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Pope Francis’ death right after Easter sounds miraculous – but patients and caregivers often work together to delay dying

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theconversation.com – Michelle Riba, Clinical Professor of Psychiatry, University of Michigan – 2025-04-24 07:58:00

Pope Francis died after celebrating Easter with his congregants.
AP Photo/Gregorio Borgia

Michelle Riba, University of Michigan

On the morning of Easter Monday, after his final public address the day prior, Pope Francis died at age 88, closing 12 years of leading the Catholic Church. He joins the phenomena of people “holding on” until after an anticipated date or event, such as the holidays or a birthday, before dying.

It sometimes seems like some patients are able to stay alive out of sheer willpower. But for many people, behind the scenes are a village of people and an ongoing series of conversations that help patients be able to celebrate their child’s graduation or travel to a place they’ve always wanted to go.

We asked Dr. Michelle Riba, director of the psycho-oncology program at the University of Michigan Rogel Cancer Center, to explain how meaning matters just as much as medicine at the end of life.

What factors come into play at the end of life?

Psychosocial factors that affect a person’s mental health and well-being – such as stress, social support, depression and anxiety, and socioeconomic status – play an important part of all parts of life, but especially at the end of life. End of life refers to the days, weeks or months after somebody is told that they have a disease that can be fatal.

Questions about meaning and what’s important to a patient and their family are important at all times. But when somebody is diagnosed with a grave illness, these questions become particularly important to acknowledge in medical conversations. As many doctors like to say, patients aren’t the disease, they have a disease.

We want to give patients control about how they want to live their lives in the most meaningful way, especially at the end. And this includes how they want to use their time, energy and resources, who they want to spend their time with and where they want to be.

How does the ‘will to live’ affect treatment and survival?

There was a new movement starting in the 1960s to 1970s that believed a person’s attitude and outlook on life could affect their health and longevity. People like minister Norman Vincent Peale promoted the idea that a positive mindset could help improve outcomes. Psychologist Martin Seligman developed the field of positive psychology that focused on subjective well-being by promoting resilience and human flourishing. The idea that you could do better if you were optimistic resonated with many people, including physicians.

Then surgeon Bernie Siegel proposed the specific idea that staying positive after a cancer diagnosis could extend your life, and that became a major focus of the movement. However, there was little to no data to support his claims. The studies researchers conducted to figure out whether it was true that people who were more positive lived longer or had a lower prevalence of cancer than those who did not were either flawed or did not consistently show this effect.

Eventually Siegel’s ideas were disproved. But for a long time, they affected how patients felt about themselves and how their families addressed illness. My own patients would tell me, “How can I be positive? I can’t eat, I’m in pain and I’m sick.” They felt guilty that they couldn’t feel positive and optimistic, and that caused extra stress and reduced their quality of life.

Patient in hospital gown sitting on bed, talking with a doctor in a hospital room
Learning about what matters most to a patient requires asking them.
FG Trade/E+ via Getty Images

Additionally, the social determinants of health – such as a patient’s environment, race, education and wealth – are also very important to their health and longevity. Having a good social life, money and not being discriminated against makes it easier to stay positive and do better in life. During the COVID-19 pandemic, people were less likely to do well if you didn’t have money, or if you were a certain race.

Research shows that patients who have severe mental illness such as schizophrenia and bipolar disorder often live about 20 years less than somebody who doesn’t. And it’s not just because of the disease. Having a severe mental illness means that you probably can’t work, you probably don’t have financial means, and you may not have family support.

How can doctors help patients feel like they have more control?

In studying how patients could feel more confident, physicians like me realized that having control over their destiny, if you will, didn’t necessarily mean patients had to stay positive. Rather, it meant understanding the things that gave them joy and meaning before their diagnosis, and how clinicians could help them continue to do these things.

For example, a patient who could no longer work because of their cancer or their treatments might miss their sense of routine. Working with them to make a schedule of all their medical appointments and enjoyable activities might help them take control over their days. The structure may provide meaning and help them cope better.

A marathon runner who loses their ability to balance due to a brain tumor is another example. If this patient found meaning and pleasure in running but could no longer run, what could we do to help them regain some of this joy? This might look like starting physical therapy and rehab, or finding alternative activities they can do.

If going to their place of worship is important to a patient but they’re no longer able to, we could see if their rabbi, imam or minister could see them at their home.

