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Anthropic Says That Claude Contains Its Own Kind of Emotions

Anthropic Says That Claude Contains Its Own Kind of Emotions

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Claude has been through a lot lately—a public fallout with the Pentagon, leaked source code—so it makes sense that it would be feeling a little blue. Except, it’s an AI model, so it can’t feel. Right?

Well, sort of. A new study from Anthropic suggests models have digital representations of human emotions like happiness, sadness, joy, and fear, within clusters of artificial neurons—and these representations activate in response to different cues.

Researchers at the company probed the inner workings of Claude Sonnet 3.5 and found that so-called “functional emotions” seem to affect Claude’s behavior, altering the model’s outputs and actions.

Anthropic’s findings may help ordinary users make sense of how chatbots actually work. When Claude says it is happy to see you, for example, a state inside the model that corresponds to “happiness” may be activated. And Claude may then be a little more inclined to say something cheery or put extra effort into vibe coding.

“What was surprising to us was the degree to which Claude’s behavior is routing through the model’s representations of these emotions,” says Jack Lindsey, a researcher at Anthropic who studies Claude’s artificial neurons.

“Function Emotions”

Anthropic was founded by ex-OpenAI employees who believe that AI could become hard to control as it becomes more powerful. In addition to building a successful competitor to ChatGPT, the company has pioneered efforts to understand how AI models misbehave, partly by probing the workings of neural networks using what’s known as mechanistic interpretability. This involves studying how artificial neurons light up or activate when fed different inputs or when generating various outputs.

Previous research has shown that the neural networks used to build large language models contain representations of human concepts. But the fact that “functional emotions” appear to affect a model’s behavior is new.

While Anthropic’s latest study might encourage people to see Claude as conscious, the reality is more complicated. Claude might contain a representation of “ticklishness,” but that does not mean that it actually knows what it feels like to be tickled.

Inner Monologue

To understand how Claude might represent emotions, the Anthropic team analyzed the model’s inner workings as it was fed text related to 171 different emotional concepts. They identified patterns of activity, or “emotion vectors,” that consistently appeared when Claude was fed other emotionally evocative input. Crucially, they also saw these emotion vectors activate when Claude was put in difficult situations.

The findings are relevant to why AI models sometimes break their guardrails.

The researchers found a strong emotional vector for “desperation” when Claude was pushed to complete impossible coding tasks, which then prompted it to try cheating on the coding test. They also found “desperation” in the model’s activations in another experimental scenario where Claude chose to blackmail a user to avoid being shut down.

“As the model is failing the tests, these desperation neurons are lighting up more and more,” Lindsey says. “And at some point this causes it to start taking these drastic measures.”

Lindsey says it might be necessary to rethink how models are currently given guardrails through alignment post-training, which involves giving it rewards for certain outputs. By forcing a model to pretend not to express its functional emotions, “you’re probably not going to get the thing you want, which is an emotionless Claude,” Lindsey says, veering a bit into anthropomorphization. “You’re gonna get a sort of psychologically damaged Claude.”

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Since LIGO’s Nobel-winning discovery of gravitational waves—ripples in spacetime—the U.S.-based detector has been picking up on hundreds of signals from black hole mergers. And, after a decade of studying gravitational waves, researchers believe a significant fraction of black holes may come from cosmic chain reactions.

A recent paper published in Physical Review Letters describes an analysis of 155 pairs of binary black holes, identified by LIGO and its sisters, Virgo and KAGRA, in Italy and Japan, respectively. According to the study, about 14% of merging black holes may be what’s called “second-generation black holes,” or black holes that form from previous mergers of two smaller black holes. This “hierarchical” backstory is vastly different from the textbook version of how black holes emerge from the explosive death of a star.

“Overall in the universe, black holes are merging all the time,” Cailin Plunkett, the study’s first author and a graduate student at the Massachusetts Institute of Technology, told MIT News. “Now we’re seeing a relatively consistent picture where there’s a decent percentage of black holes that are coming from this repeated pathway.”

Tracking the invisible

Gravitational waves that reach Earth’s detectors typically come from extremely intense events. Over the years, LIGO has picked up some truly perplexing signals. For example, last summer it found the most colossal black hole merger ever—and if that wasn’t wild enough, the black holes that took part in the merger lie within a cosmic “dead zone” for black holes.

This zone refers to a range of black hole masses in which, physically speaking, black holes can’t form through ordinary stellar collapse. From these discoveries, astronomers realized just how little we knew about black holes, which are challenging to investigate directly. In that sense, it was a no-brainer that the ever-growing catalog of LIGO’s gravitational signals would turn up entirely new insights about black holes.

“It is increasingly clear, both from individual events and population analyses, that massive black holes exist in [this] range,” the researchers wrote in the latest paper. “These observations have spurred further investigation into mechanisms that can populate this gap.”

A wobbly imprint

The latest research represents one such investigation. During mergers, the two black holes spiral toward each other along an orbital plane. When one or both black hole spins are misaligned, the orbital plane can wobble, or “precess,” the researchers explained to MIT News. The degree to which the disk wobbles acts as a parameter from which researchers can measure the masses and spins of the merging black holes.

One telling sign of hierarchical mergers is that they’re “lopsided,” meaning one of the pair has a much higher spin and mass than the other. For the study, the team created an analytic model to capture the kind of wobble that would have emerged from second-generation black holes. Around 14% of merging black holes followed this pattern, and the second-generation black holes identified had a very specific range of masses, at around 20 solar masses or 40 solar masses and above.

Of mysterious origins

To be fair, that might not sound like a whole lot. But it demonstrates that a sizeable portion of known black holes indeed follow this pattern. As for why, the team suspects hierarchical mergers emerge from dense stellar environments. Simply, when multiple neighboring stars die and collapse into black holes, the dense environment can make it easier for those black holes to find each other and merge. That could further lead to the formation of second-generation black holes. Theoretically, this could “repeat potentially ad infinitum, by virtue of the fact that you have a ton of stars and black holes in this really dense environment,” Plunkett said.

But an ensuing mystery concerns those black holes in the 40-and-above regime, which coincides with the aforementioned “death zones” for black hole masses. According to stellar evolution theory, black holes born of supernovas shouldn’t leave any black holes above roughly 45 solar masses, explained Plunkett.

