This is way too funny not to reblog. At least the “undercut” was popular in North Korea before it even hit the States!
This is way too funny not to reblog. At least the “undercut” was popular in North Korea before it even hit the States!
Japanese visual artist Hikaru Cho is known for her transformative paintings — turning things into something else through her application of paint. In this series, she turns various food items into looking like other food items, such as the banana above into a cucumber. She also did the same with turning an egg into an eggplant, and a tomato into an orange.
But Hikaru Cho does really amazing (and creepy) work with transformative painting on humans. Here are some of her best works:
Her site’s definitely worth checking out!
Your skin like dawn
Mine like musk
One paints the beginning
of a certain end
The other, the end of a
Do you think the above poem is written by a human, or generated by a computer program? That’s what site bot or not seeks to achieve: a Turing test where people try to discriminate computer-generated poems from human-written ones. By the way, the above poem, “Passing Time,” was written by a human Maya Angelou.
I have previously written about reconciling the idea of programming code as poetry, whether it’s possible to achieve “poetic beauty” in code. I posited that there might possibly be an inherent “poetic beauty” in poetry that we recognise when we decide a piece of writing is a poem. With bot or not, the idea of what makes poems poetry is taken in a different step: by identifying whether the “poems” are human or computer-generated, there must be something common with human effort that is visibly distinguishable.
That means that any piece of writing that is seen as containing enough of the essence of “poetic beauty” to be counted as a poem, can be further sub-divided into being human effort or algorithm-derived. There must be commonality between “computer poems” and “human poems” that we can decisively say, “Yes, this is the work of a human” or “This is clearly computer-generated.”
The site lists some of the top poems easily recognised as human or computer-written.
Generated by botpoet using JGnoetry (93% said “bot”)Published on desserts and from pink. Symptoms Start, 2013 as other poetry does anyone Word in thailand and write reading one mother Order they deserve. Well, 2013 recently released My pants and serve throw from is a beautiful Insane surreal once hours playing once.
Generated by botpoet using JGnoetry (87% said “bot”)New poetry I might help you currently Have been, snapping male, but it’s a rocket, Kid man been forever idea of My wearing a punk and brought old robot Smog thing. Professional grown-up looking In dusty old men remaining his tibia.
Generated by Jim Carpenter using Erica T Carter (85% said “bot”)The name ghosts second, destroying. The quite normal letter to the dutch throne after one year destroying the still stuck ridged snow, interpreted. Undimmed radiance curves, primming like the column. Lounging, as other as very high score. Loafing a booby. Lounging and spends. Normal occasion joints. Early letter gets the personal experience from the individual. Getting, tarries however in cell. Lounging. Obsesses the disturbed surface. Obsesses the abyss.
As can be seen, the above poems are mostly verbiage, and make no sense. We compare that with the poems most recognised as being written by humans.
William Blake (87% said “human”)Little Fly, Thy summer's play My thoughtless hand Has brushed away. Am not I A fly like thee? Or art not thou A man like me? For I dance And drink, and sing, Till some blind hand Shall brush my wing. If thought is life And strength and breath And the want Of thought is death; Then am I A happy fly, If I live, Or if I die.
Shelby Asquith (86% said “human)His smile was loud, and me in my silence— I thought it was meant for me. Momma warned me about boys like him. Told me that the kinds of boys that shined a little too bright might just be trying to distract me from the balled fists, the fury. I was a fly and he lured me straight into the light. And oh how he burned me, how he burned me.
Rabindranath Tagore (84% said “human”)O Fool, try to carry thyself upon thy own shoulders! O beggar, to come beg at thy own door! Leave all thy burdens on his hands who can bear all, and never look behind in regret. Thy desire at once puts out the light from the lamp it touches with its breath. It is unholy---take not thy gifts through its unclean hands. Accept only what is offered by sacred love.
