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How To Be Spiritual In A Material World
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27 Jun 2023
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A neuroscientist shares the 4 ‘highly coveted’ skills that set introverts apart: ‘Their brains work differently’

Being the most talkative person in the room may be a good way to get people’s attention, but it doesn’t necessarily mean you have the best ideas.

As a neuroscientist, I’ve worked with large companies like Google and Deloitte on how to attract and retain top talent, and I’ve found that employers tend to favor extroverts.

But there are some surprising strengths that introverts bring to the table, and they shouldn’t be overlooked.

As bestselling author Susan Cain points out in her book, Quiet: The Power of Introverts in a World That Can’t Stop Talking: “Extroverts are more likely to focus on what’s happening around them. It’s as if extroverts are seeing ‘what is,’ while their introverted peers are asking ‘what if.’” 

What sets introverts apart from extroverts

Don’t get me wrong: Both extroverts and introverts have wonderful qualities. But research shows that introverts may have the upper hand.

Here are four highly coveted skills that set introverts apart from everyone else:

1.   Introverts think more.

Gray matter, which exists in the outer most layer of the brain, serves to process and release new information in the brain.

One Harvard study found that introverts’ brains work differently, and have thicker gray matter compared to extroverts. In people who are strongly extroverted, gray matter was consistently thinner. Introverts also showed more activity in the frontal lobes, where analysis and rational thought take place.

Another study that scanned brains of both introverts and extroverts found that, even in a relaxed state, the introverted brain was more active, with increased blood flow.

2.   Introverts can focus longer.

When Albert Einstein — a known introvert — was a child, his teachers thought he was a quiet loner who seemed a million miles away, lost in his thoughts.

Einstein said: “It’s that I stay with problems longer.” This ability to focus intensely is a key characteristic of introverts, who often have more extended focus than extroverts.

Because they enjoy spending time alone, introverts tend to be more willing than extroverts to put in the hours alone necessary to master a skill.

3.   Introverts are often “gifted” in a specific field.

On average, introverts and extroverts are the same in terms of intelligence. But statistics show that around 70% of gifted people are introverts.

People are considered “gifted” when they exhibit above-average intelligence or a superior talent for something, such as music, art or math.

If your workplace is dominated by extroverts who criticize those who prefer to work alone — or skip after-work cocktails — as “not team players,” it may inadvertently alienate gifted people.

4.   Introverts do the right thing.

Introverts tend to be less swayed by external events and driven more by their inner moral compass.

A 2013 study on social conformity found that extroverts are more willing to go along with the opinion of the majority, even if it’s wrong. Extroverts are more likely than introverts to succumb to social pressure.

The researchers concluded: “The higher the pressure, a larger number of conforming responses are given by extroverts.” In contrast, “there is no difference in conforming responses given to high- and low-pressure levels by introverts.”

How to create a workplace where introverts thrive

Introverts are often exhausted in their workplace because many of their colleagues don’t know how to harness the power introversion.

Here’s how managers can create an introvert-friendly workplace:

  • Respect boundaries.It takes up to 23 minutes for a person to regain focus after they’ve been interrupted. Don’t expect people to answer every email or Slack message immediately.
  • Brainstorm alone.Letting people shout ideas at each other in a room sounds like fun. But research shows that if you want to maximize creativity, let people generate ideas by themselves before sharing them in a group. Bonus: Your introverts will be far more comfortable sharing.
  • Shorten meetings.Many introverts, as you can probably guess, are not fans of meetings. Let go of the idea that the entire office has to be invited to every meeting so that no one feels left out.
  • Don’t force a certain type of communication.The introverts in your office may prefer emails, while the extroverts might enjoy handling business on the phone. Encourage people to decide how they want to communicate (e.g., turning cameras on or off), even if it differs from yours.
  • Provide the option of privacy.Extroverts may love to see everybody all the time, but introverts tend to need privacy. The solution is a flexible work environment that provides silence and private space for introverts, and lively, interactive open space for extroverts.

As an introvert, my general message to employers is, “Let my people rest.” Like it or not, the future of work is all about more choices, autonomy, and a culture that embraces introversion.

 

 

Original article here

 

 

10 Jun 2023
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How Dreams Reveal Brain Disorders

For 30 years Isabelle Arnulf, head of the sleep disorders clinic at Pitié-Salpêtrière Hospital in Paris, has studied sleep and its associated disorders. During her career, Arnulf, who is also a professor of neurology at Sorbonne University in France, has researched a broad range of sleep conditions: sleepwalking, rapid eye movement (REM) sleep behavior disorder, lucid dreaming, sleep in Parkinson’s disease and hypersomnia, or excessive daytime sleepiness. As part of these studies, Arnulf investigated how these disorders affect dream states. In an interview with Scientific American’s French-language sister publication Pour la Science, the neurologist talks about whether depression or trauma affects dreaming and whether one should worry about recurring nightmares.

[An edited transcript of the interview follows.]

 

Our dreams are often bizarre and disturbing and are sometimes frightening. Do some betray a pathology?

Attempts have been made to identify “dream types” that are characteristic of various mental disorders, but this has not gone very far. People with a psychotic disorder, especially people with schizophrenia, have particular dreams that resemble their way of reasoning during the day—flat, disjointed, limited and undiversified content that portrays the dreamer in everyday life situations. Autistic patients also have fairly lackluster dreams, but the reverse is not true; it is not because you have this kind of dream that you are autistic or schizophrenic. In other words, dreams alone are not enough to diagnose mental illness. Some of the characteristics of a dream may yield some hints, however. For example, the frequency of nightmares is generally higher in people with a mental disorder, whether depression, anxiety or post-traumatic stress disorder.

Could frequent nightmares provide a warning about a person’s mental health risks?

The link has been particularly well established with the risk of suicide among young people, which has been on the rise in recent years. Doctors are wrong half the time about projecting someone’s suicide risk. This is why recurrent nightmares have recently begun to be included in the diagnosis because they are correlated with a higher risk of suicide. The stakes are high, because this would make it possible to direct young people with depression to get help earlier with programs such as VigilanS, coordinated by Guillaume Vaiva at the Lille University Hospital in France, which monitors people who have attempted suicide to prevent repeat attempts.

Are there specific qualities that characterize the nightmares of depressed people?

In depression, dreams are very negative, mirroring the mental state of depressed people during the day. This parallel was brought to light in the early 2000s by researcher Dieter Riemann and his colleagues at the University of Freiburg in Germany. By analyzing the dreams of depressed patients treated with antidepressants, they discovered that as the treatment began to take effect after several weeks, the content of these dreams became less and less gloomy, and the mood of the patients improved.

At first glance, one might think that the overall tone of the dreams reflects the depressed state of the patient. But things are not so simple, unfortunately, because in the first weeks of treatment, antidepressants often suppress REM sleep—the phase when we dream the most—and it usually takes a month for patients to remember their dreams again. It’s difficult to understand the link between dreams and depression and to know which of the two is influencing the other. To better understand this link, we can study two categories of dreamers—those who have what are called lucid dreams and people with REM sleep behavior disorder (RBD).

What is special about these dreamers?

In RBD, which we have been studying for 15 years, patients try to implement the actions in their dreams: some eat an imaginary sandwich while sleeping, and others struggle in bed, grappling with invisible adversaries. This enactment is usually caused by a lesion of the brain stem, which means that their movements, often inhibited in a normal dream, are not blocked during REM sleep. And this, unfortunately, involves the risk of accident or injury for them or, [say], their spouse because they do not control what they are doing. But this phenomenon also offers direct access to dreams, much more than the often imprecise stories that sleepers tell when they wake up if they remember [their dreams].

What about lucid dreamers?

They actually recognize that they are dreaming without waking up. As a result, it is possible to agree beforehand on a code for them to communicate certain information to us during their dreams.

In one experiment, we asked lucid dreamers to find a swimming pool in their dream and to turn their eyes to the right twice when they dipped underwater and when they emerged. We have thus shown that our breathing during sleep reflects how we breathe in our dreams; the periods spent underwater corresponded to remarkable apneas of the sleepers! But of course, these types of signals are not always easy to arrange in certain dreams—for instance, in an intense sequence of dream events like a chase. Today we try to use simpler signals, asking the dreamer to punctuate the pleasant parts of their dreams with three small smiles and the unpleasant parts with three slight frowns, which we capture with electrodes placed respectively on the zygomaticus muscles and on those of the forehead.

These two categories of dreamers, in any case, offer privileged access to dreams. If we could find a few who suffer from depression—which is what our team member Jean-Baptiste Maranci of Pitié-Salpêtrière Hospital is trying to do—then we would have a unique way of studying the links between dreams and mood.

What form would a study take?

One of the characteristics of depressed people is that they wake up sadder in the morning than in the evening, whereas normally it’s the opposite: we tend to be happier when we wake up than when we go to sleep. We therefore believe that sleep and dreams serve to attenuate negative emotions and that this process is dysfunctional in people with depression. Maranci’s project consists of identifying markers associated with positive and negative feelings experienced in dreams, among all the signals recorded in the sleep laboratory. A sudden feeling of joy could, for example, result in a change in cerebral activity that is associated with particular eye movement and increased heart rate and breathing. We conducted this research “live” in lucid dreamers who communicated their emotions through signals agreed on with the team of experimenters. Our idea is then to use artificial intelligence for the analyses in order to take into account a large number of parameters.

After this first phase, we will try to find the emotional markers identified in sleepers like you and me. The goal is to better understand how we regulate our emotions during sleep and how quickly we relive and “digest” negative emotions. Then the same research will be carried out in depressed people in order to determine why this mechanism works badly in them.

What is the “emotional digestion” in dreams?

Matthew Walker, a professor of neuroscience and psychology at the University of California, Berkeley, has suggested that the function of sleep and dreams is to degrade emotional memories of the day—to reset the amygdala (the brain region where the emotions are experienced) to preserve the memories associated with the emotions but without the emotions themselves. This is currently the leading theory.