“Yet we have seen black holes that are that massive,” she mused. “And the question is: Where did they come from?”

For now, it’s hard to say when we’ll get an answer to that question, if ever. But one thing seems to be clear: black holes are a lot weirder than we could ever imagine.

#Scientists #Black #Holes #Born #Black #HolesBlack holes,Gravitational wave,LIGO">Scientists Say Some Black Holes Are Born From Other Black Holes Since LIGO’s Nobel-winning discovery of gravitational waves—ripples in spacetime—the U.S.-based detector has been picking up on hundreds of signals from black hole mergers. And, after a decade of studying gravitational waves, researchers believe a significant fraction of black holes may come from cosmic chain reactions. A recent paper published in Physical Review Letters describes an analysis of 155 pairs of binary black holes, identified by LIGO and its sisters, Virgo and KAGRA, in Italy and Japan, respectively. According to the study, about 14% of merging black holes may be what’s called “second-generation black holes,” or black holes that form from previous mergers of two smaller black holes. This “hierarchical” backstory is vastly different from the textbook version of how black holes emerge from the explosive death of a star. “Overall in the universe, black holes are merging all the time,” Cailin Plunkett, the study’s first author and a graduate student at the Massachusetts Institute of Technology, told MIT News. “Now we’re seeing a relatively consistent picture where there’s a decent percentage of black holes that are coming from this repeated pathway.” Tracking the invisible Gravitational waves that reach Earth’s detectors typically come from extremely intense events. Over the years, LIGO has picked up some truly perplexing signals. For example, last summer it found the most colossal black hole merger ever—and if that wasn’t wild enough, the black holes that took part in the merger lie within a cosmic “dead zone” for black holes. This zone refers to a range of black hole masses in which, physically speaking, black holes can’t form through ordinary stellar collapse. From these discoveries, astronomers realized just how little we knew about black holes, which are challenging to investigate directly. In that sense, it was a no-brainer that the ever-growing catalog of LIGO’s gravitational signals would turn up entirely new insights about black holes. “It is increasingly clear, both from individual events and population analyses, that massive black holes exist in [this] range,” the researchers wrote in the latest paper. “These observations have spurred further investigation into mechanisms that can populate this gap.” A wobbly imprint The latest research represents one such investigation. During mergers, the two black holes spiral toward each other along an orbital plane. When one or both black hole spins are misaligned, the orbital plane can wobble, or “precess,” the researchers explained to MIT News. The degree to which the disk wobbles acts as a parameter from which researchers can measure the masses and spins of the merging black holes. One telling sign of hierarchical mergers is that they’re “lopsided,” meaning one of the pair has a much higher spin and mass than the other. For the study, the team created an analytic model to capture the kind of wobble that would have emerged from second-generation black holes. Around 14% of merging black holes followed this pattern, and the second-generation black holes identified had a very specific range of masses, at around 20 solar masses or 40 solar masses and above. Of mysterious origins To be fair, that might not sound like a whole lot. But it demonstrates that a sizeable portion of known black holes indeed follow this pattern. As for why, the team suspects hierarchical mergers emerge from dense stellar environments. Simply, when multiple neighboring stars die and collapse into black holes, the dense environment can make it easier for those black holes to find each other and merge. That could further lead to the formation of second-generation black holes. Theoretically, this could “repeat potentially ad infinitum, by virtue of the fact that you have a ton of stars and black holes in this really dense environment,” Plunkett said. But an ensuing mystery concerns those black holes in the 40-and-above regime, which coincides with the aforementioned “death zones” for black hole masses. According to stellar evolution theory, black holes born of supernovas shouldn’t leave any black holes above roughly 45 solar masses, explained Plunkett. “Yet we have seen black holes that are that massive,” she mused. “And the question is: Where did they come from?” For now, it’s hard to say when we’ll get an answer to that question, if ever. But one thing seems to be clear: black holes are a lot weirder than we could ever imagine. #Scientists #Black #Holes #Born #Black #HolesBlack holes,Gravitational wave,LIGO

Physical Review Letters describes an analysis of 155 pairs of binary black holes, identified by LIGO and its sisters, Virgo and KAGRA, in Italy and Japan, respectively. According to the study, about 14% of merging black holes may be what’s called “second-generation black holes,” or black holes that form from previous mergers of two smaller black holes. This “hierarchical” backstory is vastly different from the textbook version of how black holes emerge from the explosive death of a star.

“Overall in the universe, black holes are merging all the time,” Cailin Plunkett, the study’s first author and a graduate student at the Massachusetts Institute of Technology, told MIT News. “Now we’re seeing a relatively consistent picture where there’s a decent percentage of black holes that are coming from this repeated pathway.”

Tracking the invisible

Gravitational waves that reach Earth’s detectors typically come from extremely intense events. Over the years, LIGO has picked up some truly perplexing signals. For example, last summer it found the most colossal black hole merger ever—and if that wasn’t wild enough, the black holes that took part in the merger lie within a cosmic “dead zone” for black holes.

This zone refers to a range of black hole masses in which, physically speaking, black holes can’t form through ordinary stellar collapse. From these discoveries, astronomers realized just how little we knew about black holes, which are challenging to investigate directly. In that sense, it was a no-brainer that the ever-growing catalog of LIGO’s gravitational signals would turn up entirely new insights about black holes.

“It is increasingly clear, both from individual events and population analyses, that massive black holes exist in [this] range,” the researchers wrote in the latest paper. “These observations have spurred further investigation into mechanisms that can populate this gap.”

A wobbly imprint

The latest research represents one such investigation. During mergers, the two black holes spiral toward each other along an orbital plane. When one or both black hole spins are misaligned, the orbital plane can wobble, or “precess,” the researchers explained to MIT News. The degree to which the disk wobbles acts as a parameter from which researchers can measure the masses and spins of the merging black holes.

One telling sign of hierarchical mergers is that they’re “lopsided,” meaning one of the pair has a much higher spin and mass than the other. For the study, the team created an analytic model to capture the kind of wobble that would have emerged from second-generation black holes. Around 14% of merging black holes followed this pattern, and the second-generation black holes identified had a very specific range of masses, at around 20 solar masses or 40 solar masses and above.

