Crazy dreams help us make sense of our memories
A new theory suggests that dreams' illogical logic has an important purpose.
17 November, 2020
Credit: Paul Fleet/Adobe Stock
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<p>For a while now, the leading theory about what we're doing when we dream is that we're sorting through our experiences of the last day or so, consolidating some stuff into memories for long-term storage, and discarding the rest. That doesn't explain, though, why our dreams are so often so exquisitely weird.</p><p>A new theory proposes our brains toss in all that crazy as a way of helping us process our daily experiences, much in the way that programmers add unrelated, random-ish nonsense, or "noise," into machine learning data sets to help computers discern useful, predictive patterns in the data they're fed.</p>
Overfitting
<p>The goal of machine learning is to supply an algorithm with a data set, a "training set," in which patterns can be recognized and from which predictions that apply to other unseen data sets can be derived.</p><p>If machine learning learns its training set too well, it merely spits out a prediction that precisely — and uselessly — matches that data instead of underlying patterns within it that could serve as predictions likely to be true of other thus-far unseen data. In such a case, the algorithm describes what the data set <em>is</em> rather than what it <em>means</em>. This is called "overfitting."</p><img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNDc4NTQ4Ni9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTY2NDM4NDk1Mn0.bMHbBbt7Nz0vmmQ8fdBKaO-Ycpme5eOCxbjPLEHq9XQ/img.jpg?width=980" id="5049a" class="rm-shortcode" data-rm-shortcode-id="f9a6823125e01f4d69ce13d1eef84486" data-rm-shortcode-name="rebelmouse-image" />Big Think
The value of noise
<p>To keep machine learning from becoming too fixated on the specific data points in the set being analyzed, programmers may introduce extra, unrelated data as noise or corrupted inputs that are less self-similar than the real data being analyzed.</p><p>This noise typically has nothing to do with the project at hand. It's there, metaphorically speaking, to "distract" and even confuse the algorithm, forcing it to step back a bit to a vantage point at which patterns in the data may be more readily perceived and not drawn from the specific details within the data set.</p><p>Unfortunately, overfitting also occurs a lot in the real world as people race to draw conclusions from insufficient data points — xkcd has a fun example of how this can happen with <a href="https://xkcd.com/1122/" target="_blank">election "facts."</a></p><p>(In machine learning, there's also "underfitting," where an algorithm is too simple to track enough aspects of the data set to glean its patterns.)</p><img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNDc4NTQ5My9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYyMDE5NjY1M30.iS2bq7WEQLeS34zNFPnXwzAZZn9blCyI-KVuXmcHI6o/img.jpg?width=980" id="cd486" class="rm-shortcode" data-rm-shortcode-id="c49cfbbffceb00e3f37f00e0fef859d9" data-rm-shortcode-name="rebelmouse-image" />Credit: agsandrew/Adobe Stock
Nightly noise
<p>There remains a lot we don't know about how much storage space our noggins contain. However, it's obvious that if the brain remembered absolutely everything we experienced in every detail, that would be an awful lot to remember. So it seems the brain consolidates experiences as we dream. To do this, it must make sense of them. It must have a system for figuring out what's important enough to remember and what's unimportant enough to forget rather than just dumping the whole thing into our long-term memory.</p><p>Performing such a wholesale dump would be an awful lot like overfitting: simply documenting what we've experienced without sorting through it to ascertain its meaning.</p><p>This is where the new theory, the <a href="https://arxiv.org/pdf/2007.09560.pdf" target="_blank">Overfitting Brain Hypothesis</a> (OBH) proposed by Erik Hoel of Tufts University, comes in. Suggesting that perhaps the brain's sleeping analysis of experiences is akin to machine learning, he proposes that the illogical narratives in dreams are the biological equivalent of the noise programmers inject into algorithms to keep them from overfitting their data. He says that this may supply just enough off-pattern nonsense to force our brains to see the forest and not the trees in our daily data, our experiences.</p><p>Our experiences, of course, are delivered to us as sensory input, so Hoel suggests that dreams are sensory-input noise, biologically-realistic noise injection with a narrative twist:</p><p style="margin-left: 20px;">"Specifically, there is good evidence that dreams are based on the stochastic percolation of signals through the hierarchical structure of the cortex, activating the default-mode network. Note that there is growing evidence that most of these signals originate in a top-down manner, meaning that the 'corrupted inputs' will bear statistical similarities to the models and representations of the brain. In other words, they are derived from a stochastic exploration of the hierarchical structure of the brain. This leads to the kind structured hallucinations that are common during dreams."</p><p>Put plainly, our dreams are just realistic enough to engross us and carry us along, but are just different enough from our experiences —our "training set" — to effectively serve as noise.</p><p>It's an interesting theory.</p><p>Obviously, we don't know the extent to which our biological mental process actually resemble the comparatively simpler, man-made machine learning. Still, the OBH is worth thinking about, maybe at least more worth thinking about than whatever <em>that</em> was last night.</p>
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Objects in lucid dreams are perceived as real, study discovers
It's all about smooth pursuit.