Especially with Blake’s poem, these top poems display some things that computers can scarcely replicate. In Blake’s poem, there is a very strong meter and rhyme scheme going on. The other two read very coherently, where the ideas contained within the lines agree with each other, and a message flows from the poem to the reader. What does it take for a piece of writing/poem to resemble human effort? We look at computer-generated poems that people thought looked human.
Generated by Janus Node using Janus Node (69% said “human”)you are inscribed in the lines on the ceiling you are inscribed in the depths of the storm
A Wounded Deer Leaps Highest
Generated by Poets using Ray Kurzweil’s Cybernetic Poet (67% said “human”)A wounded deer leaps highest, I've heard the daffodil I've heard the flag to-day I've heard the hunter tell; 'Tis but the ecstasy of death, And then the brake is almost done, And sunrise grows so near sunrise grows so near That we can touch the despair and frenzied hope of all the ages.
Generated by Every Google User using Google Predictive Search (67% said “human”)some men just want to watch the world burn some men just want to watch the world learn some men just want breakfast
In these instances, how did algorithmically-generated sequences of words suddenly gain the verisimilitude of human effort, whereas the computer-like computer-generated attempts shown above failed? Finally, we look at poems written by humans that people thought were computer generated.
Deanna Ferguson (76% said “bot”)cut opinions tear tasteful hungers huge ground swell partisan have-not thought green opinions hidden slide hub from sprung in weather yah bold erect tender perfect term transparent till I two minute topless formed A necessarily sorry sloppy strands hot opinions oh like an apple a lie, a liar kick back filial oh well hybrid opinions happen not stopped
Cinema Calendar Of The Abstract Heart – 09
Tristan Tzara (69% said “bot”)the fibres give in to your starry warmth a lamp is called green and sees carefully stepping into a season of fever the wind has swept the rivers' magic and i've perforated the nerve by the clear frozen lake has snapped the sabre but the dance round terrace tables shuts in the shock of the marble shudder new sober
Gertrude Stein (69% said “bot”)Red flags the reason for pretty flags. And ribbons. Ribbons of flags And wearing material Reason for wearing material. Give pleasure. Can you give me the regions. The regions and the land. The regions and wheels. All wheels are perfect. Enthusiasm.
That we have seen the most human-like human/computer-written poems, and most computer-like human/computer-written poems, can we draw parallels for what constitutes human effort in poetry? On the technical side, can we say that, as per Blake’s poem, prosodic and auditory cues such as stress, meter, and rhyme give poems a sense of human effort, such as where by reciting “Tiger, tiger, burning bright/In the forests of the night,” we can not only hear the rhyme but feel a sense of constant rhythm to the poem?
Surely that can only be achieved by humans? Not rightly so. Assuming a bot program has access to a dictionary, and the stress, meter, and phonetics of all words contained therein all mapped out, how hard would it be to code for something that reads like human poetry? (Following is not real code, but an idea of how the code should behave)
<write human poetry>
component: alternate stress, strong-weak
component: vowels at end of line match; SET1:AAB SET2:CCB
component structure line1: [pronoun][conjunction][pronoun]
component structure line2: [verb][preposition][location]
component structure line3: [verb][noun]
component structure line1: [pronoun][verb]
component structure line2: [conjunction][verb][noun]
component structure line3: [conjunction][pronoun][verb]
And I just described:
Jack and Jill
went up the hill
to fetch a pail of water.
Jack fell down
and broke his crown
and Jill came tumbling after.
A bot could browse through a dictionary and probably come up with something similar. Granted, I “retro-wrote” the code, where I already had a poem in mind and wrote the “code” after, but if I can break “Jack and Jill” down into an algorithm that can be reproduced, using auditory and prosodic cues, then surely it is solely not that that determines human effort in poetry? However, if a program relies solely on prosodic and auditory cues, what’s to prevent it from putting in random words that fit those cues but make no sense in sequence? For example:
Bird and ball
swirled by the mall
and cocked a round of seaweed
Truth flew out
where running lout
and cops were sniffing soft beads
The prosodic and auditory cues of the above poem match “Jack and Jill” yet it makes no sense, and it is likely that people would judge it to be written by a bot. So what else is required for poems to be recognised as human effort?
