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New model frames human reinforcement learning in the context of memory and habits

Humans and most other animals are known to be strongly driven by expected rewards or adverse consequences. The process of acquiring new skills or adjusting behaviors in response to positive outcomes is known as reinforcement learning (RL).

RL has been widely studied over the past decades and has even been adapted to train some computational models, such as some deep learning algorithms. Existing models of RL suggest that this type of learning is linked to dopaminergic pathways (i.e., neural pathways that respond to differences between expected and experienced outcomes).

Anne G. E. Collins, a researcher at University of California, Berkeley, recently developed a new model of RL specific to situations in which people’s choices have uncertain context-dependent outcomes, and they try to learn the actions that will lead to rewards. Her paper, published in Nature Human Behaviour, challenges the assumption that existing RL algorithms faithfully mirror psychological and neural mechanisms.

Blinking less may mean brain is working harder, study shows

Blinking is a human reflex most often performed without thinking, like breathing. Although research on blinking is usually related to vision, a new Concordia study examines how blinking is connected to cognitive function, such as filtering out background noise to focus on what someone is trying to say to us in a crowded room.

In an article published in the journal Trends in Hearing, the researchers describe two experiments designed to measure how eye blinking changes in response to stimuli under different conditions.

They found that people naturally blink less when they are working harder to understand speech in noisy environments, suggesting that the act of blinking reflects the mental effort behind everyday listening. The research further showed that blink patterns remained stable across different lighting conditions—meaning people blinked just as much whether lighting was bright, dim or dark.

Researchers detect early brain changes linked to future psychosis development

Researchers from the Yong Loo Lin School of Medicine, National University of Singapore (NUS Medicine), and NHG Health’s Institute of Mental Health (IMH) have mapped how brain networks differ in individuals at Clinical High Risk (CHR) for psychosis, providing a new perspective on the mechanisms underlying the disease onset.

Published in Molecular Psychiatry, the study utilized advanced neuroimaging methods to identify early, network-level changes in more than 3,000 individuals at varying levels of risk.

The study—led by Dr. Siwei Liu, Senior Research Scientist, and Associate Professor Juan Helen Zhou, Director, both at the Center for Translational Magnetic Resonance Research (TMR), NUS Medicine, and in collaboration with Associate Professor Jimmy Lee, Senior Consultant Psychiatrist and Clinician-Scientist at IMH—sought to determine how brain networks can reveal signs in young individuals with heightened clinical risk of developing psychosis.

AI headphones automatically learn who you’re talking to—and let you hear them better

Holding a conversation in a crowded room often leads to the frustrating “cocktail party problem,” or the challenge of separating the voices of conversation partners from a hubbub. It’s a mentally taxing situation that can be exacerbated by hearing impairment.

As a solution to this common conundrum, researchers at the University of Washington have developed smart headphones that proactively isolate all the wearer’s conversation partners in a noisy soundscape. The headphones are powered by an AI model that detects the cadence of a conversation and another model that mutes any voices that don’t follow that pattern, along with other unwanted background noises. The prototype uses off-the-shelf hardware and can identify conversation partners using just two to four seconds of audio.

The system’s developers think the technology could one day help users of hearing aids, earbuds and smart glasses to filter their soundscapes without the need to manually direct the AI’s “attention.”

Infant-inspired framework helps robots learn to interact with objects

Over the past decades, roboticists have introduced a wide range of advanced systems that can move around in their surroundings and complete various tasks. Most of these robots can effectively collect images and other data in their surroundings, using computer vision algorithms to interpret it and plan their future actions.

In addition, many robots leverage large language models (LLMs) or other natural language processing (NLP) models to interpret instructions, make sense of what users are saying and answer them in specific languages. Despite their ability to both make sense of their surroundings and communicate with users, most robotic systems still struggle when tackling tasks that require them to touch, grasp and manipulate objects, or come in physical contact with people.

Researchers at Tongji University and State Key Laboratory of Intelligent Autonomous Systems recently developed a new framework designed to improve the process via which robots learn to physically interact with their surroundings.

First human DNA-cutting enzyme that senses physical tension discovered

An international research team has identified a human protein, ANKLE1, as the first DNA-cutting enzyme (nuclease) in mammals capable of detecting and responding to physical tension in DNA. This “tension-sensing” mechanism plays a vital role in maintaining genetic integrity during cell division—a process that, when disrupted, can lead to cancer and other serious diseases.