19 September, 2018
Man inside an ice caver under the Vatnajokull glacier, Vatnajokull National Park, East Iceland, Iceland. Photo by Marco Bottigelli / Getty Images
- While lucid dreaming, we use the same eye movement patterns as when we observe physical actions.
- However, we use different eye patterns when we imagine movement.
- Researchers believe this might help add to our understanding of consciousness.
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<p>In 2016, Dr. Tadas Stumbrys discovered that lucid dreaming helps <a href="https://bigthink.com/21st-century-spirituality/lucid-dreaming" target="_blank">improve physical performance</a>. Dividing volunteers into four groups, lucid dreamers outperformed the physical practice and mental rehearsal groups by statistically significant margins. As he said at the time, "A recent brain imaging study showed that brain activity in the sensorimotor cortex that is responsible for controlling our physical movements is similar during imagined and lucidly dreamed movement, thereby allowing motor learning to occur."</p><blockquote style="margin-left: 20px;"></blockquote><p>We've long known that imagining movements are their own sort of "practice," but an interesting <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6098118/" target="_blank">new study</a>, published in <em>Nature Communications</em>, confirms that we use the same eye movement patterns when we lucid dream as when we observe physically actions, but <em>not</em> when we imagine movement. </p><p>Three researchers — Stanford University's Philip Zimbardo and Stephen LaBerge; the University of Wisconsin-Madison's Benjamin Baird — tackled the longtime question of whether dreaming mimics perception or imagination, finally proving the former. They accomplished this by tracking the "smooth pursuit" eye movements of seven volunteers, each of whom spent between one and eight nights in their laboratory. </p>
<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8xODYzNzI2Mi9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYzNjYyODIzN30.j-G-h_tvTUMb0kM11w7lVY8WOgt79ma85V8l7KZc0LQ/img.jpg?width=1245&coordinates=0%2C98%2C0%2C98&height=700" id="2441b" class="rm-shortcode" data-rm-shortcode-id="0739ee54a2b5919bb8a144f6a20c2f9e" data-rm-shortcode-name="rebelmouse-image" />
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<p>To understand smooth pursuit you can <a href="https://blogs.scientificamerican.com/illusion-chasers/what-lucid-dreams-look-like/" target="_blank">try this experiment</a>: Track your index finger, held out at arm's length, from left to right several times. Your eyes follow the pattern in a reliably smooth pattern. On the contrary, when you try to imagine the same exact movement, your eyes will not flow smoothly left to right, but jump ahead to particular points. This is due to your saccadic system, a name derived from the French word for "jolt." In both lucid dreaming and awakened perception, your smooth pursuit system is engaged. </p><p>The researchers write that vividness relies on intensity of neural activation. Imagining images compete with our normal sensory process, but when asleep our sensory input system — absorbing the world around us — is suppressed. External objects that could bombard our perception processes are eliminated. They continue, </p><blockquote>Our findings suggest that, in this respect, the visual imagery that occurs during REM sleep is more similar to perception than imagination. . . . Under conditions of low levels of competing sensory input and high levels of activation in extrastriate visual cortices (conditions associated with REM sleep), the intensity of neural activation underlying the imagery of visual motion (and therefore its vividness) is able to reach levels typically only associated with waking perception.</blockquote><span style="display:block;position:relative;padding-top:56.25%;" class="rm-shortcode" data-rm-shortcode-id="b2affe55c0c0bb9b5772dcb01df10c42"><iframe type="lazy-iframe" data-runner-src="https://www.youtube.com/embed/h7eYt4ZmkR8?rel=0" width="100%" height="auto" frameborder="0" scrolling="no" style="position:absolute;top:0;left:0;width:100%;height:100%;"></iframe></span><p>Beyond solving a longtime debate tracing back to Aristotle, the researchers believe this research is another piece in helping solve the consciousness puzzle. Seasoned lucid dreamers — it's a skill <a href="https://bigthink.com/21st-century-spirituality/new-study-confirms-three-methods-for-controlling-your-dreams" target="_blank">you can actually practice</a> — have the ability to control their actions while unconscious. This link between consciousness and neurophysiological processes adds another intriguing layer to the link between our waking and sleeping lives. The more we study that link, the more we find out that the two states are not as separate as imagined.</p><p><em>--</em></p><p><em>Stay in touch with Derek on <a href="http://www.twitter.com/derekberes" target="_blank">Twitter</a> and <a href="https://www.facebook.com/DerekBeresdotcom" target="_blank">Facebook</a>.</em></p>
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The range of dream states are just as complex as waking states.
