Taming the endless scroll? Short-form videos, digital routines and neurocognitive outcomes in youth

Mobile phone short video use negatively impacts attention functions: an EEG study

The pervasive nature of short-form video platforms has seamlessly integrated into daily routines, yet it is important to recognize their potential adverse effects on both physical and mental health. Prior research has identified a detrimental impact of excessive short-form video consumption on attentional behavior, but the underlying neural mechanisms remain unexplored. In the current study, we aimed to investigate the effect of short-form video use on attentional functions, measured through the Attention Network Test (ANT). A total of 48 participants, consisting of 35 females and 13 males, with a mean age of 21.8 years, were recruited. The Mobile Phone Short Video Addiction Tendency Questionnaire (MPSVATQ) and self-control scale (SCS) were conducted to assess the short video usage behavior and self-control ability. Electroencephalogram (EEG) data were recorded during the completion of the ANT task. The correlation analysis showed a significant negative relationship between MPSVATQ and theta power index reflecting the executive control in the prefrontal region (r = -0.395, p = 0.007), this result was not observed by using theta power index of the resting-state EEG data. Furthermore, a significant negative correlation was identified between MPSVATQ and SCS outcomes (r = -0.320, p = 0.026). These results suggest that an increased tendency towards mobile phone short video addiction could negatively impact self-control and diminish executive control within the realm of attentional functions. This study sheds light on the adverse consequences stemming from short video consumption and underscores the importance of developing interventions to mitigate short video addiction.

No effect of different types of media on well-being

It is often assumed that traditional forms of media such as books enhance well-being, whereas new media do not. However, we lack evidence for such claims and media research is mainly focused on how much time people spend with a medium, but not whether someone used a medium or not. We explored the effect of media use during one week on well-being at the end of the week, differentiating time spent with a medium and use versus nonuse, over a wide range of different media types: music, TV, films, video games, (e-)books, (digital) magazines, and audiobooks. Results from a six-week longitudinal study representative of the UK population 16 years and older (N = 2159) showed that effects were generally small; between-person relations but rarely within-person effects; mostly for use versus nonuse and not time spent with a medium; and on affective well-being, not life satisfaction.

Reciprocal Relationships between Trajectories of Depressive Symptoms and Screen Media Use during Adolescence

Adolescents are constantly connected with each other and the digital landscape through a myriad of screen media devices. Unprecedented access to the wider world and hence a variety of activities, particularly since the introduction of mobile technology, has given rise to questions regarding the impact of this changing media environment on the mental health of young people. Depressive symptoms are one of the most common disabling health issues in adolescence and although research has examined associations between screen use and symptoms of depression, longitudinal investigations are rare and fewer still consider trajectories of change in symptoms. Given the plethora of devices and normalisation of their use, understanding potential longitudinal associations with mental health is crucial. A sample of 1,749 (47% female) adolescents (10–17 years) participated in six waves of data collection over two years. Symptoms of depression, time spent on screens, and on separate screen activities (social networking, gaming, web browsing, TV/passive) were self-reported. Latent growth curve modelling revealed three trajectories of depressive symptoms (low-stable, high-decreasing, and low-increasing) and there were important differences across these groups on screen use. Some small, positive associations were evident between depressive symptoms and later screen use, and between screen use and later depressive symptoms. However, a Random Intercept Cross Lagged Panel Model revealed no consistent support for a longitudinal association. The study highlights the importance of considering differential trajectories of depressive symptoms and specific forms of screen activity to understand these relationships.

How do social media use, gaming frequency, and internalizing symptoms predict each other over time in early-to-middle adolescence?

Abstract Background The effects of adolescent digital technology use (e.g. social media, gaming) on their mental health are a major public health concern, but existing evidence is of mixed quality and findings have been inconclusive. Methods Separating within-person effects from between-person effects, a random-intercept cross-lagged panel model was applied to three annual waves of data (T1, T2, T3) on social media use, gaming, and internalizing symptoms among N = 25 629 adolescents (51% girls, average age 12 years, 7 months (SD = 3.58 months) at baseline) in Greater Manchester, England. Results Longitudinal relationships varied by gender, such that more frequent gaming at T2 predicted less time spent on social media use at T3 in girls (but not boys), and more frequent internalizing symptoms at T2 predicted reductions in gaming frequency at T3 in boys (but not girls). There was no evidence that time spent on social media or gaming frequency predicted later internalizing symptoms among girls or boys. Sensitivity analyses that distinguished active versus passive social media use replicated these findings. Conclusions The findings of this study do not support the widely held view that adolescent technology use is a major causal factor in their mental health difficulties.

