Sleeping With Any Light Raises Risk of Obesity  Diabetes and More

Even dim light can disrupt sleep, raising the risk of serious health issues in older adults, a new study found. Dogs and cats who share their human’s bed tend to have a “higher trust level and a tighter bond with the humans that are in their lives. It’s a big display of trust on their part,” Varble said.

Sleep myths that may be keeping you from a good night’s rest. “Exposure to any amount of light during the sleep period was correlated with the higher prevalence of diabetes, obesity and hypertension in both older men and women,” senior author Phyllis Zee, chief of sleep medicine at Northwestern University Feinberg School of Medicine in Chicago, told CNN.

“People should do their best to avoid or minimize the amount of light they are exposed to during sleep,” she added. A study published earlier this year by Zee and her team examined the role of light in sleep for healthy adults in their 20s. Sleeping for only one night with a dim light, such as a TV set with the sound off, raised the blood sugar and heart rate of the young people during the sleep lab experiment.

An elevated heart rate at night has been shown in prior studies to be a risk factor for future heart disease and early death, while higher blood sugar levels are a sign of insulin resistance, which can ultimately lead to type 2 diabetes. The dim light entered the eyelids and disrupted sleep in the young adults despite the fact that participants slept with their eyes closed, Zee said. Yet even that tiny amount of light created a deficit of slow wave and rapid eye movement sleep, the stages of slumber in which most cellular renewal occurs, she said.

Objective Measurements

The new study, published Wednesday in the journal Sleep, focused on seniors who “already are at higher risk for diabetes and cardiovascular disease,” said coauthor Dr. Minjee Kim, an assistant professor of neurology at Northwestern University Feinberg School of Medicine, in a statement. “We wanted to see if there was a difference in frequencies of these diseases related to light exposure at night,” Kim said. Instead of pulling people into a sleep lab, the new study used a real-world setting.

Researchers gave 552 men and women between the ages of 63 and 84 an actigraph, a small device worn like a wristwatch that measures sleep cycles, average movement and light exposure. We’re actually measuring the amount of light the person is exposed to with a sensor on their body and comparing that to their sleep and wake activity over a 24-hour period,” Zee said. “What I think is different and notable in our study is that we have really objective data with this method.”

Fewer than half of the adults in the study got five hours of darkness at night. Zee and her team said they were surprised to find that fewer than half of the men and women in the study consistently slept in darkness for at least five hours each day. “More than 53% or so had some light during the night in the room,” she said. “In a secondary analysis, we found those who had higher amounts of light at night were also the most likely to have diabetes, obesity or hypertension.” In addition, Zee said, people who slept with higher levels of light were more likely to go to bed later and get up later, and “we know late sleepers tend to also have a higher risk for cardiovascular and metabolic disorders.”

What to do

Strategies for reducing light levels at night include positioning your bed away from windows or using light-blocking window shades. Don’t charge laptops and cellphones in your bedroom where melatonin-altering blue light can disrupt your sleep. If low levels of light persist, try a sleep mask to shelter your eyes. Using melatonin for sleep is on the rise, study says, despite potential health harms. If you have to get up, don’t turn on lights if you don’t have to, Zee advised. If you do, keep them as dim as possible and illuminated only for brief periods of time.

Older adults often have to get up at night to visit the bathroom, due to health issues or side effects from medications, Zee said, so advising that age group to turn out all lights might put them at risk of falling. In that case, consider using nightlights positioned very low to the ground, and choose lights with an amber or red color. That spectrum of light has a longer wavelength, and is less intrusive and disruptive to our circadian rhythm, or body clock, than shorter wavelengths such as blue light.

Source: Sleeping with any light raises risk of obesity, diabetes and more, study finds – CNN

Heart rate increases in light room, and body can’t rest properly 

We showed your heart rate increases when you sleep in a moderately lit room,” said Daniela Grimaldi, MD, PhD, co-first author of the study and a research assistant professor of Neurology in the Division of Sleep Medicine. “Even though you are asleep, your autonomic nervous system is activated. That’s bad. Usually, your heart rate together with other cardiovascular parameters are lower at night and higher during the day.”

There are sympathetic and parasympathetic nervous systems that regulate our physiology during the day and night. Sympathetic takes charge during the day and parasympathetic is supposed to control physiology at night, when it conveys restoration to the entire body.

How nighttime light during sleep can lead to diabetes and obesity

Investigators found insulin resistance occurred the morning after people slept in a light room. Insulin resistance is when cells in your muscles, fat and liver don’t respond well to insulin and can’t use glucose from your blood for energy. To make up for it, your pancreas makes more insulin. Over time, your blood sugar goes up. An earlier study published in JAMA Internal Medicine looked at a large population of healthy people who had exposure to light during sleep. They were more overweight and obese, Zee said.

“Now we are showing a mechanism that might be fundamental to explain why this happens. We show it’s affecting your ability to regulate glucose,” Zee said. The participants in the study weren’t aware of the biological changes in their bodies at night. “But the brain senses it,” Grimaldi said. “It acts like the brain of somebody whose sleep is light and fragmented. The sleep physiology is not resting the way it’s supposed to.”

Exposure to artificial light at night during sleep is common

Exposure to artificial light at night during sleep is common, either from indoor light emitting devices or from sources outside the home, particularly in large urban areas. A significant proportion of individuals (up to 40 percent) sleep with a bedside lamp on or with a light on in the bedroom, or keep a television on.

Light and its relationship to health is double edged.

“In addition to sleep, nutrition and exercise, light exposure during the daytime is an important factor for health, but during the night we show that even modest intensity of light can impair measures of heart and endocrine health,” Zee said. The study tested the effect of sleeping with 100 lux (moderate light) compared to 3 lux (dim light) in participants over a single night. The investigators discovered that moderate light exposure caused the body to go into a higher alert state.