The other thing you’ll notice where human-like poetry trumps computer-like poetry is coherence of ideas. In the poems that read human, most of them have ideas that agree with either a general theme, or the lines preceding and following them. The ideas contained in each line also display a progression, where there is something being explored or developed. The computer-like poems tend to show disjointedness of ideas.
Perhaps humans are predisposed to pass continuity and coherence as hallmarks of humanity.
Is coherence then unique to humans, or can computers imitate coherence as well? Let’s see if we can imitate coherence with an algorithm as well, with added prosodic and auditory cues. To achieve that, we need thematic cues. I’m going to use the following poem, “I wandered lonely as a cloud” by William Wordsworth.
I wandered lonely as a cloud
That floats on high o’er vales and hills,
When all at once I saw a crowd,
A host, of golden daffodils;
Beside the lake, beneath the trees,
Fluttering and dancing in the breeze.
<write coherent poem>
component: alternate stress, weak-strong
component: SET last line: [alternate stress, weak-strong]=false
component: vowels at end of line match; SET:ABABCC
component: [CENTRAL X(n)] designate IDEA
component: [CENTRAL X(n)]; X=verb, noun, adverb, preposition, adjective
component: [CENTRAL X(n)] must agree with THEME
component: [CENTRAL X(n)] must agree with [CENTRAL X(n±≥1)]
component: [CENTRAL X(n)] either expand or progress [CENTRAL X(n±≥1)]
component structure line1: [pronoun][CENTRAL verb(1)][adjective][preposition][CENTRAL noun2]
component structure line2: [conjunction][CENTRAL verb3][adjective][preposition][CENTRAL noun4][conjunction][CENTRAL noun5]
component structure line3: [conjunction][adverb][pronoun][CENTRAL verb6][noun]
component structure line4: [noun][adjective][CENTRAL noun7]
component structure line5: [preposition][CENTRAL noun8][preposition][CENTRAL noun9]
component structure line6: [CENTRAL verb9][conjunction][CENTRAL verb10][preposition][CENTRAL noun11]
(1) verb-noun agrees with (2); (2) agrees with THEME
(3) verb-noun agrees with (2);(4),(5) agrees with (2); (4),(5) agrees with THEME
(6) verb-noun agrees with (7)
(7) agrees with THEME
(7) preposition agrees with (8),(9); (8),(9) agrees with THEME
(10),(11) verb-noun agrees with (7); (10),(11) agrees with THEME
Does the above make any sense? It took me a while to try to break down “I wandered lonely as a cloud” into a vague enough algorithm that in my opinion still represents the poem while hypothetically still able to reproduce another poem. Let me explain the above “code.”
The poem has various components, including a weak-strong stress meter; but the last line of the set breaks the meter. The last phonetic features of certain lines must match; in this case ABABCC. Within the poem, there are certain things, designated as [CENTRAL X] where X can be a verb, noun, preposition, adverb, or adjective. These [CENTRAL X] designate a contained IDEA, which is a sense of what that line means. The [CENTRAL X] must agree with a preset THEME, which in this case, is “nature”; where the words must be somehow relevant to “nature.” such as “hill,” “daffodil,” and “cloud” being all words related to “nature.” Not only must [CENTRAL X] agree with THEME, it also has to agree with each other, one or more preceding or following it. It does so not only grammatically, but also has to expand or progress it in a logical way.
IDEA1 contains [CENTRAL (1),(2)], which progresses into IDEA2, containing [CENTRAL (3),(4),(5)]. IDEA3 contains [CENTRAL (6),(7)] which progresses into IDEA4, containing [CENTRAL (8),(9),(10),(11)].