Understanding the influence of digital technology on human cognitive functions: A narrative review

In the era of rapid digitalization, the widespread integration of digital technology into various aspects of daily life has sparked significant interest in understanding its impact on cognitive mental processes. While the emerging data suggests that its influence may be positive or negative, the depth of evidence regarding neurobiological mechanisms remains limited. This review aims to synthesize previously published studies and develop a comprehensive framework that systematically categorizes digital technologies, the cognitive functions they impact, and developmental stages around the concept of neuroplasticity, while clearly illustrating their interconnections. Despite acknowledged limitations, through an exhaustive approach, this paper intends to offer a dynamic perspective on the effects of digital media on the human brain, before the onset of addiction.

The association between adolescent well-being and digital technology use

The widespread use of digital technologies by young people has spurred speculation that their regular use negatively impacts psychological well-being. Current empirical evidence supporting this idea is largely based on secondary analyses of large-scale social datasets. Though these datasets provide a valuable resource for highly powered investigations, their many variables and observations are often explored with an analytical flexibility that marks small effects as statistically significant, thereby leading to potential false positives and conflicting results. Here we address these methodological challenges by applying specification curve analysis (SCA) across three large-scale social datasets (total n = 355,358) to rigorously examine correlational evidence for the effects of digital technology on adolescents. The association we find between digital technology use and adolescent well-being is negative but small, explaining at most 0.4% of the variation in well-being. Taking the broader context of the data into account suggests that these effects are too small to warrant policy change.

The Cognitive Debt of Digging Through Preprints

Your Brain on MIT Media Lab

Generative AI Use and Depressive Symptoms Among US Adults

Importance Generative artificial intelligence (AI) has rapidly entered mainstream use in the US, but its association with mental health has not been characterized. Objective To examine the associations of the extent and type of generative AI use among US adults with negative affective symptoms in a large, nationally representative sample. Design, Setting, and Participants This survey study used data from a 50-state US internet nonprobability survey conducted between April and May 2025. Survey respondents were aged 18 years and older. Data were analyzed in August 2025. Exposure Participants self-reported generative AI and social media use. Main Outcomes and Measures The outcome of interest, negative affect, was measured using the Patient Health Questionnaire 9-item (PHQ-9). Results There were 20 847 unique participants, with mean (SD) age 47.3 (17.1) years and 10 327 (49.5%) female, 10 386 (49.8%) male, and 134 (0.6%) nonbinary participants; 2152 participants (10.3%) reported using AI at least daily, including 1053 participants (5.1%) who reported daily use and 1099 participants (5.3%) who reported use multiple times per day. Among participants who used daily or more frequently, 1033 (48.0%) reported use for work, 246 (11.4%) for school, and 1875 (87.1%) for personal applications. In survey-weighted regression models, daily or more frequent AI use was significantly more common among men, younger adults, those with higher education and income, and those in urban settings. Greater AI use was associated with greater levels of depressive symptoms in sociodemographic-adjusted regression models: (daily use: β = 1.08 [95% CI, 0.55-1.62]; multiple times per day: β = 0.86 [95% CI, 0.35-1.37]) compared with nonuse, and with greater likelihood of reporting at least moderate depressive symptoms (odds ratio [OR], 1.29 [95% CI, 1.15-1.46]); similar patterns were observed for anxiety and irritability. The highest estimates were observed among individuals using AI for personal use (β = 0.31 [95% CI, 0.10-0.52]) and those aged 25 to 44 years (β = 1.22 [95% CI, 0.70-1.74]) or 45 to 64 years (β = 1.38 [95% CI, 0.72-2.05]). Conclusions and Relevance This survey study found that AI use was significantly associated with greater depressive symptoms, with magnitude of differences varying by age group. Further work is needed to understand whether these associations are causal and explain heterogeneous effects.

