CP v2

Category: Relationships

  • Eye-tracking training hints at new ways to build social skills in children with multiple disabilities

    Researchers at the University of Geneva say eye-tracking tools may help children and teenagers with multiple disabilities strengthen key social and emotional abilities. The findings challenge the long-held assumption that many of these patients are effectively untestable with standard methods.

    Multiple disabilities typically combine severe intellectual and motor impairments and can leave a person profoundly dependent on caregivers. Communication is often limited to subtle cues such as changes in muscle tone, eye movements, or facial expressions, which can be difficult to interpret consistently.

    What the study tested

    Building on earlier work showing that some patients demonstrate clear visual preferences, the team partnered with the University of Lille to test a year-long, personalised training approach. The study, published in Acta Psychologica, followed nine participants aged 7 to 18 through 40 to 100 individual sessions.

    The training relied on eye-controlled educational video games, allowing participants to interact without speech or hand movement. Researchers used several software tools, including the open-source Gazeplay platform alongside custom-built programmes designed to target specific socio-emotional skills.

    Early gains, careful conclusions

    By the end of the programme, all participants improved their visual exploration patterns, a basic skill that can support more complex communication. Each participant also progressed in at least one measured area, such as social orientation, joint attention, emotion discrimination, or socio-moral judgement.

    Scientists emphasised that the results are preliminary given the small sample, but they argue the improvements point to learning capacities that may be overlooked in clinical practice. The researchers say the work could inform new assessment and support methods, especially where traditional testing fails.

    Why it could matter next

    Assistive technologies are increasingly used to help non-speaking or motor-impaired people express choices and engage with others, but evidence for training specific social skills remains limited. The researchers say eye-tracking offers a measurable, low-demand way to personalise interventions and track change over time.

    Further studies with larger groups will be needed to confirm how durable these gains are and which training elements matter most. Still, the team says the approach could broaden access to communication and learning opportunities for a population often excluded from research and tailored therapies.

  • American Heart Association warns sleep is more than hours: Why timing and regularity may shape cardiovascular risk

    Sleep health is not just about getting enough hours, according to a new American Heart Association scientific statement that reviews how multiple sleep traits may influence cardiometabolic risk. The statement was published in Circulation: Cardiovascular Quality and Outcomes.

    The authors describe sleep as a multidimensional pattern that includes duration, continuity, timing, satisfaction, regularity and daytime functioning. They argue that focusing only on total hours can miss key signals tied to blood pressure, blood sugar, cholesterol and body weight.

    Why sleep timing and regularity matter

    The statement notes that most adults generally need 7 to 9 hours of sleep, but both short sleep and long sleep have been linked in studies to higher cardiometabolic risk. Research summarized in the review also connects late bedtimes and inconsistent sleep schedules with outcomes such as obesity, elevated blood pressure and insulin resistance.

    Regularity is highlighted as a growing area of concern because weekday-weekend swings can create social jetlag. Large population studies have associated more consistent sleep-wake timing with lower cardiovascular mortality risk, though the authors stress that more trials are needed.

    Beyond hours: continuity and daytime function

    Sleep continuity, including difficulty falling asleep or repeated awakenings, is also discussed as a possible contributor to cardiovascular disease risk. Disturbed sleep continuity has been associated in research with higher risks that include hypertension, myocardial infarction and atrial fibrillation.

    Daytime functioning, such as excessive sleepiness, may reflect poor sleep quality or sleep disorders like obstructive sleep apnea. The statement links daytime sleepiness to higher rates of cardiovascular disease and stroke, and notes that addressing underlying causes can be part of risk reduction.

    Gaps in sleep health across communities

    The authors also review evidence that sleep health differs across populations due to social and environmental conditions. Noise, light exposure, housing and neighborhood safety, as well as socioeconomic stressors, have all been associated with poorer sleep patterns.

    The statement calls for clinicians to ask more detailed sleep questions and document problems that may warrant screening or treatment. It also urges more research to determine whether improving sleep dimensions beyond duration leads to measurable cardiometabolic benefits.

    Sleep is already included in the American Heart Association Life’s Essential 8 framework, but only sleep duration is currently scored. The authors say validated methods for assessing other sleep dimensions could eventually refine how cardiovascular risk is evaluated and managed.