The study, titled “ANKLE1 processes chromatin bridges by cleaving mechanically stressed DNA,” published in Nature Communications, represents a major advance in the understanding of cellular DNA protection.

The research was conducted through a cross-disciplinary collaboration between Professor Gary Ying Wai Chan’s laboratory at the School of Biological Sciences, The University of Hong Kong (HKU) and Dr. Artem Efremov’s biophysics team at Shenzhen Bay Laboratory (SZBL), with additional contributions from researchers at the Hong Kong University of Science and Technology (HKUST) and the Francis Crick Institute in London.

Mini-vortices in nanopores accelerate ion transport for faster supercapacitor charging

Tiny cavities in energy storage devices form small vortices that help with charging, according to a research team led by TU Darmstadt. This previously unknown phenomenon could advance the development of faster storage devices.

Solar and wind are the energy sources of the future, but they are subject to significant natural fluctuations. Storage solutions are therefore particularly important for a successful energy transition. Rechargeable batteries achieve very high energy densities by storing energy chemically. However, this high energy density comes at the price of long charging times and a dependence on precious raw materials such as cobalt.

In contrast to rechargeable batteries, so-called supercapacitors store energy in electric double layers: a voltage is applied between two electrodes. They are immersed in a liquid in which tiny charged particles, ions, float. The positive and negative ions move in opposite directions and accumulate in charged, nanometer-thick layers, the electric double layers, on the surfaces of the electrodes. In order to provide as much surface area as possible for the accumulation of ions, supercapacitors use porous electrodes that have many tiny pores, like a sponge.

New nanomagnet production process improves efficiency and cuts costs

Researchers at HZDR have partnered with the Norwegian University of Science and Technology in Trondheim, and the Institute of Nuclear Physics in the Polish Academy of Sciences to develop a method that facilitates the manufacture of particularly efficient magnetic nanomaterials in a relatively simple process based on inexpensive raw materials.

Using a highly focused ion beam, they imprint magnetic nanostrips consisting of tiny, vertically aligned nanomagnets onto the materials. As the researchers have reported in the journal Advanced Functional Materials, this geometry makes the material highly sensitive to external magnetic fields and current pulses.

Nanomagnets play a key role in modern information technologies. They facilitate fast data storage, precise magnetic sensors, novel developments in spintronics, and, in the future, quantum computing. The foundations of all these applications are functional materials with particular magnetic structures that can be customized on the nanoscale and precisely controlled.

Student researcher leads discovery of fastest gamma-ray burst ever recorded

Sarah Dalessi, a fifth-year student in the College of Science at The University of Alabama in Huntsville (UAH), a part of The University of Alabama System, is the lead author of a paper published in The Astrophysical Journal detailing the discovery of the fastest gamma-ray burst (GRB) ever recorded.

GRB 230307A is a gamma-ray burst in the ultrarelativistic category, meaning the velocity of the GRB’s jet, a focused beam of high-energy particles and photons, came within 99.99998% of the speed of light—186,000 miles per second—making it the fastest GRB ever observed. The observation was made possible with data from the Fermi Gamma-ray Burst Monitor, one of two instruments on NASA’s Fermi Gamma-ray Space Telescope.

“The Lorentz factor is the measure of speed of the jet here, and 1,600 is the highest we ever measured,” explains Dr. Peter Veres, an assistant professor who works in the UAH Center for Space Plasma and Aeronomic Research (CSPAR) and is co-author on the study.

Durable catalyst shields itself for affordable green hydrogen production

An international research team led by Professor Philip C.Y. Chow at The University of Hong Kong (HKU) has unveiled a new catalyst that overcomes a major challenge in producing green hydrogen at scale. This innovation makes the process of producing oxygen efficiently and reliably in the harsh acidic environment used by today’s most promising industrial electrolyzers.

Spearheaded by Ci Lin, a Ph.D. student in HKU’s Department of Mechanical Engineering, the team’s work was published in ACS Energy Letters.

Green hydrogen is seen as a clean fuel that can help reduce carbon emissions across industries like steelmaking, chemical production, long-distance transportation, and seasonal energy storage. Proton exchange membrane (PEM) electrolyzers are preferred for their compact design and rapid response, but they operate in acidic conditions that are exceptionally demanding on the oxygen evolution reaction (OER) catalyst.

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