How The Brain Distinguishes Memories From Perceptions

Kristina Armitage/Quanta Magazine

The neural representations of a perceived image and the memory of it are almost the same. New work shows how and why they are different. Memory and perception seem like entirely distinct experiences, and neuroscientists used to be confident that the brain produced them differently, too. But in the 1990s neuroimaging studies revealed that parts of the brain that were thought to be active only during sensory perception are also active during the recall of memories.

“It started to raise the question of whether a memory representation is actually different from a perceptual representation at all,” said Sam Ling, an associate professor of neuroscience and director of the Visual Neuroscience Lab at Boston University. Could our memory of a beautiful forest glade, for example, be just a re-creation of the neural activity that previously enabled us to see it?

“The argument has swung from being this debate over whether there’s even any involvement of sensory cortices to saying ‘Oh, wait a minute, is there any difference?’” said Christopher Baker, an investigator at the National Institute of Mental Health who runs the learning and plasticity unit. “The pendulum has swung from one side to the other, but it’s swung too far.”

Even if there is a very strong neurological similarity between memories and experiences, we know that they can’t be exactly the same. “People don’t get confused between them,” said Serra Favila, a postdoctoral scientist at Columbia University and the lead author of a recent Nature Communications study. Her team’s work has identified at least one of the ways in which memories and perceptions of images are assembled differently at the neurological level.

Blurry Spots

When we look at the world, visual information about it streams through the photoreceptors of the retina and into the visual cortex, where it is processed sequentially in different groups of neurons. Each group adds new levels of complexity to the image: Simple dots of light turn into lines and edges, then contours, then shapes, then complete scenes that embody what we’re seeing.

In the new study, the researchers focused on a feature of vision processing that’s very important in the early groups of neurons: where things are located in space. The pixels and contours making up an image need to be in the correct places or else the brain will create a shuffled, unrecognizable distortion of what we’re seeing.

The researchers trained participants to memorize the positions of four different patterns on a backdrop that resembled a dartboard. Each pattern was placed in a very specific location on the board and associated with a color at the center of the board. Each participant was tested to make sure that they had memorized this information correctly — that if they saw a green dot, for example, they knew the star shape was at the far left position.

Then, as the participants perceived and remembered the locations of the patterns, the researchers recorded their brain activity. The brain scans allowed the researchers to map out how neurons recorded where something was as well as how they later remembered it. Each neuron attends to one space, or “receptive field,” in the expanse of your vision, such as the lower left corner.

A neuron is “only going to fire when you put something in that little spot,” Favila said. Neurons that are tuned to a certain spot in space tend to cluster together, making their activity easy to detect in brain scans. Previous studies of visual perception established that neurons in the early, lower levels of processing have small receptive fields, and neurons in later, higher levels have larger ones.

This makes sense because the higher-tier neurons are compiling signals from many lower-tier neurons, drawing in information across a wider patch of the visual field. But the bigger receptive field also means lower spatial precision, producing an effect like putting a large blob of ink over North America on a map to indicate New Jersey. In effect, visual processing during perception is a matter of small crisp dots evolving into larger, blurrier but more meaningful blobs.

But when Favila and her colleagues looked at how perceptions and memories were represented in the various areas of the visual cortex, they discovered major differences. As participants recalled the images, the receptive fields in the highest level of visual processing were the same size they had been during perception — but the receptive fields stayed that size down through all the other levels painting the mental image. The remembered image was a large, blurry blob at every stage.

This suggests that when the memory of the image was stored, only the highest-level representation of it was kept. When the memory was experienced again, all the areas of the visual cortex were activated — but their activity was based on the less precise version as an input. So depending on whether information is coming from the retina or from wherever memories are stored, the brain handles and processes it very differently.

Some of the precision of the original perception gets lost on its way into memory, and “you can’t magically get it back,” Favila said. A “really beautiful” aspect of this study was that the researchers could read out the information about a memory directly from the brain rather than rely on the human subject to report what they were seeing, said Adam Steel, a postdoctoral researcher at Dartmouth College. “The empirical work that they did, I think, is really outstanding.”

