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original research

Appendix to JAWWS: An Incrementally Rewritten Paragraph

Yesterday, I published a post describing an idea to improve scientific style by rewriting papers as part of a new science journal. I originally wanted to conclude the post with a demonstration of how the rewriting could be done, but I didn’t want to add too much length. Here it is as an appendix.

We start with a paragraph taken more or less at random from a biology paper titled “Shedding light on the ‘dark side’ of phylogenetic comparative methods“, published by Cooper et al. in 2016. Then, in five steps, we’ll incrementally improve it — at least according to my preferences! Let me know if it fits your own idea of good scientific writing as well.

1. Original

Most models of trait evolution are based on the Brownian motion model (Cavalli-Sforza & Edwards 1967; Felsenstein 1973). The Ornstein–Uhlenbeck (OU) model can be thought of as a modification of the Brownian model with an additional parameter that measures the strength of return towards a theoretical optimum shared across a clade or subset of species (Hansen 1997; Butler & King 2004). OU models have become increasingly popular as they tend to fit the data better than Brownian motion models, and have attractive biological interpretations (Cooper et al. 2016b). For example, fit to an OU model has been seen as evidence of evolutionary constraints, stabilising selection, niche conservatism and selective regimes (Wiens et al. 2010; Beaulieu et al. 2012; Christin et al. 2013; Mahler et al. 2013). However, the OU model has several well-known caveats (see Ives & Garland 2010; Boettiger, Coop & Ralph 2012; Hansen & Bartoszek 2012; Ho & Ané 2013, 2014). For example, it is frequently incorrectly favoured over simpler models when using likelihood ratio tests, particularly for small data sets that are commonly used in these analyses (the median number of taxa used for OU studies is 58; Cooper et al. 2016b). Additionally, very small amounts of error in data sets can result in an OU model being favoured over Brownian motion simply because OU can accommodate more variance towards the tips of the phylogeny, rather than due to any interesting biological process (Boettiger, Coop & Ralph 2012; Pennell et al. 2015). Finally, the literature describing the OU model is clear that a simple explanation of clade-wide stabilising selection is unlikely to account for data fitting an OU model (e.g. Hansen 1997; Hansen & Orzack 2005), but users of the model often state that this is the case. Unfortunately, these limitations are rarely taken into account in empirical studies.

Okay, first things first: let’s banish all those horrendous inline citations to footnotes.

2. With footnotes

Most models of trait evolution are based on the Brownian motion model.1Cavalli-Sforza & Edwards 1967; Felsenstein 1973 The Ornstein–Uhlenbeck (OU) model can be thought of as a modification of the Brownian model with an additional parameter that measures the strength of return towards a theoretical optimum shared across a clade or subset of species.2Hansen 1997; Butler & King 2004 OU models have become increasingly popular as they tend to fit the data better than Brownian motion models, and have attractive biological interpretations.3Cooper et al. 2016b For example, fit to an OU model has been seen as evidence of evolutionary constraints, stabilising selection, niche conservatism and selective regimes.4Wiens et al. 2010; Beaulieu et al. 2012; Christin et al. 2013; Mahler et al. 2013 However, the OU model has several well-known caveats.5see Ives & Garland 2010; Boettiger, Coop & Ralph 2012; Hansen & Bartoszek 2012; Ho & Ané 2013, 2014 For example, it is frequently incorrectly favoured over simpler models when using likelihood ratio tests, particularly for small data sets that are commonly used in these analyses.6the median number of taxa used for OU studies is 58; Cooper et al. 2016b Additionally, very small amounts of error in data sets can result in an OU model being favoured over Brownian motion simply because OU can accommodate more variance towards the tips of the phylogeny, rather than due to any interesting biological process.7Boettiger, Coop & Ralph 2012; Pennell et al. 2015 Finally, the literature describing the OU model is clear that a simple explanation of clade-wide stabilising selection is unlikely to account for data fitting an OU model,8e.g. Hansen 1997; Hansen & Orzack 2005 but users of the model often state that this is the case. Unfortunately, these limitations are rarely taken into account in empirical studies.

Much better.

Does this need to be a single paragraph? No, it doesn’t. Let’s not go overboard with cutting it up, but I think a three-fold division makes sense.