Of mysterious origins

To be fair, that might not sound like a whole lot. But it demonstrates that a sizeable portion of known black holes indeed follow this pattern. As for why, the team suspects hierarchical mergers emerge from dense stellar environments. Simply, when multiple neighboring stars die and collapse into black holes, the dense environment can make it easier for those black holes to find each other and merge. That could further lead to the formation of second-generation black holes. Theoretically, this could “repeat potentially ad infinitum, by virtue of the fact that you have a ton of stars and black holes in this really dense environment,” Plunkett said.

But an ensuing mystery concerns those black holes in the 40-and-above regime, which coincides with the aforementioned “death zones” for black hole masses. According to stellar evolution theory, black holes born of supernovas shouldn’t leave any black holes above roughly 45 solar masses, explained Plunkett.

“Yet we have seen black holes that are that massive,” she mused. “And the question is: Where did they come from?”

For now, it’s hard to say when we’ll get an answer to that question, if ever. But one thing seems to be clear: black holes are a lot weirder than we could ever imagine.

#Scientists #Black #Holes #Born #Black #HolesBlack holes,Gravitational wave,LIGO">Scientists Say Some Black Holes Are Born From Other Black HolesScientists Say Some Black Holes Are Born From Other Black Holes Since LIGO’s Nobel-winning discovery of gravitational waves—ripples in spacetime—the U.S.-based detector has been picking up on hundreds of signals from black hole mergers. And, after a decade of studying gravitational waves, researchers believe a significant fraction of black holes may come from cosmic chain reactions. A recent paper published in Physical Review Letters describes an analysis of 155 pairs of binary black holes, identified by LIGO and its sisters, Virgo and KAGRA, in Italy and Japan, respectively. According to the study, about 14% of merging black holes may be what’s called “second-generation black holes,” or black holes that form from previous mergers of two smaller black holes. This “hierarchical” backstory is vastly different from the textbook version of how black holes emerge from the explosive death of a star. “Overall in the universe, black holes are merging all the time,” Cailin Plunkett, the study’s first author and a graduate student at the Massachusetts Institute of Technology, told MIT News. “Now we’re seeing a relatively consistent picture where there’s a decent percentage of black holes that are coming from this repeated pathway.” Tracking the invisible Gravitational waves that reach Earth’s detectors typically come from extremely intense events. Over the years, LIGO has picked up some truly perplexing signals. For example, last summer it found the most colossal black hole merger ever—and if that wasn’t wild enough, the black holes that took part in the merger lie within a cosmic “dead zone” for black holes. This zone refers to a range of black hole masses in which, physically speaking, black holes can’t form through ordinary stellar collapse. From these discoveries, astronomers realized just how little we knew about black holes, which are challenging to investigate directly. In that sense, it was a no-brainer that the ever-growing catalog of LIGO’s gravitational signals would turn up entirely new insights about black holes. “It is increasingly clear, both from individual events and population analyses, that massive black holes exist in [this] range,” the researchers wrote in the latest paper. “These observations have spurred further investigation into mechanisms that can populate this gap.” A wobbly imprint The latest research represents one such investigation. During mergers, the two black holes spiral toward each other along an orbital plane. When one or both black hole spins are misaligned, the orbital plane can wobble, or “precess,” the researchers explained to MIT News. The degree to which the disk wobbles acts as a parameter from which researchers can measure the masses and spins of the merging black holes. One telling sign of hierarchical mergers is that they’re “lopsided,” meaning one of the pair has a much higher spin and mass than the other. For the study, the team created an analytic model to capture the kind of wobble that would have emerged from second-generation black holes. Around 14% of merging black holes followed this pattern, and the second-generation black holes identified had a very specific range of masses, at around 20 solar masses or 40 solar masses and above. Of mysterious origins To be fair, that might not sound like a whole lot. But it demonstrates that a sizeable portion of known black holes indeed follow this pattern. As for why, the team suspects hierarchical mergers emerge from dense stellar environments. Simply, when multiple neighboring stars die and collapse into black holes, the dense environment can make it easier for those black holes to find each other and merge. That could further lead to the formation of second-generation black holes. Theoretically, this could “repeat potentially ad infinitum, by virtue of the fact that you have a ton of stars and black holes in this really dense environment,” Plunkett said. But an ensuing mystery concerns those black holes in the 40-and-above regime, which coincides with the aforementioned “death zones” for black hole masses. According to stellar evolution theory, black holes born of supernovas shouldn’t leave any black holes above roughly 45 solar masses, explained Plunkett. “Yet we have seen black holes that are that massive,” she mused. “And the question is: Where did they come from?” For now, it’s hard to say when we’ll get an answer to that question, if ever. But one thing seems to be clear: black holes are a lot weirder than we could ever imagine. #Scientists #Black #Holes #Born #Black #HolesBlack holes,Gravitational wave,LIGO

Since LIGO’s Nobel-winning discovery of gravitational waves—ripples in spacetime—the U.S.-based detector has been picking up on hundreds of signals from black hole mergers. And, after a decade of studying gravitational waves, researchers believe a significant fraction of black holes may come from cosmic chain reactions.

A recent paper published in Physical Review Letters describes an analysis of 155 pairs of binary black holes, identified by LIGO and its sisters, Virgo and KAGRA, in Italy and Japan, respectively. According to the study, about 14% of merging black holes may be what’s called “second-generation black holes,” or black holes that form from previous mergers of two smaller black holes. This “hierarchical” backstory is vastly different from the textbook version of how black holes emerge from the explosive death of a star.

“Overall in the universe, black holes are merging all the time,” Cailin Plunkett, the study’s first author and a graduate student at the Massachusetts Institute of Technology, told MIT News. “Now we’re seeing a relatively consistent picture where there’s a decent percentage of black holes that are coming from this repeated pathway.”

Tracking the invisible

Gravitational waves that reach Earth’s detectors typically come from extremely intense events. Over the years, LIGO has picked up some truly perplexing signals. For example, last summer it found the most colossal black hole merger ever—and if that wasn’t wild enough, the black holes that took part in the merger lie within a cosmic “dead zone” for black holes.

