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Don't worry about making a mistake. It's how we learn.
A new study at UPenn found that effective learning includes mistakes—just not too many.
- Humans learn best when avoiding too much complexity and getting the gist of situations, according to a new study by researchers at the University of Pennsylvania.
- Instead of remember every detail, we learn by categorizing situations through pattern recognition.
- We wouldn't retain much if we considered a high level of complexity with every piece of information.
Humans learn in patterns. Take a bush that you pass every day. It's not particularly attractive; it just happens to exist along your normal route. One day you notice a brownish tail sticking out of one side. A nose pops out of the other side. The bush happens to be roughly the size of a tiger. The only thought you have is run.
You didn't need to see the entire tiger to get out of there. Enough of a pattern had emerged for you to get the gist.
Getting the gist is how we learn, according to a new study by researchers at the University of Pennsylvania. Published in Nature Communications, the paper looks at the balance between simplicity and complexity. Human learning falls somewhere in the middle of this spectrum: enough to get an idea, not enough to avoid mistakes. Mistakes are an integral aspect of learning.
The team, consisting of physics Ph.D. student Christopher Lynn, neuroscience Ph.D. student Ari Kahn, and professor Danielle Bassett, recruited 360 volunteers. Each participant stared at five grey squares on a computer screen, with every square corresponding to a keyboard key. Two squares simultaneously turned red. Participants were asked to tap the corresponding keys every time this happened.
While volunteers suspected the color changes were random, the researchers knew better. The sequences were generated using one of two networks: a modular network and a lattice network. Though nearly identical at a small scale, the patterns produced appear different from a macro level. Lynn explains why this matters:
"A computer would not care about this difference in large-scale structure, but it's being picked up by the brain. Subjects could better understand the modular network's underlying structure and anticipate the upcoming image."
The Science of Learning: How to Turn Information into Intelligence | Barbara Oakley
Comparing a human brain to a computer is inaccurate, they say. Computers understand information on a micro level. Every tiny detail matters. One errant symbol in one line of code can bring down an entire network. Humans learn by staring at the forest, not the trees. This allows us to avoid complexity, which is important if the goal is to understand a lot of information. It also means we're going to make mistakes. As Kahn phrases it,
"Understanding structure, or how these elements relate to one another, can emerge from an imperfect encoding of the information. If someone were perfectly able to encode all of the incoming information, they wouldn't necessarily understand the same kind of grouping of experiences that they do if there's a little bit of fuzziness to it."
Recognizing that something is like something else is a major reason we can consume so much data. In cognitive psychology this categorization process is known as chunking: individual pieces of data broken down and grouped together to form a whole. It is a highly efficient process that also leaves us prone to errors.
Ten percent of participants had high beta values, meaning they were extra cautious. They didn't want to make errors. Twenty percent exhibited low beta values—highly error-prone. The bulk of the group fell somewhere in-between.
Fans of a recent anti-vaccination film could be said to exhibit low-beta value. Vaccines are one of the most beneficial protective measures ever discovered. You can't actually estimate how many lives have been saved; that's not how proactive measures work. You can look at population charts, however. When vaccines were first put into clinical use there were over a billion people on the planet. That's after 350,000 years of Homo sapiens development. We're approaching eight billion people just 139 years after Louis Pasteur's vaccine experiments. (Germ theory, food distribution, antibiotics, and technology also play a role, though vaccines are relevant.)
Vaccination has never been a perfect science. As with every medical intervention, they're complex. Low-beta thinkers eschew complexity for simplicity. Many confuse a few trees for the forest. This is important during a time in which information is being weaponized to promote agendas. Sifting through complexity is exhausting; thus more people take the easiest route.
Not that learning should be too complex. As stated, only one in 10 people overly complicate their thinking. Most people sit in the middle, making mistakes while mostly getting the gist.
The researchers hope that this information will help address psychiatric conditions (such as schizophrenia) in the future. They cite the emerging field of computational psychiatry, "which uses powerful data analysis, machine learning, and artificial intelligence to tease apart the underlying factors behind extreme and unusual behaviors."
Don't get frustrated with your mistakes. We all make them. The key is to recognize them and learn from the experience. Mostly, the gist is enough.
- Jonah Lehrer on Learning From Mistakes - Big Think ›
- Why admitting mistakes is essential for personal growth. - Big Think ›
- Cognitive Science Explains Why We Keep Repeating Mistakes ... ›
Why mega-eruptions like the ones that covered North America in ash are the least of your worries.
- The supervolcano under Yellowstone produced three massive eruptions over the past few million years.
- Each eruption covered much of what is now the western United States in an ash layer several feet deep.