In this state, the heart rate increases as well as the force with which the heart contracts and the rate of how fast the blood is conducted to your blood vessels for oxygenated blood flow.

Zee’s top tips for reducing light during sleep

  1. Don’t turn lights on. If you need to have a light on (which older adults may want for safety), make it a dim light that is closer to the floor.
  2. Color is important. Amber or a red or orange light is less stimulating for the brain. Don’t use white or blue light and keep it far away from the sleeping person.
  3. Blackout shades or eye masks are good if you can’t control the outdoor light. Move your bed so the outdoor light isn’t shining on your face.

More contents:

6 clever tips for a great night’s sleep NewsNet5, Ohio

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Dementia a Progressive Loss of Cognitive Function Marked By Memory Problems

Dementia is an umbrella term that refers to age-related cognitive decline caused by a variety of factors as well as by the aging process, in some people. The term is also used to refer to a range of symptoms, from some minor difficulty functioning to severe impairment. The most common form of dementia is Alzheimer’s Disease, a condition that affects more than 5 million Americans. There is currently no cure for most types of dementia, but certain treatments can help alleviate the symptoms temporarily.

What are the warning signs of dementia?

When a person experiences memory and thinking problems that prevent them from functioning normally on an ongoing basis, they have dementia. There are three major red flags for dementia: either the individual, their family, or a doctor gets concerned that there has been a significant decline in memory and thinking ability; their performance on thinking or memory tests is impaired; and/or issues related to thinking and memory problems are interfering with everyday activities, from the complex (cleaning, cooking, taking medicine) to the simple (bathing, dressing, eating, and using the bathroom).

How do you get dementia?

Dementia is not a diagnosis—it says nothing about the underlying cause of thinking and memory impairment. Dementia can be caused by a variety of factors, including thyroid disorders, vitamin deficiencies, side effects of prescriptions, depression, anxiety, infections, strokes, Parkinson’s disease, and other medical problems. In some cases, cognitive impairment may be reversible if diagnosed and treated early enough.

Source: Dementia | Psychology Today

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Related contents:

“Dementia”. medlineplus.gov. Retrieved 20 January 2022.

Mini-Mental State Examination (MMSE) for the detection of dementia in clinically unevaluated people aged 65 and over in community and primary care populations” (PDF)

“Differential diagnosis dementia”. NICE. Retrieved 20 January 2022.

 The American Psychiatric Publishing Textbook of Psychiatry. American Psychiatric Pub. p. 311. ISBN 978-1-58562-257-3. Archived from the original on 2017-09-08.

Dementia prevention, intervention, and care: 2020 report of the Lancet Commission”. Lancet. 396 (10248): 413–446. doi:10.1016/S0140-6736(20)30367-6. PMC 7392084. PMID 32738937.

Global, regional, and national burden of Alzheimer’s disease and other dementias, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016″. Lancet Neurol. 18 (1): 88–106. doi:10.1016/S1474-4422(18)30403-4. PMC 6291454. PMID 30497964.

Olfactory dysfunction in the pathophysiological continuum of dementia” (PDF). Ageing Research Reviews. 55: 100956. doi:10.1016/j.arr.2019.100956. PMID 31479764. S2CID 201742825.

Research criteria for the diagnosis of prodromal dementia with Lewy bodies”. Neurology (Review). 94 (17): 743–55. doi:10.1212/WNL.0000000000009323. PMC 7274845. PMID 32241955.

Memory loss : a practical guide for clinicians. [Edinburgh?]: Elsevier Saunders. ISBN 978-1-4160-3597-8.

Mortality and Morbidity Statistics”. icd.who.int. Retrieved 20 January 2022.

Screening for cognitive impairment in older adults: A systematic review for the U.S. Preventive Services Task Force”. Annals of Internal Medicine. 159 (9): 601–12. doi:10.7326/0003-4819-159-9-201311050-00730. PMID 24145578.

Assessment and management of behavioral and psychological symptoms of dementia”. BMJ. 350: h369. doi:10.1136/bmj.h369. PMC 4707529. PMID 25731881.

Management of Behavioral and Psychological Symptoms of Dementia”. Noro Psikiyatri Arsivi. 51 (4): 303–12. doi:10.5152/npa.2014.7405. PMC 5353163. PMID 28360647.

Inhibition in Cognition”. http://www.apa.org. Retrieved 7 February 2021.

Psychiatric Comorbidity in Persons With Dementia: Assessment and Treatment

The Art of Deep Listening To Resolve Conflict

A lack of effective listening between colleagues is one of the main causes of workplace conflicts, a problem that has been on the increase during the pandemic.

Before we have even stepped into the room, we are likely to have our own agenda which disrupts our ability to truly listen and resolve issues. But what can be done about it to improve communication and resolve conflict, and why does it matter?

Poor listening and communication are at the root of many relationship breakdowns, conflicts and disputes and lead to talent loss, poor productivity, low morale, missing deadlines, failure to complete on projects, loss of sales and a breakdown in trust and relationships.

In business truly listening to employees, colleagues and stakeholders means seriously entertaining their ideas, thoughts and feelings, whilst simultaneously putting your own ideas and instinctive responses on hold.

Why The Pandemic Made Listening Harder

Being asked to work from home and attend frequent online meetings has meant that we have less access to verbal and non-verbal cues, body language, lipreading and facial emotional reading. Turn-taking is difficult in these sorts of meetings.

If listening and speaking are harder, then people have less opportunity to express themselves. In addition, we may be distracted by other things going on at home and our mood and mental health may have been suffering. A lost ability to socialize at work means that meetings are often now solely functional. Furthermore, whilst wearing them may be required, masks have increased communication and listening problems too.

