Though we are often friends with people similar to ourselves, it is unclear if neural responses to perceptual stimuli are also similar. Here, authors show that the similarity of neural responses evoked by a range of videos was highest for close friends and decreased with increasing social distance.
Using electrocorticography (ECoG), we probed the neurocognitive substrates of mentalizing at the level of neuronal populations. We found that mentalizing about the self and others recruited near-identical cortical sites (Fig. 5a, b) in a common spatiotemporal sequence (Figs. 5 c and 6). Within our ROIs, activations began in visual cortex, followed by temporoparietal DMN regions (TPJ, ATL, and PMC), and lastly in mPFC regions (amPFC, dmPFC, and vmPFC; Fig. 3e, f). Critically, regions with later activations exhibited greater functional specialization for mentalizing as measured by three metrics: functional specificity for mentalizing versus arithmetic (Figs. 3 c, d and 4b), self/other differentiation in activation timing (Fig. 5c, d), and temporal associations with behavioral responses (Fig. 4D and Table 1). Taken together, these results reveal a common neurocognitive sequence28,29,30,31 for self-and other-mentalizing, beginning in visual cortex (low specialization), ascending through temporoparietal DMN areas (intermediate specialization), then reaching its apex in mPFC regions (high specialization).
Our results are consistent with gradient-based models of brain function, which posit that concrete sensorimotor processing in unimodal regions (e.g., visual cortex) gradually yields to increasingly abstract and inferential processing in ‘high-level’ transmodal regions like mPFC41,42. We found that the strength of self/other differences in activation timing increased along a gradient from visual cortex to mPFC. Specifically, other-mentalizing evoked slower (Fig. 5c) and lengthier (Fig. 5d) activations than self-mentalizing throughout successive DMN ROIs. These self/other functional differences corresponded with self/other differences in RTBehav (Supplementary Fig. 4), although the two were often dissociable (Table 1). Thus, perhaps because we know ourselves better than others, other-mentalizing may require lengthier processing at more abstract and inferential levels of representation, ultimately resulting in slower behavioral responses.
What might our results imply about extant fMRI findings? Hundreds of fMRI studies consistently suggest that: TPJ and dmPFC are most crucial for mentalizing6,8,11,12,43,44,45,46, and dmPFC is selective for thinking about others over oneself 32,33,34,35. However, when examined with ECoG, we found that both pieces of received wisdom are not what they seem. Below, we discuss both issues before moving onto our peculiar vmPFC results, and then conclude with systems-level discussion.
Analysis of the genome and proteome shows that eukaryotic evolution gave rise to the regulatory function of chromatin.
Two meters of DNA
DNA, or deoxyribonucleic acid, is a molecule composed of two long strands of nucleotides that coil around each other to form a double helix. It is the hereditary material in humans and almost all other organisms that carries genetic instructions for development, functioning, growth, and reproduction. Nearly every cell in a person’s body has the same DNA. Most DNA is located in the cell nucleus (where it is called nuclear DNA), but a small amount of DNA can also be found in the mitochondria (where it is called mitochondrial DNA or mtDNA).
Although quantum computing is not commercially available, CISA (Cybersecurity and Infrastructure Security Agency) urges organizations to prepare for the dawn of this new age, which is expected to bring groundbreaking changes in cryptography, and how we protect our secrets.
The agency published a paper earlier in the week, calling for leaders to start preparing for the migration to stronger secret guarding systems, exploring risk mitigation methods, and participating in developing new standards.
NASA’s new Space Launch System had some technical issues, but astronauts say the real reason the US hasn’t returned to the moon is political.
Summary: A newly developed artificial intelligence model can detect Parkinson’s disease by reading a person’s breathing patterns. The algorithm can also discern the severity of Parkinson’s disease and track progression over time.
Source: MIT
Parkinson’s disease is notoriously difficult to diagnose as it relies primarily on the appearance of motor symptoms such as tremors, stiffness, and slowness, but these symptoms often appear several years after the disease onset.
Meta is developing a machine learning model that scans these citations and cross-references their content to Wikipedia articles to verify that not only the topics line up, but specific figures cited are accurate.
This isn’t just a matter of picking out numbers and making sure they match; Meta’s AI will need to “understand” the content of cited sources (though “understand” is a misnomer, as complexity theory researcher Melanie Mitchell would tell you, because AI is still in the “narrow” phase, meaning it’s a tool for highly sophisticated pattern recognition, while “understanding” is a word used for human cognition, which is still a very different thing).
Meta’s model will “understand” content not by comparing text strings and making sure they contain the same words, but by comparing mathematical representations of blocks of text, which it arrives at using natural language understanding (NLU) techniques.
A new study in Science overthrew the whole gamebook. Led by Dr. David Baker at the University of Washington, a team tapped into an AI’s “imagination” to dream up a myriad of functional sites from scratch. It’s a machine mind’s “creativity” at its best—a deep learning algorithm that predicts the general area of a protein’s functional site, but then further sculpts the structure.
As a reality check, the team used the new software to generate drugs that battle cancer and design vaccines against common, if sometimes deadly, viruses. In one case, the digital mind came up with a solution that, when tested in isolated cells, was a perfect match for an existing antibody against a common virus. In other words, the algorithm “imagined” a hotspot from a viral protein, making it vulnerable as a target to design new treatments.
The algorithm is deep learning’s first foray into building proteins around their functions, opening a door to treatments that were previously unimaginable. But the software isn’t limited to natural protein hotspots. “The proteins we find in nature are amazing molecules, but designed proteins can do so much more,” said Baker in a press release. The algorithm is “doing things that none of us thought it would be capable of.”
Well, now we have a robot version of this classic Serengeti scene.
The fawn in this case is a robotic dog at the University of California, Berkeley. And it’s likewise a surprisingly quick learner (relative to the rest of robot-kind). The robot is also special because, unlike other flashier robots you might have seen online, it uses artificial intelligence to teach itself how to walk.
Continue reading “This Robot Dog Has an AI Brain and Taught Itself to Walk in Just an Hour” »
While the planet is too large and hot for life, it could help us study more Earth-like exoplanets.
WASP-39b, a distant gas giant “hot Jupiter,” shows signs of water vapor and carbon dioxide, helping Webb scientists hone in on how to find them on smaller planets.