A Feature or a Bug?

But why are memories recalled in this “blurrier” way? To find out, the researchers created a model of the visual cortex that had different levels of neurons with receptive fields of increasing size. They then simulated an evoked memory by sending a signal through the levels in reverse order. As in the brain scans, the spatial blurriness seen in the level with the largest receptive field persisted through all the rest. That suggests that the remembered image forms in this way due to the hierarchical nature of the visual system, Favila said.

One theory about why the visual system is arranged hierarchically is that it helps with object recognition. If receptive fields were tiny, the brain would need to integrate more information to make sense of what was in view; that could make it hard to recognize something big like the Eiffel Tower, Favila said. The “blurrier” memory image might be the “consequence of having a system that’s been optimized for things like object recognition.”

Yasemin Saplakoglu

By: Yasemin Saplakoglu

Source: How the Brain Distinguishes Memories From Perceptions | Quanta Magazine

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Meditation Can Better The Brain. Are We Morally Obligated To Meditate



Studies have shown that meditation can change your neural circuitry in ways that make you more compassionate.Getty Images

A growing body of neuroscience research shows that meditation can make us better to each other.Finding the best ways to do good.Eight weeks ago, I started meditating every day. I knew I’d be going home to visit my family at the end of December, and well, I have a bad habit of regressing into a 13-year-old whenever I’m around them.

All my old immaturities and anxieties get activated. I become a more reactive, less compassionate version of myself. But this holiday season, I was determined to avoid fighting with my family. I would be kind and even-tempered throughout the visit. I knew that in order to have a chance in hell of achieving this, I’d need a secret weapon.

That’s where the meditation came in. Starting in 2005, Harvard neuroscientist Sara Lazar began to publish some mind-blowing findings: Meditation can literally change the structure of your brain, thickening key areas of the cortex that help you control your attention and emotions. Your brain — and possibly, by extension, your behavior — can reap the benefits if you practice meditation for half an hour a day over eight weeks.

Just eight weeks? I thought when I read the research. This seems too good to be true! I was intrigued, if skeptical. Above all, I was curious to know more. And I wasn’t the only one. By 2014, there had been enough follow-up studies to warrant a meta-analysis, which showed that meditators’ brains tend to be enlarged in a bunch of regions, including the insula (involved in emotional self-awareness), parts of the cingulate cortex and orbitofrontal cortex (involved in self-regulation), and parts of the prefrontal cortex (involved in attention).

A host of other studies showed that meditation can also change your neural circuitry in ways that make you more compassionate, as well as more inclined to have positive feelings toward a victim of suffering and to see things from their perspective.

Further research suggested that meditation can change not only your internal emotional states but also your actual behavior. One study found that people made charitable donations at a higher rate after being trained in meditation for just two weeks. Another study found that people who get that same measly amount of meditation training are about three times more likely than non-meditators to give up their chair when they see someone on crutches and in pain.

Still skeptical, I fell down an internet rabbit hole and soon found many more neuroscientific studies. Looking closely at them, I did find that a fair number are methodologically flawed (more on that below). But there were many others that seemed sound. Taken together, the literature on meditation suggested that the practice can help us get better at relating to one another. It confronted me with evidence that a few weeks of meditation can improve me as a person.

I say “confronted” because the evidence really did feel like a challenge, even a dare. If it takes such a small amount of time and effort to get better at regulating my emotions, paying attention to other people, seeing things from their point of view, and acting altruistically, then … well … am I not morally obligated to do it?

The science behind mindfulness meditation and how we pay attention to others

The word “meditation” actually refers to many different practices. In the West, the most well-known set of practices is “mindfulness meditation.” When people talk about that, they’re typically thinking of a practice for training our attention.

Here’s how Jon Kabat-Zinn, a scientist who helped popularize mindfulness in the West, defines it: “Mindfulness is awareness that arises through paying attention, on purpose, in the present moment, non-judgmentally.”