According to Walker, dreams are a kind of mental theater where we relive the trials that have happened to us but integrated into more or less crazy scenarios. The peculiarities of dreams make this repetition of a day’s events easier to live with. Difficult events are replayed without the physical manifestations of strong emotions. Researchers have observed, for example, sleepers who suffered horrors in dreams (as evidenced by the stories they shared when they woke up). But they didn’t show the slightest quickening of a heartbeat. On the other hand, these events are sometimes mixed with positive or bizarre elements. You might be reprimanded by your boss in the dream when suddenly a kitten comes to lick his ear, which lessens the emotions you’re experiencing in the dream.

In the end, this re-exposure to the events of the day could lead to the progressive extinction of negative emotions. In our brain, a dialogue occurs between the amygdala, which is very strongly activated during REM sleep, the hippocampus, where the information of the day is stored, and the neocortex, the seat of long-term memory. Thanks to this three-way dialogue, the brain stores the new information by ridding it of its emotional context to consolidate it in a more definitive way in the neocortex.

Do you mean that negative dreams are good for us?

Yes, absolutely. But be careful; it is believed that in nightmares, this beneficial mechanism malfunctions to the point of interrupting the dream in progress and waking the sleeper, so the process of emotional integration cannot go to the end. Why does the sleeper wake up? There are several possibilities. Maybe the person was just sleeping badly, or the emotional intensity of the nightmares was too strong. This is particularly the case in post-traumatic stress disorder, following various types of extreme ordeal—torture, war, rape. The emotion to be appeased is then so violent that the brain cannot manage it. It constantly repeats the traumatic memory, which awakens the sleeper. Finally, about 5 percent of the population has had nightmares since birth without anyone knowing how to explain it.

How should one react to a recurring nightmare?

For a long time, we approached recurring nightmares through the prism of psychoanalysis, [which explained them as] unresolved trauma that we would have to work to resolve. But the fact is: we don’t know.

In the case of repeated nightmares, before going to see a psychologist, in my opinion, it is necessary to make a medical diagnosis rather than be diverted down the wrong pathway. When I started my research, a journalist from Le Monde came to see me for a sleep apnea screening. In the course of the conversation, he told me that he has been having the same nightmare for 10 years in which he was sticking his head through the neck of a bottle and choking. With his psychoanalyst, they came to the conclusion that he was reliving his birth. In fact, he was really choking, with one apnea incident each minute of sleep each night. We offered him a CPAP [continuous positive airway pressure] machine, and the nightmares were gone the first night!

In some cases, a psychological origin can be involved. We have bad dreams during a stressful period, probably because the brain has a greater need to digest negative emotions and wants to simulate the threats we face, which is another purported function of dreams.

In general, there are many forms of nightmares, which have different origins and which a doctor will be able to differentiate. Young people who scream at night and sometimes get out of bed are thus subject to night terrors, a disorder that is similar to sleepwalking but that is not associated with mental health problems. Others [experience] a phenomenon of half awakening called “sleep paralysis”: a very unpleasant moment when the sleeper tries to wake up without being able to move, often with the feeling that a harmful creature is crushing their chest or that the person is possessed by a demon. Lack of sleep facilitates this phenomenon. Others who suffer from RBD make the motions of fighting in their bed to defend themselves against lions or attackers. Some nightmares are caused by drug treatments, for which a doctor can offer alternative medications. So it is necessary to exclude each of these possibilities before starting a therapy targeted specifically for nightmares.

Nightmares are not inevitable. We have to stop thinking that all it takes to stop a round of nightmares is to have things go well when we’re awake during the day. Bad nights themselves produce an unpleasant feeling in the morning, and people then fear going to bed and repeating the same scenario, which maintains this vicious cycle. This was shown by several British and North American teams as early as the 1980s. So tackling bad dreams is a therapy in itself that is actually effective.

What are some of the treatments for nightmares?

Techniques such as mental image rehearsal, as well as drug therapies, are highly effective, according to at least one review of scientific literature. The principle is to slightly modify elements of the nightmare and mentally visualize the new scenario before falling asleep. To give you an example, a patient abused by a priest at the age of 11 who came to consult me for another problem told me that she dreams every night that a devil in a red cassock wants to rape her. I suggested that she try to imagine something happening to the devil, perhaps his foot tripping over his cassock or something else. She decided that a large crucifix would fall on him. By mentally repeating this new scenario in the evening, she managed to transform her nightmare and calm herself.

Confronting the bad dream is therefore therapeutic on its own. Although, I repeat, it is better to start by consulting a doctor, ideally a sleep specialist, in order to exclude a number of organic causes. This is all the more important because certain types of dreams point to neurodegenerative pathologies, such as Parkinson’s disease or Lewy body dementia.

Are there dreams that can help diagnose neurodegenerative diseases?

Agitated dreams—those in which patients mimic everything they are experiencing in a dream—are characteristic of RBD. More than 80 percent of patients suffering from this disorder develop a neurodegenerative pathology within 10 to 15 years of the onset of RBD. Most often, it is Parkinson’s disease. In fact, the brain’s failure to inhibit movements during dreams is a sign that the brain is already beginning to be damaged because [this phenomenon] results from the damage to the area of the brain stem that normally inhibits movement. It is therefore a very strong warning sign.

But we must be careful not to confuse this disorder with somnambulism. In RBD, the affected person is rather restless at the end of the night, is generally more than 50 years old and does not get out of bed, whereas a sleepwalker often wanders around the house and is often much younger. Sleepwalking does not signal any hidden disease, neither neurological nor psychiatric. The studies that have investigated it have just observed that patients are slightly more anxious than average.

In general, do people with Parkinson’s have different sorts of dreams?

According to a 2011 study at Egas Moniz Hospital in Lisbon, their dreams have a more aggressive tone and more often involve animals. In addition, the unusual qualities of their dreams appear to correspond to damage in their frontal lobes.

What about people with Alzheimer’s disease?

The difficulty is that they remember their dreams less. In addition, because their cortex is damaged first, the characteristic patterns of sleep are less discernible on their electroencephalograms, which complicates research. Many also awaken early, which doesn’t make things any easier. We only know that there is no physical acting out, unlike in dreams of RBD patients.

This information is crucial because it makes it possible to distinguish Alzheimer’s disease from another pathology, Lewy body dementia, which represents the third leading cause of dementia globally, after Alzheimer’s disease and vascular dementia. Affecting up to 5 percent of the [older people in the] general population—30 percent of dementia cases—it is manifested by cognitive losses similar to those observed in Alzheimer’s disease and is often confused with it. But [Lewy body dementia] is accompanied by RBD, which is not the case with Alzheimer’s disease. As a result, this readily available biomarker can distinguish between these two dementias, and it is necessary to avoid giving neuroleptics (antipsychotics) to people with Lewy body dementia (which is sometimes done for an Alzheimer’s patient but can be toxic if it is administered to patients with Lewy body disease).

Dreams are therefore likely to provide valuable information about our mental and neurological health. They are still not made use of enough because they have long been the preserve of psychoanalysis. But things are progressing: more and more doctors are interested in them, having understood that taking them into account can help in diagnosis.

 

 

Original article here

29 May 2023
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The big idea: why colour is in the eye of the beholder

In February 2015, a Scottish woman uploaded a photograph of a dress to the internet. Within 48 hours the blurry snapshot had gone viral, provoking spirited debate around the world. The disagreement centred on the dress’s colour: some people were convinced it was blue and black while others were adamant it was white and gold.

Everyone, it seemed, was incredulous. People couldn’t understand how, faced with exactly the same photograph of exactly the same dress, they could reach such different and firmly held conclusions about its appearance. The confusion was grounded in a fundamental misunderstanding about colour – one that, despite mounting evidence to the contrary, shows little sign of disappearing.

For a long time, people believed that colours were objective, physical properties of objects or of the light that bounced off them. Even today, science teachers regale their students with stories about Isaac Newton and his prism experiment, telling them how different wavelengths of light produce the rainbow of hues around us.

But this theory isn’t really true. Different wavelengths of light do exist independently of us but they only become colours inside our bodies. Colour is ultimately a neurological process whereby photons are detected by light-sensitive cells in our eyes, transformed into electrical signals and sent to our brain, where, in a series of complex calculations, our visual cortex converts them into “colour”.

Most experts now agree that colour, as commonly understood, doesn’t inhabit the physical world at all but exists in the eyes or minds of its beholders. They argue that if a tree fell in a forest and no one was there to see it, its leaves would be colourless – and so would everything else. To put it another way: there is no such thing as colour; there are only the people who perceive it.

This is why no two people will ever see exactly the same colours. Every person’s visual system is unique and so, therefore, are their perceptions. About 8% of men are colour-blind and see fewer colours than everyone else; a small number of lucky women might, thanks to a genetic duplication on the X chromosome, be able to distinguish many more than the rest of us.

Animals inhabit very different chromatic worlds too. Most mammals are red-green colour-blind; bulls might be famous for their hatred of red capes, but the colour itself is invisible to them – they are actually enraged by the fabric’s movements. By contrast, most reptiles, amphibians, insects and birds perceive more colours than us. Bees see ultraviolet light, discerning elaborate patterns in flowers that we cannot perceive, while snakes see infrared radiation, detecting the warm bodies of prey from a distance.

 

People generally name only the colours they consider socially or culturally important

 

“Colour,” Umberto Eco once said, “is not an easy matter.” It is indeed elusive and illusive. Pretty much everything we take to be self-evident about it really isn’t self-evident at all. Scientists have shown that the sky isn’t blue, the sun isn’t yellow, snow isn’t white, black isn’t dark and darkness isn’t black.

One cause of the problem – or perhaps its symptom – is language. In English we divide colour space into 11 basic terms – black, white, red, yellow, green, blue, purple, brown, grey, orange and pink – but other languages do things differently. Many don’t have words for pink, brown and yellow, and some use one word for both green and blue. The Tiv people in west Africa use only three basic colour terms (black, white, red), and at least one Indigenous community has no specific words for any colours, only “light” and “dark”.

The vocabulary of these languages isn’t dictated by the prismatic spectrum but, once again, by what is happening inside their speakers’ heads. People generally name only the colours they consider socially or culturally important. The Aztecs, who were enthusiastic farmers, used more than a dozen words for green; the Mursi cattleherders of Ethiopia have 11 colour terms for cows, and none for anything else.

