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For more than a century, researchers have known that people are generally very good at eyeballing quantities of four or fewer items. But performance at sizing up numbers drops markedly — becoming slower and more prone to error — in the face of larger numbers.

Now scientists have discovered why: the human brain uses one mechanism to assess four or fewer items and a different one for when there are five or more. The findings, obtained by recording the neuron activity of 17 human participants, settle a long-standing debate on how the brain estimates how many objects a person sees. The results were published in Nature Human Behaviour on 2 October.

The finding is relevant to the understanding of the nature of thinking, says psychologist Lisa Feigenson, the co-director of the Johns Hopkins University Laboratory for Child Development in Baltimore, Maryland. “Fundamentally, the question is one of mental architecture: what are the building blocks that give rise to human thought?”

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A kilonova is a bright blast of electromagnetic radiation that happens when two neutron stars or a neutron star and a stellar-mass black hole collide and merge.

When these collisions occur, a vast amount of material is ejected from the neutron stars, the ultradense cores of massive stars that have reached the ends of their lives. This matter is rich in neutral particles called neutrons, and in this violent sea of particles around a neutron star merger, the heaviest elements of the periodic table are forged. These include gold and platinum, radioactive materials such as uranium, and the iodine that flows through our blood. In fact, many pieces of jewelry owe their existence to a kilonova-triggering event.

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Rocks and soil collected from the asteroid Bennu and brought back to Earth last month by NASA’s OSIRIS-REx probe are rich in carbon and contain water-bearing clay minerals that date back to the birth of the solar system, scientists said Wednesday. The discovery gives critical insight into the formation of our planet and supports theories about how water may have arrived on Earth in the distant past.

The clay minerals “have water locked inside their crystal structure,” said Dante Lauretta, a planetary scientist at the University of Arizona and the principal investigator of the asteroid sample return mission, while revealing initial photographs of the material.

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In a tour de force for neuroscience, teams of researchers have published a voluminous set of brain-cell atlases for humans and other primates.

The atlases are detailed in 21 research papers appearing in Science, Science Advances and Science Translational Medicine — and could point scientists toward new strategies for addressing mental conditions ranging from Alzheimer’s disease and schizophrenia to epilepsy and ADHD.

“We need to understand the specifics of the human brain if we hope to understand human diseases,” Ed Lein, a senior investigator at Seattle’s Allen Institute, said in comments provided via video.

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The powdery material that NASA officials unveiled on Wednesday looked like asphalt or charcoal, but was easily worth more than its weight in diamonds. The fragments were from a world all their own—pieces of the asteroid Bennu, collected and returned to Earth for analysis by the OSIRIS-REx mission. The samples hold chemical clues to the formation of our solar system and the origin of life-supporting water on our planet.

The clay and minerals from the 4.5 billion-year-old rock had been preserved in space’s deep freeze since the dawn of the solar system. Last month, after a seven-year-long space mission, they parachuted to a desert in Utah, where they were whisked away by helicopter.

And now those pristine materials sit in an airtight vessel in a clean room at NASA’s Johnson Space Center, where researchers like University of Arizona planetary scientist Dante Lauretta are getting their first chance to study the sample up close.

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In a new Science Advances study, scientists from the University of Science and Technology of China have developed a dynamic network structure using laser-controlled conducting filaments for neuromorphic computing.

Neuromorphic computing is an emerging field of research that draws inspiration from the to create efficient and intelligent computer systems. At its core, relies on , which are computational models inspired by the neurons and synapses in the brain. But when it comes to creating the hardware, it can be a bit challenging.

Mott materials have emerged as suitable candidates for neuromorphic computing due to their unique transition properties. Mott transition involves a rapid change in electrical conductivity, often accompanied by a transition between insulating and metallic states.

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Forget the cloud. Northwestern University engineers have developed a new nanoelectronic device that can perform accurate machine-learning classification tasks in the most energy-efficient manner yet. Using 100-fold less energy than current technologies, the device can crunch large amounts of data and perform artificial intelligence (AI) tasks in real time without beaming data to the cloud for analysis.

With its tiny footprint, ultra-low power consumption and lack of lag time to receive analyses, the device is ideal for direct incorporation into wearable electronics (like smart watches and fitness trackers) for real-time and near-instant diagnostics.

To test the concept, engineers used the device to classify large amounts of information from publicly available electrocardiogram (ECG) datasets. Not only could the device efficiently and correctly identify an irregular heartbeat, it also was able to determine the arrhythmia subtype from among six different categories with near 95% accuracy.

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