3. Multiple paragraphs

Most models of trait evolution are based on the Brownian motion model.9Cavalli-Sforza & Edwards 1967; Felsenstein 1973

The Ornstein–Uhlenbeck (OU) model can be thought of as a modification of the Brownian model with an additional parameter that measures the strength of return towards a theoretical optimum shared across a clade or subset of species.10Hansen 1997; Butler & King 2004 OU models have become increasingly popular as they tend to fit the data better than Brownian motion models, and have attractive biological interpretations.11Cooper et al. 2016b For example, fit to an OU model has been seen as evidence of evolutionary constraints, stabilising selection, niche conservatism and selective regimes.12Wiens et al. 2010; Beaulieu et al. 2012; Christin et al. 2013; Mahler et al. 2013

However, the OU model has several well-known caveats.13see Ives & Garland 2010; Boettiger, Coop & Ralph 2012; Hansen & Bartoszek 2012; Ho & Ané 2013, 2014 For example, it is frequently incorrectly favoured over simpler models when using likelihood ratio tests, particularly for small data sets that are commonly used in these analyses.14the median number of taxa used for OU studies is 58; Cooper et al. 2016b Additionally, very small amounts of error in data sets can result in an OU model being favoured over Brownian motion simply because OU can accommodate more variance towards the tips of the phylogeny, rather than due to any interesting biological process.15Boettiger, Coop & Ralph 2012; Pennell et al. 2015 Finally, the literature describing the OU model is clear that a simple explanation of clade-wide stabilising selection is unlikely to account for data fitting an OU model,16e.g. Hansen 1997; Hansen & Orzack 2005 but users of the model often state that this is the case. Unfortunately, these limitations are rarely taken into account in empirical studies.

We haven’t rewritten anything yet — the changes so far are really low-hanging fruit! Let’s see if we can improve the text more with some rephrasing. This is trickier, because there’s a risk I change the original meaning, but it’s not impossible.

4. Some rephrasing

Most models of trait evolution are based on the Brownian motion model, in which traits evolve randomly and accrue variance over time.17Cavalli-Sforza & Edwards 1967; Felsenstein 1973

What if we add a parameter to measure how much the trait motion returns to a theoretical optimum for a given clade or set of species? Then we get a family of models called Ornstein-Uhlenbeck,18Hansen 1997; Butler & King 2004 first developed as a way to describe friction in the Brownian motion of a particle. These models have become increasingly popular, both because they tend to fit the data better than simple Brownian motion, and because they have attractive biological interpretations.19Cooper et al. 2016b For example, fit to an Ornstein-Uhlenbeck model has been seen as evidence of evolutionary constraints, stabilising selection, niche conservatism and selective regimes.20Wiens et al. 2010; Beaulieu et al. 2012; Christin et al. 2013; Mahler et al. 2013

However, Ornstein-Uhlenbeck models have several well-known caveats.21see Ives & Garland 2010; Boettiger, Coop & Ralph 2012; Hansen & Bartoszek 2012; Ho & Ané 2013, 2014 For example, they are frequently — and incorrectly — favoured over simpler Brownian models. This occurs with likelihood ratio tests, particularly for the small data sets that are commonly used in these analyses.22the median number of taxa used for Ornstein-Uhlenbeck studies is 58; Cooper et al. 2016b It also happens when there is error in the data set, even very small amounts of error, simply because Ornstein-Uhlenbeck models accommodate more variance towards the tips of the phylogeny — therefore suggesting an interesting biological process where there is none.23Boettiger, Coop & Ralph 2012; Pennell et al. 2015 Additionally, users of Ornstein-Uhlenbeck models often state that clade-wide stabilising selection accounts for data fitting the model, even though the literature describing the model warns that such a simple explanation is unlikely.24e.g. Hansen 1997; Hansen & Orzack 2005 Unfortunately, these limitations are rarely taken into account in empirical studies.

What did I do here? First, I completely got rid of the “OU” acronym. Acronyms may look like they simplify the writing, but in fact they often ask more cognitive resources from the reader, who has to constantly remember that OU means Ornstein-Uhlenbeck.

Then I rephrased several sentences to make them flow better, at least according to my taste.