These differences might even influence the colours they see. Debates about linguistic relativity – the extent to which our words shape our thoughts and perceptions – have been rumbling on for decades, and while many scholars have overstated the case for it, some have found persuasive evidence that if you don’t, say, have a word for blue, you will probably find it harder to distinguish.

The meanings of colour are no less socially constructed, which is why a single colour can mean completely different things in different places and at different times. In the west white is the colour of light, life and purity, but in parts of Asia it is the colour of death. In America red is conservative and blue progressive, while in Europe it’s the other way around. Many people today think of blue as masculine and pink as feminine, but only a hundred years ago baby boys were dressed in pink and girls in blue.

When all of this is taken together – the subjective nature of visual perception, the complicating influence of language, the role that social life and cultural traditions play in filtering our understandings of colour – it becomes really rather difficult to reach a conclusion different from that of the 18th-century philosopher David Hume: that, in the end, colour is “merely a phantasm of the senses”.

The ancient Egyptian term for “colour” was iwn – a word that also meant “skin”, “nature”, “character” and “being”, and was represented in part by a hieroglyph of human hair. To the Egyptians, colours were like people – full of life, energy, power and personality. We now understand just how completely the two are entangled. That’s because every hue we see around us is actually manufactured within us – in the same grey matter that forms language, stores memories, stokes emotions, shapes thoughts and gives rise to consciousness. Colour is, if you’ll pardon the pun, a pigment of our imaginations.

 

 

 

Original article here

22 May 2023
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How To Worry Less And Enjoy Your Life More

 

Did you know that worrying is like a rocking chair?

It gives you something to do, but it doesn’t get you anywhere.

Excessive worrying is exhausting and can become a great waste of time and energy. It puts your mind in overdrive and distracts you from doing the more important things, as well as preventing you from having a healthy, happy life.

Most people worry on a regular basis, as a natural part of life. However, the worry habit can become problematic when it becomes too intense and chronic. Some people even worry about worrying. When you are plagued by thoughts that are persistent and out of control, they end up causing interference and paralysis in your daily life.

The bad news is that constant worrying eventually takes a toll on your emotional and physical health, as well as your self-esteem, relationships, and career. When you keep hitting the panic button, the stress response is triggered, which leads to fear, then more stress and anxiety, and then more worry. An endless worry cycle is created.

The good news is that worrying is a mental habit that can be broken. You can train your brain to stay calm and remain positive. You are in charge of your thoughts and imagination. How you choose to use your mind is your responsibility. Do you continue to misuse your imagination and keep focusing on the negative or do you choose to switch to the positive?

The reason most people worry too much is often rooted in fear – ultimately a fear of something happening that cannot be controlled. When we anticipate something negative happening in the future, it is based on irrational thoughts. We are afraid of the worst-case scenario, as we have allowed our minds to wander too far on the negative side.

9 Practical Tips To Minimize Worrying 

  • Learn to distinguish between what you can control in life and what you can’t. The things you can change need an action plan with a list of concrete steps you can take to solve the problem. And for the things you cannot change, you need to learn acceptance. The Serenity Prayer sums it up perfectly: “God, grant me the serenity to accept the things I cannot change, the courage to change the things I can, and the wisdom to know the difference.”
  • Recognize your trigger points. Find out the source of your worries and gain a clearer picture of what’s driving them. Your worries will start losing power, and you will start regaining control of your mind. Self-awareness is the first step to changing your mindset.
  • Re-frame your thoughts and change your perspective. Instead of saying, “I can’t handle this,” become more self-confident and start believing that you can handle it. Make a list of the negative messages you are feeding your mind and replace them with positive affirmations. Make sure you challenge those thoughts. No amount of worrying will make life any more predictable.
  • Break the worry cycle by limiting your worrying time. Schedule a worry time each day to keep the worry contained and prevent it from contaminating your whole day. Sit down for 15 minutes and make a list of all the things you want to worry about, and then proceed to worry about them. At the end of the exercise, you will come to the realization that worrying is futile. You can become more mindful of your thoughts and where you place your energy. Choose to focus on what really matters in your life and become more productive.
  • Get up and move. Take a 20-minute walk. Stretch out and do some yoga. Exercise is a productive activity that can interrupt your worries and shift your body’s energy. It releases endorphins, which get rid of the tension and break the cycle.
  • Breathing exercises, meditation and relaxation techniques also interrupt your train of thoughts and calm the body’s response to stress. This dynamic trio helps relax your body, guide your thoughts, and keep them from getting caught up in worries.
  • Ground yourself in the present. Become mindful of your thoughts and activities. Make time to unplug and take a look at the bigger picture. Ask yourself if what you are worrying about is really going to matter in five years from now. If not, let it go and don’t spend more than five minutes worrying about it. If it does matter, then make a list of one or two things you can do now to solve the problem. Keep in mind, you may not resolve the whole problem right then and there, but at least your thoughts will be clearer and calmer, and you will be able to move forward by focusing on what matters instead of worrying about things that don’t really matter.
  • Stop overthinking and dwelling on your worries by finding a healthy distraction like reading a good book, watching a funny movie, listening to great music, taking up hobbies that you enjoy, or doing something different, such as trying a new recipe.
  • Build a support network. Enlist the help of your trusted friends and family. By sharing your thoughts and tuning into your feelings, it can help you process them as you feel supported. In some cases, chronic worry can be a symptom of GAD or generalized anxiety disorder. You can seek the help of a therapist or join a support group. Don’t keep your worries to yourself. This will cause them to build up and become overwhelming.

Next time you catch yourself worrying, instead of pressing the panic button, reach for the pause button and ask yourself, “What’s important now?” Coach Lou Holtz came up with the acronym W.I.N. or What’s Important Now. This enables you to prioritize your decisions, choices and actions. The antidote for worrying is to maintain calm, clarity, and focus. Also, by having a grateful mind, you can look for the positive in every situation.

Strengthen your mind by learning to turn off those worrying thoughts and regain control. Look at life from a less fearful and more balanced perspective.

 

 

Original article here

18 May 2023
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Chat GPT is about to revolutionize the economy. We need to decide what that looks like.

 

 

Whether it’s based on hallucinatory beliefs or not, an artificial-intelligence gold rush has started over the last several months to mine the anticipated business opportunities from generative AI models like ChatGPT. App developers, venture-backed startups, and some of the world’s largest corporations are all scrambling to make sense of the sensational text-generating bot released by OpenAI last November.

You can practically hear the shrieks from corner offices around the world: “What is our ChatGPT play? How do we make money off this?”

But while companies and executives see a clear chance to cash in, the likely impact of the technology on workers and the economy on the whole is far less obvious. Despite their limitations—chief among of them their propensity for making stuff up—ChatGPT and other recently released generative AI models hold the promise of automating all sorts of tasks that were previously thought to be solely in the realm of human creativity and reasoning, from writing to creating graphics to summarizing and analyzing data. That has left economists unsure how jobs and overall productivity might be affected.

For all the amazing advances in AI and other digital tools over the last decade, their record in improving prosperity and spurring widespread economic growth is discouraging. Although a few investors and entrepreneurs have become very rich, most people haven’t benefited. Some have even been automated out of their jobs.

Productivity growth, which is how countries become richer and more prosperous, has been dismal since around 2005 in the US and in most advanced economies (the UK is a particular basket case). The fact that the economic pie is not growing much has led to stagnant wages for many people.

What productivity growth there has been in that time is largely confined to a few sectors, such as information services, and in the US to a few cities—think San Jose, San Francisco, Seattle, and Boston.

Will ChatGPT make the already troubling income and wealth inequality in the US and many other countries even worse? Or could it help? Could it in fact provide a much-needed boost to productivity?

ChatGPT, with its human-like writing abilities, and OpenAI’s other recent release DALL-E 2, which generates images on demand, use large language models trained on huge amounts of data. The same is true of rivals such as Claude from Anthropic and Bard from Google. These so-called foundational models, such as GPT-3.5 from OpenAI, which ChatGPT is based on, or Google’s competing language model LaMDA, which powers Bard, have evolved rapidly in recent years.

They keep getting more powerful: they’re trained on ever more data, and the number of parameters—the variables in the models that get tweaked—is rising dramatically. Earlier this month, OpenAI released its newest version, GPT-4. While OpenAI won’t say exactly how much bigger it is, one can guess; GPT-3, with some 175 billion parameters, was about 100 times larger than GPT-2.

But it was the release of ChatGPT late last year that changed everything for many users. It’s incredibly easy to use and compelling in its ability to rapidly create human-like text, including recipes, workout plans, and—perhaps most surprising—computer code. For many non-experts, including a growing number of entrepreneurs and businesspeople, the user-friendly chat model—less abstract and more practical than the impressive but often esoteric advances that have been brewing in academia and a handful of high-tech companies over the last few years—is clear evidence that the AI revolution has real potential.

Venture capitalists and other investors are pouring billions into companies based on generative AI, and the list of apps and services driven by large language models is growing longer every day.

 

Will ChatGPT make the already troubling income and wealth inequality in the US and many other countries even worse? Or could it help?

 

Among the big players, Microsoft has invested a reported $10 billion in OpenAI and its ChatGPT, hoping the technology will bring new life to its long-struggling Bing search engine and fresh capabilities to its Office products. In early March, Salesforce said it will introduce a ChatGPT app in its popular Slack product; at the same time, it announced a $250 million fund to invest in generative AI startups. The list goes on, from Coca-Cola to GM. Everyone has a ChatGPT play.

Meanwhile, Google announced it is going to use its new generative AI tools in Gmail, Docs, and some of its other widely used products.

Still, there are no obvious killer apps yet. And as businesses scramble for ways to use the technology, economists say a rare window has opened for rethinking how to get the most benefits from the new generation of AI.

“We’re talking in such a moment because you can touch this technology. Now you can play with it without needing any coding skills. A lot of people can start imagining how this impacts their workflow, their job prospects,” says Katya Klinova, the head of research on AI, labor, and the economy at the Partnership on AI in San Francisco.

“The question is who is going to benefit? And who will be left behind?” says Klinova, who is working on a report outlining the potential job impacts of generative AI and providing recommendations for using it to increase shared prosperity.