  • Brain generosity study points to the basolateral amygdala as a key social-distance switch

    Neuroscientists investigating why people are generous to some but not others have identified a brain region that appears to fine-tune giving based on emotional closeness. The work, published in Proceedings of the National Academy of Sciences, focuses on the basolateral amygdala, part of the limbic system linked to emotion and social learning.

    The international team studied people with Urbach-Wiethe disease, an extremely rare condition that can cause selective damage to the basolateral amygdala while leaving much of the brain intact. Fewer than 150 cases have been documented worldwide, with one of the larger patient groups living in Namaqualand in northern South Africa.

    A natural experiment in social decisions

    Because the disease affects a specific region, researchers describe it as a quasi-natural experiment for probing prosocial behaviour. Previous research has connected the amygdala to processing emotional cues such as facial expressions, but its role in generosity has been harder to pin down.

    To test generosity in a controlled way, participants took part in dictator games, a standard tool in behavioural economics. They were asked to divide money between themselves and another person, with the recipient varying from close friends to acquaintances, neighbours, or strangers.

    Generous to friends, not strangers

    People with basolateral amygdala damage were as generous as healthy comparison participants when deciding about close friends. However, when the recipient was socially more distant, they tended to keep more money for themselves than the control group.

    The researchers conclude that the basolateral amygdala is not required for altruism in general, but helps calibrate how generous someone is depending on social distance. When that calibration is impaired, self-interest appears to dominate unless a strong emotional bond is present.

    Why the findings may matter

    By linking social-distance sensitivity to a specific brain circuit, the study adds context to how biology and lived experience jointly shape social behaviour. The authors say the results could also inform research into conditions where social decision-making differs from typical patterns, including autism spectrum disorder and psychopathy.

    They caution that the findings come from a rare patient group and do not imply that one brain area single-handedly determines moral choices. Still, the work suggests that therapies aimed at improving social functioning may benefit from targeting how people evaluate emotional closeness and context during decisions.

  • Cornell study suggests scent can predict platonic chemistry in minutes, and conversations may reshape what we smell

    New research from Cornell University suggests that scent plays a measurable role in how quickly strangers decide whether they could become friends. The study found that, within minutes, people’s reactions to another person’s everyday smell aligned with their impressions after a brief face-to-face chat.

    The work, published in Scientific Reports, examined how odor-based preferences interact with early social encounters. Researchers focused on friendship formation rather than romantic attraction, an area that has historically dominated social olfactory research.

    How the speed-friending test worked

    The study recruited heterosexual women who took part in four-minute speed-friending conversations. Participants also evaluated the everyday scent of other participants using T-shirts that had absorbed their normal, day-to-day odor.

    Researchers reported that individual, highly personal scent preferences predicted how much participants liked their interaction partners after the short conversations. Those patterns were not driven by a single universally appealing or unpleasant smell, but by idiosyncratic taste that varied by person.

    Everyday odor, not lab-made purity

    Instead of isolating a so-called natural body odor, the research leaned into what the authors describe as a signature scent shaped by daily life. That includes choices such as hygiene products, household environment, pets, and other common exposures that can influence how someone smells in real settings.

    In that sense, the study frames scent as part of a broader social signal people carry into first meetings, even if they are not consciously aware of it. The findings suggest smell is registered alongside conversation cues, body language, and other first-impression factors.

    Can a chat change how someone smells?

    One of the most notable findings was that the interaction appeared to work both ways. Participants’ ratings from the live conversation predicted shifts in how they later judged that same person’s T-shirt scent.

    That pattern suggests social experience can reshape odor perception, linking a person’s smell to the quality of the encounter. The researchers argue this feedback loop may help explain why people sometimes warm to someone over time, including on a sensory level.

    While the study focuses on a specific group and a controlled experimental design, it adds to evidence that scent influences social bonding beyond dating. The authors say future work could test whether similar effects appear across broader populations and in more natural social settings.

  • Penn State wearable sticker pairs biosensors and AI to spot genuine emotions, even behind a calm face

    Researchers at Penn State say they have developed a stretchable, rechargeable sticker designed to detect genuine emotions by combining facial movement data with physiological signals such as skin temperature and heart rate.