I also added a short explanation of what Brownian and Ornstein-Uhlenbeck models are. That might not be necessary, but it’s always good to make life easier for the reader. Even if you defined the terms earlier in the paper, repetition is useful to avoid asking the reader an effort to remember. And even if everyone reading your paper is expected to know what Brownian motion is, there’ll be some student somewhere thanking you for reminding them.25I considered doing this with the “evolutionary constraints, stabilising selection, niche conservatism and selective regimes” enumeration too, but these are mere examples, less critical to the main idea of the section. Adding definitions would make the sentence quite long and detract from the main flow. Also I don’t know what the definitions are and don’t feel like researching lol.

This is already pretty good, and still close enough to the original. What if I try to go further?

5. More rephrasing

Most models of trait evolution are based on the Brownian motion model.26Cavalli-Sforza & Edwards 1967; Felsenstein 1973 Brownian motion was originally used to describe the random movement of a particle through space. In the context of trait evolution, it assumes that a trait (say, beak size in some group of bird species) changes randomly, with some species evolving a larger beak, some a smaller one, and so on. Brownian motion implies that variance in beak size, across the group of species, increases over time.

This is a very simple model. What if we refined it by adding a parameter? Suppose there is a theoretical optimal beak size for this group of species. The new parameter measures how much the trait tends to return to this optimum. This gives us a type of model called Ornstein-Uhlenbeck,27Hansen 1997; Butler & King 2004 first developed as a way to add friction to the Brownian motion of a particle.

Ornstein-Uhlenbeck models have become increasingly popular in trait evolution, for two reasons.28Cooper et al. 2016b First, they tend to fit the data better than simple Brownian motion. Second, they have attractive biological interpretations. For example, fit to an Ornstein-Uhlenbeck model has been seen as evidence of a number of processes, including evolutionary constraints, stabilising selection, niche conservatism and selective regimes.29Wiens et al. 2010; Beaulieu et al. 2012; Christin et al. 2013; Mahler et al. 2013

Despite this, Ornstein-Uhlenbeck models are not perfect, and have several well-known caveats.30see Ives & Garland 2010; Boettiger, Coop & Ralph 2012; Hansen & Bartoszek 2012; Ho & Ané 2013, 2014 Sometimes you really should use a simpler model! It is common, but incorrect, to favour an Ornstein-Uhlenbeck model over a Brownian model after performing likelihood ratio tests, particularly for the small data sets that are often used in these analyses.31the median number of taxa used for Ornstein-Uhlenbeck studies is 58; Cooper et al. 2016b Then there is the issue of error in data sets. Even a very small amount of error can lead researchers to pick an Ornstein-Uhlenbeck model, simply because they accommodate more variance towards the tips of the phylogeny — therefore suggesting interesting biological processes where there is none.32Boettiger, Coop & Ralph 2012; Pennell et al. 2015

Additionally, users of Ornstein-Uhlenbeck models often state that the reason their data fits the model is clade-wide stabilising selection (for instance, selection for intermediate beak sizes, rather than extreme ones, across the group of birds). Yet the literature describing the model warns that such simple explanations are unlikely.33e.g. Hansen 1997; Hansen & Orzack 2005

Unfortunately, these limitations are rarely taken into account in empirical studies.

Okay, many things to notice here. First, I added an example, bird beak size. I’m not 100% sure I understand the topic well enough for my example to be particularly good, but I think it’s decent. I also added more explanation of what Brownian models are in trait evolution. Then I rephrased other sentences to make the tone less formal.

As a result, this version is longer than the previous ones. It seemed justified to cut it up into more paragraphs to accommodate the extra length. It’s plausible that the authors originally tried to include too much content in too few words, perhaps to satisfy a length constraint posed by the journal.

Let’s do one more round…

6. Rephrasing, extreme edition

Suppose you want to model the evolution of beak size in some fictional family of birds. There are 20 bird species in the family, all with different average beak sizes. You want to create a model of how their beaks changed over time, so you can reimagine the beak of the family’s ancestor and understand what happened exactly.

Most people who try to model the evolution of a biological trait use some sort of Brownian motion model.34Cavalli-Sforza & Edwards 1967; Felsenstein 1973 Brownian motion, originally, refers to the random movement of a particle in a liquid or gas. The mathematical analogy here is that beak size evolves randomly: it becomes very large in some species, very small in others, with various degrees of intermediate forms between the extremes. Therefore, across the 20 species, the variance in beak size increases over time.