Why Listening Matters

When we communicate, we are subconsciously conducting a test for trust and respect. The test is continuous, it happens from moment-to-moment and is based on what people see, hear or feel. What they want to know more than anything else is ‘Do I matter?’ and ‘Am I heard?’

We also pay most attention to the things that directly concern us or are relevant to our own situation, our own needs, interests, fears and concerns, which means we can often listen from our own point of view rather than the speakers.

The message that a person or organization intends to give is frequently not the message that the other receives. Even when we feel we are expressing ourselves with great clarity, if either or both sides don’t truly listen to what is being said or don’t share the same meaning in the message there will be failures in communication. Not feeling heard can affect work relationships which can result in deep resentment, frustration and conflict.

Tips of how to use deep listening to resolve conflict.

  1. Understand that every conflict has two components: emotional and rational. When a person experiences high emotion in response to a situation or an exchange with another person, the rational, thinking part of the brain will not come into play until they have dealt with the emotional hijacking of the brain. It is physically impossible for someone to switch to logical thinking when their amygdala has created an emotional fight or flight response.
  • Acknowledge a person’s emotional state with an empathetic response. In instances where an emotional response is taking place, the first step to resolving the situation involves expressing empathy. You do this by saying something like ‘It sounds like you are feeling very frustrated’, or ‘I can see that you are upset by this’.
  • Be curious about what it is that is bothering them. If you are aware of and respectful of the other person’s needs, interests, fears and concerns then that is a great opening for good communication. Equally understand that the surface level of conflict is usually just that and there may be deeper issues involved; you may be missing subtle cues or underlying messages. Try not to interrupt or jump to conclusions.
  • Stand in the other person’s shoes. Even if only for a brief moment in time, try to see the world as the other person sees it, rather than how you see it. If you can do this then the person that you are communicating with will begin to have trust in you.
  • Show you are listening. Make eye contact, be present, don’t multi-task at the same time, turn your phone and the tv off, and pay attention to what the other person is saying rather thinking about your own response. Speaking to someone who gives the impression that they are not listening will only escalate the situation further.
  • Reflect back. Unless we take the important step of reflecting back to the speaker what we thought we heard and checking that our interpretation is correct, then we have no real way of knowing that we have understood accurately. Don’t tell them what they are feeling but summarise the important bits by using phrases like ‘I think you are saying’…’ and ‘If I heard you correctly…’
  • You don’t need to have all the answers.  Sometimes people just want to offload or vent and they don’t want fixing.  It is ok to not always know what to say. The important thing is to be present and there for them and to have created a safe space for them to tell you how they are feeling.
  • Tell them your reaction if relevant. Give the speaker some information about your response to their message. Don’t attack on what has been said but add some value to the conversation, describing your reaction rather than criticising the speaker.

By: Jane Gunn , Renowned Mediator and Conflict Specialist, http://www.janegunn.co.uk

Source: The art of deep listening to resolve conflict – HR News

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You Can’t Outrun Your Fork But That Doesn’t Mean Exercise Can’t Help You Lose Weight

1Every January, millions of individuals make New Year’s resolutions to lose weight or eat healthier, if not both. To achieve this goal, many individuals will begin strenuous exercise programs that incorporate too much exercise too soon, leading to fitness burnout or injury. Overtraining can actually prevent you from losing weight.

As a health neuroscientist, I have been studying the brain and cognitive mechanisms underlying dietary behaviours and the role exercise plays in helping people improve their diets for over 10 years.

Energy and exercise

The truth is that you simply cannot exercise away a poor diet and expect to lose weight (if that is your goal). Humans are very good at conserving energy and will account for any calories burned through exercise by consuming more calories later in the day or by being less physically active throughout the rest of the day.

That being said, you can — and should — use exercise to help you lose weight and maintain your weight loss. But not to offset calories consumed.

If you are looking to lose weight, the only way to do it is by controlling your calorie intake. The best and most effective way of doing that is limiting the consumption of ultra-processed foods — typical “junk foods” and fast-food meals. Even if you are not trying to lose weight, reducing ultra-processed food consumption is good for mental and physical health.

Regular exercise makes it easier to do this by improving the brain and cognitive processes that help us regulate junk food consumption, and by reducing stress. And the best part is, as little as 20 minutes of brisk walking is all you need to get the beneficial effects.

Why we over-consume junk foods

We know that we shouldn’t overeat candy, cookies, cake and chips, or drink sugary sodas. Diets that are high in these ultra-processed foods cause us to gain weight. But they are just so hard to resist.

Ultra-processed junk foods have been designed to be as tasty and rewarding as possible. When we are exposed to media advertisements, or actual food items (for example, chocolate bars in the checkout lane at grocery stores), brain activity in regions associated with reward processing increases. This reward-related brain activity results in increased food cravings and the drive to eat, even when we are not hungry.

A brain region known as the dorsolateral prefrontal cortex (dlPFC) helps us limit the consumption of ultra-processed foods by both decreasing activity in these reward regions to reduce food cravings and by initiating the cognitive processes needed to exert conscious control over food choices.

When using functional brain imaging to examine brain responses, neuroscientists have shown that increased activity in the dlPFC helps us control food cravings and select healthier food items by decreasing activity in the reward regions of the brain. Conversely, when activity in the dlPFC is decreased, we have a harder time resisting the temptation of appealing junk foods and will consume more snack food.

Exercise can help regulate food consumption

Exercise boosts brain plasticity, which is the brain’s ability to adapt its functions based on new input. Boosting brain plasticity makes it easier to change our habits and lifestyle. More and more evidence has shown that regular physical activity can increase prefrontal brain function and improve cognition.

These exercise-induced increases in prefrontal brain function and cognition makes it easier to regulate or limit our consumption of junk foods. And we can see the effects with as little as 20 minutes of moderate intensity exercise.