This zone refers to a range of black hole masses in which, physically speaking, black holes can’t form through ordinary stellar collapse. From these discoveries, astronomers realized just how little we knew about black holes, which are challenging to investigate directly. In that sense, it was a no-brainer that the ever-growing catalog of LIGO’s gravitational signals would turn up entirely new insights about black holes.

“It is increasingly clear, both from individual events and population analyses, that massive black holes exist in [this] range,” the researchers wrote in the latest paper. “These observations have spurred further investigation into mechanisms that can populate this gap.”

A wobbly imprint

The latest research represents one such investigation. During mergers, the two black holes spiral toward each other along an orbital plane. When one or both black hole spins are misaligned, the orbital plane can wobble, or “precess,” the researchers explained to MIT News. The degree to which the disk wobbles acts as a parameter from which researchers can measure the masses and spins of the merging black holes.

One telling sign of hierarchical mergers is that they’re “lopsided,” meaning one of the pair has a much higher spin and mass than the other. For the study, the team created an analytic model to capture the kind of wobble that would have emerged from second-generation black holes. Around 14% of merging black holes followed this pattern, and the second-generation black holes identified had a very specific range of masses, at around 20 solar masses or 40 solar masses and above.

Of mysterious origins

To be fair, that might not sound like a whole lot. But it demonstrates that a sizeable portion of known black holes indeed follow this pattern. As for why, the team suspects hierarchical mergers emerge from dense stellar environments. Simply, when multiple neighboring stars die and collapse into black holes, the dense environment can make it easier for those black holes to find each other and merge. That could further lead to the formation of second-generation black holes. Theoretically, this could “repeat potentially ad infinitum, by virtue of the fact that you have a ton of stars and black holes in this really dense environment,” Plunkett said.

But an ensuing mystery concerns those black holes in the 40-and-above regime, which coincides with the aforementioned “death zones” for black hole masses. According to stellar evolution theory, black holes born of supernovas shouldn’t leave any black holes above roughly 45 solar masses, explained Plunkett.

“Yet we have seen black holes that are that massive,” she mused. “And the question is: Where did they come from?”

For now, it’s hard to say when we’ll get an answer to that question, if ever. But one thing seems to be clear: black holes are a lot weirder than we could ever imagine.

#Scientists #Black #Holes #Born #Black #HolesBlack holes,Gravitational wave,LIGO

Ready for the 2026 World Cup final? You might think you are, but your body is going to have to be prepared to put in some work—especially if your favorite team makes it.

Research shows that watching high-pressure matches can raise your heart rate, increase your stress levels, and put extra strain on your cardiovascular system.

According to a recent study from researchers at Bielefeld University in Germany, fans’ physiological stress increases by about 41 percent during a soccer final compared to a normal day. Heart rate also rose significantly, jumping from 70.9 beats per minute to 78.7 beats per minute—a difference even when compared to other weekends.

Researchers at Bielefeld tracked 229 fans of the German club Arminia Bielefeld for three months. Participants wore smartwatches that continuously recorded heart rate and an estimated stress index based on heart rate variability, allowing researchers to compare the day of the 2025 German Cup final with the days leading up to the match.

The physiological reaction to the soccer final began long before the match began. The researchers saw fans’ stress levels begin to rise in the morning and peak just before kickoff. Even after the final whistle, viewers showed signs of elevated stress.

Where you watch the game also makes a difference. The study found that fans who watched at the stadium had an average heart rate of 94.2 beats per minute compared to 79.4 among those who watched the match on television. After their team’s first goal, those in the stands saw their heart rate climb to an average of up to 108 beats per minute—a much more intense response than that observed in other contexts.

Alcohol consumption appeared to amplify that effect. Participants who reported drinking during the game had a heart rate approximately 5 percent higher than the rest of the fans during the match and nearly 12 percent higher after their team’s first goal. Although the researchers did not assess medical risks, they note that alcohol can increase cardiovascular strain when people are in an emotional state.

During the first few minutes of the match, when the outcome was still uncertain, heart rates reached their highest levels. Once the game seemed to be decided, fans’ heart rates dropped.

However, two goals scored in the final minutes caused them to spike again, even though the chances of a comeback were practically nil. (You can only imagine how fast Argentina fans’ hearts were thumping during this week’s furious comeback against Egypt.) For the authors, this reflects that the body responds not only to the objective chances of winning but also to emotions such as hope, pride, or attachment to the team.

The findings align with the results of previous studies on the physiological impacts of soccer. That incluides a study published in the New England Journal of Medicine after the 2006 World Cup in Germany, found that the risk of suffering an acute cardiovascular event nearly triples during German national team matches among people with preexisting heart conditions.

Subsequent research shows that matches can lead to an increase in stress hormones such as cortisol and found that fans who identify more strongly with their team exhibit more intense biological responses during decisive matches.

This article originally appeared on WIRED en Español and has been translated from Spanish.

#Watching #Soccer #Final #Body #Scienceworld cup 2026,sports,health,soccer,stress,fandom">What Watching a Soccer Final Does to Your Body, According to ScienceReady for the 2026 World Cup final? You might think you are, but your body is going to have to be prepared to put in some work—especially if your favorite team makes it.Research shows that watching high-pressure matches can raise your heart rate, increase your stress levels, and put extra strain on your cardiovascular system.According to a recent study from researchers at Bielefeld University in Germany, fans’ physiological stress increases by about 41 percent during a soccer final compared to a normal day. Heart rate also rose significantly, jumping from 70.9 beats per minute to 78.7 beats per minute—a difference even when compared to other weekends.Researchers at Bielefeld tracked 229 fans of the German club Arminia Bielefeld for three months. Participants wore smartwatches that continuously recorded heart rate and an estimated stress index based on heart rate variability, allowing researchers to compare the day of the 2025 German Cup final with the days leading up to the match.The physiological reaction to the soccer final began long before the match began. The researchers saw fans’ stress levels begin to rise in the morning and peak just before kickoff. Even after the final whistle, viewers showed signs of elevated stress.Where you watch the game also makes a difference. The study found that fans who watched at the stadium had an average heart rate of 94.2 beats per minute compared to 79.4 among those who watched the match on television. After their team’s first goal, those in the stands saw their heart rate climb to an average of up to 108 beats per minute—a much more intense response than that observed in other contexts.Alcohol consumption appeared to amplify that effect. Participants who reported drinking during the game had a heart rate approximately 5 percent higher than the rest of the fans during the match and nearly 12 percent higher after their team’s first goal. Although the researchers did not assess medical risks, they note that alcohol can increase cardiovascular strain when people are in an emotional state.During the first few minutes of the match, when the outcome was still uncertain, heart rates reached their highest levels. Once the game seemed to be decided, fans’ heart rates dropped.However, two goals scored in the final minutes caused them to spike again, even though the chances of a comeback were practically nil. (You can only imagine how fast Argentina fans’ hearts were thumping during this week’s furious comeback against Egypt.) For the authors, this reflects that the body responds not only to the objective chances of winning but also to emotions such as hope, pride, or attachment to the team.The findings align with the results of previous studies on the physiological impacts of soccer. That incluides a study published in the New England Journal of Medicine after the 2006 World Cup in Germany, found that the risk of suffering an acute cardiovascular event nearly triples during German national team matches among people with preexisting heart conditions.Subsequent research shows that matches can lead to an increase in stress hormones such as cortisol and found that fans who identify more strongly with their team exhibit more intense biological responses during decisive matches.This article originally appeared on WIRED en Español and has been translated from Spanish.#Watching #Soccer #Final #Body #Scienceworld cup 2026,sports,health,soccer,stress,fandom