Brownian motion is a very simple model. What if we add a parameter to get a slightly more complicated one? Let’s assume there’s a theoretical optimal beak size for our family of birds — maybe because the seeds they eat have a constant average diameter. The new parameter measures how much beak size tends to return to the optimum during its evolution. This gives us a type of model called Ornstein-Uhlenbeck,35Hansen 1997; Butler & King 2004 first developed as a way to add friction to the Brownian motion of a particle. We can imagine the “friction” to be the resistance against deviating from the optimum.

Ornstein-Uhlenbeck models have become increasingly popular, for two reasons.36Cooper et al. 2016b First, they often fit real-life data better than simple Brownian motion. Second, they are easy to interpret biologically. For example, maybe our birds don’t have as extreme beak sizes as we’d expect from a Brownian model, so it makes sense to assume there’s some force pulling the trait towards an intermediate optimum. That force might be an evolutionary constraint, stabilising selection (i.e. selection against extremes), niche conservatism (the tendency to keep ancestral traits), or selective regimes. Studies using Ornstein-Uhlenbeck models have been seen as evidence for each of these patterns.37Wiens et al. 2010; Beaulieu et al. 2012; Christin et al. 2013; Mahler et al. 2013

Of course, Ornstein-Uhlenbeck aren’t perfect, and in fact have several well-known caveats.38see Ives & Garland 2010; Boettiger, Coop & Ralph 2012; Hansen & Bartoszek 2012; Ho & Ané 2013, 2014 For example, simpler models are sometimes better. It’s common for researchers to incorrectly choose Ornstein-Uhlenbeck instead of Brownian motion when using likelihood ratio tests to compare models, a problem especially present due to the small data sets that are often used in these analyses.39the median number of taxa used for Ornstein-Uhlenbeck studies is 58; Cooper et al. 2016b Then there is the issue of error in data sets (e.g. when your beak size data isn’t fully accurate). Even a very small amount of error can lead researchers to pick an Ornstein-Uhlenbeck model, simply because it’s better at accommodating variance among closely related species at the tips of a phylogenetic tree. This can suggest interesting biological processes where there are none.40Boettiger, Coop & Ralph 2012; Pennell et al. 2015

One particular mistake that users of Ornstein-Uhlenbeck models often make is to assume that their data fits the model due to clade-wise stabilising selection (e.g. selection for intermediate beak sizes, rather than extreme ones, across the family of birds). Yet the literature warns against exactly that — according to the papers describing the models, such simple explanations are unlikely.41e.g. Hansen 1997; Hansen & Orzack 2005

Unfortunately, these limitations are rarely taken into account in empirical studies.

This is longer still than the previous version! At this point I’m convinced the original paragraph was artificially short. That is, it packed far more information than a text of its size normally should.

This is a common problem in science writing. Whenever you write something, there’s a tradeoff between brevity, clarity, amount of information, and complexity: you can only maximize three of them. Since science papers often deal with a lot of complex information, and have word limits, clarity often gets the short end of the stick.

Version 6 is a good example of sacrificing brevity to get more clarity. In this case it’s important to keep the amount of information constant, because I don’t want to change what the original authors were saying. It is possible that they were saying too many things. On the other hand, this is only one paragraph in a longer paper, so maybe it made sense to simply mention some ideas without developing them.

I tried a Version 7 in which I aimed for a shorter paragraph, on the scale of the original one, but I failed. To be able to keep all the information, I would have to sacrifice the extra explanations and the bird beak example, and we’d be back to square one. This suggests that both the original paragraph and my rewritten version are on different points on the tradeoff curve. The original is brief, information-rich, and complex dense; my version is information-rich, complex, and clear.. To get brief and clear would require taking some information out, which I can’t do as a rewriter.

It is my opinion that sacrificing clarity is the worst possible world, at least in most contexts. We could then rephrase my project as attempting to emphasize clarity above all else — after all, brevity, information richness and complexity serve no purpose if they fail to communicate what they want to.

