It’s possible that I shall make an ass of myself. But in that case one can always get out of it with a little dialectic. I have, of course, so worded my proposition as to be right either way (K.Marx, Letter to F.Engels on the Indian Mutiny)
Monday, August 10, 2026
AI model captures how humans read, paving the way to personalised text and better augmented reality
Researchers now understand not just how our eyes move when we read, but also how we build meaning from text
Researchers at Aalto University, together with international partners, have developed the most accurate model yet of how humans read. The new model uses reinforcement learning, a type of AI used in robotics, to explain—and recreate—the choices readers make as they move through text.
‘For the first time we’ve used AI methods to understand—not just mimic—how people read,’ says Professor Antti Oulasvirta from Aalto University. In a study to be published on Monday, August 10, in Nature Human Behaviour, researchers say the model could power smarter Augmented Reality (AR) displays and tailor complex texts to different readers and everyday situations.
Earlier models learned from large datasets pairing text snippets with eye tracking data, then mimicked human behaviour, but they lacked true understanding of the content and didn’t generalise well across languages or contexts, explains Oulasvirta. In contrast, the new model follows the psychological mechanisms readers use to direct attention, revealing how understanding is built as the eyes move through words, sentences and paragraphs.
Understanding how human memory serves reading is the key to unlocking enormous potential for customisable apps, services or products, according to Oulasvirta.
‘We read all the time, yet throughout written history we have read texts that have been produced for mass use and not for an individual person and a specific situation,’ he says. ‘Now we are in a position to change that.’
How it works
The new model is guided by resource rationality—the idea that while reading, we constantly decide where to look next to improve our understanding as much as possible within the time available. Decisions about gaze allocation are made at three levels: word, sentence and text. They are influenced by factors such as a reader’s language, memory capacity and their vision and eye speed. For example, a fast reader with a good memory may jump briskly from one paragraph to the next, whereas a reader with a poorer memory is more likely to loop back.
‘Reading feels effortless, but your brain is constantly deciding where to look, what to skip, and when to backtrack—spending attention like a budget to maximize understanding,’ says Professor Shengdong Zhao from City University of Hong Kong.
The researchers added reader characteristics as parameters so that each could be adjusted, then let the model learn for itself the best strategy for directing attention.
‘We placed the model in a world with millions of texts. Then, using AI-based reinforcement learning, we trained it to optimise eye movements so that it truly understands what it reads,’ Oulasvirta explains.
As it reads, the model forms a condensed description of the text’s content. When a crucial word or clause is missing, the gaze can be directed to gather that information. The model’s understanding can be tested by asking what it retained from the text within the given time and constraints.
When the researchers compared the model’s attention-allocation decisions with real human eye-tracking data they found that its decisions mirrored readers’ behaviour. In practice, they had succeeded in building a model of an average reader that can be tailored to different reader profiles.
What’s next?
The development paves the way to new reading support tools and personalised text design. For example, the model could be used to enable smart glasses that pace and lay out on-screen text to fit the situation and the user’s needs, or to customise texts to suit users.
‘We could take the same source text—say, a convoluted piece of legal writing—and with little effort produce versions that are more comprehensible for different readers,’ Oulasvirta says.
The next step for the team will be to evaluate how the model can be used to help individuals suffering from dyslexia and low language proficiency.
‘We want to help users in real-time situations, for example, by designing text that helps drivers without distracting them,’ says Oulasvirta. ‘Now we have this new understanding of something that’s so central to our lives, it’s just a matter of exploring all the possibilities.’
In addition to Aalto University, the study involved researchers from The Hong Kong University of Science and Technology, City University of Hong Kong, and the National University of Singapore.
Hierarchical Resource Rationality Explains Human Reading Behavior
Article Publication Date
10-Aug-2026
Governor Hochul announces Empire AI Beta fully online as federal government takes inspiration from New York to launch state and regional AI infrastructure hubs
New York's Empire AI served as model for new national science foundation to build out regional AI research infrastructure
New York's New $40 Million Supercomputer Gives Researchers Across New York Access to World-Class AI Computing Power
New York's Empire AI Served as Model for New National Science Foundation To Build Out Regional AI Research Infrastructure
Governor Kathy Hochul today announced that Empire AI Beta is officially online, giving researchers at New York's leading public and private universities access to the most powerful academic research computer in the country and marking a major milestone in New York's effort to lead the nation in responsible artificial intelligence for the public good. As convened by Governor Hochul and consortium partners, the Empire AI initiative is already serving as a national model for public-interest AI use. As the federal National Science Foundation has announced a major investment to support regional AI infrastructure and shared research capacity through their new State and Regional AI Infrastructure Hubs initiative, Empire AI is already powering world-class research and serving academics, students and communities across the state.