Additionally, helping patients continue doing what’s meaningful for them also gives them hope. It helps them know that their physicians feel they’re worth doing that for, and that there’s a life beyond cancer treatment.

How do a patient’s goals factor into their treatment plan?

When doctors give patients hope, patients tend to have better outcomes. That doesn’t mean we’re telling patients something false, or that they’re going to live a longer time. Rather, doctors can help patients improve or maintain their quality of life and achieve certain goals.

For example, a patient may be thinking of attending their child’s graduation two months from now. Their care team can talk to them about how they might be able to do this, or think of other ways they can celebrate.

Hand with hospital band around wrist holding another hand
Feeling supported during a serious illness can make a big difference.
Joshua Hoehne/Unsplash, CC BY-SA

My mother passed away from cancer a month after I graduated high school. I remember she couldn’t participate in a lot of senior prom activities, like helping me get a dress or do my hair. But my date and I and another couple were allowed to go to her hospital room just before the prom so she could see us all dressed up. And it was one of the most meaningful moments of my life. Though she couldn’t be there for graduation or all the other preparations and celebrations, it mattered to my mother and me that she was able to see my friends and me before prom. Also, very meaningfully, my friends were so kind and thoughtful to make that effort on our behalf.

There have been observations that some patients with terminal illness manage to hold on until after a certain holiday or date. A 1988 study found that the number of Jewish people who died before Passover was lower than expected, and the number of deaths after Passover was higher than expected. While this study had flaws and limitations, other researchers have made similar observations for deaths for specific groups after holidays like Christmas, the Mid-Autumn Festival and birthdays.

But these studies don’t address whether those specific holidays were actually what these patients really cared about. It may be that people made it through something else important to them. It may be that they were able to be with the people they loved at the end. It may be something else entirely. We don’t really know what’s important for someone unless we ask.

Allowing patients and their families to think about what matters most to them and how we can help them achieve their goals is part of our job as physicians.

How do you balance a patient’s medical care with their goals?

Being diagnosed with a terminal illness can be a traumatic event. Patients often can remember where and when they heard the news about a certain illness or scan or problem. How to help people process, understand and live with this to the best of their ability is really the key to having the best quality of life. This means giving them choices and helping them see some ways to address it for themselves and their families.

Sometimes that can be really hard. For patients who really want to travel somewhere, we might figure out a way to defer specific treatments or procedures, or set up appointments for them to be done at the local hospital or clinic. But there’s not much we can do for a patient who wants to attend their young child’s wedding when that won’t be for decades in the future. The medical team does everything it can within reason, and it tries to make sure the patients and their loved ones understand the risks and benefits.

Patient crying while a health care worker consoles them
Receiving bad news can be a traumatic event.
Maskot/DigitalVision via Getty Images

Doctors and patients may also have different goals that can be difficult to meet at the same time. Figuring out how to juggle these agendas and listening to each other during these conversations can be challenging but important.

Everybody is trying to do what they think is right and best for the patient. This means taking care of the whole person, not just the disease. Whether that means reaching a certain holiday or special event, or just gathering together with the people they love, taking the time and effort to understand what is important for the patient and their family is key to good care.The Conversation

Michelle Riba, Clinical Professor of Psychiatry, University of Michigan

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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The Conversation

AI is giving a boost to efforts to monitor health via radar

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theconversation.com – Chandler Bauder, Electronics Engineer, U.S. Naval Research Laboratory – 2025-04-30 07:48:00

AI-powered radar could enable contactless health monitoring in the home.
Chandler Bauder

Chandler Bauder, U.S. Naval Research Laboratory and Aly Fathy, University of Tennessee

If you wanted to check someone’s pulse from across the room, for example to remotely monitor an elderly relative, how could you do it? You might think it’s impossible, because common health-monitoring devices such as fingertip pulse oximeters and smartwatches have to be in contact with the body.

However, researchers are developing technologies that can monitor a person’s vital signs at a distance. One of those technologies is radar.

We are electrical engineers who study radar systems. We have combined advances in radar technology and artificial intelligence to reliably monitor breathing and heart rate without contacting the body.

Noncontact health monitoring has the potential to be more comfortable and easier to use than traditional methods, particularly for people looking to monitor their vital signs at home.