And here’s what mindfulness meditation practice often involves: You sit down, close your eyes, and focus on feeling your breath go in and out. When you feel your attention drifting to the thoughts that inevitably arise, you notice, and then gently bring your attention back to your breath.

This combination of attention training and direct observation is the basic practice. Sounds simple, right? But according to some studies, it can have profound effects on your brain.

In a 2012 study, people who were new to meditation underwent eight weeks of mindful attention training, practicing for around four hours each week. Before the training, they got fMRIs, scans that show where brain activity is occurring.

While they were in the MRI scanner, they viewed a series of pictures, some of which were upsetting (like a photo of a burn victim). After eight weeks of mindfulness meditation, when they viewed the upsetting pictures in the scanner again, they showed reduced activity in a crucial brain region: the amygdala.

The amygdala is our brain’s threat detector. It scans our environment for danger, and when it perceives a threat, it sets off our fight-flight-freeze response, which includes releasing stress hormones like cortisol and adrenaline. It glues our attention to the threat, making it hard for us to focus on anything else…

Read more…..

Source: Meditation can better the brain. Are we morally obligated to meditate? – Vox

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Related contents:

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Meditation Could Be as Effective for Anxiety as Medication, Study Says Psychology Today

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Brain Drain: Scientists Look at Why Mental Exertion Triggers Exhaustion

It’s a familiar feeling on a Friday evening. After finishing a gruelling day’s work, you finally agree with friends on where to meet for a night out. But by the time you have figured out what to wear and where you left your keys, a night on the sofa begins to sound more appealing than one on the tiles.

Now, scientists think they may be able to explain why you feel so weary before you have even reached the bus stop: your brain has slowed down to manage the strain. The brain could suffer from something similar to the painful buildup of lactate in muscles during physical exercise. This could be why hard mental yards – and resisting the temptation to give up throughout the day – feel equally taxing.

Prolonged mental activity leads to the accumulation of a potentially toxic neurotransmitter in the prefrontal cortex, according to a study published in Current Biology. The researchers suggest the brain slows down its activity to manage the buildup, offering an explanation to why we feel tired.

“Even when you resist scratching an itch, for example, your brain is exerting cognitive control,” said Antonius Wiehler of the Paris Brain Institute, the first author of the study. Repeated demands on cognitive control functions can lead to fatigue, he said. The prefrontal cortex is the region of decision-making and cognitive control, which is applied when the brain overrides an impulse or fights any kind of temptation.

The team monitored the brain chemistry of 40 participants while they completed repetitive tasks on a computer. They formed two groups, who performed either hard tasks or easy tasks for over six hours. The researchers measured levels of a neurotransmitter in the prefrontal cortex. They found a greater accumulation of glutamate in participants who were given the harder tasks.

Work that involves a lot of thinking requires the brain to repeatedly resist the temptation to do something less demanding. Unsurprisingly, this can leave people feeling tired, but the brain chemistry behind it has remained unclear. Now, researchers suggest cognitive control may lead to the accumulation of glutamate in the brain – of which high levels can be harmful because it overexcites neural cells.

“We found that glutamate was accumulating in the region of the brain which controls the tasks we set participants,” said Wiehler. “Our understanding is that the brain has some kind of clearance mechanism to counteract this, which may slow down activity.” The researchers posit that mental fatigue could be linked to recycling the glutamate that builds up during neural activity. “The accumulated glutamate needs to be cleared away, which we think is likely happening during sleep,” said Wiehler.

When participants were asked to report their level of fatigue, no definitive link between glutamate and fatigue was found – the groups performing hard and easy tasks recorded the same tiredness. Researchers said this could be due to fatigue being subjective, and those doing the easy task were unaware of the difficulty of the other.

“The fact that glutamate levels don’t track the reported fatigue is slightly disappointing, but not surprising because there is often a dissociation between biological features and self-reported fatigue,” said Dr Anna Kuppuswamy from the Institute of Neurology at University College London, who was not involved in the study.