I have shown that people consume less ultra-processed food such as chips or milk chocolate after 20 minutes of moderate-intensity exercise (in our study, this was a brisk walk at 5.6-6.1 kilometres per hour on a treadmill with a slight incline). Research has also shown that both a single session of high-intensity interval training and a 12-week high-intensity aerobic exercise program can reduce preferences or appetite for high-calorie junk foods. Similar effects are seen when people engage in moderate aerobic exercise or strength training.

The key takeaway here is that regular exercise can reduce how much people want junk foods and improve their ability to resist the temptation of these appealing foods by improving brain function and cognition. This makes it easier to limit the consumption of these foods to achieve healthier eating and weight loss goals.

Exercise also helps reduce stress

When people are stressed, the body releases a hormone called cortisol, which activates what is known as the fight-or-flight response. When cortisol levels are high, the brain thinks it needs more fuel, resulting in increased cravings for sugary or salty ultra-processed foods.

Participation in regular exercise or a single bout of exercise reduces perceived stress levels and cortisol levels. Exercise also helps reduce unhealthy drink and food consumption when people are stressed.

Stress can also impact how the brain functions. Research has shown that stress can result in decreased activity in the prefrontal cortex and increased activity in reward regions of the brain when looking at pictures of food. This makes it harder to resist the temptation of appealing junk foods.

By offsetting the impact of stress on prefrontal brain function, exercise makes it easier to maintain your goals of healthier eating or reducing junk food consumption. Twenty minutes of brisk walking can help the prefrontal cortex recover from temporary changes in activity, like the ones seen when people are stressed.

Next time you are feeling stressed, try going for a brisk 20-minute walk. It could prevent you from stress-eating.

What exercise is best?

Researchers often get asked what is the best exercise and how much exercise to do.

At the end of the day, the best exercise is one you enjoy and can sustain over time. High-intensity interval training (HIIT), aerobic exercise, meditation and mindfulness, yoga and strength training are all effective in helping improve diet by targeting prefrontal brain function and reducing stress.

If you are beginning a new exercise routine this new year, ease into it, be kind to yourself, listen to your body and remember that a little goes a long way.

By: Cassandra J. Lowe

I am a CIHR and Canada First Research Excellence Fund (CFREF; BrainsCAN) funded Postdoctoral Fellow at Western University. My research examines the cognitive and neural factors that increase the likelihood individuals will over consume appealing “junk foods” (e.g., chips, chocolate, candy, fast-food meals)….

Source: You can’t outrun your fork. But that doesn’t mean exercise can’t help you lose weight or change your diet.

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The Science of Mind Reading

One night in October, 2009, a young man lay in an fMRI scanner in Liège, Belgium. Five years earlier, he’d suffered a head trauma in a motorcycle accident, and since then he hadn’t spoken. He was said to be in a “vegetative state.” A neuroscientist named Martin Monti sat in the next room, along with a few other researchers. For years, Monti and his postdoctoral adviser, Adrian Owen, had been studying vegetative patients, and they had developed two controversial hypotheses.

First, they believed that someone could lose the ability to move or even blink while still being conscious; second, they thought that they had devised a method for communicating with such “locked-in” people by detecting their unspoken thoughts.

In a sense, their strategy was simple. Neurons use oxygen, which is carried through the bloodstream inside molecules of hemoglobin. Hemoglobin contains iron, and, by tracking the iron, the magnets in fMRI machines can build maps of brain activity. Picking out signs of consciousness amid the swirl seemed nearly impossible. But, through trial and error, Owen’s group had devised a clever protocol.

They’d discovered that if a person imagined walking around her house there was a spike of activity in her parahippocampal gyrus—a finger-shaped area buried deep in the temporal lobe. Imagining playing tennis, by contrast, activated the premotor cortex, which sits on a ridge near the skull. The activity was clear enough to be seen in real time with an fMRI machine. In a 2006 study published in the journal Science, the researchers reported that they had asked a locked-in person to think about tennis, and seen, on her brain scan, that she had done so.

With the young man, known as Patient 23, Monti and Owen were taking a further step: attempting to have a conversation. They would pose a question and tell him that he could signal “yes” by imagining playing tennis, or “no” by thinking about walking around his house. In the scanner control room, a monitor displayed a cross-section of Patient 23’s brain. As different areas consumed blood oxygen, they shimmered red, then bright orange. Monti knew where to look to spot the yes and the no signals.

He switched on the intercom and explained the system to Patient 23. Then he asked the first question: “Is your father’s name Alexander?” The man’s premotor cortex lit up. He was thinking about tennis—yes.

“Is your father’s name Thomas?”

Activity in the parahippocampal gyrus. He was imagining walking around his house—no.

“Do you have any brothers?”

Tennis—yes.

“Do you have any sisters?”

House—no.

“Before your injury, was your last vacation in the United States?”

Tennis—yes.

The answers were correct. Astonished, Monti called Owen, who was away at a conference. Owen thought that they should ask more questions. The group ran through some possibilities. “Do you like pizza?” was dismissed as being too imprecise. They decided to probe more deeply. Monti turned the intercom back on.

That winter, the results of the study were published in The New England Journal of Medicine. The paper caused a sensation. The Los Angeles Times wrote a story about it, with the headline “Brains of Vegetative Patients Show Life.” Owen eventually estimated that twenty per cent of patients who were presumed to be vegetative were actually awake. This was a discovery of enormous practical consequence: in subsequent years, through painstaking fMRI sessions, Owen’s group found many patients who could interact with loved ones and answer questions about their own care.

The conversations improved their odds of recovery. Still, from a purely scientific perspective, there was something unsatisfying about the method that Monti and Owen had developed with Patient 23. Although they had used the words “tennis” and “house” in communicating with him, they’d had no way of knowing for sure that he was thinking about those specific things. They had been able to say only that, in response to those prompts, thinking was happening in the associated brain areas. “Whether the person was imagining playing tennis, football, hockey, swimming—we don’t know,” Monti told me recently.