"New York State built Empire AI to show that artificial intelligence can be developed for the public good and with Empire AI Beta officially online, New York is giving our researchers the most powerful academic AI research computer in the country," Governor Hochul said. "The National Science Foundation's new hubs embrace the same core principle behind Empire AI — when government, universities, philanthropy and industry come together, they can deliver outstanding results."
Empire AI Board Chairman Tom Secunda said, "Empire AI is showing the nation what dedicated partners across government, research institutions, and philanthropy can build together: a scientific asset no institution could create on its own, advancing the public good. Thanks to Governor Hochul's leadership and vision, New York is setting the standard for how the United States can build and maintain AI infrastructure by researchers and for researchers."
SUNY Chancellor John B. King Jr. said, "Empire AI Beta is a testament to Governor Hochul's leadership and the power of New York State higher education to lead the way in the use of AI to accelerate research that saves lives and strengthens our communities. Thanks to Empire AI, SUNY's researchers are making advances every day in fields like health care, public safety and emerging technologies, all while demonstrating responsible environmental stewardship."
CUNY Chancellor Félix V. Matos Rodríguez said, "Empire AI Beta represents a monumental leap forward for public higher education, ensuring that world-class computing power is not reserved solely for tech giants, but placed directly into the hands of our diverse students, faculty, and scholars. By democratizing access to cutting-edge AI infrastructure across CUNY and our partner institutions, New York is setting a national standard for research that drives social mobility, ethical innovation, and real-world solutions for the communities we serve."
Empire AI Research Computing Director Kiran Keshav said, "Turning on Beta is a major leap forward for Empire AI and for academic research across New York. Researchers who were once limited by access to computing power can now ask bigger questions, test more ambitious ideas and move faster from theory to discovery. From medical diagnostics and climate modeling to safer infrastructure and more trustworthy AI systems, this system will help New York's researchers do work that would not otherwise be possible."
State Senator April N.M. Baskin said, "Having the most powerful academic research computer in the country at the University at Buffalo is a tremendous achievement for Western New York. Empire AI will expand opportunities for students and researchers to lead groundbreaking discoveries while ensuring artificial intelligence is developed responsibly and for the public good. I'm proud that the University at Buffalo is at the center of this effort, helping shape the future of AI for the benefit of all New Yorkers as Empire AI continues to grow."
State Senator Jeremy Zellner said, "Innovation and responsibility go hand in hand. Empire AI shows that New York can lead the world in artificial intelligence by investing in public research, supporting our universities, and ensuring these technologies are developed in ways that benefit everyone. I applaud Governor Hochul for her leadership in making this investment and for putting New York at the forefront of AI innovation."
Assembly Majority Leader Crystal Peoples-Stokes said, "I am excited to see Empire AI Beta come online. New York has no shortage of challenges where Empire AI can offer analyzed solutions to address societal ills. With over 300 projects currently queued up, I look forward to seeing Empire AI in action through our partners in research and higher education and am confident in Empire AI's ability to help Governor Hochul, her administration and the State Legislature effectuate leadership for the greater good of New York State."
Housed at the State University of New York at Buffalo, Empire AI Beta is a $40 million NVIDIA-powered supercomputer that dramatically expands the computing power available to academic researchers across New York State. The system delivers an 11-fold increase in AI training capacity, a 40-fold boost in AI inference and an 8-fold expansion in data storage compared to Empire AI Alpha, the consortium's initial system launched in 2024. With over 300 research projects already queued up to use the system, Beta will accelerate work across fields including health care, climate science, advanced manufacturing, education, cybersecurity, public safety and other areas that directly benefit New Yorkers.
The launch of Beta represents the next major step in Empire AI's phased buildout. Alpha, the consortium's initial system made possible by philanthropic support from the Simons Foundation, has already supported more than 130 research projects and hundreds of researchers across New York. Beta now brings a transformative increase in capacity, while construction continues on Empire AI's permanent, full-scale Gamma facility at the University at Buffalo, which is expected to be completed by the end of 2027.