The researchers monitored only glutamate but suggest other related substances could be linked to fatigue. “The study measures a single neurotransmitter in a very specific part of the brain, so we have to look at it more globally,” said Kuppuswamy.

But the results were encouraging, she added. “We know that during physical exercise lactate accumulates in the muscles, leading to muscle fatigue. It is kind of intuitive that something similar happens in the brain and this is good first evidence to suggest that.”

By:

Source: Brain drain: scientists look at why mental exertion triggers exhaustion | Neuroscience | The Guardian

Critics:

Mental exhaustion is a feeling of extreme tiredness, characterized by other feelings including apathy, cynicism, and irritability. You may be mentally exhausted if you’ve recently undergone long-term stress, find it hard to focus on tasks or lack interest in activities you usually enjoy.

Mental exhaustion often happens as a result of overuse, like physical overuse injuries. Even though you can’t point to it, it has more in common with repetitive stress injuries, like carpal tunnel or tennis elbow. Rather than overstressing a muscle group, mental and emotional exhaustion come from overstressing your mind.

Mental exhaustion is completely possible and is probably more common than it should be. After a long period of stress or time of intense emotions, mental exhaustion is bound to happen. Just like our bodies show symptoms after we push too hard, our minds are bound to display signs of mental exhaustion if we don’t take proper care.

Mental exhaustion can be caused by many things. Typically, though, people feel mentally tired after experiencing long-term stress. This is especially true if the stressors increase a person’s cognitive load or reduce their resources. 

For example, you may be responsible for completing a challenging project with many moving parts and tradeoffs. This would require a high level of project management skills and political savvy (e.g., increased load). 

Another example would be traveling for work. Constantly changing time zones would leave you feeling jet-lagged (reduced resources). Many stressors involve both reduced resources and increased load. 

Work travel to an unfamiliar country where you don’t speak the language amps up the cognitive load. Taking care of a sick family member may involve coordinating medical care and interpreting unfamiliar terms while managing emotions (increased load). But you may also be getting less sleep (reduced resources). Over time, increased responsibility and stress plus poor self-care can result in mental exhaustion.

Though a wide variety of stressors can cause you to feel mentally drained, we’ve boiled down the 7 most common causes of mental exhaustion below. 

1. Chronic stress

This is the most frequent cause of mental exhaustion. Chronic stress keeps your brain — and body — on high alert all the time. Over time, this begins to wear away at your well-being. Chronic stress can also lead to empathy or compassion fatigue. It can become difficult to muster an emotional response to the constant strain.

2. Uncertainty

The human stress response was designed to work efficiently in the face of short-term stress (think fight-or-flight). However, it’s a much less effective response to a constant, nagging feeling of uncertainty. Unfortunately, uncertainty has become far too normal of a feeling since the start of COVID-19 pandemic. This has made mental exhaustion more common than ever. 

3. Work stress

Stress at work can take many forms. It can arise from a values mismatch, difficulty managing tasks and priorities, or a high-demand, risk-oriented job. Some jobs (or programs of study) involve a lot of new learning. They could also require processing and making sense of a lot of information. Whatever the reason, it’s not always possible to leave work at work. Left unchecked, workplace stress can even evolve into burnout. Your work stress could bleed into your weekends and ultimately, develop into a bad case of the Sunday scaries

4. Family issues

Few things are more stressful than worrying about a family member. Being a caregiver for young children, sick relatives, or aging parents can be mentally taxing. Even if everyone’s healthy, families can bring all kinds of stressors. Divorce, disagreements, and estrangements have a way of following you into all areas of your life. Ultimately, family troubles can be a big cause of mental exhaustion.

5. Juggling multiple commitments

In addition to caring for family, many people have other commitments on their plate — those commitments come with details, schedules, logistics, and challenges. Balancing an intensive school or training program, a second job, or a freelance business can leave you feeling like you’re never “off.” If you’re not able to, or don’t know how to, manage your priorities, you’re at risk of becoming mentally drained.