How radar works

Radar is commonly known for measuring the speed of cars, making weather forecasts and detecting obstacles at sea and in the air. It works by sending out electromagnetic waves that travel at the speed of light, waiting for them to bounce off objects in their path, and sensing them when they return to the device.

Radar can tell how far away things are, how fast they’re moving, and even their shape by analyzing the properties of the reflected waves.

Radar can also be used to monitor vital signs such as breathing and heart rate. Each breath or heartbeat causes your chest to move ever so slightly – movement that’s hard for people to see or feel. However, today’s radars are sensitive enough to detect these tiny movements, even from across a room.

Advantages of radar

There are other technologies that can be used to measure health remotely. Camera-based techniques can use infrared light to monitor changes in the surface of the skin in the same manner as pulse oximeters, revealing information about your heart’s activity. Computer vision systems can also monitor breathing and other activities, such as sleep, and they can detect when someone falls.

However, cameras often fail in cases where the body is obstructed by blankets or clothes, or when lighting is inadequate. There are also concerns that different skin tones reflect infrared light differently, causing inaccurate readings for people with darker skin. Additionally, depending on high-resolution cameras for long-term health monitoring brings up serious concerns about patient privacy.

side-by-side images, one of a person and the other a verticle series of nested blobs of color
Radar sees the world in terms of how strongly objects in its view reflect the transmitted signals. The resolution of images it can generate are much lower than images cameras produce.
Chandler Bauder

Radar, on the other hand, solves many of these problems. The wavelengths of the transmitted waves are much longer than those of visible or infrared light, allowing the waves to pass through blankets, clothing and even walls. The measurements aren’t affected by lighting or skin tone, making them more reliable in different conditions.

Radar imagery is also extremely low resolution – think old Game Boy graphics versus a modern 4K TV – so it doesn’t capture enough detail to be used to identify someone, but it can still monitor important activities. While it does project energy, the amount does not pose a health hazard. The health-monitoring radars operate at frequencies and power levels similar to the phone in your pocket.

Radar + AI

Radar is powerful, but it has a big challenge: It picks up everything that moves. Since it can detect tiny chest movements from the heart beating, it also picks up larger movements from the head, limbs or other people nearby. This makes it difficult for traditional processing techniques to extract vital signs clearly.

To address this problem we created a kind of “brain” to make the radar smarter. This brain, which we named mm-MuRe, is a neural network – a type of artificial intelligence – that learns directly from raw radar signals and estimates chest movements. This approach is called end-to-end learning. It means that, unlike other radar plus AI techniques, the network figures out on its own how to ignore the noise and focus only on the important signals.

a diagram with two cartoon representations of people on one side, a brain on the other and vertical curved lines in betwenn
In our study, we used AI to transform raw, unprocessed radar signals into vital signs waveforms of one or two people.
Chandler Bauder

We found that this AI enhancement not only gives more accurate results, it also works faster than traditional methods. It handles multiple people at once, for example an elderly couple, and adapts to new situations, even those it didn’t see during training – such as when people are sitting at different heights, riding in a car or standing close together.

Implications for health care

Reliable remote health monitoring using radar and AI could be a major boon for health care. With no need to touch the patient’s skin, risks of rashes, contamination and discomfort could be greatly reduced. It’s especially helpful in long-term care, where reducing wires and devices can make life significantly easier for patients and caregivers.

Imagine a nursing home where radar quietly watches over residents, alerting caregivers immediately if someone has breathing trouble, falls or needs help. It can be implemented as a home system that checks your breathing while you sleep – no wearables required. Doctors could even use radar to remotely monitor patients recovering from surgery or illness.

This technology is moving quickly toward real-world use. In the future, checking your health could be as simple as walking into a room, with invisible waves and smart AI working silently to take your vital signs.The Conversation

Chandler Bauder, Electronics Engineer, U.S. Naval Research Laboratory and Aly Fathy, Professor of Electrical Engineering, University of Tennessee

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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Note: The following A.I. based commentary is not part of the original article, reproduced above, but is offered in the hopes that it will promote greater media literacy and critical thinking, by making any potential bias more visible to the reader –Staff Editor.