2026 World Cup final? You might think you are, but your body is going to have to be prepared to put in some work—especially if your favorite team makes it.

Research shows that watching high-pressure matches can raise your heart rate, increase your stress levels, and put extra strain on your cardiovascular system.

According to a recent study from researchers at Bielefeld University in Germany, fans’ physiological stress increases by about 41 percent during a soccer final compared to a normal day. Heart rate also rose significantly, jumping from 70.9 beats per minute to 78.7 beats per minute—a difference even when compared to other weekends.

Researchers at Bielefeld tracked 229 fans of the German club Arminia Bielefeld for three months. Participants wore smartwatches that continuously recorded heart rate and an estimated stress index based on heart rate variability, allowing researchers to compare the day of the 2025 German Cup final with the days leading up to the match.

The physiological reaction to the soccer final began long before the match began. The researchers saw fans’ stress levels begin to rise in the morning and peak just before kickoff. Even after the final whistle, viewers showed signs of elevated stress.

Where you watch the game also makes a difference. The study found that fans who watched at the stadium had an average heart rate of 94.2 beats per minute compared to 79.4 among those who watched the match on television. After their team’s first goal, those in the stands saw their heart rate climb to an average of up to 108 beats per minute—a much more intense response than that observed in other contexts.

Alcohol consumption appeared to amplify that effect. Participants who reported drinking during the game had a heart rate approximately 5 percent higher than the rest of the fans during the match and nearly 12 percent higher after their team’s first goal. Although the researchers did not assess medical risks, they note that alcohol can increase cardiovascular strain when people are in an emotional state.

During the first few minutes of the match, when the outcome was still uncertain, heart rates reached their highest levels. Once the game seemed to be decided, fans’ heart rates dropped.

However, two goals scored in the final minutes caused them to spike again, even though the chances of a comeback were practically nil. (You can only imagine how fast Argentina fans’ hearts were thumping during this week’s furious comeback against Egypt.) For the authors, this reflects that the body responds not only to the objective chances of winning but also to emotions such as hope, pride, or attachment to the team.

The findings align with the results of previous studies on the physiological impacts of soccer. That incluides a study published in the New England Journal of Medicine after the 2006 World Cup in Germany, found that the risk of suffering an acute cardiovascular event nearly triples during German national team matches among people with preexisting heart conditions.

Subsequent research shows that matches can lead to an increase in stress hormones such as cortisol and found that fans who identify more strongly with their team exhibit more intense biological responses during decisive matches.

This article originally appeared on WIRED en Español and has been translated from Spanish.

#Watching #Soccer #Final #Body #Scienceworld cup 2026,sports,health,soccer,stress,fandom">What Watching a Soccer Final Does to Your Body, According to Science

Ready for the 2026 World Cup final? You might think you are, but your body is going to have to be prepared to put in some work—especially if your favorite team makes it.

Research shows that watching high-pressure matches can raise your heart rate, increase your stress levels, and put extra strain on your cardiovascular system.

According to a recent study from researchers at Bielefeld University in Germany, fans’ physiological stress increases by about 41 percent during a soccer final compared to a normal day. Heart rate also rose significantly, jumping from 70.9 beats per minute to 78.7 beats per minute—a difference even when compared to other weekends.

Researchers at Bielefeld tracked 229 fans of the German club Arminia Bielefeld for three months. Participants wore smartwatches that continuously recorded heart rate and an estimated stress index based on heart rate variability, allowing researchers to compare the day of the 2025 German Cup final with the days leading up to the match.

The physiological reaction to the soccer final began long before the match began. The researchers saw fans’ stress levels begin to rise in the morning and peak just before kickoff. Even after the final whistle, viewers showed signs of elevated stress.

Where you watch the game also makes a difference. The study found that fans who watched at the stadium had an average heart rate of 94.2 beats per minute compared to 79.4 among those who watched the match on television. After their team’s first goal, those in the stands saw their heart rate climb to an average of up to 108 beats per minute—a much more intense response than that observed in other contexts.

Alcohol consumption appeared to amplify that effect. Participants who reported drinking during the game had a heart rate approximately 5 percent higher than the rest of the fans during the match and nearly 12 percent higher after their team’s first goal. Although the researchers did not assess medical risks, they note that alcohol can increase cardiovascular strain when people are in an emotional state.

During the first few minutes of the match, when the outcome was still uncertain, heart rates reached their highest levels. Once the game seemed to be decided, fans’ heart rates dropped.

However, two goals scored in the final minutes caused them to spike again, even though the chances of a comeback were practically nil. (You can only imagine how fast Argentina fans’ hearts were thumping during this week’s furious comeback against Egypt.) For the authors, this reflects that the body responds not only to the objective chances of winning but also to emotions such as hope, pride, or attachment to the team.

The findings align with the results of previous studies on the physiological impacts of soccer. That incluides a study published in the New England Journal of Medicine after the 2006 World Cup in Germany, found that the risk of suffering an acute cardiovascular event nearly triples during German national team matches among people with preexisting heart conditions.