Categories
original research

Of Emoji and Hieroglyphs

Emoji are pictograms that are used to add nuance and meaning to electronic written text. They were invented in Japan in the 1990s and are now widely used across the world. Random examples: 🤾‍♂️ 😒 🦑 🔊 💚

Egyptian hieroglyphs are characters, mostly based on real objects, that were used to write the Ancient Egyptian language. They were invented around the 32nd century BC and fell into disuse by Late Antiquity. Random examples:1If you only see squares, that means you need to install a font that supports those Unicode characters. Most browsers will display them automatically, but I’m not sure about the details. 𓊛 𓋊 𓃕 𓌗 𓎁

There’s an obvious parallel to be drawn between the two, which multiple people have pointed out, usually with cries of “Thousands of years of language evolution and we’re back to using pictograms!” Even I tweeted about a few months ago:

As Twitter threads go, this was a reasonably popular one, which means there was some value in investigating the links between emoji and hieroglyphs. But maybe not enough to write more than a few tweets, and so the matter was put to rest.

Then I read Clo’s excellent piece on emoji and our relationship with them, and it made me want to revisit the topic. So I embarked on a small and silly side project.

The result is being released today. It is a browser extension. It is called Emoji to Hieroglyphs. It replaces the former with the latter whenever possible as you browse the web. It’s stupid and fun. And it can be downloaded here.

How it works

Emoji to Hieroglyphs is based on the famous cloud-to-butt extension — which replaces “the cloud” with “your butt” all over the internet — because I don’t really know any JavaScript so it was simpler to steal code from somewhere. Good thing that cloud-to-butt is released under the “Do What The F*ck You Want To Public License”, which I’m also using for Emoji to Hieroglyphs.

The extension searches text in web pages for certain emoji, and replaces them with the closest hieroglyphic visual equivalent I could find. Here are some examples:

🤸 → 𓀡

✍️ → 𓃈

🐇 → 𓃹

⛵ → 𓊝

(Of course, the extension needs to be uninstalled for these examples to make sense.)

Not all emoji have a hieroglyphic equivalent. As of today, there are 3,521 emoji in Unicode 13.1, but only 1,071 hieroglyphs. A lot of the extra emoji are things that didn’t exist in Ancient Egypt, such as soccer ⚽, helicopters 🚁, Japan 🗾, or jack-o-lanterns 🎃. Many others represent something that did exist along the banks of the Nile, but that the Egyptians didn’t bother making a hieroglyph for, e.g. skulls 💀, grapes 🍇, or crabs 🦀. I assume the Ancient Egyptians had emotions, but there aren’t any hieroglyphs to represent them directly, so smileys such as 😄, 😍, 🤯, or 🤑 are also not affected by my extension.

Not all hieroglyphs have an emoji equivalent, either. Many are just too abstract, like 𓊖, which is supposed to mean “village.” Several others are combinations, like 𓆲, combining an owl and a branch; I could’ve used it to replace 🪵🦉 and 🦉🪵, and indeed I did this for a few combos, but usually that’s just not very interesting. A few hieroglyphs represent things that the Unicode Consortium has prudishly decided not to depict as emoji, such as breasts or phalluses.2Ancient Egyptian has three hieroglyphs for the penis: 𓂸, 𓂹 (phallus combined with cloth), and 𓂺 (phallus with emission). I considered replacing the eggplant emoji 🍆 with 𓂸, but then I decided it’d be confusing and offensive for people using it as, uh, an actual eggplant. And a lot are just too specific to Ancient Egypt. For instance, there are regrettably not yet emoji for “pyramid,”3although I used it to replace the Tokyo Tower emoji 🗼, because why not “mummy-shaped god,” “crocodile on shrine,” or “human-headed bird with bowl with smoke.”

𓉴 𓁰 𓆋 𓅽

Maybe in Unicode 14.

I did manage to create more than 300 mappings, not counting all the skin tone and gender emoji variations, which I have for the most part merged together. Everyone is an Egyptian in my extension! Also, almost everyone is male, because there are only a few specifically female hieroglyphs, usually related to pregnancy or child rearing. Don’t blame me, blame the Ancients.

The most affected emoji categories are people (except smileys), animals, plants, and a bunch of random objects such as containers or bread-like foods.

Here’s a screenshot from Emojipedia’s list of people emoji, modified with the extension:

I should note that I created mappings only based on the visual appearance of the symbols. The word “doctor” in Ancient Egyptian is written with three glyphs, 𓌕𓏌𓀃,4The arrow should be above the pot, but I can’t do that in linear text. but I didn’t map the emoji 🧑‍⚕️ to that combination since it wouldn’t be very evocative. Such a mapping would be more akin to a translation, which isn’t the goal here.