Political Bias Rating: Centrist

The article is focused on a scientific and technological development related to health monitoring using radar and artificial intelligence. It provides an overview of the research process, technical details, and potential health care applications without expressing a clear ideological stance. The tone remains neutral, emphasizing the technical capabilities and benefits of the technology, particularly in long-term care and home health monitoring. While it does mention potential privacy concerns with other methods like cameras, it does so without taking a political position, focusing instead on the advantages of radar. The content adheres to factual reporting and avoids overt bias or advocacy, presenting the information in a straightforward and informative manner.

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Forensics tool ‘reanimates’ the ‘brains’ of AIs that fail in order to understand what went wrong

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theconversation.com – David Oygenblik, Ph.D. Student in Electrical and Computer Engineering, Georgia Institute of Technology – 2025-04-30 07:47:00

Tesla crashes are only the most glaring of AI failures.
South Jordan Police Department via APPEAR

David Oygenblik, Georgia Institute of Technology and Brendan Saltaformaggio, Georgia Institute of Technology

From drones delivering medical supplies to digital assistants performing everyday tasks, AI-powered systems are becoming increasingly embedded in everyday life. The creators of these innovations promise transformative benefits. For some people, mainstream applications such as ChatGPT and Claude can seem like magic. But these systems are not magical, nor are they foolproof – they can and do regularly fail to work as intended.

AI systems can malfunction due to technical design flaws or biased training data. They can also suffer from vulnerabilities in their code, which can be exploited by malicious hackers. Isolating the cause of an AI failure is imperative for fixing the system.

But AI systems are typically opaque, even to their creators. The challenge is how to investigate AI systems after they fail or fall victim to attack. There are techniques for inspecting AI systems, but they require access to the AI system’s internal data. This access is not guaranteed, especially to forensic investigators called in to determine the cause of a proprietary AI system failure, making investigation impossible.

We are computer scientists who study digital forensics. Our team at the Georgia Institute of Technology has built a system, AI Psychiatry, or AIP, that can recreate the scenario in which an AI failed in order to determine what went wrong. The system addresses the challenges of AI forensics by recovering and “reanimating” a suspect AI model so it can be systematically tested.

Uncertainty of AI

Imagine a self-driving car veers off the road for no easily discernible reason and then crashes. Logs and sensor data might suggest that a faulty camera caused the AI to misinterpret a road sign as a command to swerve. After a mission-critical failure such as an autonomous vehicle crash, investigators need to determine exactly what caused the error.

Was the crash triggered by a malicious attack on the AI? In this hypothetical case, the camera’s faultiness could be the result of a security vulnerability or bug in its software that was exploited by a hacker. If investigators find such a vulnerability, they have to determine whether that caused the crash. But making that determination is no small feat.

Although there are forensic methods for recovering some evidence from failures of drones, autonomous vehicles and other so-called cyber-physical systems, none can capture the clues required to fully investigate the AI in that system. Advanced AIs can even update their decision-making – and consequently the clues – continuously, making it impossible to investigate the most up-to-date models with existing methods.

YouTube video
Researchers are working on making AI systems more transparent, but unless and until those efforts transform the field, there will be a need for forensics tools to at least understand AI failures.

Pathology for AI

AI Psychiatry applies a series of forensic algorithms to isolate the data behind the AI system’s decision-making. These pieces are then reassembled into a functional model that performs identically to the original model. Investigators can “reanimate” the AI in a controlled environment and test it with malicious inputs to see whether it exhibits harmful or hidden behaviors.

AI Psychiatry takes in as input a memory image, a snapshot of the bits and bytes loaded when the AI was operational. The memory image at the time of the crash in the autonomous vehicle scenario holds crucial clues about the internal state and decision-making processes of the AI controlling the vehicle. With AI Psychiatry, investigators can now lift the exact AI model from memory, dissect its bits and bytes, and load the model into a secure environment for testing.

Our team tested AI Psychiatry on 30 AI models, 24 of which were intentionally “backdoored” to produce incorrect outcomes under specific triggers. The system was successfully able to recover, rehost and test every model, including models commonly used in real-world scenarios such as street sign recognition in autonomous vehicles.

Thus far, our tests suggest that AI Psychiatry can effectively solve the digital mystery behind a failure such as an autonomous car crash that previously would have left more questions than answers. And if it does not find a vulnerability in the car’s AI system, AI Psychiatry allows investigators to rule out the AI and look for other causes such as a faulty camera.