The optimistic view: it will prove to be a powerful tool for many workers, improving their capabilities and expertise, while providing a boost to the overall economy. The pessimistic one: companies will simply use it to destroy what once looked like automation-proof jobs, well-paying ones that require creative skills and logical reasoning; a few high-tech companies and tech elites will get even richer, but it will do little for overall economic growth.

Helping the least skilled

The question of ChatGPT’s impact on the workplace isn’t just a theoretical one.

In the most recent analysis, OpenAI’s Tyna Eloundou, Sam Manning, and Pamela Mishkin, with the University of Pennsylvania’s Daniel Rock, found that large language models such as GPT could have some effect on 80% of the US workforce. They further estimated that the AI models, including GPT-4 and other anticipated software tools, would heavily affect 19% of jobs, with at least 50% of the tasks in those jobs “exposed.” In contrast to what we saw in earlier waves of automation, higher-income jobs would be most affected, they suggest. Some of the people whose jobs are most vulnerable: writers, web and digital designers, financial quantitative analysts, and—just in case you were thinking of a career change—blockchain engineers.

“There is no question that [generative AI] is going to be used—it’s not just a novelty,” says David Autor, an MIT labor economist and a leading expert on the impact of technology on jobs. “Law firms are already using it, and that’s just one example. It opens up a range of tasks that can be automated.”

Autor has spent years documenting how advanced digital technologies have destroyed many manufacturing and routine clerical jobs that once paid well. But he says ChatGPT and other examples of generative AI have changed the calculation.

Previously, AI had automated some office work, but it was those rote step-by-step tasks that could be coded for a machine. Now it can perform tasks that we have viewed  as creative, such as writing and producing graphics. “It’s pretty apparent to anyone who’s paying attention that generative AI opens the door to computerization of a lot of kinds of tasks that we think of as not easily automated,” he says.

 

Generative AI could help a wide swath of people gain the skills to compete with those who have more education and expertise.

 

The worry is not so much that ChatGPT will lead to large-scale unemployment—as Autor points out, there are plenty of jobs in the US—but that companies will replace relatively well-paying white-collar jobs with this new form of automation, sending those workers off to lower-paying service employment while the few who are best able to exploit the new technology reap all the benefits.

In this scenario, tech-savvy workers and companies could quickly take up the AI tools, becoming so much more productive that they dominate their workplaces and their sectors. Those with fewer skills and little technical acumen to begin with would be left further behind.

But Autor also sees a more positive possible outcome: generative AI could help a wide swath of people gain the skills to compete with those who have more education and expertise.

One of the first rigorous studies done on the productivity impact of ChatGPT suggests that such an outcome might be possible.

Two MIT economics graduate students, Shakked Noy and Whitney Zhang, ran an experiment involving hundreds of college-educated professionals working in areas like marketing and HR; they asked half to use ChatGPT in their daily tasks and the others not to. ChatGPT raised overall productivity (not too surprisingly), but here’s the really interesting result: the AI tool helped the least skilled and accomplished workers the most, decreasing the performance gap between employees. In other words, the poor writers got much better; the good writers simply got a little faster.

The preliminary findings suggest that ChatGPT and other generative AIs could, in the jargon of economists, “upskill” people who are having trouble finding work. There are lots of experienced workers “lying fallow” after being displaced from office and manufacturing jobs over the last few decades, Autor says. If generative AI can be used as a practical tool to broaden their expertise and provide them with the specialized skills required in areas such as health care or teaching, where there are plenty of jobs, it could revitalize our workforce.

Determining which scenario wins out will require a more deliberate effort to think about how we want to exploit the technology.

“I don’t think we should take it as the technology is loose on the world and we must adapt to it. Because it’s in the process of being created, it can be used and developed in a variety of ways,” says Autor. “It’s hard to overstate the importance of designing what it’s there for.”

Simply put, we are at a juncture where either less-skilled workers will increasingly be able to take on what is now thought of as knowledge work, or the most talented knowledge workers will radically scale up their existing advantages over everyone else. Which outcome we get depends largely on how employers implement tools like ChatGPT. But the more hopeful option is well within our reach.

Beyond human-like

There are some reasons to be pessimistic, however. Last spring, in “The Turing Trap: The Promise & Peril of Human-Like Artificial Intelligence,” the Stanford economist Erik Brynjolfsson warned that AI creators were too obsessed with mimicking human intelligence rather than finding ways to use the technology to allow people to do new tasks and extend their capabilities.

The pursuit of human-like capabilities, Brynjolfsson argued, has led to technologies that simply replace people with machines, driving down wages and exacerbating inequality of wealth and income. It is, he wrote, “the single biggest explanation” for the rising concentration of wealth.

A year later, he says ChatGPT, with its human-sounding outputs, “is like the poster child for what I warned about”: it has “turbocharged” the discussion around how the new technologies can be used to give people new abilities rather than simply replacing them.

Despite his worries that AI developers will continue to blindly outdo each other in mimicking human-like capabilities in their creations, Brynjolfsson, the director of the Stanford Digital Economy Lab, is generally a techno-optimist when it comes to artificial intelligence. Two years ago, he predicted a productivity boom from AI and other digital technologies, and these days he’s bullish on the impact of the new AI models.

Much of Brynjolfsson’s optimism comes from the conviction that businesses could greatly benefit from using generative AI such as ChatGPT to expand their offerings and improve the productivity of their workforce. “It’s a great creativity tool. It’s great at helping you to do novel things. It’s not simply doing the same thing cheaper,” says Brynjolfsson. As long as companies and developers can “stay away from the mentality of thinking that humans aren’t needed,” he says, “it’s going to be very important.”

Within a decade, he predicts, generative AI could add trillions of dollars in economic growth in the US. “A majority of our economy is basically knowledge workers and information workers,” he says. “And it’s hard to think of any type of information workers that won’t be at least partly affected.”

When that productivity boost will come—if it does—is an economic guessing game. Maybe we just need to be patient.

In 1987, Robert Solow, the MIT economist who won the Nobel Prize that year for explaining how innovation drives economic growth, famously said, “You can see the computer age everywhere except in the productivity statistics.” It wasn’t until later, in the mid and late 1990s, that the impacts—particularly from advances in semiconductors—began showing up in the productivity data as businesses found ways to take advantage of ever cheaper computational power and related advances in software.

Could the same thing happen with AI? Avi Goldfarb, an economist at the University of Toronto, says it depends on whether we can figure out how to use the latest technology to transform businesses as we did in the earlier computer age.

So far, he says, companies have just been dropping in AI to do tasks a little bit better: “It’ll increase efficiency—it might incrementally increase productivity—but ultimately, the net benefits are going to be small. Because all you’re doing is the same thing a little bit better.” But, he says, “the technology doesn’t just allow us to do what we’ve always done a little bit better or a little bit cheaper. It might allow us to create new processes to create value to customers.”

The verdict on when—even if—that will happen with generative AI remains uncertain. “Once we figure out what good writing at scale allows industries to do differently, or—in the context of Dall-E—what graphic design at scale allows us to do differently, that’s when we’re going to experience the big productivity boost,” Goldfarb says. “But if that is next week or next year or 10 years from now, I have no idea.”

Power struggle

When Anton Korinek, an economist at the University of Virginia and a fellow at the Brookings Institution, got access to the new generation of large language models such as ChatGPT, he did what a lot of us did: he began playing around with them to see how they might help his work. He carefully documented their performance in a paper in February, noting how well they handled 25 “use cases,” from brainstorming and editing text (very useful) to coding (pretty good with some help) to doing math (not great).

ChatGPT did explain one of the most fundamental principles in economics incorrectly, says Korinek: “It screwed up really badly.” But the mistake, easily spotted, was quickly forgiven in light of the benefits. “I can tell you that it makes me, as a cognitive worker, more productive,” he says. “Hands down, no question for me that I’m more productive when I use a language model.”

When GPT-4 came out, he tested its performance on the same 25 questions that he documented in February, and it performed far better. There were fewer instances of making stuff up; it also did much better on the math assignments, says Korinek.

Since ChatGPT and other AI bots automate cognitive work, as opposed to physical tasks that require investments in equipment and infrastructure, a boost to economic productivity could happen far more quickly than in past technological revolutions, says Korinek. “I think we may see a greater boost to productivity by the end of the year—certainly by 2024,” he says.

 

Who will control the future of this amazing technology?

 

What’s more, he says, in the longer term, the way the AI models can make researchers like himself more productive has the potential to drive technological progress.

That potential of large language models is already turning up in research in the physical sciences. Berend Smit, who runs a chemical engineering lab at EPFL in Lausanne, Switzerland, is an expert on using machine learning to discover new materials. Last year, after one of his graduate students, Kevin Maik Jablonka, showed some interesting results using GPT-3, Smit asked him to demonstrate that GPT-3 is, in fact, useless for the kinds of sophisticated machine-learning studies his group does to predict the properties of compounds.

“He failed completely,” jokes Smit.

It turns out that after being fine-tuned for a few minutes with a few relevant examples, the model performs as well as advanced machine-learning tools specially developed for chemistry in answering basic questions about things like the solubility of a compound or its reactivity. Simply give it the name of a compound, and it can predict various properties based on the structure.

As in other areas of work, large language models could help expand the expertise and capabilities of non-experts—in this case, chemists with little knowledge of complex machine-learning tools. Because it’s as simple as a literature search, Jablonka says, “it could bring machine learning to the masses of chemists.”

These impressive—and surprising—results are just a tantalizing hint of how powerful the new forms of AI could be across a wide swath of creative work, including scientific discovery, and how shockingly easy they are to use. But this also points to some fundamental questions.

As the potential impact of generative AI on the economy and jobs becomes more imminent, who will define the vision for how these tools should be designed and deployed? Who will control the future of this amazing technology?

Diane Coyle, an economist at Cambridge University in the UK, says one concern is the potential for large language models to be dominated by the same big companies that rule much of the digital world. Google and Meta are offering their own large language models alongside OpenAI, she points out, and the large computational costs required to run the software create a barrier to entry for anyone looking to compete.

The worry is that these companies have similar “advertising-driven business models,” Coyle says. “So obviously you get a certain uniformity of thought, if you don’t have different kinds of people with different kinds of incentives.”