#cybersafety #ageverification #privacy #minors #ai | Parry Aftab

Internet Age-Verification Causing More Risks than It’s Worth This is not a new issue. Regulators and policymakers have raised it for the last 28 years as the way the keep kids safer online. They compare it to having to flash an ID to purchase regulated content or items (porn, cigarettes, alcohol, restricted medications, age-restricted movies, amusement rides, etc.) But verification online is very different and vastly riskier than flashing an ID in real life. For one, it’s collecting personally-identifiable information about our kids. For two, it puts that information in the too-often untrustworthy digital hands of tech companies. For three, while porn, alcohol and regulated drugs may be a clear-cut case for restricting access to adults, access to the Internet is very different. Yet, regulations begun in the UK and adopted globally have begun a weighted cybersafety approach that, in my humble opinion, prefers excluding young people from digital technology over teaching the cybersafety and digital life skills. That, in my 32 years of work in the fields of cybersafety and protection of minors online, was and remains a mistake. One of my best known quotes, repeated in Congressional and state legislative testimony and in the media is: “The greatest single risk our children face online is being denied access. We have solutions for everything else.” (my testimony before the FTC) Some proponents of age-verification have opted to denying them access unless they give up confirmable personally-identifiable info, such as selfies, feeding AI with PII, and govt or school credentials. I argue that this gives social networks, game providers and digital app operators a wealth of PII to be used for profiling, marketing and collecting far too much. With their privacy at stake, too often incompetent, malicious and greedy producers and mega-tech industry players, can’t and shouldn’t be trusted with the personal info of our most vulnerable - our kids. And AI will now be tasked with collecting everything it can find about our kids to be able to “age-verify.” (It’s ability to age-verify is less reliable with youth as their data-sets are more limited.) I served on a task force appointed by 49 state attorneys general, charged to research and weigh-in on this issue. We concluded that age-verification wasn’t feasible. While this task force was formed many years ago, with the exception of a fortified AI, not much as changed. Our children’s privacy remains, or should remain, paramount. We have robust filtering and blocking technologies. The adult industry has adopted an age-gated model. And parents can restrict access using device-specific tools. And, many well-intentioned age-verification laws were adopted too quickly for thoughtful discourse. Or by governments more prone to censorship/content restrictions. I fear in our quest to protect our kids we have thrown our babies out with the bath water. #cybersafety #ageverification #privacy #minors #AI

#precision #scaling #generalization #impact | Jean-Rémi King

Well, we did not expect that: in 10 days: 1.6K ⭐ on Github, 70K downloads on HuggingFace 🤗, >6M views on socials. 🧠 TRIBE v2, our new foundation model of the human brain responses to sight, sound, and language, has led to a surge of community demos, dozens of PRs and issues, and a level of engagement that rivals major flagships model at Meta (e.g. Llama4: 3.6M views, and DINOv3: 900K): https://lnkd.in/eQgqyDvf As this raised several questions, let me emphasize some elements of clarification: 1. #Precision: This model exclusively uses fMRI recordings. While powerful, these data are slow-paced proxy measurements of brain activity, and aggregate responses ~100k-3M neurons per data point. This means we are very far from a neuronal-level modeling of the brain. 2. #Scaling: We leveraged ~1,000 hours of naturalistic fMRI data. This is very substantial for a functional imaging study with naturalistic data; but still this is "small data" compared to medical or biology foundation models in general (e.g. structural MRI models are typically tens of thousands of individual brains). 3. #Generalization: The model shows surprising out-of-domain generalization, but it is **not** a replacement for new data collection. The model will almost surely fail in areas it hasn't seen: task-specific behaviors, memory protocols, touch sensation etc. And these are the "known unknowns" - I expect we'll discover many more unknown unknowns along the way. 4. #Impact: The goal of this model, and of our team in general, is fundamental research in neuroscience. While I am optimistic about how such foundation models will help clinical diagnosis, prognosis, and patient care, the physics of MRI is such that there is no clear path for making this kind of technology directly usable to consumer/wearable products. We're in for the science, hence the open sourcing: 📄 Paper: https://lnkd.in/e7cbunJp 💻 Code: https://lnkd.in/ebwBVuJp ▶️ Demo: https://lnkd.in/eEUVxP4S 🤗 Model: https://lnkd.in/e2T8nPJP

Brainrot: Deskilling and Addiction are Overlooked AI Risks | Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency

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From gifted to high potential and twice exceptional: A state-of-the-art meta-review

Despite the abundant literature on intelligence and high potential individuals, there is still a lack of international consensus on the terminology and clinical characteristics associated to this population. It has been argued that unstandardized use of diagnosis tools and research methods make comparisons and interpretations of scientific and epidemiological evidence difficult in this field. If multiple cognitive and psychological models have attempted to explain the mechanisms underlying high potentiality, there is a need to confront new scientific evidence with the old, to uproot a global understanding of what constitutes the neurocognitive profile of high-potential in gifted individuals. Another particularly relevant aspect of applied research on high potentiality concerns the challenges faced by individuals referred to as "twice exceptional" in the field of education and in their socio-affective life. Some individuals have demonstrated high forms of intelligence together with learning, affective or neurodevelopmental disorders posing the question as to whether compensating or exacerbating psycho-cognitive mechanisms might underlie their observed behavior. Elucidating same will prove relevant to questions concerning the possible need for differential diagnosis tools, specialized educational and clinical support. A meta-review of the latest findings from neuroscience to developmental psychology, might help in the conception and reviewing of intervention strategies.

Distinguishing Person-Specific from Situation-Specific Variation in Media Use: A Meta-Analysis - Anna Schnauber-Stockmann, Michael Scharkow, Veronika Karnowski, Teresa K. Naab, Daniela Schlütz, Paul Pressmann, 2025

Media use varies between persons (person-specific variation) and within persons (situation-specific variation, that is, the same individual uses media different...

Individual Experience vs. The Cochrane Review

On my decade-long exploration seeking a scientific language for singular evidence.

🚨 We're very happy to introduce TRIBE v2: a foundation model of the human brain's responses to sight, sound, and language. Leveraging 1,000+ hours of fMRI across 720 subjects, it generalizes… | Stéphane d'Ascoli | 16 comments

🚨 We're very happy to introduce TRIBE v2: a foundation model of the human brain's responses to sight, sound, and language. Leveraging 1,000+ hours of fMRI across 720 subjects, it generalizes zero-shot to new stimuli, tasks and people, finetunes efficiently, and enables in-silico experiments. ❓How does it work? Stemming from our v1, which won the Algonauts 2025 challenge, TRIBE v2 combines video, audio, and language embeddings to predict brain activity for any brain, then adapts to each individual. Key results: 📊 High-quality predictions — TRIBE v2 predicts brain activity across cortical and subcortical regions, significantly better than standard linear models, with a log-linear scaling law and no plateau in sight. 🎯 Zero-shot generalization — Without retraining, the predictions of TRIBE v2 are more correlated with group-averaged brain responses than almost any individual fMRI scan! A short finetuning step vastly improves over linear models trained, from scratch, on each individual. 🧪 In-silico experiments — Can we do useful experiments with TRIBE v2? Yes: classic vision and language paradigms replicate in-silico. It zero-shot recovers the FFA, PPA, EBA, VWFA, Broca's lateralization, and syntactic responses in STG — all without training on these artificial tasks. 🔍 Interpretability & multimodality — ICA on the weights rediscovers known functional networks (auditory, language, motion, default mode, visual) from naturalistic data alone. Ablating modalities further maps how vision, audition, and language integrate, with the largest gains at the temporo-parietal-occipital junction. 🧠 This effort is a step toward a foundation model of the human brain. Much remains to be understood, but we hope this opens a path for neuroscience, AI, and medical research alike. All code, weights, and a live demo are open — find it useful or mistaken in some conditions? Let us know, new test cases can only help improving this effort. 📄 Paper: https://lnkd.in/e7cbunJp 💻 Code: https://lnkd.in/ebwBVuJp ▶️ Demo: https://lnkd.in/eEUVxP4S 🤗 Model: https://lnkd.in/e2T8nPJP Joint work with Jérémy RAPIN, Yohann Benchetrit, Teon Brooks, Katie Begany, Joséphine Raugel, Hubert Banville and Jean-Rémi King. 🙏 Special thanks to Elisa Cascardi, Diego Marcos, Dominic Giardini, AI at Meta, and the open-source and neuroscience communities (in particular Lune Bellec and Bertrand Thirion for the amazing Courtois NeuroMod and IBC datasets) | 16 comments on LinkedIn