Not just for autonomous vehicles

AI Psychiatry’s main algorithm is generic: It focuses on the universal components that all AI models must have to make decisions. This makes our approach readily extendable to any AI models that use popular AI development frameworks. Anyone working to investigate a possible AI failure can use our system to assess a model without prior knowledge of its exact architecture.

Whether the AI is a bot that makes product recommendations or a system that guides autonomous drone fleets, AI Psychiatry can recover and rehost the AI for analysis. AI Psychiatry is entirely open source for any investigator to use.

AI Psychiatry can also serve as a valuable tool for conducting audits on AI systems before problems arise. With government agencies from law enforcement to child protective services integrating AI systems into their workflows, AI audits are becoming an increasingly common oversight requirement at the state level. With a tool like AI Psychiatry in hand, auditors can apply a consistent forensic methodology across diverse AI platforms and deployments.

In the long run, this will pay meaningful dividends both for the creators of AI systems and everyone affected by the tasks they perform.The Conversation

David Oygenblik, Ph.D. Student in Electrical and Computer Engineering, Georgia Institute of Technology and Brendan Saltaformaggio, Associate Professor of Cybersecurity and Privacy, and Electrical and Computer Engineering, Georgia Institute of Technology

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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The post Forensics tool ‘reanimates’ the ‘brains’ of AIs that fail in order to understand what went wrong appeared first on theconversation.com



Note: The following A.I. based commentary is not part of the original article, reproduced above, but is offered in the hopes that it will promote greater media literacy and critical thinking, by making any potential bias more visible to the reader –Staff Editor.

Political Bias Rating: Centrist

The article focuses on the development of a forensic tool, AI Psychiatry, designed to investigate the failure of AI systems. It provides technical insights into how this tool can help investigate and address AI failures, particularly in autonomous vehicles, without promoting any ideological stance. The content is centered on technological advancements and their practical applications, with an emphasis on problem-solving and transparency in AI systems. The tone is neutral, focusing on factual reporting about AI forensics and the technical capabilities of the system. There is no discernible political bias in the article, as it largely sticks to technical and academic subjects without introducing political viewpoints.

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The Conversation

Young bats learn to be discriminating when listening for their next meal

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theconversation.com – Logan S. James, Research Associate in Animal Behavior, The University of Texas at Austin – 2025-04-29 18:07:00

A frog-eating bat approaches a túngara frog, one of its preferred foods.
Grant Maslowski

Logan S. James, The University of Texas at Austin; Rachel Page, Smithsonian Institution, and Ximena Bernal, Purdue University

It is late at night, and we are silently watching a bat in a roost through a night-vision camera. From a nearby speaker comes a long, rattling trill.

Cane toad’s rattling trill call.

The bat briefly perks up and wiggles its ears as it listens to the sound before dropping its head back down, uninterested.

Next from the speaker comes a higher-pitched “whine” followed by a “chuck.”

Túngara frog’s ‘whine chuck’ call.

The bat vigorously shakes its ears and then spreads its wings as it launches from the roost and dives down to attack the speaker.

Bats show tremendous variation in the foods they eat to survive. Some species specialize on fruits, others on insects, others on flower nectar. There are even species that catch fish with their feet.

Bat eating frog
The calls male frogs use to attract mates also attract eavesdropping predators. Here, a frog-eating bat consumes an unlucky male túngara frog.
Marcos Guerra, Smithsonian Tropical Research Institute

At the Smithsonian Tropical Research Institute in Panama, we’ve been studying one species, the fringe-lipped bat (Trachops cirrhosus), for decades. This bat is a carnivore that specializes in feeding on frogs.

Male frogs from many species call to attract female frogs. Frog-eating bats eavesdrop on those calls to find their next meal. But how do the bats come to associate sounds and prey?

We were interested in understanding how predators that eavesdrop on their prey acquire the ability to discriminate between tasty and dangerous meals. We combined our expertise on animal behavior, bat cognition and frog communication to investigate.

How do bats know the sound of a tasty meal?

There are nearly 8,000 frog and toad species in the world, and each one has a unique call. For instance, the first rattling call that we played from our speaker came from a large and toxic cane toad. The second “whine chuck” came from the túngara frog, a preferred prey species for these bats. Just as herpetologists can tell a frog species by its call, frog-eating bats can use these calls to identify the best meal.