Coyle acknowledges that there are no easy fixes, but she says one possibility is a publicly funded international research organization for generative AI, modeled after CERN, the Geneva-based intergovernmental European nuclear research body where the World Wide Web was created in 1989. It would be equipped with the huge computing power needed to run the models and the scientific expertise to further develop the technology.

Such an effort outside of Big Tech, says Coyle, would “bring some diversity to the incentives that the creators of the models face when they’re producing them.”

While it remains uncertain which public policies would help make sure that large language models best serve the public interest, says Coyle, it’s becoming clear that the choices about how we use the technology can’t be left to a few dominant companies and the market alone.

History provides us with plenty of examples of how important government-funded research can be in developing technologies that bring about widespread prosperity. Long before the invention of the web at CERN, another publicly funded effort in the late 1960s gave rise to the internet, when the US Department of Defense supported ARPANET, which pioneered ways for multiple computers to communicate with each other.

In Power and Progress: Our 1000-Year Struggle Over Technology & Prosperity, the MIT economists Daron Acemoglu and Simon Johnson provide a compelling walk through the history of technological progress and its mixed record in creating widespread prosperity. Their point is that it’s critical to deliberately steer technological advances in ways that provide broad benefits and don’t just make the elite richer.

From the decades after World War II until the early 1970s, the US economy was marked by rapid technological changes; wages for most workers rose while income inequality dropped sharply. The reason, Acemoglu and Johnson say, is that technological advances were used to create new tasks and jobs, while social and political pressures helped ensure that workers shared the benefits more equally with their employers than they do now.

In contrast, they write, the more recent rapid adoption of manufacturing robots in “the industrial heartland of the American economy in the Midwest” over the last few decades simply destroyed jobs and led to a “prolonged regional decline.”

The book, which comes out in May, is particularly relevant for understanding what today’s rapid progress in AI could bring and how decisions about the best way to use the breakthroughs will affect us all going forward. In a recent interview, Acemoglu said they were writing the book when GPT-3 was first released. And, he adds half-jokingly, “we foresaw ChatGPT.”

Acemoglu maintains that the creators of AI “are going in the wrong direction.” The entire architecture behind the AI “is in the automation mode,” he says. “But there is nothing inherent about generative AI or AI in general that should push us in this direction. It’s the business models and the vision of the people in OpenAI and Microsoft and the venture capital community.”

If you believe we can steer a technology’s trajectory, then an obvious question is: Who is “we”? And this is where Acemoglu and Johnson are most provocative. They write: “Society and its powerful gatekeepers need to stop being mesmerized by tech billionaires and their agenda … One does not need to be an AI expert to have a say about the direction of progress and the future of our society forged by these technologies.”

The creators of ChatGPT and the businesspeople involved in bringing it to market, notably OpenAI’s CEO, Sam Altman, deserve much credit for offering the new AI sensation to the public. Its potential is vast. But that doesn’t mean we must accept their vision and aspirations for where we want the technology to go and how it should be used.

According to their narrative, the end goal is artificial general intelligence, which, if all goes well, will lead to great economic wealth and abundances. Altman, for one, has promoted the vision at great length recently, providing further justification for his longtime advocacy of a universal basic income (UBI) to feed the non-technocrats among us. For some, it sounds tempting. No work and free money! Sweet!

It’s the assumptions underlying the narrative that are most troubling—namely, that AI is headed on an inevitable job-destroying path and most of us are just along for the (free?) ride. This view barely acknowledges the possibility that generative AI could lead to a creativity and productivity boom for workers far beyond the tech-savvy elites by helping to unlock their talents and brains. There is little discussion of the idea of using the technology to produce widespread prosperity by expanding human capabilities and expertise throughout the working population.

 

Companies can decide to use ChatGPT to give workers more abilities—or to simply cut jobs and trim costs.

 

As Acemoglu and Johnson write: “We are heading toward greater inequality not inevitably but because of faulty choices about who has power in society and the direction of technology … In fact, UBI fully buys into the vision of the business and tech elite that they are the enlightened, talented people who should generously finance the rest.”

Acemoglu and Johnson write of various tools for achieving “a more balanced technology portfolio,” from tax reforms and other government policies that might encourage the creation of more worker-friendly AI to reforms that might wean academia off Big Tech’s funding for computer science research and business schools.

But, the economists acknowledge, such reforms are “a tall order,” and a social push to redirect technological change is “not just around the corner.”

The good news is that, in fact, we can decide how we choose to use ChatGPT and other large language models. As countless apps based on the technology are rushed to market, businesses and individual users will have a chance to choose how they want to exploit it; companies can decide to use ChatGPT to give workers more abilities—or to simply cut jobs and trim costs.

Another positive development: there is at least some momentum behind open-source projects in generative AI, which could break Big Tech’s grip on the models. Notably, last year more than a thousand international researchers collaborated on a large language model called Bloom that can create text in languages such as French, Spanish, and Arabic. And if Coyle and others are right, increased public funding for AI research could help change the course of future breakthroughs.

Stanford’s Brynjolfsson refuses to say he’s optimistic about how it will play out. Still, his enthusiasm for the technology these days is clear. “We can have one of the best decades ever if we use the technology in the right direction,” he says. “But it’s not inevitable.”

 

 

Original article here

 

 

10 May 2023
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5 Ways to Future-Proof Your Career in the Age of AI

Since last fall, artificial intelligence — in particular ChatGPT and its recently unveiled successor, GPT-4 — has taken center stage in conversations about the future of business, work, and learning. ChatGPT became the fastest-growing consumer application in history — outpacing Instagram and even TikTok — and Google lost $100 billion in market cap after a botched AI product demonstration raised questions about its ability to compete.

The stakes are high, not just for companies, but also for individuals hoping to navigate potentially massive implications for their own careers. For years, pundits have assured us that the key to surviving AI disruption was leaning into creativity and other (theoretically) unique human skill sets. But the new wave of AI is rapidly demonstrating that it can do far more than crunch numbers and analyze data: It can also create professional-level art and design (see DALL-E and Midjourney, among others) and create articles and copywriting that could displace many journalists and marketers.

We believe it’s no longer useful to debate whether this tool is “smarter “or “better” than us. The tools are here, and they’re already being widely used. What matters to us — Tomas, the author of the new book I, Human: AI, Automation, and the Quest to Reclaim What Makes Us Unique, and Dorie, a consultant and keynote speaker on personal branding and career development — is how we can better ourselves by using them.

Of course, there are nearly infinite tactical applications for AI. For instance, we can speed up our productivity by using GPT-4 or other tools (such as Microsoft’s Bing, which now integrates GPT-4, or its recently launched Copilot for all things work) to assist with brainstorming, creating drafts of emails, or performing rapid and complex research.

But above and beyond specific use cases, we’re interested in exploring the question of whether there might still be strategies that professionals can deploy to generate unique value, even as AI begins to showcase its prodigious (and exponentially increasing) power. In other words, what can we do personally to stave off the displacement that may happen as a result of AI and future-proof ourselves in the age of intelligent machines? Here are five strategies we find especially critical.

Avoid predictability.

It’s important to remember that AI isn’t generating new insights; it’s a prediction engine that merely guesses the most likely next word. At the micro-level, it’s helpful: “thank” is indeed often followed by “you.” But at a macro level, its suggestions tend to homogenize, and they’re only as good as the wisdom of the crowds, which is often the exact opposite of wisdom. In the famous words of Oscar Wilde, who would probably not have been a heavy user of ChatGPT: “Everything popular is wrong.”

And yet, this aspect of generative AI can be a powerful tool if you use it right. For instance, if you want to understand how most people think or feel about something, including their prejudices and misconceptions, you can use GPT-4 to access this common knowledge — or ignorance. But if we simply deploy the tools without question, their algorithms and nudges may turn us into more predictable creatures who eventually all start to sound the same.

Consider that whenever we let Gmail autocomplete our searches or emails, we relinquish a bit of originality and uniqueness, turning AI’s prediction into a self-fulfilling prophecy that makes us more predictable. While GPT-4 is a powerful tool for ideation and initial drafts, if you want to stand out, you may be better served (in some cases) by doing the opposite of what it suggests, because you’ll be bucking conventional wisdom. Just as some corporations have recognized that boilerplate “corporate speak” turns off customers, we may discover the advantage of sounding like ourselves — and embracing our own personality, serendipity, and unpredictability — when everyone else is turning to AI.

Hone the skills that machines strive to emulate.

There’s no question: GPT-4 has been trained to be respectful and polite (especially now that previous — ahem — vulnerabilities have come to light). Its responses display empathy (“I am sorry my answer upset you”), self-awareness (“I’m just an AI model and my answers are based on training data”), and even creativity (producing ingenious haikus about inequality, impersonating Elon Musk, and generating an infinite number of cheese jokes in multiple languages). But — to reiterate — those responses are based on text prediction, and AI is not capable of experiencing or displaying the human version of these soft skills. As Tomas has argued extensively, humans are wired to respond to genuine emotions — so actually knowing and caring about what others think and feel, truly understanding yourself, and being capable of creating something machines cannot is an essential strategy to set yourself apart in the age of AI.

Double down on “the real world.”

GPT-4 — like AI in general — is confined to the digital world, inhabiting a virtual cage of 0s and 1s. Sadly, so are many human activities these days. But it’s essential to recognize that one thing AI can’t disrupt is our analog, in-person connections with others, so it’s important to carve out time and safeguard those. As Harvard Professor Arthur C. Brooks summarizes the research, “Technology that crowds out our real-life interaction with others will lower our well-being and thus must be managed with great care in our lives.” Artifacts of pre-pandemic life like having meals with colleagues, attending conferences, and initiating conversation with a stranger may seem less pressing now that we’ve gone so long without them. But they represent an opportunity to build connections and gain insights that simply aren’t possible through AI — and thus, they represent a unique competitive advantage we still possess.

In a similar vein, original research — actually talking to people and identifying new insights — becomes critical, because AI can only connect past dots and information it’s already been presented with. When you tap into information that isn’t (yet) online through your lived experience or novel interviews and conversations, you’re adding something genuinely new to the cultural conversation that wouldn’t be possible through AI.

Develop your personal brand. 