Subsequent research shows that matches can lead to an increase in stress hormones such as cortisol and found that fans who identify more strongly with their team exhibit more intense biological responses during decisive matches.

This article originally appeared on WIRED en Español and has been translated from Spanish.

#Watching #Soccer #Final #Body #Scienceworld cup 2026,sports,health,soccer,stress,fandom

These days, new versions of AI chatbots don’t just launch; they’re unshackled and released to the public following government scrutiny. OpenAI’s new GPT-5.6 models were – like Anthropic’s Claude Mythos and Fable – apparently too powerful to just launch; but now, after some tinkering, they’re available to you, dear customer.

In practice, it simply means that the new GPT-5.6 models are very powerful and smarter than before. In its introductory post, OpenAI shared a bunch of graphs showing just how much better GPT-5.6 is than the competition, whilst using fewer tokens and generally costing less.

OK, great. But GPT-5.6 is not just one model; it comes in three distinct flavors: Sol, Terra, and Luna. So what do different kinds of users get, what should they pay for, and which models should they (mostly) use? Let’s dive in.

Free users get (almost) nothing

Sorry; if you’re not a paying customer, you’ll have to make do with OpenAI’s previous flagship model, GPT-5.5. Any sort of access to GPT-5.6 models requires a subscription of some sort. Fortunately, GPT-5.5 is still quite capable at most tasks, but if you want the best of the best, you’ll have to cough up the dough.

There’s an exception to this: Free and Go users can access GPT-5.6 through ChatGPT Work. More on that below.

If you’re a Plus or Business user, you can only get Sol (the most powerful model) at medium and higher effort settings. There’s another, higher level of performance called Sol Pro, but that’s only available for Pro and Enterprise users.

In terms of availability per one million tokens, the prices are: $5 input and $30 output for Sol. $2.5 input and $15 output for Terra, and $1 input and $6 output for Luna.

SEE ALSO: Visa is connecting with ChatGPT to let AI agents automatically make purchases

Sol, Terra, or Luna?

Why are there three models in the first place? Well, OpenAI always had a multi-tier model; for example, previously users were able to choose a “mini” version of the main model to get results done cheaper. Now, the model has been split into three tiers.

If you’re a paying customer, you’re free to use all three. But you know how it is in the world of LLMs: If you pick the smartest one, your usage limits will get hit faster (yes, there are always usage limits, even if you throw a ton of money at OpenAI).

In the simplest sense, GPT-5.6 Sol is the smartest model, Terra is in between (with roughly GPT-5.5 level of performance), and Luna is the cheapest, fastest, but also least capable of the bunch.

The breakdown is as follows: Terra is a “balanced” model for everyday work. That’s the one you should be asking most of your questions. Don’t underestimate it, though, as OpenAI claims it performs better than Anthropic’s Fable 5 in some cases.

Luna is cost-efficient, and should be used for easy, non crucial everyday tasks; think recipes and movie recommendations. Again, OpenAI says it outperforms Anthropic’s Opus 4.8 in some cases, so it’s not a slouch, either.

Sol should be reserved for coding, deep research, planning, and cybersecurity: The most demanding tasks. Of course it comes at a (literal) cost: While OpenAI claims it spends less tokens than Anthropic’s Fable 5, Sol will still hit usage limits a lot faster than the other variants.

Fun fact: If you ask GPT-5.5 about any of this, right now, it’ll give you completely wrong answers. Hopefully OpenAI will fix this soon.

Wait, what’s this ChatGPT Work thing, then?

Oh yeah, OpenAI also launched ChatGPT Work, which is a new agent in ChatGPT that can access and take actions on your apps and files, and work in the background until a task is finished. It’s powered by Codex (OpenAI’s software engineering agent) and GPT-5.6. Think about it as your buddy that will go through your emails and files, browse the web, fetch the relevant data, and create that presentation your boss wants before the day is done.

ChatGPT Work is rolling out to Pro, Enterprise and Edu users first on web and mobile; this will be expanded to Plus and Business users “over the next few days.”

On the desktop, Work is available for everyone, including Free users.

Oh, and one more thing: The fact that ChatGPT Work has a built-in browser also means that OpenAI is sunsetting its standalone web browser, Atlas. Sorry.

How about GPT Live?

GPT Live is a new version of ChatGPT Voice and it will show up when you start talking to ChatGPT.

We’ve covered this in more depth here, but the bottom line is that GPT Live can listen and speak at the same time, allowing it to keep up a more realistic conversation.

Wrapping it all up

The new GPT-5.6 model is smart. It comes in three flavors: Luna, Terra, and Sol, with Sol being the most capable variant, Luna the most affordable one, and Terra somewhere in the middle. You can currently only get them on paid tiers, unless you’re using ChatGPT Work on desktop. And ChatGPT Voice has also gotten smarter with GPT-Live underneath, a model that can listen and speak at the same time.

