does ageing affect the memories of these individuals? Do their mental time-travel abilities depend on age? Can they learn to control the accumulation of memories? We have many questions, and everything remains to be discovered. An exciting avenue of research lies ahead,” concludes La Corte.</p>
<p>The case report, “<a href="https://doi.org/10.1080/13554794.2025.2537950" target="_blank" rel="noopener">Autobiographical hypermnesia as a particular form of mental time travel</a>,” was published August 1, 2025.</p></p>
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<td><a href="https://www.psypost.org/ai-powered-brain-stimulation-system-can-boost-focus-for-those-who-struggle-with-attention/" 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-powered brain stimulation system can boost focus for those who struggle with attention</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Sep 1st 2025, 12:00</div>
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<p><p>A new study published in <em><a href="https://doi.org/10.1038/s41746-025-01744-6" target="_blank">npj Digital Medicine</a></em> provides evidence that a personalized, artificial intelligence–powered brain stimulation system can enhance sustained attention in people completing cognitive tasks at home. The system uses non-invasive electrical brain stimulation and an adaptive algorithm to tailor stimulation to each person’s baseline attention level and head size. The results indicate that this personalized approach was especially effective for individuals who initially had lower attention performance, suggesting potential for future use in both everyday settings and clinical populations.</p>
<p>Maintaining attention over long periods is essential for many real-world tasks—whether it’s staying alert while driving, focusing on schoolwork, or remaining engaged during meetings. When attention falters, the consequences can range from missed learning opportunities to dangerous accidents. And for people with neurological or psychiatric conditions like attention-deficit/hyperactivity disorder, depression, or brain injury, the stakes are even higher.</p>
<p>Despite this, existing methods to improve attention, such as cognitive training or medications, have limitations. Brain stimulation, especially methods involving low electrical currents like transcranial electrical stimulation, has shown some promise in boosting cognitive performance. But most studies use fixed stimulation protocols that do not account for differences between individuals. Results have been inconsistent, likely because a single dose or method does not work equally well for everyone.</p>
<p>Additionally, most previous studies have been conducted in controlled laboratory settings, which reduces their relevance to real-life environments. In this context, the research team aimed to address two key challenges: how to personalize brain stimulation for each person, and how to deliver it effectively in a real-world setting such as a participant’s home.</p>
<p>“Most of our research is lab-based and doesn’t always translate outside the lab, where we hope it will work. We have noticed, along with other researchers, that not everyone benefits from the stimulation, so how can we improve that? There’s also a broader societal challenge in maintaining attention at a good level,” said study author Roi Cohen Kadosh, head of the School of Psychology and professor of cognitive neuroscience at the University of Surrey. </p>
<p>To develop a personalized system, the researchers created a framework that uses artificial intelligence to adapt stimulation settings for each individual. The system centers on a technique called transcranial random noise stimulation. Unlike more intense or invasive methods, this technique uses painless, low-intensity electrical currents delivered through electrodes placed on the scalp.</p>
<p>The researchers trained a machine learning algorithm using what’s called personalized Bayesian optimization. This method adjusts the intensity of the stimulation by analyzing each person’s baseline attention score and head circumference—a factor that affects how much current reaches the brain. As more people used the system, the algorithm refined its recommendations, improving over time.</p>
<p>The researchers tested the system across three experiments. To measure sustained attention, the researchers used a task developed by the US Air Force Research Laboratory. Participants acted as air traffic controllers monitoring aircraft on a screen. They had to press a button for “safe” aircraft paths and withhold their response for “critical” paths that could lead to collisions. Only about 11 percent of the trials were critical, mimicking the low-frequency but high-risk situations faced in real monitoring jobs.</p>
<p>The main performance metric was a score called A-prime, which reflects the participant’s ability to distinguish safe from unsafe trials. This score became the target the AI system tried to optimize by adjusting stimulation intensity. An improvement in this score after stimulation indicated better sustained attention.</p>
<p>In the first experiment, 103 participants used the neurostimulation device at home while completing the air traffic control simulation. Participants measured their own head size, completed a baseline attention task, and then received stimulation adjusted by the algorithm. The system identified an inverted U-shaped pattern in the data—suggesting that there is an optimal intensity for stimulation, and going above or below that level tends to worsen performance. The optimal dose also varied based on head size and baseline attention levels.</p>
<p>The findings provide evidence that “we can improve people’s attention outside of the lab using personalized neurostimulation to maximize the benefit,” Cohen Kadosh told PsyPost.</p>
<p>The second experiment used simulated data to compare the personalized approach against other optimization methods, including random selection and non-personalized algorithms. The personalized method outperformed both alternatives, although its advantage narrowed as noise in the data increased. This suggests that the AI system is more effective when the input data are reliable, which has implications for future deployment in uncontrolled environments.</p>
<p>In the third experiment, the researchers directly compared the personalized stimulation method to a fixed-intensity stimulation and a sham condition. This phase included 37 participants who completed three sessions, each with a different type of stimulation. </p>
<p>Only those with lower initial attention scores showed significant improvement from the personalized approach. Those who already performed well at baseline did not see further gains. This pattern “fits with the idea that there is a limit to how much we can improve cognition,” Cohen Kadosh said.</p>
<p>Participants did not report differences in side effects across conditions, and stimulation intensity did not predict discomfort or adverse outcomes. This suggests the method is safe for at-home use, at least in healthy adults.</p>
<p>While the study suggests that personalized brain stimulation can enhance attention in people with lower baseline performance, several limitations remain. The device and algorithm were tested only in healthy young adults, so it is unclear whether the same benefits would occur in people with attention-related conditions such as ADHD or long COVID. The researchers say that future work will need to extend to clinical populations.</p>
<p>Some participants also experienced technical issues, including device malfunctions and task crashes, leading to data exclusions. In particular, the remote nature of the experiment posed challenges for ensuring consistent engagement, as participants could lose motivation during repetitive tasks. The researchers note that about 27 percent of the sessions had to be excluded due to highly variable baseline performance, likely reflecting fluctuating motivation or attention.</p>
<p>Another limitation is that the algorithm was developed to optimize overall performance rather than track moment-to-moment fluctuations in attention. While this makes the system easier to use and more scalable, it may not capture the full complexity of how attention waxes and wanes in real time.</p>
<p>The study also raises broader ethical questions about the future use of cognitive enhancement technologies. The researchers point out that the personalized nature of their system might help reduce disparities, since it appears to work best for individuals starting from a lower performance baseline. Unlike some neuroenhancement tools that might widen performance gaps, this system could help narrow them. Still, questions about access, data privacy, and long-term safety will need to be addressed as the technology advances.</p>
<p>The study, “<a href="https://doi.org/10.1038/s41746-025-01744-6" target="_blank">Personalized home based neurostimulation via AI optimization augments sustained attention</a>,” was authored by Roi Cohen Kadosh, Delia Ciobotaru, Malin I. Karstens, and Vu Nguyen.</p></p>
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<p><strong>Forwarded by:<br />
Michael Reeder LCPC<br />
Baltimore, MD</strong></p>
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