AI tools have reached sufficient quality that they may well decimate the lower and middle ends of the market in many professions (for instance, copywriters and designers on freelance marketplaces, or those who work with cost-conscious customers eager to shake off expenses in favor of a free option). In some cases, AI may even match the quality with professionals at the highest echelons — but it’s almost certain those industry leaders won’t get displaced, and it’s because of the strength of their brands. Just as art-world buyers will pay exponentially more for a “real Rembrandt” rather than an equally beautiful painting by one of his lesser-known contemporaries, corporate leaders will likely continue to pay a premium to work with people viewed as the “top of their field” — partly as a statement of quality, and partly as a brand statement about whom they associate with and what they value. As one example, even the local tire shop or florist can use AI to create a logo. But only those with discernment and real money — so the thinking goes — can afford to use an elite group of agencies. The rise of AI doesn’t change the fact, crucially tied to human nature, that branding matters.

Cultivate expertise.

GPT-4 and other AI technologies are prodigious researchers that can summon a cavalcade of facts almost instantly. Unfortunately, some of those facts aren’t true — a form of the “hallucinations” that have, heretofore, plagued AI. (Indeed, one reader contacted Dorie asking where he could locate one of Dorie’s Harvard Business Review articles that ChatGPT had referenced. Unfortunately, this particular article didn’t exist.) Thus, while AI is an extraordinarily valuable tool, it can’t always be trusted to deliver accurate results — at least at this point. That’s why it’s so valuable to develop recognized expertise in your field. Even if AI performs “first draft” functions, it still has to be double-checked by a trusted and reliable source. If that’s you, you’ll continue to be sought out because you have the authority to vet AI’s responses.

. . .

AI technology has the power to transform our professional lives — perhaps in the very near future. By following these strategies, we believe you can find ways to identify and provide unique value, even as GPT-4 and other technologies advance. Even in changing times, that’s the clearest path to career insurance for yourself.

 

 

 

Original article here

28 Apr 2023
Comments: 0

Why Six Hours Of Sleep Is As Bad As None At All

Not getting enough sleep is detrimental to both your health and productivity. Yawn. We’ve heard it all before. But results from one study impress just how bad a cumulative lack of sleep can be on performance. Subjects in a lab-based sleep study who were allowed to get only six hours of sleep a night for two weeks straight functioned as poorly as those who were forced to stay awake for two days straight. The kicker is the people who slept six hours per night thought they were doing just fine.

This sleep deprivation study, published in the journal Sleep, took 48 adults and restricted their sleep to a maximum of four, six, or eight hours a night for two weeks; one unlucky subset was deprived of sleep for three days straight.

During their time in the lab, the participants were tested every two hours (unless they were asleep, of course) on their cognitive performance as well as their reaction time. They also answered questions about their mood and any symptoms they were experiencing, basically, “How sleepy do you feel?”

Why Six Hours of Sleep Isn’t Enough

As you can imagine, the subjects who were allowed to sleep eight hours per night had the highest performance on average. Subjects who got only four hours a night did worse each day. The group who got six hours of sleep seemed to be holding their own, until around day 10 of the study.

In the last few days of the experiment, the subjects who were restricted to a maximum of six hours of sleep per night showed cognitive performance that was as bad as the people who weren’t allowed to sleep at all. Getting only six hours of shut-eye was as bad as not sleeping for two days straight. The group who got only four hours of sleep each night performed just as poorly, but they hit their low sooner.

One of the most alarming results from the sleep study is that the six-hour sleep group didn’t rate their sleepiness as being all that bad, even as their cognitive performance was going downhill. The no-sleep group progressively rated their sleepiness level higher and higher. By the end of the experiment, their sleepiness had jumped by two levels. But the six-hour group only jumped one level. Those findings raise the question about how people cope when they get insufficient sleep, perhaps suggesting that they’re in denial (willful or otherwise) about their present state.

We Have No Idea How Much We Sleep

Complicating matters is the fact that people are terrible at knowing how much time they actually spend asleep. According to the Behavioral Risk Factor Surveillance System survey, as reported by the CDC, more than 35% of Americans sleep less than seven hours in a typical day. That’s one out of every three people. However, those who suffer from sleep problems don’t accurately estimate how much they sleep each night.

If you think you sleep seven hours a night, as one out of every three Americans does, it’s entirely possible you’re only getting six.

Research from University of Chicago, for instance, shows that people are as likely to overestimate how much they sleep as underestimate it. Another sleep study published in Epidemiology, indicates people generally overestimate their nightly sleep by around 0.8 hours. The same study also estimates that for every hour beyond six that people sleep, they overestimate sleep by about half an hour. If you think you sleep seven hours a night, as one out of every three Americans does, it’s entirely possible you’re only getting six.

So no one knows how much or little they’re sleeping, and when they don’t sleep enough, they believe they’re doing better than they are.

Even just a little bit of sleep deprivation, in this case, six rather than eight hours of sleep across two weeks, accumulates to jaw-dropping results. Cumulative sleep deprivation isn’t a new concept by any means, but it’s rare to find research results that are so clear about the effects.

Fixing Sleep: Easier Said Than Done

Figuring out how to get enough sleep, consistently, is a tough nut to crack. The same advice experts have batted around for decades is probably a good place to start: Have a consistent bedtime; don’t look at electronic screens at least 30 minutes before bed; limit alcohol intake (alcohol makes many people sleepy, but it can also decrease the quality and duration of sleep); and get enough exercise.

Other advice that you’ll hear less often, but which is equally valid, is to lose excess weight. Sleep apnea and obesity have a very high correlation, according to the National Sleep Foundation. What’s more, obese workers already suffer from more lost productive time than normal weight and overweight workers.

Other causes of sleep problems include physical, neurological, and psychological issues. Even stress and worry can negatively affect sleep. The CDC has called lack of sleep a health problem, and for good reason. Diet, exercise, mental health, and physical health all affect our ability to sleep, and in return, our ability to perform to our best.

Fixing bad sleep habits to get enough sleep is easier said than done. But if you’re functioning as if you hadn’t slept for two days straight, isn’t it worthwhile?

 

 

Original article here

24 Apr 2023
Comments: 0

A Power Law Keeps the Brain’s Perceptions Balanced

 

The human brain is often described in the language of tipping points: It toes a careful line between high and low activity, between dense and sparse networks, between order and disorder. Now, by analyzing firing patterns from a record number of neurons, researchers have uncovered yet another tipping point — this time, in the neural code, the mathematical relationship between incoming sensory information and the brain’s neural representation of that information. Their findings, published in Nature in June 2019, suggest that the brain strikes a balance between encoding as much information as possible and responding flexibly to noise, which allows it to prioritize the most significant features of a stimulus rather than endlessly cataloging smaller details. The way it accomplishes this feat could offer fresh insights into how artificial intelligence systems might work, too.

A balancing act is not what the scientists initially set out to find. Their work began with a simpler question: Does the visual cortex represent various stimuli with many different response patterns, or does it use similar patterns over and over again? Researchers refer to the neural activity in the latter scenario as low dimensional: The neural code associated with it would have a very limited vocabulary, but it would also be resilient to small perturbations in sensory inputs. Imagine a one-dimensional code in which a stimulus is simply represented as either good or bad. The amount of firing by individual neurons might vary with the input, but the neurons as a population would be highly correlated, their firing patterns always either increasing or decreasing together in the same overall arrangement. Even if some neurons misfired, a stimulus would most likely still get correctly labeled.

At the other extreme, high-dimensional neural activity is far less correlated. Since information can be graphed or distributed across many dimensions, not just along a few axes like “good-bad,” the system can encode far more detail about a stimulus. The trade-off is that there’s less redundancy in such a system — you can’t deduce the overall state from any individual value — which makes it easier for the system to get thrown off.

For the past couple of decades, research indicated that neural systems generally favored low-dimensional representations. Although the natural world contains an absolutely massive amount of information, the brain seemed to be discarding much of that in favor of simpler neural descriptions. But later analyses showed that this conclusion could be chalked up to weaknesses in the experiments themselves: The lab animals were presented with only a few stimuli, or very simple stimuli, and researchers could only record from a limited number of neurons at a time. “Of course those experiments gave those results,” said Kenneth Harris, a neuroscientist at University College London.

“They couldn’t do anything different.”

So Harris and his colleagues revisited the problem, after creating a new technique for recording from 10,000 neurons simultaneously. As they showed mice nearly 3,000 images of natural scenes, they monitored the responses in the animals’ visual cortex and found a range of patterns that fit with a higher-dimensional picture of neural activity.

But the researchers also discovered something puzzling about that activity. The neurons didn’t care about all the dimensions equally: A few dimensions, or firing patterns, captured most of the neural responses to the visual stimuli. Adding other dimensions further increased that predictive power only by smaller and smaller increments. This decay followed what’s known as a power law, a special mathematical relationship “that’s been found almost everywhere people look for it,” said Jakob Macke, a computational neuroscientist at the Technical University of Munich who did not participate in the study.

Harris and his colleagues were stumped about what it might signify. Although recent studies have called the relevance (and even prevalence) of power laws into question — Harris quipped that even “the distribution of the number of exclamation marks in tweets from Donald Trump follows a power law” — there was something special about this one. It consistently had a particular slope, an exponent that couldn’t be explained by the mathematical structure of the stimuli.

“This sort of thing, this quantitative regularity in the data,” Harris said, “just doesn’t happen in biology. … We had absolutely no idea what it meant” — but it seemed to mean something.

In search of an explanation, they turned to previous mathematical work on the differentiability of functions. They found that if the power law mapping input to output decayed any slower, small changes in input would be able to generate large changes in output. The researchers referred to this as a breakdown in smoothness: The outputs produced by the underlying code were not always continuous.

It’s like being on the border of fractality, according to the co-leaders of the study, Carsen Stringer and Marius Pachitariu, both of whom worked in Harris’s lab and are now researchers at the Howard Hughes Medical Institute’s Janelia Research Campus in Virginia. “If you think of a fractal like the coastline of England,” Stringer said, “if you’re moving just a little bit along that coastline, you’re going to be changing very quickly, because there’s lots of jagged edges.”

In brain terms, that meant two very similar images could be represented by very different neural activity. “And that’s problematic,” she added. “If just one pixel changes, or if the image moves a bit, you don’t want your representation to totally change.”