Topics Artificial Intelligence OpenAI

#GPT5.6 #Sol #Terra #Luna">GPT-5.6 Sol, Terra, and Luna are here. See which one’s best for you. These days, new versions of AI chatbots don’t just launch; they’re unshackled and released to the public following government scrutiny. OpenAI’s new GPT-5.6 models were – like Anthropic’s Claude Mythos and Fable – apparently too powerful to just launch; but now, after some tinkering, they’re available to you, dear customer.In practice, it simply means that the new GPT-5.6 models are very powerful and smarter than before. In its introductory post, OpenAI shared a bunch of graphs showing just how much better GPT-5.6 is than the competition, whilst using fewer tokens and generally costing less. OK, great. But GPT-5.6 is not just one model; it comes in three distinct flavors: Sol, Terra, and Luna. So what do different kinds of users get, what should they pay for, and which models should they (mostly) use? Let’s dive in. Free users get (almost) nothingSorry; if you’re not a paying customer, you’ll have to make do with OpenAI’s previous flagship model, GPT-5.5. Any sort of access to GPT-5.6 models requires a subscription of some sort. Fortunately, GPT-5.5 is still quite capable at most tasks, but if you want the best of the best, you’ll have to cough up the dough. There’s an exception to this: Free and Go users can access GPT-5.6 through ChatGPT Work. More on that below. If you’re a Plus or Business user, you can only get Sol (the most powerful model) at medium and higher effort settings. There’s another, higher level of performance called Sol Pro, but that’s only available for Pro and Enterprise users. In terms of availability per one million tokens, the prices are: input and output for Sol. .5 input and output for Terra, and input and output for Luna. SEE ALSO: Visa is connecting with ChatGPT to let AI agents automatically make purchases Sol, Terra, or Luna?Why are there three models in the first place? Well, OpenAI always had a multi-tier model; for example, previously users were able to choose a “mini” version of the main model to get results done cheaper. Now, the model has been split into three tiers. If you’re a paying customer, you’re free to use all three. But you know how it is in the world of LLMs: If you pick the smartest one, your usage limits will get hit faster (yes, there are always usage limits, even if you throw a ton of money at OpenAI). Mashable Light Speed In the simplest sense, GPT-5.6 Sol is the smartest model, Terra is in between (with roughly GPT-5.5 level of performance), and Luna is the cheapest, fastest, but also least capable of the bunch. The breakdown is as follows: Terra is a “balanced” model for everyday work. That’s the one you should be asking most of your questions. Don’t underestimate it, though, as OpenAI claims it performs better than Anthropic’s Fable 5 in some cases. Luna is cost-efficient, and should be used for easy, non crucial everyday tasks; think recipes and movie recommendations. Again, OpenAI says it outperforms Anthropic’s Opus 4.8 in some cases, so it’s not a slouch, either.Sol should be reserved for coding, deep research, planning, and cybersecurity: The most demanding tasks. Of course it comes at a (literal) cost: While OpenAI claims it spends less tokens than Anthropic’s Fable 5, Sol will still hit usage limits a lot faster than the other variants. Fun fact: If you ask GPT-5.5 about any of this, right now, it’ll give you completely wrong answers. Hopefully OpenAI will fix this soon.Wait, what’s this ChatGPT Work thing, then?Oh yeah, OpenAI also launched ChatGPT Work, which is a new agent in ChatGPT that can access and take actions on your apps and files, and work in the background until a task is finished. It’s powered by Codex (OpenAI’s software engineering agent) and GPT-5.6. Think about it as your buddy that will go through your emails and files, browse the web, fetch the relevant data, and create that presentation your boss wants before the day is done. ChatGPT Work is rolling out to Pro, Enterprise and Edu users first on web and mobile; this will be expanded to Plus and Business users “over the next few days.”On the desktop, Work is available for everyone, including Free users. Oh, and one more thing: The fact that ChatGPT Work has a built-in browser also means that OpenAI is sunsetting its standalone web browser, Atlas. Sorry. How about GPT Live?GPT Live is a new version of ChatGPT Voice and it will show up when you start talking to ChatGPT. We’ve covered this in more depth here, but the bottom line is that GPT Live can listen and speak at the same time, allowing it to keep up a more realistic conversation.Wrapping it all upThe new GPT-5.6 model is smart. It comes in three flavors: Luna, Terra, and Sol, with Sol being the most capable variant, Luna the most affordable one, and Terra somewhere in the middle. You can currently only get them on paid tiers, unless you’re using ChatGPT Work on desktop. And ChatGPT Voice has also gotten smarter with GPT-Live underneath, a model that can listen and speak at the same time. Topics Artificial Intelligence OpenAI #GPT5.6 #Sol #Terra #Luna

Fable – apparently too powerful to just launch; but now, after some tinkering, they’re available to you, dear customer.

In practice, it simply means that the new GPT-5.6 models are very powerful and smarter than before. In its introductory post, OpenAI shared a bunch of graphs showing just how much better GPT-5.6 is than the competition, whilst using fewer tokens and generally costing less.

OK, great. But GPT-5.6 is not just one model; it comes in three distinct flavors: Sol, Terra, and Luna. So what do different kinds of users get, what should they pay for, and which models should they (mostly) use? Let’s dive in.

Free users get (almost) nothing

Sorry; if you’re not a paying customer, you’ll have to make do with OpenAI’s previous flagship model, GPT-5.5. Any sort of access to GPT-5.6 models requires a subscription of some sort. Fortunately, GPT-5.5 is still quite capable at most tasks, but if you want the best of the best, you’ll have to cough up the dough.

There’s an exception to this: Free and Go users can access GPT-5.6 through ChatGPT Work. More on that below.

If you’re a Plus or Business user, you can only get Sol (the most powerful model) at medium and higher effort settings. There’s another, higher level of performance called Sol Pro, but that’s only available for Pro and Enterprise users.

In terms of availability per one million tokens, the prices are: $5 input and $30 output for Sol. $2.5 input and $15 output for Terra, and $1 input and $6 output for Luna.

SEE ALSO: Visa is connecting with ChatGPT to let AI agents automatically make purchases

Sol, Terra, or Luna?

Why are there three models in the first place? Well, OpenAI always had a multi-tier model; for example, previously users were able to choose a “mini” version of the main model to get results done cheaper. Now, the model has been split into three tiers.

If you’re a paying customer, you’re free to use all three. But you know how it is in the world of LLMs: If you pick the smartest one, your usage limits will get hit faster (yes, there are always usage limits, even if you throw a ton of money at OpenAI).

In the simplest sense, GPT-5.6 Sol is the smartest model, Terra is in between (with roughly GPT-5.5 level of performance), and Luna is the cheapest, fastest, but also least capable of the bunch.

The breakdown is as follows: Terra is a “balanced” model for everyday work. That’s the one you should be asking most of your questions. Don’t underestimate it, though, as OpenAI claims it performs better than Anthropic’s Fable 5 in some cases.

Luna is cost-efficient, and should be used for easy, non crucial everyday tasks; think recipes and movie recommendations. Again, OpenAI says it outperforms Anthropic’s Opus 4.8 in some cases, so it’s not a slouch, either.

Sol should be reserved for coding, deep research, planning, and cybersecurity: The most demanding tasks. Of course it comes at a (literal) cost: While OpenAI claims it spends less tokens than Anthropic’s Fable 5, Sol will still hit usage limits a lot faster than the other variants.

Fun fact: If you ask GPT-5.5 about any of this, right now, it’ll give you completely wrong answers. Hopefully OpenAI will fix this soon.