During the past few decades, the state of neuroscientific mind reading has advanced substantially. Cognitive psychologists armed with an fMRI machine can tell whether a person is having depressive thoughts; they can see which concepts a student has mastered by comparing his brain patterns with those of his teacher. By analyzing brain scans, a computer system can edit together crude reconstructions of movie clips you’ve watched. One research group has used similar technology to accurately describe the dreams of sleeping subjects.

In another lab, scientists have scanned the brains of people who are reading the J. D. Salinger short story “Pretty Mouth and Green My Eyes,” in which it is unclear until the end whether or not a character is having an affair. From brain scans alone, the researchers can tell which interpretation readers are leaning toward, and watch as they change their minds.

I first heard about these studies from Ken Norman, the fifty-year-old chair of the psychology department at Princeton University and an expert on thought decoding. Norman works at the Princeton Neuroscience Institute, which is housed in a glass structure, constructed in 2013, that spills over a low hill on the south side of campus. P.N.I. was conceived as a center where psychologists, neuroscientists, and computer scientists could blend their approaches to studying the mind; M.I.T. and Stanford have invested in similar cross-disciplinary institutes.

At P.N.I., undergraduates still participate in old-school psych experiments involving surveys and flash cards. But upstairs, in a lab that studies child development, toddlers wear tiny hats outfitted with infrared brain scanners, and in the basement the skulls of genetically engineered mice are sliced open, allowing individual neurons to be controlled with lasers. A server room with its own high-performance computing cluster analyzes the data generated from these experiments.

Norman, whose jovial intelligence and unruly beard give him the air of a high-school science teacher, occupies an office on the ground floor, with a view of a grassy field. The bookshelves behind his desk contain the intellectual DNA of the institute, with William James next to texts on machine learning. Norman explained that fMRI machines hadn’t advanced that much; instead, artificial intelligence had transformed how scientists read neural data.

This had helped shed light on an ancient philosophical mystery. For centuries, scientists had dreamed of locating thought inside the head but had run up against the vexing question of what it means for thoughts to exist in physical space. When Erasistratus, an ancient Greek anatomist, dissected the brain, he suspected that its many folds were the key to intelligence, but he could not say how thoughts were packed into the convoluted mass.

In the seventeenth century, Descartes suggested that mental life arose in the pineal gland, but he didn’t have a good theory of what might be found there. Our mental worlds contain everything from the taste of bad wine to the idea of bad taste. How can so many thoughts nestle within a few pounds of tissue?

Now, Norman explained, researchers had developed a mathematical way of understanding thoughts. Drawing on insights from machine learning, they conceived of thoughts as collections of points in a dense “meaning space.” They could see how these points were interrelated and encoded by neurons. By cracking the code, they were beginning to produce an inventory of the mind. “The space of possible thoughts that people can think is big—but it’s not infinitely big,” Norman said. A detailed map of the concepts in our minds might soon be within reach.

Norman invited me to watch an experiment in thought decoding. A postdoctoral student named Manoj Kumar led us into a locked basement lab at P.N.I., where a young woman was lying in the tube of an fMRI scanner. A screen mounted a few inches above her face played a slide show of stock images: an empty beach, a cave, a forest.

“We want to get the brain patterns that are associated with different subclasses of scenes,” Norman said.

As the woman watched the slide show, the scanner tracked patterns of activation among her neurons. These patterns would be analyzed in terms of “voxels”—areas of activation that are roughly a cubic millimetre in size. In some ways, the fMRI data was extremely coarse: each voxel represented the oxygen consumption of about a million neurons, and could be updated only every few seconds, significantly more slowly than neurons fire.

But, Norman said, “it turned out that that information was in the data we were collecting—we just weren’t being as smart as we possibly could about how we’d churn through that data.” The breakthrough came when researchers figured out how to track patterns playing out across tens of thousands of voxels at a time, as though each were a key on a piano, and thoughts were chords.

The origins of this approach, I learned, dated back nearly seventy years, to the work of a psychologist named Charles Osgood. When he was a kid, Osgood received a copy of Roget’s Thesaurus as a gift. Poring over the book, Osgood recalled, he formed a “vivid image of words as clusters of starlike points in an immense space.” In his postgraduate days, when his colleagues were debating how cognition could be shaped by culture, Osgood thought back on this image. He wondered if, using the idea of “semantic space,” it might be possible to map the differences among various styles of thinking.

Osgood conducted an experiment. He asked people to rate twenty concepts on fifty different scales. The concepts ranged widely: BOULDER, ME, TORNADO, MOTHER. So did the scales, which were defined by opposites: fair-unfair, hot-cold, fragrant-foul. Some ratings were difficult: is a TORNADO fragrant or foul? But the idea was that the method would reveal fine and even elusive shades of similarity and difference among concepts.

“Most English-speaking Americans feel that there is a difference, somehow, between ‘good’ and ‘nice’ but find it difficult to explain,” Osgood wrote. His surveys found that, at least for nineteen-fifties college students, the two concepts overlapped much of the time. They diverged for nouns that had a male or female slant. MOTHER might be rated nice but not good, and COP vice versa. Osgood concluded that “good” was “somewhat stronger, rougher, more angular, and larger” than “nice.”

Osgood became known not for the results of his surveys but for the method he invented to analyze them. He began by arranging his data in an imaginary space with fifty dimensions—one for fair-unfair, a second for hot-cold, a third for fragrant-foul, and so on. Any given concept, like TORNADO, had a rating on each dimension—and, therefore, was situated in what was known as high-dimensional space. Many concepts had similar locations on multiple axes: kind-cruel and honest-dishonest, for instance. Osgood combined these dimensions. Then he looked for new similarities, and combined dimensions again, in a process called “factor analysis.”