6. Emotional stress

There are dozens of things that can cause emotional stress. No matter the cause, the experience is similar. Constant negative feelings, events, and circumstances can make it difficult to relax. This emotional exhaustion can quickly lead to mental fatigue.

7. Poor self-care

Without gas in the tank, you won’t get very far. When you get busy or you’re feeling a bit down, it’s easy to neglect self-care. However, over time this will affect your ability to be resilient in the face of stressful situations. If you have a chronic illness, like multiple sclerosis or chronic fatigue syndrome, brain fog may be a side effect. Rather than pushing through, it’s important to be especially diligent about self-care.

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Desk Work Hack: Boost Productivity With Frequent Breaks

Humans are not designed to sit for long periods. Doing so can distort the spine, strain muscles, and drain energy levels. A good ergonomic chair can reverse these effects by supporting healthy sitting postures. Adding frequent short breaks can take things to another level.

Learn how microbreaks can supercharge the effects of a healthy ergonomic sitting routine.Integrating regular breaks into a deskwork routine yields three important benefits. First, frequent walking breaks help to ward off the dangers of sedentary behavior. Second, the human brain can only process complex tasks in short bursts.

If you push your brain beyond its limit, both focus and performance degrade.Third, downtime is when the brain does its deepest data processing. By stepping away from a task, your brain gets the time it needs to digest its previous work. Working against these realities makes full-time sitting a drag. By the end of a workday, you will likely feel stiff, sore, and mentally fried.In contrast, working in sync with these principles yields tremendous benefits:

  1. Get more work done in shorter bursts of intense activity.
  2. Physical activity energizes both the body and the mind.
  3. Mental downtime is when the brain’s problem-solving skills work best.

Benefits of Frequent Work Breaks

This section explains how physical and mental efficiency declines when pushed too hard. By taking advantage of this biological reality, desk workers can boost their health and productivity — with less time spent sitting!

Movement boosts physical and mental wellness

A good ergonomic chair will keep your spine in alignment while sitting for long periods. That lessens the physical stress caused by sitting. Even so, it doesn’t change the fact that humans are not designed for sitting! Sitting for long periods underworks muscles, making them weaker over time. Research has also linked extended sitting with increased blood pressure, high blood sugar, and fat buildup around the waist.

bloodpressure

positions

spinalcord

vertebraFor long periods, the ergonomic solution is to sit in dynamic neutral positions. The neutral aspect looks the same in all types of ergonomic chairs. Sit with your feet planted, your lower back supported, and your head balanced evenly above the shoulders.The dynamic aspect involves moving while you sit. Neutral posture combined with small position changes is called ‘active’ or ‘dynamic’ sitting.This tactic engages back, leg, and abdominal muscles while you sit. It yields plenty of benefits:

  • Better spinal positioning with less pressure on the vertebrae.
  • Recurring core muscle contractions burn more fat tissue.
  • Increased control and awareness of your body’s position.

Going beyond seated movement

Moving while sitting keeps muscles active. To take this concept to the next level, simply get out of your chair and move your body. That could mean taking a walk, grabbing a drink, or even doing some quick stretches. Esports Physical Therapist Dr. Joshua Lee shared the benefits of mini-exercise breaks with ChairsFX. “The body craves movement. Short rest breaks with exercises are like little snacks. Your body can use these throughout a gaming session to keep you energized.”

Gateway to a Massive Brain Boost

If you suffer fatigue while sitting, test this concept. Get up from your chair and walk around. That will stimulate core muscles and improve circulation. At the same time, stepping away from your task switches the brain from a focused mode to a dreamy, diffused one. That gives it the time it needs to process and store its most recent work. As a result, when you sit down, you’ll feel more energetic, focused, and mentally prepared to handle your desk work challenges.