- The last eruption was 640,000 years ago, but that doesn't mean the next eruption is overdue.
The end of the world as we know it
Panoramic view of Yellowstone National Park
Image: Heinrich Berann for the National Park Service – public domain
Of the many freak ways to shuffle off this mortal coil – lightning strikes, shark bites, falling pianos – here's one you can safely scratch off your worry list: an outbreak of the Yellowstone supervolcano.
As the map below shows, previous eruptions at Yellowstone were so massive that the ash fall covered most of what is now the western United States. A similar event today would not only claim countless lives directly, but also create enough subsidiary disruption to kill off global civilisation as we know it. A relatively recent eruption of the Toba supervolcano in Indonesia may have come close to killing off the human species (see further below).
However, just because a scenario is grim does not mean that it is likely (insert topical political joke here). In this case, the doom mongers claiming an eruption is 'overdue' are wrong. Yellowstone is not a library book or an oil change. Just because the previous mega-eruption happened long ago doesn't mean the next one is imminent.
Ash beds of North America
Ash beds deposited by major volcanic eruptions in North America.
Image: USGS – public domain
This map shows the location of the Yellowstone plateau and the ash beds deposited by its three most recent major outbreaks, plus two other eruptions – one similarly massive, the other the most recent one in North America.
The Huckleberry Ridge eruption occurred 2.1 million years ago. It ejected 2,450 km3 (588 cubic miles) of material, making it the largest known eruption in Yellowstone's history and in fact the largest eruption in North America in the past few million years.
This is the oldest of the three most recent caldera-forming eruptions of the Yellowstone hotspot. It created the Island Park Caldera, which lies partially in Yellowstone National Park, Wyoming and westward into Idaho. Ash from this eruption covered an area from southern California to North Dakota, and southern Idaho to northern Texas.
About 1.3 million years ago, the Mesa Falls eruption ejected 280 km3 (67 cubic miles) of material and created the Henry's Fork Caldera, located in Idaho, west of Yellowstone.
It was the smallest of the three major Yellowstone eruptions, both in terms of material ejected and area covered: 'only' most of present-day Wyoming, Colorado, Kansas and Nebraska, and about half of South Dakota.
The Lava Creek eruption was the most recent major eruption of Yellowstone: about 640,000 years ago. It was the second-largest eruption in North America in the past few million years, creating the Yellowstone Caldera.
It ejected only about 1,000 km3 (240 cubic miles) of material, i.e. less than half of the Huckleberry Ridge eruption. However, its debris is spread out over a significantly wider area: basically, Huckleberry Ridge plus larger slices of both Canada and Mexico, plus most of Texas, Louisiana, Arkansas, and Missouri.
This eruption occurred about 760,000 years ago. It was centered on southern California, where it created the Long Valley Caldera, and spewed out 580 km3 (139 cubic miles) of material. This makes it North America's third-largest eruption of the past few million years.
The material ejected by this eruption is known as the Bishop ash bed, and covers the central and western parts of the Lava Creek ash bed.
Mount St Helens
The eruption of Mount St Helens in 1980 was the deadliest and most destructive volcanic event in U.S. history: it created a mile-wide crater, killed 57 people and created economic damage in the neighborhood of $1 billion.
Yet by Yellowstone standards, it was tiny: Mount St Helens only ejected 0.25 km3 (0.06 cubic miles) of material, most of the ash settling in a relatively narrow band across Washington State and Idaho. By comparison, the Lava Creek eruption left a large swathe of North America in up to two metres of debris.
The difference between quakes and faults
The volume of dense rock equivalent (DRE) ejected by the Huckleberry Ridge event dwarfs all other North American eruptions. It is itself overshadowed by the DRE ejected at the most recent eruption at Toba (present-day Indonesia). This was one of the largest known eruptions ever and a relatively recent one: only 75,000 years ago. It is thought to have caused a global volcanic winter which lasted up to a decade and may be responsible for the bottleneck in human evolution: around that time, the total human population suddenly and drastically plummeted to between 1,000 and 10,000 breeding pairs.
Image: USGS – public domain
So, what are the chances of something that massive happening anytime soon? The aforementioned mongers of doom often claim that major eruptions occur at intervals of 600,000 years and point out that the last one was 640,000 years ago. Except that (a) the first interval was about 200,000 years longer, (b) two intervals is not a lot to base a prediction on, and (c) those intervals don't really mean anything anyway. Not in the case of volcanic eruptions, at least.
Earthquakes can be 'overdue' because the stress on fault lines is built up consistently over long periods, which means quakes can be predicted with a relative degree of accuracy. But this is not how volcanoes behave. They do not accumulate magma at constant rates. And the subterranean pressure that causes the magma to erupt does not follow a schedule.