When you reduce a sauce, you meld and deepen the essential flavors. Osgood did something similar with factor analysis. Eventually, he was able to map all the concepts onto a space with just three dimensions. The first dimension was “evaluative”—a blend of scales like good-bad, beautiful-ugly, and kind-cruel. The second had to do with “potency”: it consolidated scales like large-small and strong-weak. The third measured how “active” or “passive” a concept was. Osgood could use these three key factors to locate any concept in an abstract space. Ideas with similar coördinates, he argued, were neighbors in meaning.

For decades, Osgood’s technique found modest use in a kind of personality test. Its true potential didn’t emerge until the nineteen-eighties, when researchers at Bell Labs were trying to solve what they called the “vocabulary problem.” People tend to employ lots of names for the same thing. This was an obstacle for computer users, who accessed programs by typing words on a command line. George Furnas, who worked in the organization’s human-computer-interaction group, described using the company’s internal phone book.

“You’re in your office, at Bell Labs, and someone has stolen your calculator,” he said. “You start putting in ‘police,’ or ‘support,’ or ‘theft,’ and it doesn’t give you what you want. Finally, you put in ‘security,’ and it gives you that. But it actually gives you two things: something about the Bell Savings and Security Plan, and also the thing you’re looking for.” Furnas’s group wanted to automate the finding of synonyms for commands and search terms.

They updated Osgood’s approach. Instead of surveying undergraduates, they used computers to analyze the words in about two thousand technical reports. The reports themselves—on topics ranging from graph theory to user-interface design—suggested the dimensions of the space; when multiple reports used similar groups of words, their dimensions could be combined.

In the end, the Bell Labs researchers made a space that was more complex than Osgood’s. It had a few hundred dimensions. Many of these dimensions described abstract or “latent” qualities that the words had in common—connections that wouldn’t be apparent to most English speakers. The researchers called their technique “latent semantic analysis,” or L.S.A.

At first, Bell Labs used L.S.A. to create a better internal search engine. Then, in 1997, Susan Dumais, one of Furnas’s colleagues, collaborated with a Bell Labs cognitive scientist, Thomas Landauer, to develop an A.I. system based on it. After processing Grolier’s American Academic Encyclopedia, a work intended for young students, the A.I. scored respectably on the multiple-choice Test of English as a Foreign Language. That year, the two researchers co-wrote a paper that addressed the question “How do people know as much as they do with as little information as they get?”

They suggested that our minds might use something like L.S.A., making sense of the world by reducing it to its most important differences and similarities, and employing this distilled knowledge to understand new things. Watching a Disney movie, for instance, I immediately identify a character as “the bad guy”: Scar, from “The Lion King,” and Jafar, from “Aladdin,” just seem close together. Perhaps my brain uses factor analysis to distill thousands of attributes—height, fashion sense, tone of voice—into a single point in an abstract space. The perception of bad-guy-ness becomes a matter of proximity.

In the following years, scientists applied L.S.A. to ever-larger data sets. In 2013, researchers at Google unleashed a descendant of it onto the text of the whole World Wide Web. Google’s algorithm turned each word into a “vector,” or point, in high-dimensional space. The vectors generated by the researchers’ program, word2vec, are eerily accurate: if you take the vector for “king” and subtract the vector for “man,” then add the vector for “woman,” the closest nearby vector is “queen.”

Word vectors became the basis of a much improved Google Translate, and enabled the auto-completion of sentences in Gmail. Other companies, including Apple and Amazon, built similar systems. Eventually, researchers realized that the “vectorization” made popular by L.S.A. and word2vec could be used to map all sorts of things. Today’s facial-recognition systems have dimensions that represent the length of the nose and the curl of the lips, and faces are described using a string of coördinates in “face space.” Chess A.I.s use a similar trick to “vectorize” positions on the board.

The technique has become so central to the field of artificial intelligence that, in 2017, a new, hundred-and-thirty-five-million-dollar A.I. research center in Toronto was named the Vector Institute. Matthew Botvinick, a professor at Princeton whose lab was across the hall from Norman’s, and who is now the head of neuroscience at DeepMind, Alphabet’s A.I. subsidiary, told me that distilling relevant similarities and differences into vectors was “the secret sauce underlying all of these A.I. advances.”

In 2001, a scientist named Jim Haxby brought machine learning to brain imaging: he realized that voxels of neural activity could serve as dimensions in a kind of thought space. Haxby went on to work at Princeton, where he collaborated with Norman. The two scientists, together with other researchers, concluded that just a few hundred dimensions were sufficient to capture the shades of similarity and difference in most fMRI data. At the Princeton lab, the young woman watched the slide show in the scanner.

With each new image—beach, cave, forest—her neurons fired in a new pattern. These patterns would be recorded as voxels, then processed by software and transformed into vectors. The images had been chosen because their vectors would end up far apart from one another: they were good landmarks for making a map. Watching the images, my mind was taking a trip through thought space, too.

The larger goal of thought decoding is to understand how our brains mirror the world. To this end, researchers have sought to watch as the same experiences affect many people’s minds simultaneously. Norman told me that his Princeton colleague Uri Hasson has found movies especially useful in this regard. They “pull people’s brains through thought space in synch,” Norman said. “What makes Alfred Hitchcock the master of suspense is that all the people who are watching the movie are having their brains yanked in unison. It’s like mind control in the literal sense.”

One afternoon, I sat in on Norman’s undergraduate class “fMRI Decoding: Reading Minds Using Brain Scans.” As students filed into the auditorium, setting their laptops and water bottles on tables, Norman entered wearing tortoiseshell glasses and earphones, his hair dishevelled.