    The team argues the approach could help clinicians understand what patients feel in real time, especially when facial expressions alone are misleading or emotions are intentionally concealed.

    In a study published in Nano Letters, the researchers describe a BandAid-sized patch that measures several body signals linked to emotional states, including temperature, humidity, heart rate and blood oxygen levels.

    The device is built from thin, flexible layers of metals such as platinum and gold, shaped to remain sensitive even when bent, pulled or twisted during natural facial movement.

    How the emotion-tracking patch works

    To reduce measurement errors, the sensors are arranged so they operate independently, with protective layers intended to prevent stretching or moisture from distorting readings from neighboring components.

    Alongside the biosignals, facial strain sensors capture subtle changes in expression, and the system fuses those inputs to separate acted emotions from those tied to physiological responses.

    AI training and early accuracy results

    The researchers trained an AI model using repeated facial-expression performances across six categories: happiness, surprise, fear, sadness, anger and disgust.

    In the reported tests, the model classified performed facial expressions with 96.28% accuracy, based on data collected while participants repeatedly displayed each expression.

    To probe real emotions, participants watched video clips intended to elicit feelings while the patch tracked physiological changes associated with emotional arousal.

    The system identified emotions with 88.83% accuracy in those tests, with sensor readings aligning with known links between emotions and changes in metrics such as skin temperature and heart rate.

    Potential uses in telemedicine care

    The patch wirelessly transmits measurements to mobile devices and cloud systems, which the researchers say could support remote monitoring in telemedicine settings.

    The team also says the device is designed to avoid collecting personal information beyond sensor signals, aiming to reduce privacy risks while still enabling clinical interpretation.

    While the work remains at a research stage, the authors suggest the platform could eventually support broader health applications, including monitoring non-verbal patients and tracking conditions where behavioral signals are difficult to assess.

    The project was supported by funding from the U.S. National Institutes of Health and the U.S. National Science Foundation, according to the researchers.

  • Primary progressive aphasia study tests brain stimulation to boost speech therapy and what it could mean for patients

    Primary progressive aphasia, or PPA, is a neurodegenerative condition that gradually erodes a person’s ability to communicate, often affecting speech, writing, and word retrieval. There is currently no drug proven to halt or reverse the underlying progression, so care typically focuses on supportive speech-language therapy.

    Researchers at the University of Arizona are reporting encouraging results from an approach that pairs standard speech therapy with transcranial direct current stimulation, a noninvasive method that delivers a low electrical current through electrodes placed on the scalp. The goal is to amplify therapy gains by targeting brain networks involved in language.

    How the trial was designed

    The study, published in the Journal of Speech, Language, and Hearing Research, focused on logopenic PPA, a subtype often marked by difficulty finding words and repeating phrases. The team used neuroimaging to help identify stimulation targets while avoiding brain areas that were already significantly atrophied.

    Twelve participants with written language deficits completed two treatment phases in a randomized order, separated by a two-month break. In one phase, they received speech therapy plus active stimulation, and in the other, the same therapy paired with placebo stimulation.

    What improved after stimulation

    Participants improved after both phases, but the gains were stronger and lasted longer when active stimulation was added, the researchers reported. They observed clearer writing outcomes, including fewer spelling errors and better-formed, more meaningful sentences.

    Senior author Aneta Kielar said the team’s rationale reflects how language relies not only on meaning but also on retrieving a word’s sound structure during speaking and writing. Lead researcher Katlyn Nickels noted that PPA has only been widely characterized for several decades, leaving key aspects of treatment and recovery underexplored.

    Why neuroplasticity matters in PPA

    The researchers propose that stimulation may help promote neuroplasticity, the brain’s ability to reorganize and strengthen connections that support learning. In this view, brain stimulation does not replace therapy but may boost the effects of training by making language networks more responsive.

    They also emphasized that transcranial direct current stimulation is generally described in the field as relatively inexpensive and straightforward to administer under appropriate clinical protocols. Next, the group plans to study genetic, cognitive, and neural markers that could help predict which patients benefit most and how durable improvements can be in real-world care.

    The work was supported by multiple state and university health research programs, according to the authors. While larger studies are needed to confirm results and define best practices, the findings add to growing interest in combining rehabilitation with targeted neuromodulation for neurodegenerative language disorders.