What's more, previous super-eruptions do not necessarily imply future ones. Scientists are not convinced that there ever will be another big eruption at Yellowstone. Smaller eruptions, however, are much likelier. Since the Lava Creek eruption, there have been about 30 smaller outbreaks at Yellowstone, the last lava flow being about 70,000 years ago.
As for the immediate future (give or take a century): the magma chamber beneath Yellowstone is only 5 percent to 15 percent molten. Most scientists agree that is as un-alarming as it sounds. And that its statistically more relevant to worry about death by lightning, shark, or piano.
Strange Maps #1041
Got a strange map? Let me know at email@example.com.
The potential of CRISPR technology is incredible, but the threats are too serious to ignore.
- CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) is a revolutionary technology that gives scientists the ability to alter DNA. On the one hand, this tool could mean the elimination of certain diseases. On the other, there are concerns (both ethical and practical) about its misuse and the yet-unknown consequences of such experimentation.
- "The technique could be misused in horrible ways," says counter-terrorism expert Richard A. Clarke. Clarke lists biological weapons as one of the potential threats, "Threats for which we don't have any known antidote." CRISPR co-inventor, biochemist Jennifer Doudna, echos the concern, recounting a nightmare involving the technology, eugenics, and a meeting with Adolf Hitler.
- Should this kind of tool even exist? Do the positives outweigh the potential dangers? How could something like this ever be regulated, and should it be? These questions and more are considered by Doudna, Clarke, evolutionary biologist Richard Dawkins, psychologist Steven Pinker, and physician Siddhartha Mukherjee.
Measuring a person's movements and poses, smart clothes could be used for athletic training, rehabilitation, or health-monitoring.
In recent years there have been exciting breakthroughs in wearable technologies, like smartwatches that can monitor your breathing and blood oxygen levels.
But what about a wearable that can detect how you move as you do a physical activity or play a sport, and could potentially even offer feedback on how to improve your technique?
And, as a major bonus, what if the wearable were something you'd actually already be wearing, like a shirt of a pair of socks?
That's the idea behind a new set of MIT-designed clothing that use special fibers to sense a person's movement via touch. Among other things, the researchers showed that their clothes can actually determine things like if someone is sitting, walking, or doing particular poses.
The group from MIT's Computer Science and Artificial Intelligence Lab (CSAIL) says that their clothes could be used for athletic training and rehabilitation. With patients' permission, they could even help passively monitor the health of residents in assisted-care facilities and determine if, for example, someone has fallen or is unconscious.
The researchers have developed a range of prototypes, from socks and gloves to a full vest. The team's "tactile electronics" use a mix of more typical textile fibers alongside a small amount of custom-made functional fibers that sense pressure from the person wearing the garment.
According to CSAIL graduate student Yiyue Luo, a key advantage of the team's design is that, unlike many existing wearable electronics, theirs can be incorporated into traditional large-scale clothing production. The machine-knitted tactile textiles are soft, stretchable, breathable, and can take a wide range of forms.
"Traditionally it's been hard to develop a mass-production wearable that provides high-accuracy data across a large number of sensors," says Luo, lead author on a new paper about the project that is appearing in this month's edition of Nature Electronics. "When you manufacture lots of sensor arrays, some of them will not work and some of them will work worse than others, so we developed a self-correcting mechanism that uses a self-supervised machine learning algorithm to recognize and adjust when certain sensors in the design are off-base."
The team's clothes have a range of capabilities. Their socks predict motion by looking at how different sequences of tactile footprints correlate to different poses as the user transitions from one pose to another. The full-sized vest can also detect the wearers' pose, activity, and the texture of the contacted surfaces.
The authors imagine a coach using the sensor to analyze people's postures and give suggestions on improvement. It could also be used by an experienced athlete to record their posture so that beginners can learn from them. In the long term, they even imagine that robots could be trained to learn how to do different activities using data from the wearables.
"Imagine robots that are no longer tactilely blind, and that have 'skins' that can provide tactile sensing just like we have as humans," says corresponding author Wan Shou, a postdoc at CSAIL. "Clothing with high-resolution tactile sensing opens up a lot of exciting new application areas for researchers to explore in the years to come."
The paper was co-written by MIT professors Antonio Torralba, Wojciech Matusik, and Tomás Palacios, alongside PhD students Yunzhu Li, Pratyusha Sharma, and Beichen Li; postdoc Kui Wu; and research engineer Michael Foshey.
The work was partially funded by Toyota Research Institute.