The post Our Dreams Have Many Purposes appeared first on ORBITER.
Practicing Skills In Your Sleep Can Be as Effective as Physical Training
Just imagining movement fires the same neurons as if we were actually moving. A new study shows we can wake our sleeping mind to practice motor skills in our dreams.
23 October, 2016
Lucid dreaming. (Image: Shutterstock)
Mental training is arguably as important as physical fitness. That argument is gaining strength as a growing body of literature unravels the once-mysterious connections between consciousness and movement. We know that the murky domain of subconscious and autonomic actions greatly influences our waking lives. Now we’re learning how to train our unconscious selves for the benefit of our daily actions.
<p>Neuroscientist Rodolfo Llinás <a href="https://mitpress.mit.edu/books/i-vortex" target="_blank">attributes this skill</a> to the art of prediction. Before consciousness arose in multicellular creatures, primitive life developed a nervous system to navigate through its environment—a biological property he terms ‘motricity.’ By predicting where it has to move the organism ensures its survival.</p> <p>Interestingly, Llinás noticed that thinking fires motor neurons, the pathway we use to move our bodies. He believes thinking is an internalized form of movement; what we call consciousness is a mental representation of this phenomenon. Our mental maps allow us to predict how to navigate our environment. Combined with memory, our inner GPS creates and constantly updates this road map of prediction: move here, don’t go there, act this way but not like that.</p> <p>Athletes have understood this for some time: visually picture yourself performing your sport and you strengthen the motor connections between brain and body. Indeed, just picturing movement fires the same neurons as if you were doing it. Motricity meets metacognition.</p> <p>This we’ve known for some time. But what about training while asleep? We know proper rest leads to better performance. Plenty of evidence proves the all-night cram to be a myth. A better time to retain information is after cardiovascular exercise. This makes sense. We’re designed for movement. Hunched over books all night without sleep is not an optimal training program. Eight hours of shuteye followed by a quick 5k is.</p> <p>What’s happening during those eight hours, though? More to the point, can we use that time for training as well?</p> <p>Turns out we can. Dr. Tadas Stumbrys <a href="http://brainblogger.com/2016/09/08/can-you-improve-physical-skills-while-dreaming/" target="_blank">decided to investigate</a> whether sleep training had any positive correlation with physical performance. The vehicle he used was lucid dreaming, a unique mental state in which consciousness and the unconscious seemingly merge. While some people naturally acquire this skill, there are training methods to help you learn to take control of your dreams, affording you a Matrix-style cinema screen inside your head every night.</p> <p>Stumbyrs and team had four groups—“frequent lucid dreamers (25%), a mental practice group (23%), a physical practice group (24%) and a control (no practice) group (24%)”—engage in a sequential finger tapping exercise. Alarms rung in the night to signify the commencement of their assigned practice. They were measured the following day for improvements.</p> <p>In the end all three practice groups improved their physical skill. Interestingly, the lucid dreaming group improved more than the physical practice group by 3 percent and the mental rehearsal group by 8 percent.</p> <p>The neural mechanisms responsible for physical movement are similar between the three states of consciousness studied: waking, lucid dreaming, and daydreaming:</p> <blockquote>
<p>A recent brain imaging study showed that brain activity in the sensorimotor cortex that is responsible for controlling our physical movements is similar during imagined and lucidly dreamed movement, thereby allowing motor learning to occur.</p>
</blockquote> <p>In his book, <em>Waking, Dreaming, Being</em>, philosophy professor Evan Thompson investigates consciousness through the lens of lucid dreaming, a topic he’s studied for decades. Dreaming isn’t random, he writes, but a “spontaneous mental simulation” that helps us imagine ourselves in the world. While awake we envision a reality we’d like to create and then pursue its manifestation—metacognition and motricity. This is also possible when you know you’re dreaming.</p> <p>Borrowing from Buddhist philosophy, Thompson writes that the key is to understand “witnessing awareness,” which is essentially what we do while conscious and unconscious. This is the process of the little me inside my head picturing the physical me in the ‘real’ world. Just don’t confuse this for dualism. This is all one me.</p> <p>One way to strengthen this inner dialogue of thought and action, of perception, prediction, and movement, is meditation. This discipline of self-awareness helps cultivate an ability to step back from daily patterns and envision yourself as part of the larger world, which changes your relationship to your actions. The same holds true while lucid dreaming.</p> <p>Once you’re able to sustain the lucid dream state, he writes, you can transform it:</p> <blockquote>