Once complete, the Gamma facility will also be the most efficient high-powered computing center in the nation. By integrating into University at Buffalo's buildout of a thermal energy network in a closed loop system, process heat from Empire AI will be used to heat buildings on campus, dramatically improving the school's ability to meet net zero goals.
Empire AI member institutions include the State University of New York, the City University of New York, Columbia University, Cornell University, New York University, Rensselaer Polytechnic Institute, the University of Rochester, Rochester Institute of Technology, the Icahn School of Medicine at Mount Sinai and the Flatiron Institute at the Simons Foundation.
The Governor announced Empire AI in her 2024 State of the State to create a state-of-the-art artificial intelligence center at the State University at Buffalo to be used by New York's leading institutions to promote responsible research and development, create jobs, and unlock AI opportunities focused on public good. With Empire AI Beta fully online, New York is already delivering on the computing power, institutional partnership and research capacity that NSF is looking to replicate.
Empire AI is backed by more than $500 million in public and private funding, and is made up of 10 member universities and research institutions. In May 2025, Governor Hochul secured funding to expand access for SUNY researchers at the State University of New York at Albany, State University of New York at Binghamton, State University of New York at Buffalo and State University of New York at Stony Brook, and support the addition of new members including the University of Rochester, the Rochester Institute of Technology, and the Icahn School of Medicine at Mount Sinai. They joined the seven founding members of Empire AI: SUNY, CUNY, Columbia University, Cornell University, New York University, Rensselaer Polytechnic Institute and the Flatiron Institute.
In her 2026 State of the State agenda, Governor Hochul proposed the launch of Empire AI Beta, which will accelerate Empire AI's performance to 11 times its former scale, making it the world's most advanced academic supercomputer. Governor Hochul also announced a record-breaking gift to the State University of New York at Binghamton to create the first independent university AI research center in the United States, the Center for AI Responsibility and Research at Binghamton University. The $30 million philanthropic gift, the largest academic gift in the university's history, is coupled with a $25 million research capital investment by SUNY.
About the State University of New York The State University of New York is the largest comprehensive system of higher education in the United States, and more than 95 percent of all New Yorkers live within 30 miles of any one of SUNY’s 64 colleges and universities. Across the system, SUNY has four academic health centers, five hospitals, four medical schools, two dental schools, a law school, the country’s oldest school of maritime, the state's only college of optometry, 12 Educational Opportunity Centers, over 30 ATTAIN digital literacy labs, and manages one US Department of Energy National Laboratory. In total, SUNY serves about 1.7 million students across its portfolio of credit- and non-credit-bearing courses and programs, continuing education, and community outreach programs. SUNY oversees nearly a quarter of academic research in New York. Research expenditures system-wide are nearly $1.5 billion in fiscal year 2025, including significant contributions from students and faculty. There are more than three million SUNY alumni worldwide, and annually one in three New Yorkers who earn a college degree is a SUNY alum. To learn more about how SUNY creates opportunities, visit suny.edu.
Brain possesses greater self-repair capacity than previously assumed
The image on the left shows a brain lesion (diameter: just under 0.5 mm). Around the perimeter of the lesion, the newly discovered “regenerative” astrocytes begin to seal the defect by forming long cellular extensions (shown in red). Newly formed cell nuclei (shown in blue) migrate along the cellular extensions toward the damaged area. Unaltered astrocytes (shown in green) surround the lesion area. The image on the right shows an enlargement of the marked area in the left image.
Credit: Institute of Pharmacology and Toxicology, University of Zurich
The brain evidently can regenerate itself better than previously assumed after injuries or certain autoimmune diseases. Using a mouse model, researchers at the University of Zurich have demonstrated that special supporting and nourishing cells repopulate damaged areas of the brain by initially sending only newly formed cell nuclei there.
Glial cells are supporting and nourishing cells in the brain. Star-shaped glial cells called astrocytes are vital to the functioning of neurons. They supply the nerve cells with nutrients, help to regulate blood flow and keep brain tissue healthy. It had long been assumed that when astrocytes are lost – as happens, for instance, in brain injuries or autoimmune diseases such as rare neuromyelitis optica spectrum disorder, in which the body's own antibodies destroy these cells – the adult brain cannot fully replace them.