Conversely, if the power law decayed any faster, the neural representations would become lower dimensional. They would encode less information, emphasizing some key dimensions while ignoring the rest.

Taken together, those principles implied that the representations were as detailed and high dimensional as they could get while still remaining smooth.

According to Harris, one way to interpret the finding is that with a slower decay, too much emphasis would be placed on less important dimensions (because if the curve relating neural activity to dimension were to get flatter, it would indicate that neural populations cared about all the dimensions more equally). Representations of the finer details in a stimulus would swamp the representation of the bigger features: The visual cortex would always be hypersensitive to certain trivial details, which would in turn make it difficult to formulate coherent perceptions and decisions. Meanwhile, with a faster decay, more weight than necessary would be placed on the larger features, overwhelming smaller features that might be relevant, too.

The brain seems to get it just right. “This is in a cool sweet spot in between,” said Eric Shea-Brown, a mathematical neuroscientist at the University of Washington who was not involved in the study. “It’s a balance between being smooth and systematic, in terms of mapping like inputs to like responses, but other than that, expressing as much as possible about the input.”

Harris and his team performed another experiment to test their idea. The particular slope of the power law they found depended on incoming stimuli being high dimensional, as any complex image is bound to be. But they calculated that if incoming visual inputs were simpler and lower dimensional, the slope would have to be steeper to avoid a breakdown in smoothness.

That’s exactly what they saw when they analyzed the neural activity of mice presented with low-dimensional images.

The researchers now want to determine the biological mechanism that makes this power law possible. They also hope to continue probing the role it might play in other brain regions, in other tasks or behaviors, and in models of disease.

One tantalizing context that they’re starting to explore is artificial intelligence. Deep learning systems have their own problem with breakdowns in smoothness: After training, they might be able to accurately label an image as a panda, but changes made to just a handful of pixels — which would be practically invisible to the human eye — might lead them to classify the image as a chimpanzee instead. “It’s a pathological feature of these networks,” Harris said. “There are always going to be some details that they’re oversensitive to.”

Computer scientists have been trying to determine why this happens, and Harris thinks his team’s findings might offer some clues. Preliminary analyses of deep learning networks have revealed that some of their layers typically obey power laws that decay more slowly than those observed in the mouse experiments. Harris, Stringer and their colleagues suspect that these networks might be vulnerable because, unlike networks in the brain, they produce representations that aren’t totally continuous. Perhaps, Harris said, it might be possible to apply the lessons of the power law he’s been studying to deep learning networks to make them more stable. But this research is still in its early days, according to Macke, who is also studying power laws in deep learning networks.

Shea-Brown still thinks it’s a good place to start. “Continuous and smooth relationships,” he said, “seem obviously important for creating the ability to generalize and compare different types of situations in an environment.” Scientists are starting to understand how the brain uses its full network of neurons to encode representations of the world. Now, with “this surprising and beautiful result,” they have both “a new target … and a very useful reference point” in hand for thinking about that code.

Harris noted that the unexpected presence of this power law in the visual cortex “was just something that came out in the data.” Now that other research questions can be pursued using his group’s technique for imaging and analyzing thousands of neurons at once, “this thing with the power law will probably be some very basic first finding,” with many other unanticipated insights on the horizon. “This whole approach is going to completely change the way we think about things.”

 

 

Original article here

05 Apr 2023
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The idea that everything from spoons to stones is conscious is gaining academic credibility

Consciousness permeates reality. Rather than being just a unique feature of human subjective experience, it’s the foundation of the universe, present in every particle and all physical matter.

This sounds like easily-dismissible bunkum, but as traditional attempts to explain consciousness continue to fail, the “panpsychist” view is increasingly being taken seriously by credible philosophers, neuroscientists, and physicists, including figures such as neuroscientist Christof Koch and physicist Roger Penrose.

“Why should we think common sense is a good guide to what the universe is like?” says Philip Goff, a philosophy professor at Central European University in Budapest, Hungary. “Einstein tells us weird things about the nature of time that counters common sense; quantum mechanics runs counter to common sense. Our intuitive reaction isn’t necessarily a good guide to the nature of reality.”

David Chalmers, a philosophy of mind professor at New York University, laid out the “hard problem of consciousness” in 1995, demonstrating that there was still no answer to the question of what causes consciousness. Traditionally, two dominant perspectives, materialism and dualism, have provided a framework for solving this problem. Both lead to seemingly intractable complications.

The materialist viewpoint states that consciousness is derived entirely from physical matter. It’s unclear, though, exactly how this could work. “It’s very hard to get consciousness out of non-consciousness,” says Chalmers. “Physics is just structure. It can explain biology, but there’s a gap: Consciousness.” Dualism holds that consciousness is separate and distinct from physical matter—but that then raises the question of how consciousness interacts and has an effect on the physical world.

Panpsychism offers an attractive alternative solution: Consciousness is a fundamental feature of physical matter; every single particle in existence has an “unimaginably simple” form of consciousness, says Goff. These particles then come together to form more complex forms of consciousness, such as humans’ subjective experiences. This isn’t meant to imply that particles have a coherent worldview or actively think, merely that there’s some inherent subjective experience of consciousness in even the tiniest particle.

Panpsychism doesn’t necessarily imply that every inanimate object is conscious. “Panpsychists usually don’t take tables and other artifacts to be conscious as a whole,” writes Hedda Hassel Mørch, a philosophy researcher at New York University’s Center for Mind, Brain, and Consciousness, in an email. “Rather, the table could be understood as a collection of particles that each have their own very simple form of consciousness.”

But, then again, panpsychism could very well imply that conscious tables exist: One interpretation of the theory holds that “any system is conscious,” says Chalmers. “Rocks will be conscious, spoons will be conscious, the Earth will be conscious. Any kind of aggregation gives you consciousness.”

Interest in panpsychism has grown in part thanks to the increased academic focus on consciousness itself following on from Chalmers’ “hard problem” paper. Philosophers at NYU, home to one of the leading philosophy-of-mind departments, have made panpsychism a feature of serious study. There have been several credible academic books on the subject in recent years, and popular articles taking panpsychism seriously.

One of the most popular and credible contemporary neuroscience theories on consciousness, Giulio Tononi’s Integrated Information Theory, further lends credence to panpsychism. Tononi argues that something will have a form of “consciousness” if the information contained within the structure is sufficiently “integrated,” or unified, and so the whole is more than the sum of its parts. Because it applies to all structures—not just the human brain—Integrated Information Theory shares the panpsychist view that physical matter has innate conscious experience.

Goff, who has written an academic book on consciousness and is working on another that approaches the subject from a more popular-science perspective, notes that there were credible theories on the subject dating back to the 1920s. Thinkers including philosopher Bertrand Russell and physicist Arthur Eddington made a serious case for panpsychism, but the field lost momentum after World War II, when philosophy became largely focused on analytic philosophical questions of language and logic. Interest picked up again in the 2000s, thanks both to recognition of the “hard problem” and to increased adoption of the structural-realist approach in physics, explains Chalmers. This approach views physics as describing structure, and not the underlying nonstructural elements.

“Physical science tells us a lot less about the nature of matter than we tend to assume,” says Goff. “Eddington”—the English scientist who experimentally confirmed Einstein’s theory of general relativity in the early 20th century—“argued there’s a gap in our picture of the universe. We know what matter does but not what it is. We can put consciousness into this gap.”

In Eddington’s view, Goff writes in an email, it’s “”silly” to suppose that that underlying nature has nothing to do with consciousness and then to wonder where consciousness comes from.” Stephen Hawking has previously asked: “What is it that breathes fire into the equations and makes a universe for them to describe?” Goff adds: “The Russell-Eddington proposal is that it is consciousness that breathes fire into the equations.”

The biggest problem caused by panpsychism is known as the “combination problem”: Precisely how do small particles of consciousness collectively form more complex consciousness? Consciousness may exist in all particles, but that doesn’t answer the question of how these tiny fragments of physical consciousness come together to create the more complex experience of human consciousness.

Any theory that attempts to answer that question, would effectively determine which complex systems—from inanimate objects to plants to ants—count as conscious.

An alternative panpsychist perspective holds that, rather than individual particles holding consciousness and coming together, the universe as a whole is conscious. This, says Goff, isn’t the same as believing the universe is a unified divine being; it’s more like seeing it as a “cosmic mess.” Nevertheless, it does reflect a perspective that the world is a top-down creation, where every individual thing is derived from the universe, rather than a bottom-up version where objects are built from the smallest particles. Goff believes quantum entanglement—the finding that certain particles behave as a single unified system even when they’re separated by such immense distances there can’t be a causal signal between them—suggests the universe functions as a fundamental whole rather than a collection of discrete parts.

Such theories sound incredible, and perhaps they are. But then again, so is every other possible theory that explains consciousness. “The more I think about [any theory], the less plausible it becomes,” says Chalmers. “One starts as a materialist, then turns into a dualist, then a panpsychist, then an idealist,” he adds, echoing his paper on the subject. Idealism holds that physical matter does not exist at all and conscious experience is the only thing there is. From that perspective, panpsychism is quite moderate.

Chalmers quotes his colleague, the philosopher John Perry, who says: “If you think about consciousness long enough, you either become a panpsychist or you go into administration.”

 

 

Original article here

04 Mar 2023
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How Loneliness Reshapes the Brain

 

The Neumayer III polar station sits near the edge of Antarctica’s unforgiving Ekström Ice Shelf. During the winter, when temperatures can plunge below minus 50 degrees Celsius and the winds can climb to more than 100 kilometers per hour, no one can come or go from the station. Its isolation is essential to the meteorological, atmospheric and geophysical science experiments conducted there by the mere handful of scientists who staff the station during the winter months and endure its frigid loneliness.

But a few years ago, the station also became the site for a study of loneliness itself. A team of scientists in Germany wanted to see whether the social isolation and environmental monotony marked the brains of people making long Antarctic stays. Eight expeditioners working at the Neumayer III station for 14 months agreed to have their brains scanned before and after their mission and to have their brain chemistry and cognitive performance monitored during their stay. (A ninth crew member also participated but could not have their brain scanned for medical reasons.)