<p>Use your imagination to manipulate the dream. Be playful. Change things and transform them…Explore the plasticity of the dream. In this way, the mind’s supple nature will manifest, and you’ll gain a deeper understanding of the dreamscape as a mental construct, a product of imagination.</p>
</blockquote> <p>As the study shows, the imagination is the thoroughfare for the manifestation of ideas into reality while asleep <em>and</em> awake. Envisioning the goal is the first step in physically acting on it. All life is movement, from the neuronal firings of an idea to our bodies moving through space to accomplish it. Lucid dreaming turns out to be yet another field in which to sharpen our skills, another step in this complex and fascinating journey of self-understanding and self-mastery.</p> <div class="video-full-card-placeholder" data-slug="big-think-interview-with-shelby-harris" style="border: 1px solid #ccc;">
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</div> <p>--</p> <p>Derek Beres is working on his new book, <em><a href="https://www.amazon.com/Whole-Motion-Training-Optimal-Health/dp/1631440721/ref=sr_1_1?ie=UTF8&qid=1476996488&sr=8-1&keywords=whole+motion" target="_blank">Whole Motion: Training Your Brain and Body For Optimal Health</a></em> (Carrel/Skyhorse, Spring 2017). He is based in Los Angeles. Stay in touch on <a href="https://www.facebook.com/DerekBeresdotcom" target="_blank">Facebook</a> and <a href="http://www.twitter.com/derekberes" target="_blank">Twitter</a>.</p>
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What Do a Robot's Dreams Look Like? Google Found Out
They may look odd, but it’s all part of Google’s plan to solve a huge issue in machine learning: recognizing objects in images.
18 October, 2016
Google's artificial neural networks produce some trippy images thanks to its Deep Dream program (photo credit: Michael Tyka/Google)
When Google asked its neural network to dream, the machine begin to generating some pretty wild images. They may look odd, but it’s all part of Google’s plan to solve a huge issue in machine learning: recognizing objects in images.
To be clear, Google’s software engineers didn’t ask a computer to dream, but they did ask its neural network to alter the images based on an original photo they fed into it, by applying layers. This was all part of their Deep Dream program.
The purpose was to make it better at finding patterns, which computers are none too good at. So, engineers started by “teaching” the neural network to recognize certain objects by giving it 1.2 million images, complete with object classifications the computer could understand.
These classifications allowed Google’s AI to learn to detect the different qualities of certain objects in an image, like a dog and a fork. But Google’s engineers wanted to go one step further, which is where Deep Dream comes in, which allowed the neural network to add those hallucinogenic qualities to images.
<p><img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8xODQwODUyNC9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYxOTg2NzE1NX0.7tSbcSewsmAEB3P1iNy-GK2ldYWEADF79AX2EcaKFzg/img.jpg?width=980" id="eb01e" class="rm-shortcode" data-rm-shortcode-id="31450292a3bc90ae1e70b193e7db3460" data-rm-shortcode-name="rebelmouse-image"><br><br>Google wanted to make its neural network better at detection to the point where it could pick out other objects in an image that may not contain that object (think of it as seeing the outline of a dog in the clouds). Deep Dream gave the computer the ability to change the rules and parameters of the images, which in turn allowed Google’s AI to recognize objects the images didn’t necessarily contain. So, an image might contain an image of a foot, but when it examined a few pixels of that image, it may have seen the outline of what looked like a dog’s nose. <br><br>So, when researchers began to ask its neural network to tell them what other objects they might be able to see in an image of a mountain, tree, or plant, it came up with these interpretations:<br><br><img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8xODQwODUyNS9vcmlnaW4ucG5nIiwiZXhwaXJlc19hdCI6MTYxOTA1NjkwMX0.Il_qt54jJhjT735wcfnpXwDC5-CH_zqLh5cPQ1M2ofc/img.png?width=980" id="b6106" class="rm-shortcode" data-rm-shortcode-id="77ec0e5c396700a47391f3894ec48319" data-rm-shortcode-name="rebelmouse-image"><br><em>(Photo Credit: Michael Tyka/Google)</em></p> <p>“The techniques presented here help us understand and visualize how neural networks are able to carry out difficult classification tasks, improve network architecture, and check what the network has learned during training,” software engineers Alexander Mordvintsev and Christopher Olah, and intern Mike Tyka <a href="https://research.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html" target="_blank">wrote in a post about Deep Dream</a>. “It also makes us wonder whether <strong>neural networks could </strong>become a tool for artists—a new way to remix visual concepts—or perhaps even <strong>shed a little light on the roots of the creative process in general.”</strong></p> <p>Just for fun, Google has opened up the tool to the public and you can generate your own Deep Dream art here: <a href="http://deepdreamgenerator.com/" target="_blank">deepdreamgenerator.com</a></p> <p><strong> </strong></p> <div class="video-full-card-placeholder" data-slug="the-future-of-machine-learning" style="border: 1px solid #ccc;">
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