Regenerative astrocytes repair damaged tissue
A new study by co-lead authors Marina Herwerth and Matthias Wyss from the Institute of Pharmacology and Toxicology at the University of Zurich (UZH) has now overturned that assumption: their research team headed by Bruno Weber discovered a specialized group of “regenerative” astrocytes in the brains of living mice that step in on the perimeter of the damaged area of the brain to rebuild the cells. “The findings of our study reveal a previously unknown ability of the adult brain to repair itself. They point toward new ways of supporting recovery from ailments involving the loss of astrocytes,” Weber says.
Only cell nuclei migrate
The researchers used two-photon microscopy to observe the brains of living mice in real time over a period of several weeks and mapped which genes switch on in which areas of the brain. This way they were able to identify the special astrocytes that take care of rebuilding injured tissue. But those cells don’t just divide, they also perform a remarkable feat: “they send the newly formed nuclei of their daughter cells gliding across long distances to repopulate the damaged area of the brain and knit the astrocyte network back together,” Weber explains.
Starting points for targeted regeneration
The discovery of how adult brain cell nuclei migrate through the long star-shaped extensions of astrocytes to injured tissue expands comprehension of how the brain organizes and regenerates itself after certain injuries. If those mechanisms can be selectively activated, that could help to more effectively repair damaged brain tissue, restore astrocyte networks and thus improve recovery after certain brain disorders. “We were able to identify numerous genes and signaling pathways that are temporarily activated during repair. They could serve as starting points in the future for influencing post-disease and -injury regeneration processes,” Weber stresses.
The innovative architecture proposed in the study enables reliable contact and proximity detection, paving the way for advanced electronic skin systems.
Credit: Associate Professor Jaekyun Kim from Hanyang University
The recent advancement of miniaturized and portable electronics, particularly wearable and flexible devices, has increased the demand for self-powered sensing technologies. Among these, triboelectric nanogenerators (TENGs) have received increased attention for developing highly sensitive tactile sensors. TENGs convert external mechanical stimuli into electrical signals through redistribution of electric charges. They are particularly attractive for the development of advanced electronic skin and intelligent robotics. However, conventional tribotronic devices suffer from non-tuneable sensitivity and pose challenges in integration into large-area architectures, limiting practical applications.
To address these limitations, a research team led by Associate Professor Jaekyun Kim from the Department of Photonics and Nanoelectronics at Hanyang University in South Korea has developed a novel vertically integrated dual-gated tribotronic transistor. “Our vertical dual-gate architecture not only offers gate-tunable amplification of the triboelectronic responses, but also minimizes pixel footprint, enabling high-density, large-area integration,” explains Dr. Kim. Their study was made available online on April 9, 2026, and published in Volume 153 of Nano Energy on June 15, 2026.
The proposed dual-gated tribotronic transistor features a polydimethylsiloxane (PDMS) triboelectric sensing layer as the top gate, stacked on top of a dedicated gate insulator, which in turn is positioned above an indium-tin-zinc-oxide (ITZO) thin-film transistor (TFT). This innovative configuration provides synergistic control of sensitivity.
In its default state, the bottom gate sets the baseline current flowing through the ITZO transistor. To enable contact and proximity sensing, the device first undergoes a charging phase, in which a stainless-steel plate comes into contact with the PDMS surface. This causes triboelectric charges to form at the interface. As the plate separates from the PDMS layer, these accumulated charges create a triboelectric potential that acts as the top-gate voltage, which suppresses the current flow through the ITZO transistor.
As the charged plate or another object approaches the PDMS surface again, the triboelectric potential gradually decreases, causing the transistor current to recover, based on the proximity of the plate or surface. This change in current serves as the tribotronic response, indicating contact or proximity. Meanwhile, the bottom-gate voltage sets the baseline current, allowing the sensitivity to be electrically tuned. Specifically, the researchers found that the sensitivity increased with increasing bottom-gate voltage.
The researchers also showed that increasing the contact pressure enlarges the effective contact area between the PDMS layer and the contacting object, generating more triboelectric charge and producing a stronger response.
Additionally, the device exhibited stable response and recovery times of 127 and 212 milliseconds, respectively, during each contact-separation cycle. It also maintained stable performance without noticeable degradation after 1,000 operating cycles.