  • Study finds listeners use hand gestures to anticipate the next word in conversation

    In face-to-face conversation, people do more than listen to speech: they also read the hands. New research suggests that listeners use meaningful hand gestures to predict which words a speaker is likely to say next, speeding up comprehension.

    The work, led by scientists at the Max Planck Institute for Psycholinguistics and Radboud University in Nijmegen, tested whether gestures provide advance cues about upcoming speech. The team combined behavioural measures with electroencephalography, or EEG, to track how the brain responds.

    Avatars reveal predictive listening

    To tightly control timing and movement, the researchers used realistic virtual avatars that asked everyday questions. In one experiment, the avatar paused just before a key word, such as type, while making either a relevant typing gesture, a meaningless movement, or no movement at all.

    Participants were asked to guess how the sentence would end before hearing the missing word. They predicted the target word more often when they saw the matching gesture, indicating that hand movements can guide expectations about what comes next.

    EEG signals anticipation and easier processing

    A second experiment examined brain activity while a different group simply listened to the full questions. During the silent pause before the target word, EEG patterns associated with anticipation differed depending on whether a meaningful gesture was present.

    After the target word appeared, the brain showed a reduced N400 response when the gesture matched the word, a signal commonly linked to easier semantic processing. Together, the results suggest gestures help listeners prepare for upcoming meaning rather than merely adding emphasis after the fact.

    Implications for robots and assistants

    The findings also point to practical design choices for artificial agents, including robots and virtual assistants that use embodied avatars. If gestures can help humans predict speech in natural conversation, adding well-timed, meaningful hand movements could make synthetic communicators easier to understand.

    Researchers say the broader takeaway is that language comprehension is inherently multimodal. Listeners do not wait passively for words to arrive, but actively integrate visual cues such as gestures to anticipate what a speaker is about to say.

  • Study finds earlier bedtimes and longer sleep may sharpen teens cognitive performance, even with small differences

    Adolescents who sleep a little longer and tend to fall asleep earlier show stronger brain function and do better on cognitive tests than peers with later, shorter sleep, according to researchers in the UK and China.

    The findings draw on objective sleep tracking and brain imaging, offering fresh evidence that modest changes in sleep habits may be linked to measurable differences in how the teenage brain works.

    Researchers analyzed data from the Adolescent Brain Cognitive Development study in the United States, using Fitbit sleep measures from more than 3 200 participants aged 11 to 12 and comparing them with brain scans and cognitive testing.

    They then checked whether similar patterns appeared in two additional groups aged 13 to 14, totaling about 1 190 participants, to see if the results held up beyond a single age snapshot.

    The team identified three broad sleep profiles, with average sleep times ranging from about 7 hours 10 minutes to roughly 7 hours 25 minutes, a gap of just over 15 minutes between the shortest and longest sleepers.

    Despite the small difference, adolescents in the longest-sleep group performed best on tests that assess skills such as vocabulary, reading, problem solving, and focus.

    Brain measures differed across groups

    Brain imaging also showed differences that tracked with sleep patterns, with the longest-sleep group showing the largest overall brain volume and stronger brain function measures, while the shortest-sleep group showed the smallest volume and weakest measures.

    The study did not find significant differences in school achievement between groups, suggesting standardized academic outcomes may not capture the subtler cognitive effects observed in testing.

    Heart-rate data during sleep pointed in the same direction: the longest-sleep group had the lowest heart rates across sleep states, while the shortest-sleep group had the highest.

    Lower sleeping heart rates are generally associated with better cardiovascular health and can align with more stable, higher-quality sleep, while higher rates can accompany restless sleep and frequent awakenings.

    Most teens still fell short

    Even the best sleepers in the study were not reaching the amount of sleep typically recommended for adolescents, highlighting how widespread sleep shortfalls can be in early teen years.

    The American Academy of Sleep Medicine advises that teenagers aged 13 to 18 should regularly sleep 8 to 10 hours per night for optimal health, while many fall below that range.

    Because the dataset follows participants over time, researchers reported that differences in sleep patterns and related brain and cognitive measures appeared to persist across multiple years around the main assessment window.