On the other hand, not all visual mappings are as obvious as 🐘 to 𓃰. Consider 𓆳, which is supposed to be a palm branch. Since there is no palm tree hieroglyph, I used the palm branch to replace the palm tree emoji.

🌴 → 𓆳

The link may not be crystal clear to users, but I included it anyway in the interest of having as many mappings as possible. Here are a few other examples where the emoji and hieroglyphs do represent the same object, but where the resemblance isn’t that strong:

🔥 → 𓊮

🏠 → 𓉐

💩 → 𓄽

Conversely, some mappings are just based on superficial resemblance. The sistrum is an ancient percussion instrument which, as you can imagine, doesn’t have a close emoji equivalent. But since it’s about music and sort of resembles a microphone, that’s what I decided to use it for. There are also “woman holding sistrum” and “man holding sistrum” hieroglyphs, so it made sense to replace the female and male singer emoji with those.

🎤 → 𓏣

👩‍🎤 → 𓁙

👨‍🎤 → 𓁋

Finally, not all mappings are 1:1. Sometimes multiple emoji together make a single hieroglyph.

🌊 → 𓈖

🌊🌊🌊 → 𓈗

And sometimes a single emoji is expressed through multiple hieroglyphs.

🏡 → 𓆭𓉐

👀 → 𓁹𓁹

There are a few combinations that could be considered Easter eggs. I will not tell you which.

Overall, don’t expect a lot of consistency. This is obviously just for fun, and I hope some of you do have fun with it. I had fun making it; I even learned a few things! Which we’ll get into presently.

Some linguistics

To some, emoji mark a return to a more primitive form of language. We started out with cave paintings, then we developed pictograms (character = picture), then we got more general logograms (character = word), and then we gradually invented more symbolic forms of writing, culminating in clean5alphabets aren’t actually clean, they’re super redundant and inconsistent, but let’s allow this for the sake of the argument phonetic alphabets with a few dozen characters.6At least in the West. Chinese has remained at the logogram stage, and there aren’t any strong reasons to think it’s inferior to alphabetic writing. This should make us dubious of claims that the evolution of written language has followed any sort of natural progress. And now, with the advent of mind-numbing technology such as smartphones and Twitter, we’re apparently back to pictograms.

Thus joke images such as:

and:

and:

(Two notes about this last image: first, those mappings are terrible, and second, the image on the left isn’t even a picture of actual hieroglyphs. There isn’t a hieroglyph that looks like “#”. I don’t know where it’s from, but it’s very fake.)

Many media pieces discuss the question, and they all converge on the same point: No, emoji and hieroglyphs are not the same thing. Hieroglyphs weren’t just cute drawings to decorate Egyptian temples! They were a full-fledged writing system! A single hieroglyph, say the wigeon duck, 𓅰, could be used to represent an actual wigeon, yes, but it could also represent the idea of food, or the verb “to fatten,” and it had full phonetic value just like our letters, being used to transcribe the consonant sounds wšꜣ!7The symbol “ꜣ”, if you’re curious, represents the conventional transcription of the letter aleph in Egyptology, indicating something like a glottal stop.

Whereas emoji aren’t a writing system. They are mostly cute drawings we use to decorate our sentences. They carry meaning, and are linguistically interesting, but you can’t express arbitrary sentences with them, at least not at the moment.

Perhaps, like hieroglyphs, emoji could one day represent sounds directly. Say 🥶 = “fr”, 😇 = “en”, and 🍩 = “d”. Then 🥶😇🍩 could be used to represent the spoken word “friend,” even though the symbols have mostly nothing to do with friends. Add a ship, 🛳, and now we get a hybrid word, combining phonograms and logograms: 🥶😇🍩🛳, “friendship.” But we’re unlikely to get there, because, well, we already have symbols to represent sounds. The 26 letters of the English version of the Latin alphabet, for example. Or the > 160 symbols of the International Phonetic Alphabet, if you want more comprehensiveness. The reason the Egyptians gave phonetic value to their cute little drawings is that they were all they had.

But I want to go in a somewhat different direction than both the joke images and the serious linguistics articles.

I claim that we never actually stopped using Ancient Egyptian hieroglyphs. I claim that we’re still at the stage of using cute little drawings to represent language.

Consider the letter A, the first in the Latin alphabet. Where does it come from? The Latin alphabet is descended from the Greek one, by way of the Etruscan alphabet. So the letter A comes from the Greek equivalent, Α/α, pronounced “alpha.” But where did alpha come from?