To demonstrate active tactile sensing, the researchers fabricated a 10 × 10 transistor array using the proposed architecture. After initially charging the sensing layer with a stainless-steel plate, they demonstrated pixel-level responses to finger touches as well as reliable proximity sensing at distances of up to 500 micrometers using a stainless-steel probe.
“Our research could contribute to the development of electronic skin systems that allow robots, prosthetic devices, and wearable electronics to perceive touch, pressure, and proximity more precisely,” remarks Dr. Kim. “This will lead to safer and more reliable human–machine interaction, with applications in healthcare robots, health monitoring and autonomous systems.”
Overall, this innovative architecture provides a scalable platform for programmable, mechanically robust tribotronic sensor arrays, paving the way for advanced human–machine interfaces.
About Hanyang University ERICA Hanyang University ERICA (Education Research Industry Cluster at Ansan) is a prominent research-focused campus established in 1979 in Ansan, South Korea. ERICA offers undergraduate and graduate programs. ERICA is renowned for its active industry-university cooperation, offering students hands-on experience through partnerships with various industries. This ensures that graduates are well-prepared to meet societal needs and excel in their respective fields. With state-of-the-art facilities and a supportive learning environment, Hanyang University ERICA empowers students to pursue their passions and contribute meaningfully to society, staying true to the university's founding philosophy of "Love in Deed and Truth."
About the author Dr. Jaekyun Kim is an Associate Professor and researcher in the Department of Photonics and Nanoelectronics at Hanyang University. His group focuses on oxide semiconductor thin-film transistors, tribotronic sensors, electronic skin, and active-matrix sensing systems. The group develops device platforms that integrate semiconductor electronics with functional sensing materials for tactile, proximity, and wearable applications.
Doctors may think they're saying “Don't panic." But many patients hear "Don't bother" instead.
A new study from the University of California San Diego Rady School of Management suggests that when physicians try to reassure patients by saying their symptoms are “normal,” patients may actually infer that treatment isn't necessary – and become less inclined to seek it.
Published in Nature Human Behaviour, the findings held across 14 experiments involving 9,371 participants and a wide range of health conditions, from menopause and migraines to dental pain, seasonal allergies and elevated blood glucose levels.
Why ‘normal’ can send the wrong message
The idea for the research grew from first author Seyi Lawal's interest in communication around menopause, where patients sometimes report feeling dismissed after being told disruptive symptoms are simply a normal part of aging. Could it be, she wondered, that doctors and patients were interpreting the same conversations differently?
To find out, the researchers conducted 14 studies involving members of the public and healthcare providers. Participants read realistic medical scenarios in which healthcare providers either described symptoms as "normal" or did not. The researchers then measured the participants' willingness to pursue treatment and compared it with what providers expected patients would do.
"Providers expected that normalizing a patient's symptoms would increase their treatment likelihood, or at worst have no impact, but patients actually reacted in the opposite way," said Lawal, a doctoral student at the UC San Diego Rady School of Management.
Doctors use "normal," it seems, to mean common and well understood. Patients often interpret it as meaning acceptable – or not worth treating.
Fixing the communication gap, making reassurance work
The findings come amid broader conversations about patients feeling dismissed in healthcare settings, sometimes described as “medical gaslighting.” The study identifies a communication gap that may contribute to those experiences, even when doctors are trying to help.
The good news is that miscommunication isn’t inevitable. The researchers also tested two simple ways to reduce it: pairing normalizing language with an explicit recommendation for treatment, and explaining that "normal" was meant in a statistical, not normative or prescriptive, sense.
Both approaches helped close the communication gap.
"Doctors usually have a noble goal. They mean to ease anxiety, but somehow it backfires," said senior author On Amir, professor of marketing and holder of the Wolfe Family Presidential Endowed Chair in Life Sciences Innovation and Entrepreneurship at the UC San Diego Rady School of Management. "Doctors shouldn’t stop reassuring patients. But they should make their meaning unmistakable.”
Co-author Brianna Chew, a doctoral student at the Rady School, said the same lesson applies to patients. Hearing that symptoms are “normal,” she said, shouldn't be taken to mean they are any less serious.
The key takeaway for patients: If you're unsure what your doctor means when they say a symptom is "normal," don't assume it means treatment isn't recommended and you should just live with it. Ask.
Common symptoms can still deserve attention – and treatment.