As the researchers described in 2019, in comparison to a control group, the socially isolated team lost volume in their prefrontal cortex — the region at the front of the brain, just behind the forehead, that is chiefly responsible for decision-making and problem-solving. They also had lower levels of brain-derived neurotrophic factor, a protein that nurtures the development and survival of nerve cells in the brain. The reduction persisted for at least a month and a half after the team’s return from Antarctica.

It’s uncertain how much of this was due purely to the social isolation of the experience. But the results are consistent with evidence from more recent studies that chronic loneliness significantly alters the brain in ways that only worsen the problem.

Neuroscience suggests that loneliness doesn’t necessarily result from a lack of opportunity to meet others or a fear of social interactions. Instead, circuits in our brain and changes in our behavior can trap us in a catch-22 situation: While we desire connection with others, we view them as unreliable, judgmental and unfriendly. Consequently, we keep our distance, consciously or unconsciously spurning potential opportunities for connections.

Loneliness can be difficult to study empirically because it is entirely subjective. Social isolation, a related condition, is different — it’s an objective measure of how few relationships a person has. The experience of loneliness has to be self-reported, although researchers have developed tools such as the UCLA Loneliness Scale to help with assessing the depths of an individual’s feelings.

From such work, it’s clear that the physical and psychological toll of loneliness across the globe is profound. In one survey, 22% of Americans and 23% of British people said they felt lonely always or often. And that was before the pandemic. As of October 2020, 36% of Americans reported “serious loneliness.”

Introduction

But loneliness doesn’t merely feel bad: It takes a toll on our health. It can lead to high blood pressure, stroke and heart disease. It can also double the risk of Type 2 diabetes and raise the likelihood of dementia by 40%. As a consequence, chronically lonely people tend to have an 83% higher mortality risk than those who feel less isolated.

Organizations and governments often attempt to help with loneliness by encouraging people to go out more and by setting up hobby clubs, community gardens and craft groups. Yet as the neuroscience shows, getting rid of loneliness isn’t always that simple.

A Bias Toward Rejection 

When neuroscientists from Germany and Israel set out to investigate loneliness a few years ago, they expected to find that its neural underpinnings were like those of social anxiety and involved the amygdala. Often called the fear center of the brain, the amygdala tends to activate when we face things we dread, from snakes to other humans. “We thought, ‘Social anxiety is associated with increased amygdala activity, so this should also be the case for lonely individuals,’” said Jana Lieberz, a doctoral student at the University of

Bonn in Germany who was part of the research team.

However, a study that the team published in 2022 revealed that although threatening social situations trigger more amygdala activity in people suffering from social anxiety, they do not have that effect on lonely people. Similarly, people with social anxiety have diminished activity in the reward sections of their brain, and that does not appear to be true for lonely people.

“The core features of social anxiety were not evident in loneliness,” Lieberz said. Those results suggest, she said, that treating loneliness simply by telling lonely people to go out and socialize more (the way you can treat a phobia of snakes with exposure) will often not work because it fails to address the root cause of the loneliness. In fact, a recent meta-analysis confirmed that simply providing lonely people with easier access to potential friends has no effect on subjective loneliness.

The problem with loneliness seems to be that it biases our thinking. In behavioral studies, lonely people picked up on negative social signals, such as images of rejection, within 120 milliseconds — twice as quickly as people with satisfying relationships and in less than half the time it takes to blink. Lonely people also preferred to stand farther away from strangers, trusted others less and disliked physical touch.

This may be why the emotional well-being of lonely individuals often follows “a downward spiral,” said Danilo Bzdok, an interdisciplinary researcher at McGill University with a background in neuroscience and machine learning. “They tend to end up with a more negative spin on whatever information they receive — facial expressions, texting, whatever — and that drives them even deeper into this loneliness pit.”

Faults in the Default Network

Bzdok and his colleagues conducted the largest studies to date looking for signatures of loneliness in the human brain — studies involving about 100 times more subjects than any prior ones, according to Bzdok. They used data from the UK Biobank — a biomedical database that contains the brain scans of about 40,000 residents of the United Kingdom, along with information about their social isolation and loneliness.

Their results, published in 2020 in Nature Communications, revealed that the brain’s loneliness hot spot nestles within the default network, a part of the brain that activates when we are mentally on standby. “Until 20 years ago we didn’t even know we had this system,” Bzdok said. Yet studies have shown that activity in the default network accounts for most of the brain’s consumption of energy.

Bzdok and his team showed that some regions of the default network are not only larger in chronically lonely people but also more strongly connected to other parts of the brain. Moreover, the default network seems to be involved in many of the distinctive abilities that have evolved in humans — such as language, anticipating the future and causal reasoning. More generally, the default network activates when we think about other people, including when we interpret their intentions.

The findings on default network connectivity provided neuroimaging

evidence to support previous discoveries by psychologists that lonely people tend to daydream about social interactions, get easily nostalgic about past social events, and even anthropomorphize their pets, talking to their cats as if they were human, for example. “It would require the default network to do that too,” Bzdok said.

While loneliness can lead to a rich imaginary social life, it can make real-life social encounters less rewarding. A reason why may have been identified in a 2021 study by Bzdok and his colleagues that was also based on the voluminous UK Biobank data. They looked separately at socially isolated people and at people with low social support, as measured by a lack of someone to confide in on a daily or almost daily basis. The researchers found that in all such individuals, the orbitofrontal cortex — a part of the brain linked to processing rewards — was smaller.

Last year, a large brain-imaging study based on data from more than 1,300 Japanese volunteers revealed that greater loneliness is associated with stronger functional connections in the brain area that handles visual attention. This finding supports previous reports from eye-tracking studies that lonely people tend to focus excessively on unpleasant social cues, such as being ignored by others.

A Deep, Uncomfortable Craving

And yet, although lonely people may find encounters with others uncomfortable and unrewarding, they still seem to crave connection. The late John Cacioppo, a University of Chicago neuroscientist whose research earned him the nickname “Dr. Loneliness,” hypothesized that loneliness is an evolved adaptation, similar to hunger, signaling that something has gone awry in our lives. Just as hunger motivates us to look for food, loneliness should drive us to seek out connection to others. For our ancestors on the African savanna, whose survival probably depended on having ties to a group, that social impulse might have been a matter of life or death.

Recent brain imaging data supports the idea that loneliness is that deeply rooted in our psyche. In one study, Livia Tomova, a research associate in neuroscience at the University of Cambridge, and her colleagues asked 40 people to fast for 10 hours, then to have their brains scanned while they looked at pictures of mouth-watering foods. Subsequently, the same volunteers had to spend 10 hours alone — without phones, email or even novels as surrogates for contact. Then they had a second brain scan, this time while looking at pictures of happy groups of friends. When the scientists compared the brain scans of these individuals, the brain activation patterns from when they were hungry and when they felt lonely were remarkably alike.

To Tomova, the experiment underlined an important truth about loneliness: If just 10 hours without social contact is enough to elicit essentially the same neural signals as being deprived of food, “it highlights how basic our need to connect with others is,” she said.

Bigger Brains and More Friends

Recent studies also appear to confirm an evolutionary theory called the social brain hypothesis, which proposes that a busy social life is linked to bigger brains. The idea originated as a theory about how brains might have changed through evolution, but the larger brain size seems to emerge directly from life experiences too. In general, nonhuman primates in captivity that live in larger social groups or share spaces with more cage mates have larger brains. More specifically, the primates have more gray matter in their prefrontal cortex.

Humans are not much different, research suggests. A 2022 study found that elderly lonely people often have atrophy in parts of the brain including the thalamus, which processes emotions, and the hippocampus, a memory center. These changes, the authors suggested, could help explain links between loneliness and dementia.

Of course, the chicken-and-egg question about all these findings is: Do differences in the brain predispose us to loneliness, or does loneliness rewire and shrink the brain? According to Bzdok, it’s not currently possible to solve this puzzle. He believes, however, that the causality may point both ways.

Primate studies and the results of the Neumayer III polar station experiment show that experience and social environment can exert a powerful influence on the structure of an individual’s brain, hard-wiring the changes that loneliness can cause. On the other hand, studies of twins have shown that loneliness is partly heritable: Almost 50% of the variation in individuals’ feelings of loneliness can be explained by genetic differences.

People suffering from chronic loneliness are not irretrievably locked into those feelings by nature and nurture. Studies show that cognitive therapies can be effective at reducing loneliness by training people to recognize how their behaviors and thought patterns hinder them from forming the kinds of connections they value. And better interventions for loneliness and social isolation should be possible.

Take a recent study in which Lieberz and her colleagues looked at the brain activity in people playing a trust-based game. In the brain scans of lonely people, one brain region was far less active than in social people. That region, the insula, tends to activate when we examine our gut feelings, Lieberz explained. “That might be a reason why lonely people have problems trusting others — they cannot rely on their [gut] feelings,” she said. Interventions that target trust could therefore be part of a solution to the catch-22 of loneliness.

Another idea is to encourage synchrony. Research shows that one key to how much people like and trust each other lies in how closely their behaviors and reactions match from moment to moment. This synchrony between individuals can be as simple as reciprocating a smile or mirroring body language during conversation, or as elaborate as singing in a choir or being part of a rowing team. In a study published a year ago, Lieberz and her colleagues showed that lonely people struggle to synchronize with others, and that this discordance causes the regions of their brain responsible for observing actions to go into overdrive. Coaching lonely people in how to join in with the actions of others could be another strategic intervention to consider. It won’t cure loneliness by itself, “but it may be a starting point,” Lieberz said.

And if all else fails, there could be new chemical therapies. In one experiment conducted in Switzerland, after volunteers took psilocybin, the psychoactive compound in magic mushrooms, they reported feeling less socially excluded. Scans of their brains showed less activity in areas that process painful social experiences.

While interventions such as cognitive behavioral therapy, promoting trust and synchrony, or even ingesting magic mushrooms could help treat chronic loneliness, transient feelings of solitude will most likely always remain part of the human experience. And there is nothing wrong with that, Tomova said.

She compares loneliness to stress: It’s unpleasant but not necessarily negative. “It provides energy to the body, and then we can deal with challenges,” she said. “It becomes problematic when it’s chronic because our bodies are not meant to be in this constant state. That’s when our adaptive mechanisms ultimately break down.”

 

 

Original article here