He had the class watch a clip from “Seinfeld” in which George, Susan (an N.B.C. executive he is courting), and Kramer are hanging out with Jerry in his apartment. The phone rings, and Jerry answers: it’s a telemarketer. Jerry hangs up, to cheers from the studio audience.

“Where was the event boundary in the clip?” Norman asked. The students yelled out in chorus, “When the phone rang!” Psychologists have long known that our minds divide experiences into segments; in this case, it was the phone call that caused the division.

Norman showed the class a series of slides. One described a 2017 study by Christopher Baldassano, one of his postdocs, in which people watched an episode of the BBC show “Sherlock” while in an fMRI scanner. Baldassano’s guess going into the study was that some voxel patterns would be in constant flux as the video streamed—for instance, the ones involved in color processing. Others would be more stable, such as those representing a character in the show.

The study confirmed these predictions. But Baldassano also found groups of voxels that held a stable pattern throughout each scene, then switched when it was over. He concluded that these constituted the scenes’ voxel “signatures.” Norman described another study, by Asieh Zadbood, in which subjects were asked to narrate “Sherlock” scenes—which they had watched earlier—aloud.

The audio was played to a second group, who’d never seen the show. It turned out that no matter whether someone watched a scene, described it, or heard about it, the same voxel patterns recurred. The scenes existed independently of the show, as concepts in people’s minds.

Through decades of experimental work, Norman told me later, psychologists have established the importance of scripts and scenes to our intelligence. Walking into a room, you might forget why you came in; this happens, researchers say, because passing through the doorway brings one mental scene to a close and opens another.

Conversely, while navigating a new airport, a “getting to the plane” script knits different scenes together: first the ticket counter, then the security line, then the gate, then the aisle, then your seat. And yet, until recently, it wasn’t clear what you’d find if you went looking for “scripts” and “scenes” in the brain.

In a recent P.N.I. study, Norman said, people in an fMRI scanner watched various movie clips of characters in airports. No matter the particulars of each clip, the subjects’ brains all shimmered through the same series of events, in keeping with boundary-defining moments that any of us would recognize. The scripts and the scenes were real—it was possible to detect them with a machine. What most interests Norman now is how they are learned in the first place.

How do we identify the scenes in a story? When we enter a strange airport, how do we know intuitively where to look for the security line? The extraordinary difficulty of such feats is obscured by how easy they feel—it’s rare to be confused about how to make sense of the world. But at some point everything was new. When I was a toddler, my parents must have taken me to the supermarket for the first time; the fact that, today, all supermarkets are somehow familiar dims the strangeness of that experience.

When I was learning to drive, it was overwhelming: each intersection and lane change seemed chaotic in its own way. Now I hardly have to think about them. My mind instantly factors out all but the important differences.

Norman clicked through the last of his slides. Afterward, a few students wandered over to the lectern, hoping for an audience with him. For the rest of us, the scene was over. We packed up, climbed the stairs, and walked into the afternoon sun.

Like Monti and Owen with Patient 23, today’s thought-decoding researchers mostly look for specific thoughts that have been defined in advance. But a “general-purpose thought decoder,” Norman told me, is the next logical step for the research. Such a device could speak aloud a person’s thoughts, even if those thoughts have never been observed in an fMRI machine. In 2018, Botvinick, Norman’s hall mate, co-wrote a paper in the journal Nature Communications titled “Toward a Universal Decoder of Linguistic Meaning from Brain Activation.”

Botvinick’s team had built a primitive form of what Norman described: a system that could decode novel sentences that subjects read silently to themselves. The system learned which brain patterns were evoked by certain words, and used that knowledge to guess which words were implied by the new patterns it encountered.

The work at Princeton was funded by iARPA, an R. & D. organization that’s run by the Office of the Director of National Intelligence. Brandon Minnery, the iARPA project manager for the Knowledge Representation in Neural Systems program at the time, told me that he had some applications in mind. If you knew how knowledge was represented in the brain, you might be able to distinguish between novice and expert intelligence agents. You might learn how to teach languages more effectively by seeing how closely a student’s mental representation of a word matches that of a native speaker.

Minnery’s most fanciful idea—“Never an official focus of the program,” he said—was to change how databases are indexed. Instead of labelling items by hand, you could show an item to someone sitting in an fMRI scanner—the person’s brain state could be the label. Later, to query the database, someone else could sit in the scanner and simply think of whatever she wanted. The software could compare the searcher’s brain state with the indexer’s. It would be the ultimate solution to the vocabulary problem.

Jack Gallant, a professor at Berkeley who has used thought decoding to reconstruct video montages from brain scans—as you watch a video in the scanner, the system pulls up frames from similar YouTube clips, based only on your voxel patterns—suggested that one group of people interested in decoding were Silicon Valley investors. “A future technology would be a portable hat—like a thinking hat,” he said.

He imagined a company paying people thirty thousand dollars a year to wear the thinking hat, along with video-recording eyeglasses and other sensors, allowing the system to record everything they see, hear, and think, ultimately creating an exhaustive inventory of the mind. Wearing the thinking hat, you could ask your computer a question just by imagining the words. Instantaneous translation might be possible. In theory, a pair of wearers could skip language altogether, conversing directly, mind to mind. Perhaps we could even communicate across species.

Among the challenges the designers of such a system would face, of course, is the fact that today’s fMRI machines can weigh more than twenty thousand pounds. There are efforts under way to make powerful miniature imaging devices, using lasers, ultrasound, or even microwaves. “It’s going to require some sort of punctuated-equilibrium technology revolution,” Gallant said. Still, the conceptual foundation, which goes back to the nineteen-fifties, has been laid.