Over the years, our research team has learned a great deal from frog-eating bats about how sound and echolocation are used to find prey, as well as the role of learning and memory in foraging success. In our newly published study, we focused on how associations between the sounds a bat hears and the prey quality it expects arise within the lifespan of an individual bat.

Bat capturing frog from a pond
Adult bats like the one pictured have extensive acoustic repertoires and remember specific frog calls year after year. Young bats must learn which calls to respond to – and, critically, which to ignore – over time through experience.
Grant Maslowski

We considered whether the associations between sound and a delicious meal are an evolved specialty that bats are born with. But this possibility seemed unlikely because the bat species we study has a large geographic distribution across Central and South America, and the species of frogs found across this range vary tremendously.

Instead, we hypothesized that bats learn to associate different sounds with food as they grow up. But we had to test this idea.

First, we and our collaborators spent time in the forest and at ponds to record the mating calls from 15 of the most common frog and toad species in our study area in Panama.

Researcher untangles a bat from a finely woven mistnet at night.
Rachel Page, one of the lead authors on the study, takes a bat out of a mist net in Panama.
Jorge Alemán, Smithsonian Tropical Research Institute

Then, we set up mist nets along streams in Soberanía National Park to capture wild bats for the study.

Frog call, bat response

For the testing, each bat was housed individually in a large, outdoor flight chamber. From a speaker on the ground in the center, we played calls from one frog species on loop for 30 seconds and measured the behavior of the bat, which was hanging from a cloth roost. As we expected, adult bats were generally uninterested in the sounds of species that were unpalatable, such as those with toxins or those that are too large for the bat to carry.

But it was a different story for young bats. Juveniles responded with significantly more predatory behaviors in response to the calls of toxic toads compared with the adults. They also responded more weakly than adults to the sounds of túngara frogs, a palatable, abundant prey that adult bats prefer.

Thus it seems that juvenile bats must learn the associations between sounds and food over the course of their lives. As they grow up, we believe they learn to ignore the calls of frogs that aren’t worth the trouble and zero in on the calls of frogs that will be a good meal.

To better understand how sounds drive prey associations, we measured the acoustic properties of the different calls. We found that some of the most noticeable features of the calls correlated with body size: Larger frogs produce lower-frequency calls – that is, their voices are deeper. Both the adult and juvenile bats responded more strongly to larger species, which would provide larger meals.

However, there was a clear exception in the responses of adults, where the toxic toads and very large frogs elicited much weaker responses than expected for their body size. This finding led us to hypothesize that bats have early biases to pay attention to sounds associated with larger body size. Then they must learn through experience that meal quality is not only about size. Some large meals are toxic or impossible to carry, making them unpalatable.

YouTube video
Once the researchers have studied each frog-eating bat for a few days, they safely release it where it was originally captured. Footage courtesy of Léna de Framond-Bénard and Eric de Framond-Bénard, compiled by Caroline Rogan.

After the bats spent a few days with us, we released each one back at its original site of capture. The bats departed, taking with them a small RFID tag, just like the ones pet owners use to identify their dogs and cats, in case we meet again as part of a future study.

As the bats go on with their lives in the wild, we continue our quest to deepen our understanding of the subtleties of information discrimination. How do individuals weed through information overload to make choices that make sense and benefit them? That’s the same challenge we all face each day.The Conversation

Logan S. James, Research Associate in Animal Behavior, The University of Texas at Austin; Rachel Page, Staff Scientist, Smithsonian Tropical Research Institute, Smithsonian Institution, and Ximena Bernal, Professor of Biological Sciences, Purdue University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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The post Young bats learn to be discriminating when listening for their next meal appeared first on theconversation.com



Note: The following A.I. based commentary is not part of the original article, reproduced above, but is offered in the hopes that it will promote greater media literacy and critical thinking, by making any potential bias more visible to the reader –Staff Editor.

Political Bias Rating: Centrist

The content of this article is a scientific and factual exploration of bat behavior, specifically focusing on the learning processes of young bats in identifying suitable prey based on sound cues. The language used is neutral, without any ideological stance or persuasive elements aimed at pushing a particular viewpoint. The piece primarily conveys research findings and observations made by scientists. The framing is academic and informative, with no evident political, social, or controversial implications influencing the tone. It adheres to neutral, factual reporting and does not present any discernible bias in terms of ideology or political orientation.

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