    The authors cautioned that the study cannot prove that better sleep directly causes better brain function, but they noted prior research supporting sleep’s role in memory consolidation and learning.

    What could be driving later bedtimes?

    The researchers said the next step is to better understand why some adolescents consistently go to bed later and sleep less, including potential influences such as evening screen use and individual body-clock differences.

    They argue that identifying the drivers of sleep loss could help shape practical interventions, since the results suggest even small improvements in sleep timing and duration may matter.

  • Precision Treatment for Depression: A New Data-Driven Model Aims to Match Patients With the Therapy Most Likely to Work

    Researchers are moving beyond trial-and-error care for depression with a precision approach designed to better match patients to treatments based on individual characteristics. The effort reflects growing evidence that depression symptoms and recovery paths vary widely from person to person.

    The project, led by psychologists at the University of Arizona and Radboud University, draws on patient-level data from randomized clinical trials across the world. Their protocol, published in PLOS One, outlines how they plan to build a clinical decision support tool for adult depression treatment selection.

    Why first-line care often fails

    Standard care frequently begins with a first-line medication or therapy and then shifts if symptoms persist, a process that can take months. The researchers point to prior findings that roughly half of patients do not respond to an initial treatment, highlighting the need for better targeting.

    Instead of offering broad guidelines, the planned tool would generate a single recommendation by weighing multiple factors at once. These include demographic information such as age and gender, along with clinical features like anxiety symptoms or personality-related difficulties.

    What data the model will use

    The team aggregated outcomes from more than 60 clinical trials involving nearly 10 000 patients, covering several widely used interventions. The treatments include antidepressant medications and multiple psychotherapy approaches, such as cognitive therapy, behavioral therapy, interpersonal therapy and short-term psychodynamic therapy.

    By combining many trials, the researchers aim to overcome limits that can affect prediction models built from single studies with smaller samples. They say the work required years of data cleaning and harmonization before analysis could begin.

    When it could reach clinics

    The next step is to develop the algorithm and then test it in a clinical trial to see whether tool-guided care improves outcomes compared with usual practice. If the results hold up, the system could be deployed as a simple software or web-based application used during routine assessments.

    The researchers argue that the inputs are intentionally practical, relying on information that can be collected through standard questionnaires and basic clinical intake. Their longer-term goal is to help clinicians and patients reach effective treatment faster while using existing mental health resources more efficiently.

  • Study finds AI still struggles to read social cues in video, a hurdle for self-driving cars and robots

    Humans still outperform today’s artificial intelligence at interpreting social interactions in moving scenes, a skill that underpins safer self-driving cars and more helpful assistive robots. New research from Johns Hopkins University suggests many leading models miss context that people grasp quickly.

    The team examined how well AI systems can infer intentions, relationships, and ongoing actions when people share a scene. These judgments help determine whether two pedestrians are chatting, about to cross the street, or reacting to one another.

    Testing AI against human perception

    In the study, participants watched three-second video clips and rated social features on a one-to-five scale. The clips showed people interacting, doing side-by-side activities, or acting independently.

    Researchers then asked more than 350 AI language, video, and image models to predict human ratings and expected brain responses. For large language models, the systems evaluated short, human-written captions describing the videos.

    Where models fell behind

    People largely agreed with one another across questions, but the AI models did not show the same consistency, regardless of size or training data. Video models often struggled to describe what people were doing, and image models given still frames could not reliably detect communication.

    Language models were comparatively better at predicting how humans would judge behavior, while video models were more aligned with predicted neural activity. Even so, none of the model types matched human responses across the board.

    Why reading the room is hard

    The researchers argue the gap highlights a difference between recognizing objects in static images and understanding the unfolding story in real life. They suggest a potential cause is that many AI architectures draw inspiration from brain systems tuned for static vision rather than dynamic social scenes.

    Lead author Leyla Isik said an autonomous vehicle needs to read intentions and goals, not just identify people and objects. Co-first author Kathy Garcia added that social relationships, context, and dynamics appear to be a persistent blind spot in current model development.

    The findings are being presented at the International Conference on Learning Representations, where researchers will discuss implications for AI that must interact safely with humans. The work adds to a growing body of evidence that high scores on benchmarks do not always translate to robust real-world understanding.