Wait, what’s this ChatGPT Work thing, then?

Oh yeah, OpenAI also launched ChatGPT Work, which is a new agent in ChatGPT that can access and take actions on your apps and files, and work in the background until a task is finished. It’s powered by Codex (OpenAI’s software engineering agent) and GPT-5.6. Think about it as your buddy that will go through your emails and files, browse the web, fetch the relevant data, and create that presentation your boss wants before the day is done.

ChatGPT Work is rolling out to Pro, Enterprise and Edu users first on web and mobile; this will be expanded to Plus and Business users “over the next few days.”

On the desktop, Work is available for everyone, including Free users.

Oh, and one more thing: The fact that ChatGPT Work has a built-in browser also means that OpenAI is sunsetting its standalone web browser, Atlas. Sorry.

How about GPT Live?

GPT Live is a new version of ChatGPT Voice and it will show up when you start talking to ChatGPT.

We’ve covered this in more depth here, but the bottom line is that GPT Live can listen and speak at the same time, allowing it to keep up a more realistic conversation.

Wrapping it all up

The new GPT-5.6 model is smart. It comes in three flavors: Luna, Terra, and Sol, with Sol being the most capable variant, Luna the most affordable one, and Terra somewhere in the middle. You can currently only get them on paid tiers, unless you’re using ChatGPT Work on desktop. And ChatGPT Voice has also gotten smarter with GPT-Live underneath, a model that can listen and speak at the same time.

Topics Artificial Intelligence OpenAI

#GPT5.6 #Sol #Terra #Luna">GPT-5.6 Sol, Terra, and Luna are here. See which one’s best for you.

These days, new versions of AI chatbots don’t just launch; they’re unshackled and released to the public following government scrutiny. OpenAI’s new GPT-5.6 models were – like Anthropic’s Claude Mythos and Fable – apparently too powerful to just launch; but now, after some tinkering, they’re available to you, dear customer.

In practice, it simply means that the new GPT-5.6 models are very powerful and smarter than before. In its introductory post, OpenAI shared a bunch of graphs showing just how much better GPT-5.6 is than the competition, whilst using fewer tokens and generally costing less.

OK, great. But GPT-5.6 is not just one model; it comes in three distinct flavors: Sol, Terra, and Luna. So what do different kinds of users get, what should they pay for, and which models should they (mostly) use? Let’s dive in.

Free users get (almost) nothing

Sorry; if you’re not a paying customer, you’ll have to make do with OpenAI’s previous flagship model, GPT-5.5. Any sort of access to GPT-5.6 models requires a subscription of some sort. Fortunately, GPT-5.5 is still quite capable at most tasks, but if you want the best of the best, you’ll have to cough up the dough.

There’s an exception to this: Free and Go users can access GPT-5.6 through ChatGPT Work. More on that below.

If you’re a Plus or Business user, you can only get Sol (the most powerful model) at medium and higher effort settings. There’s another, higher level of performance called Sol Pro, but that’s only available for Pro and Enterprise users.

In terms of availability per one million tokens, the prices are: $5 input and $30 output for Sol. $2.5 input and $15 output for Terra, and $1 input and $6 output for Luna.

SEE ALSO: Visa is connecting with ChatGPT to let AI agents automatically make purchases

Sol, Terra, or Luna?

Why are there three models in the first place? Well, OpenAI always had a multi-tier model; for example, previously users were able to choose a “mini” version of the main model to get results done cheaper. Now, the model has been split into three tiers.

If you’re a paying customer, you’re free to use all three. But you know how it is in the world of LLMs: If you pick the smartest one, your usage limits will get hit faster (yes, there are always usage limits, even if you throw a ton of money at OpenAI).

In the simplest sense, GPT-5.6 Sol is the smartest model, Terra is in between (with roughly GPT-5.5 level of performance), and Luna is the cheapest, fastest, but also least capable of the bunch.

The breakdown is as follows: Terra is a “balanced” model for everyday work. That’s the one you should be asking most of your questions. Don’t underestimate it, though, as OpenAI claims it performs better than Anthropic’s Fable 5 in some cases.

Luna is cost-efficient, and should be used for easy, non crucial everyday tasks; think recipes and movie recommendations. Again, OpenAI says it outperforms Anthropic’s Opus 4.8 in some cases, so it’s not a slouch, either.

Sol should be reserved for coding, deep research, planning, and cybersecurity: The most demanding tasks. Of course it comes at a (literal) cost: While OpenAI claims it spends less tokens than Anthropic’s Fable 5, Sol will still hit usage limits a lot faster than the other variants.

Fun fact: If you ask GPT-5.5 about any of this, right now, it’ll give you completely wrong answers. Hopefully OpenAI will fix this soon.

Wait, what’s this ChatGPT Work thing, then?

Oh yeah, OpenAI also launched ChatGPT Work, which is a new agent in ChatGPT that can access and take actions on your apps and files, and work in the background until a task is finished. It’s powered by Codex (OpenAI’s software engineering agent) and GPT-5.6. Think about it as your buddy that will go through your emails and files, browse the web, fetch the relevant data, and create that presentation your boss wants before the day is done.

ChatGPT Work is rolling out to Pro, Enterprise and Edu users first on web and mobile; this will be expanded to Plus and Business users “over the next few days.”

On the desktop, Work is available for everyone, including Free users.

Oh, and one more thing: The fact that ChatGPT Work has a built-in browser also means that OpenAI is sunsetting its standalone web browser, Atlas. Sorry.

How about GPT Live?

GPT Live is a new version of ChatGPT Voice and it will show up when you start talking to ChatGPT.

We’ve covered this in more depth here, but the bottom line is that GPT Live can listen and speak at the same time, allowing it to keep up a more realistic conversation.

Wrapping it all up

The new GPT-5.6 model is smart. It comes in three flavors: Luna, Terra, and Sol, with Sol being the most capable variant, Luna the most affordable one, and Terra somewhere in the middle. You can currently only get them on paid tiers, unless you’re using ChatGPT Work on desktop. And ChatGPT Voice has also gotten smarter with GPT-Live underneath, a model that can listen and speak at the same time.

Topics Artificial Intelligence OpenAI

#GPT5.6 #Sol #Terra #Luna

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