Recently, I asked Owen what the new thought-decoding technology meant for locked-in patients. Were they close to having fluent conversations using something like the general-purpose thought decoder? “Most of that stuff is group studies in healthy participants,” Owen told me. “The really tricky problem is doing it in a single person. Can you get robust enough data?” Their bare-bones protocol—thinking about tennis equals yes; thinking about walking around the house equals no—relied on straightforward signals that were statistically robust.

It turns out that the same protocol, combined with a series of yes-or-no questions (“Is the pain in the lower half of your body? On the left side?”), still works best. “Even if you could do it, it would take longer to decode them saying ‘it is in my right foot’ than to go through a simple series of yes-or-no questions,” Owen said. “For the most part, I’m quietly sitting and waiting. I have no doubt that, some point down the line, we will be able to read minds. People will be able to articulate, ‘My name is Adrian, and I’m British,’ and we’ll be able to decode that from their brain. I don’t think it’s going to happen in probably less than twenty years.”

In some ways, the story of thought decoding is reminiscent of the history of our understanding of the gene. For about a hundred years after the publication of Charles Darwin’s “On the Origin of Species,” in 1859, the gene was an abstraction, understood only as something through which traits passed from parent to child. As late as the nineteen-fifties, biologists were still asking what, exactly, a gene was made of. When James Watson and Francis Crick finally found the double helix, in 1953, it became clear how genes took physical form. Fifty years later, we could sequence the human genome; today, we can edit it.

Thoughts have been an abstraction for far longer. But now we know what they really are: patterns of neural activation that correspond to points in meaning space. The mind—the only truly private place—has become inspectable from the outside. In the future, a therapist, wanting to understand how your relationships run awry, might examine the dimensions of the patterns your brain falls into.

Some epileptic patients about to undergo surgery have intracranial probes put into their brains; researchers can now use these probes to help steer the patients’ neural patterns away from those associated with depression. With more fine-grained control, a mind could be driven wherever one liked. (The imagination reels at the possibilities, for both good and ill.) Of course, we already do this by thinking, reading, watching, talking—actions that, after I’d learned about thought decoding, struck me as oddly concrete. I could picture the patterns of my thoughts flickering inside my mind. Versions of them are now flickering in yours.

On one of my last visits to Princeton, Norman and I had lunch at a Japanese restaurant called Ajiten. We sat at a counter and went through the familiar script. The menus arrived; we looked them over. Norman noticed a dish he hadn’t seen before—“a new point in ramen space,” he said. Any minute now, a waiter was going to interrupt politely to ask if we were ready to order.

“You have to carve the world at its joints, and figure out: what are the situations that exist, and how do these situations work?” Norman said, while jazz played in the background. “And that’s a very complicated problem. It’s not like you’re instructed that the world has fifteen different ways of being, and here they are!” He laughed. “When you’re out in the world, you have to try to infer what situation you’re in.” We were in the lunch-at-a-Japanese-restaurant situation. I had never been to this particular restaurant, but nothing about it surprised me. This, it turns out, might be one of the highest accomplishments in nature.

Norman told me that a former student of his, Sam Gershman, likes using the terms “lumping” and “splitting” to describe how the mind’s meaning space evolves. When you encounter a new stimulus, do you lump it with a concept that’s familiar, or do you split off a new concept? When navigating a new airport, we lump its metal detector with those we’ve seen before, even if this one is a different model, color, and size. By contrast, the first time we raised our hands inside a millimetre-wave scanner—the device that has replaced the walk-through metal detector—we split off a new category.

Norman turned to how thought decoding fit into the larger story of the study of the mind. “I think we’re at a point in cognitive neuroscience where we understand a lot of the pieces of the puzzle,” he said. The cerebral cortex—a crumply sheet laid atop the rest of the brain—warps and compresses experience, emphasizing what’s important. It’s in constant communication with other brain areas, including the hippocampus, a seahorse-shaped structure in the inner part of the temporal lobe.

For years, the hippocampus was known only as the seat of memory; patients who’d had theirs removed lived in a perpetual present. Now we were seeing that the hippocampus stores summaries provided to it by the cortex: the sauce after it’s been reduced. We cope with reality by building a vast library of experience—but experience that has been distilled along the dimensions that matter. Norman’s research group has used fMRI technology to find voxel patterns in the cortex that are reflected in the hippocampus. Perhaps the brain is like a hiker comparing the map with the territory.

In the past few years, Norman told me, artificial neural networks that included basic models of both brain regions had proved surprisingly powerful. There was a feedback loop between the study of A.I. and the study of the real human mind, and it was getting faster. Theories about human memory were informing new designs for A.I. systems, and those systems, in turn, were suggesting ideas about what to look for in real human brains. “It’s kind of amazing to have gotten to this point,” he said.

On the walk back to campus, Norman pointed out the Princeton University Art Museum. It was a treasure, he told me.

“What’s in there?” I asked.

“Great art!” he said

After we parted ways, I returned to the museum. I went to the downstairs gallery, which contains artifacts from the ancient world. Nothing in particular grabbed me until I saw a West African hunter’s tunic. It was made of cotton dyed the color of dark leather. There were teeth hanging from it, and claws, and a turtle shell—talismans from past kills. It struck me, and I lingered for a moment before moving on.

Six months later, I went with some friends to a small house in upstate New York. On the wall, out of the corner of my eye, I noticed what looked like a blanket—a kind of fringed, hanging decoration made of wool and feathers. It had an odd shape; it seemed to pull toward something I’d seen before. I stared at it blankly. Then came a moment of recognition, along dimensions I couldn’t articulate—more active than passive, partway between alive and dead. There, the chest. There, the shoulders. The blanket and the tunic were distinct in every way, but somehow still neighbors. My mind had split, then lumped. Some voxels had shimmered. In the vast meaning space inside my head, a tiny piece of the world was finding its proper place. ♦

Source: The Science of Mind Reading | The New Yorker

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