Mental downtime supercharges cognition

The brain is a voracious energy drain that is never idle. It functions in two operating modes: focused, and ‘diffused’. In diffused mode, it demands 20% of all energy the body produces. In focused mode, power demands only go up by 5-10%. The diffused mode puts the brain in a more relaxed, dreamlike state. This mode swivels powers of reflection away from the external world toward the self. Mental downtime is when the brain can process information.Next time you stumble with a challenging problem, put this to the test. Take a break, wander around, and let your brain find a solution in its diffused state. It works!

Breaks enhances info processing

Matthew Walker is a UC Berkeley psychologist and sleep researcher. His studies show that fact-based memories are first stored in the hippocampus. During downtime, that information goes to the brain’s prefrontal cortex, which has more storage space.Dr. Walker likens the process to an email system. When the email inbox in your hippocampus is full, the brain needs downtime to clear out the emails. Until then, new information will bounce when trying to enter the hippocampus.

Power-naps work well

In the work-from-home era, adding short naps is also a potent option. Studies show that daytime naps help to sharpen concentration and accelerate processing. TCM expert Nan Lu, also endorses the power of daytime naps. As the body relaxes, so will the mind. When the mind relaxes, Qi (internal energy) can flow.

Breaks restore focus on long-term goals

Many middle managers equate staff sitting at their desks with ‘productivity’. In fact, the opposite is true! The average goldfish has an attention span of nine seconds. In the smarthphone era, the average human has an attention span of eight seconds. That is because the brain is not designed for extended focus on one thing.On top of that, everything you do throughout a workday subtracts from your cognitive resources. In fact, the brain regards constant stimulation as unimportant, so it erases such from awareness.For instance, most people aren’t aware of the sensation of clothing touching the skin. As the body becomes habituated, the stimulus stops registering in the brain.

Solve Complex Problems By Disengaging

When you start to lose focus at your desk, consider it a sign to take a short break. Doing so will boost your focus and energy levels. Disengaging also gives a better sense of the big picture. Then, it becomes easier to see a broader view.For example, a Stanford study looked at people facing mental challenges needing imagination to solve. It found that walking yielded more creative solutions than sitting.In summary, another benefit of taking breaks is that it lets you disengage from an immediate task. That gives your brain time to process information. It also puts your mind in a diffused state that yields a clearer view of big-picture goals.

Micro-break Integration Methods

If you’re new to the concept of taking frequent breaks, here are two easy methods to help you get started:

Pomodoro method

One of the most popular methods is the super-simple Pomodoro method. One 25-minute work session plus a 5-minute break equals one Pomodoro.

  1. Set a timer for 25 minutes.
  2. When the timer goes off, take a 5-minute break.
  3. After four sessions, take a longer 30-minute break.

90-minute solution

Working in 90-minute intervals syncs with our body’s natural rhythms. Fifty years ago, pioneering sleep researcher Nathan Kleitman documented the “basic rest-activity cycle“.This cycle describes 90-minute periods at night where humans move through five stages of sleep. Kleitman found that our bodies operate by the same 90-minute rhythms during the day.

During waking hours, stages shift from higher to lower alertness. Other researchers call this our “ultradian rhythm.” The gist is to work in 90-minute blocks and then take a break.An alternative is to break when signs of fatigue emerge. When we need rest, our bodies show symptoms.

These include hunger, drowsiness, fidgeting, and a loss of focus.To override these symptoms, many people use caffeine or sugary foods. Some even rely on stress hormones like cortisol and adrenaline to “power through”. Using the 90-minute solution provides a healthier option that yields more effective results.

Peak performance in 90-minute Blocks

A famous 1993 study of young violinists backs up the 90-minute method. It found that the best violinists all practiced the same way. Each worked in three increments of no more than 90 minutes each.The study found similar patterns among high-performing musicians, writers, chess players, and athletes. In brief:

  1. Take a break every 90 minutes for a fast and easy energy boost.
  2. If you’re feeling drowsy before the 90-minute mark, take a break anyway.

