The Persian polymath and philosopher of the Islamic Golden Age teaches us about self-awareness.
A new study provides validation for the recently identified phenomenon.
- Aphantasia, a recently identified psychological phenomenon, describes when people can't conjure visualizations in their mind's eye.
- A new study published in Cortex compared the visual memories of aphantasic participants with a group of controls.
- Its results found experimental validation for the condition.
Changing our understanding of the mind's eye<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNTI2NjM0Mi9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTY0ODM2ODE5NX0.SWkNBfgO1uLsAMsetcmmwOHvJqzK1UsPMxc6tL6Je9k/img.jpg?width=1245&coordinates=0%2C228%2C0%2C228&height=700" id="609a9" class="rm-shortcode" data-rm-shortcode-id="121c211fd751fb11eba0e9aa4ec53ef0" data-rm-shortcode-name="rebelmouse-image" data-width="1245" data-height="700" />
Francis Galton was the first to describe a condition that would today be recognized as aphantasia.
Visualizing the difference<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNTI2NjMzNS9vcmlnaW4ucG5nIiwiZXhwaXJlc19hdCI6MTYzMjAyMDk3M30.EYfZH3v5DRhu4ImOjpuuXdHiXbPkgTUCOxJsTQmDYA8/img.png?width=980" id="fed74" class="rm-shortcode" data-rm-shortcode-id="eb2d7c7f78e780fe09bc6d1635cdaad5" data-rm-shortcode-name="rebelmouse-image" data-width="598" data-height="245" />
On the left, an aphantastic participant's recreation of a photo from memory. On the right, the participant's recreation when the photo was available for reference.
Discovering a new reality in aphantasia?<span style="display:block;position:relative;padding-top:56.25%;" class="rm-shortcode" data-rm-shortcode-id="cc502388d1b548118d6e587ad785fe34"><iframe type="lazy-iframe" data-runner-src="https://www.youtube.com/embed/zNHDTvqbUm4?rel=0" width="100%" height="auto" frameborder="0" scrolling="no" style="position:absolute;top:0;left:0;width:100%;height:100%;"></iframe></span><p>And Bainbridge's study has joined an ever-growing panoply. A <a href="https://www.sciencedirect.com/science/article/abs/pii/S0010945217303581" target="_blank">2018 study, also published in Cortex</a>, measured the binocular rivalry—the visual phenomenon in which awareness fluctuates when different images are presented to each eye—of participants with and without aphantasia. When primed beforehand, control participants choose the primed stimuli more often than not. Meanwhile, aphantastic participants showed no such favoritism, whether primed or not. Like Bainbridge's study, these results suggest a physiological underpinning for aphantasia.</p><p>Another critical factor is growing awareness. As more studies and stories are published, more and more people are realizing they aren't alone. Such a realization can empower others to come forward and share their experiences, which in turn spurs researchers with new questions and experiences to study and hypothesize over.</p><p>Yet, there's still much work to be done. Because this psychological phenomenon has only recently been identified—Galton's observation notwithstanding—there has been sparingly little research on the condition and what research has been done has relied on participants who self-report as having aphantasia. While researchers have used the <a href="https://en.wikipedia.org/wiki/Vividness_of_Visual_Imagery_Questionnaire" target="_blank">Vividness of Visual Imagery Quiz</a> to test for aphantasia, there is currently no universal method for diagnosing the condition. And, of course, there is the ever-vexing question of how one can assess one mind's experiences from another.</p><p>"Skeptics could claim that aphantasia is itself a mere fantasy: describing our inner lives is difficult and undoubtedly liable to error," Zeman and his co-authors wrote in <a href="https://ore.exeter.ac.uk/repository/bitstream/handle/10871/17613/Lives%20without%20imagery%20Letter%20version%20FINAL%2017.5.15%20.pdf?sequence=7&isAllowed=y" target="_blank" rel="noopener noreferrer">their 2015 case study</a>. "We suspect, however, that aphantasia will prove to be a variant of neuropsychological functioning akin to synesthesia [a neurological condition in which one sense is experienced as another] and to congenital prosopagnosia [the inability to recognize faces or learn new ones]."</p><p>Time and further research will tell. But scientists need phenomenon to test and questions to experiment on. Thanks to researchers like Zeman and Bainbridge, alongside the many people who came forward to discuss their experiences, they now have both when it comes to aphantasia.</p><p>* Zeman also coined the term "<a href="https://www.sciencedirect.com/science/article/abs/pii/S0010945220301404" target="_blank">hyperphantasia</a>" to describe the condition in which people's psychological imagery is incredibly vivid and well-defined.</p>
A large study shows changes in the brain scans of lonely people in the area involved in imagination, memory, and daydreaming.
- A study of 40,000 participants shows specific signatures in the brain scans of lonely people.
- Loneliness is linked to variations in grey matter volume and connections in the brain default network.
- This area of the brain is connected to the use of imagination, memory, future planning, and daydreaming.
Scientists show what loneliness looks like in the brain<span style="display:block;position:relative;padding-top:56.25%;" class="rm-shortcode" data-rm-shortcode-id="63156dc0c87a36da00d48a02eab00822"><iframe type="lazy-iframe" data-runner-src="https://www.youtube.com/embed/wkWpqlfA_2Q?rel=0" width="100%" height="auto" frameborder="0" scrolling="no" style="position:absolute;top:0;left:0;width:100%;height:100%;"></iframe></span>
A new theory suggests that dreams' illogical logic has an important purpose.
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" data-width="1440" data-height="585" />
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" data-width="1440" data-height="810" />
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>
Schools have become captivated by the idea that students must learn a set of generalized critical-thinking skills to flourish in the contemporary world.