It came from the Phoenician alphabet, whose immediate ancestor is the Proto-Sinaitic script, considered the first alphabet in the world. The Phoenicians were a coastal people of the Levant in Antiquity. Their invention of the alphabet turned out to be quite influential, since the vast majority of the world today writes in systems descended from it: Latin and Greek, but also Cyrillic (used to write Russian, among others), Arabic, Hebrew, Ge’ez (used for Ethiopian), all of the scripts used in India and Southeast Asia, and even Mongolian. In other words, pretty much everything on this map except China, Korea, Japan, possibly Georgia and the syllabary used for indigenous languages in northern Canada.8gray = Latin, teal = Cyrillic, green = Arabic, see the original source for others

Writing systems worldwide.png

The equivalent to A and alpha in Phoenician is 𐤀, pronounced “aleph.” It has an equivalent in all those other scripts, such as Hebrew א (also called aleph). Okay. But where did aleph come from?

At this point we’re quite far out in the past, with the Proto-Sinaitic script having been in use from the 19th to the 15th centuries BC, so things get a bit murky. But the land of Canaan, where the script was used, is right next to Egypt. And 𐤀 kind of looks like a stylized ox head. So does A, for that matter, except upside down. Look at the math symbol ∀ (“for all”). Pretty easy to see an animal head with horns, right? And so it is commonly accepted that the letter A is descended from the Egyptian hieroglyph 𓃾.9Below, 𐌀 is the Etruscan or old Italic version. I’m not showing Greek Α/α because it would have to go between 𐤀 and 𐌀, but it looks more similar to A than to 𐌀. This is because the actual Greek letter that led to the Etruscan version was an archaic version that is not in Unicode. For more details and more intermediate forms, see Wikipedia on the history of A.

𓃾 → 𐤀 → 𐌀 → A/a

Yes. Each time you use the symbol A or a, which, if you write at all, probably happens dozens or hundreds of times a day, you are in fact using something that ultimately comes from the Ancient Egyptian version of “🐮”.

And all of our letters are like this! (With one exception.) Some are a bit obscure, like B, which apparently comes from the house hieroglyph:

𓉐  𐤁 → 𐌁 → B/b

But most others are pretty clear.

𓈖 → 𐤌 → 𐌌 → M/m

𓆓 → 𐤍 → 𐌍 → N/n

𓁹 → 𐤏 → 𐌏 → O/o

(And then, of course, the O became the many-eyed or multiocular O, whose Unicode version is “ꙮ”, in one hilarious and terrifying instance of a monk doodling something in his copy of the Orthodox Christian Bible.)

Here’s the full Latin emoji alphabet based on the hieroglyphic origins of the letters. Hang a version in your toddler’s bedroom, to thoroughly confuse him or her!10You can notice the exception: the letter X comes from Greek Χχ (chi), but chi was apparently a native Greek invention and wasn’t derived from Phoenician or Egyptian hieroglyphs. So I left it as is.

🐮🏠🏒🐠🤷🥄🏒🚧💪💪🤚🦯🌊🐍👁👄🐒🗣️🏹❌🥄🥄🥄X🥄🥢

𓃾𓉐𓌙𓆛𓀠𓌉𓌙𓊐𓂝𓂝𓂧𓋿𓈖𓆓𓁹𓂋𓃻𓁶𓌓𓏴𓌉𓌉𓌉X𓌉𓏭

ABCDEFGHIJKLMNOPQRSTUVWXYZ

Maybe next time I’ll create an extension to turn all Latin letters into hieroglyphs or emoji. Just to confuse everyone.

To conclude, emoji aren’t a return to anything. We’re still using symbols based on real objects, even if most of them aren’t recognizable anymore. Our system is a bit more advanced than the Egyptians’ — for one thing, we have vowels, they didn’t — but it isn’t fundamentally any different.

Of course, emoji do fulfill some needs — otherwise we wouldn’t use them. They are recognizable as objects and ideas, unlike our letters. They’re diverse. They’re fun. Maybe a good, complete writing system should feature small pictures to convey emotion, nuance, and humor. In a way, the Egyptians had a bit of that. Now we do too, thanks to emoji.

I would say it is a good development.

✨ Download the Emoji to Hieroglyphs extension here ✨