ChairsFX method

Five years ago, I switched from a cheap office chair to a gaming chair. It took me around a month to master healthy neutral sitting habits. That yielded a cascade of wellness benefits.

  1. Using a gaming chair helped me to improve my posture.
  2. With improved posture, I gained more energy, which I expended at the gym.
  3. With improved posture and health, my productivity skyrocketed.

These days, I take a walking break every time I finish a complex task. Sometimes that takes an hour; sometimes it takes 10 minutes. In general, I spend around 15 minutes of every hour walking around.Here are the highlights of my own desk productivity recipe:

Establish good feng shui

Feng shui is a 3000-year-old Chinese art that means “wind water”. Feng shui design is the arrangement of indoor spaces to achieve harmony and balance. Doing so maximizes the flow of positive energy into a space. The point is to increase the positive energy in a room to make its inhabitants happier. For purists, there are thousands of details to consider. For desk workers seeking a productivity edge, stick with the basics:

  1. Clean your office thoroughly before and after work.
  2. Keep your desk clear of clutter.
  3. Your desk should face towards the room’s main entrance.
  4. Working directly in front of or behind a window will drain your energy.
  5. Keep windows open to ensure that fresh air flows into the room. Add plants for more air cleaning power.
  6. If outside noises are a distraction, override them with white noise or nature sounds.
  7. Assemble furnishings that achieve a balance of fire, earth, metal, water, and wood elements.

Following these steps will ensure a clean, welcoming room flowing with positive energy. To learn more, check out our home office setup guide:

Adopt healthy sitting habits

Sitting with poor posture stresses the spine and forces muscles to work harder. From a cognitive perspective, sitting in a powerless, crouched position also stimulates hopelessness. That makes the brain more likely to recall depressive thoughts. Harvard Prof. Amy Cuddy says this has biological roots tracing back to the animal kingdom.

Among all species, body language reflects submission or dominance. When the body curls into a submissive pose, cognitive performance also degrades.In comparison, sitting with good posture relieves back muscles and boosts energy levels. As a result, sitting this way makes people more alert, engaged, and confident.

Take regular breaks

One component of an effective break is psychological detachment. That means mentally disengaging from work thoughts. Another key is to embrace positive thoughts while disengaged. That reverses the negative effects of work tasks. It also increases blood flow to the areas of the brain that we use for focus.By playing around with these concepts, you can develop a custom routine tailored to your needs. These days, my method of break-taking is flexible. Whenever I feel the need, I get out of my chair and move my body. Here is a summary of my approach:

  1. Break complex work down into chunks. Work through each piece from the most difficult to the easiest. An average chunk should take between 10 to 20 minutes.
  2. Take a break after completing each chunk of work. Alternatively, take a break whenever you start to lose focus.
  3. Disengage from the internet. Leave your phone at your desk. Walk with a purpose towards a drink, fresh air, yoga mat, etc.
  4. Forget about work and focus on positive, healthy sensations. For example, listen to birds chirping, or walk barefoot on grass.
  5. Return to your desk and settle in. Then, use your clear mind and excess energy to power through another chunk of work.

Conclusion

In the work-from-home era, the concept of taking many breaks through a workday makes sense. With discipline, arranging your work into chunks can yield incredible results. For one thing, working in short bursts with a primed brain will deliver more efficient production.For another, regular disengagement from the details helps you to see a project from micro and macro perspectives.

As well, regular movement will keep your body and mind feeling vibrant, focused, and alert. Start your own healthy home office routine with good feng shui, healthy sitting habits, and a good ergonomic chair. Then, make the most of your setup by mixing frequent short breaks into your routine.Doing so will help you get more work done with less sitting time. On top of that, it will help you to maintain a lithe, lean physique that takes your well-being to a higher level of bliss.

By: Source: Benefits of Micro-Breaks For Desk Workers | ChairsFX.Related ArticlesPeer-Reviewed Guidelines For Healthy SittingLumbar Support Biomechanics: The Key To Sitting StraightWhy Gaming Chairs Are Good For Your Back

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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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