You know, even after so much postulating, I’m still not sure I have successfully “retro-coded” Wordsworth’s poem. Maybe it is coherence of idea that seem unique to human effort, and that humans are predisposed to finding order in nature. My head hurts from trying to break poems down like that. Maybe someone else can do this better than I can. Feel free to leave comments.
Two days ago, I was at work transcribing an interview one of the interviewees used the word “bespoke.” That was a word I haven’t heard in a while, and I just shared my thoughts on Facebook, “I think “bespoke” might be my favourite word of the day, today.” It’s a simple word that means something one would not associate with how it looks and sounds: it basically means something that is tailor-made to individual preference.
My friend Kevin Allison, who hosts a podcast and runs the live comedy show The Risk! Show, commented, “I tried to cut it from a recent RISK story because I didn’t know what it meant.” I asked if he could not have simply checked the dictionary and he said something that struck me: “If I don’t know what a damn word means, plenty of listeners won’t either!”
This forced me to think a lot about the role of those delivering informational content, be it via print or broadcast such as newspapers (print/online) or the radio. Is it our role to expand the vocabulary of those watching/reading/listening, or should we create a bespoke program easily digestible and understandable by readers?
I’ve always been impressed with the balance of hefty words and simple terms that the New York Time achieves, and constantly learn new words or remember old ones I’ve forgotten. English is a beautiful language, the bastard amalgamation of some many languages from continental Europe, that in its plenitude are niche, forgotten gems we should celebrate.
But in our consideration of the audience, especially here in America, we (content producers) amputate ourselves to fit the ready-to-serve boxes that the audience expects. As my journalism professor once said, “Why used ‘dollar words’ when a ‘fifty cent’ one suffices?” Maybe because sometimes there is beauty in the complexity contained within the brevity of “dollar words.” The best uses of difficult words are those placed supremely in a sentence where the context allows the readers to read and extract the meaning without having to stumble over it, and upon reflection on the use of the word, appreciates that that word is not run-of-the-mill. Where a “dollar word” is pivotal in the sentence, and the meaning known only to Webster-Merriam, that is untenable. We use many clues in context and morphology to help us understand words we don’t, harkening back to ye ol’ grade school days of “reading from context.” Morphemes like “un,” “mal,” “pro,” etc. contribute to the understanding of the word, even if the reader doesn’t know the word, and writers should take that into consideration when trying to achieve beauty with their words.
But ere we lose the readers in a mire of SAT vocabulary list, there is nothing more exhausting than having to refer to a dictionary every couple of sentences. I think content producers, in an ideal world, should challenge the limits of the audience’s knowledge every now and then, and achieve a balance between inspiration and information.
Why did Anthony Gatto, the greatest juggler alive — and perhaps of all time — back away from his art to open a construction business?
The greatest juggler alive, maybe of all time, is a 40-year-old Floridian named Anthony Gatto. He holds 11 world records, has starred for years in Cirque du Soleil, and has appeared as a child onThe Tonight Show, performing in a polo shirt and shorts, juggling five rings while balancing a five-foot pole on his forehead.
His records are for keeping certain numbers of objects aloft for longer than anyone else. Eleven rings, 10 rings, nine rings, eight rings, and seven rings. Nine balls, eight balls, and seven balls. Eight clubs, seven clubs, and six clubs. To break this down a little: There’s one person in the world who can juggle eight clubs for 16 catches,1and that’s Gatto. As for seven clubs, maybe a hundred people can get a stable pattern going — for a couple of seconds. It’s difficult to evenhold seven clubs without dropping them; your hands aren’t big enough. Gatto can juggle seven clubs for more than four minutes. “That’s insane,” says David Cain, a professional juggler and juggling historian. “There’s no competition.” …Read the rest of the story here.
This fairly long article speaks to me a lot about what it means to be a performer and what my craft means to me. Anthony Gatto, whom the jugglers I grew up amongst raved about all the time, fell off of the face of the performing world, after having received so many accolades. Were there to be a “king of juggling,” even today I believe jugglers in the know would not hesitate to crown Gatto still.
Fangone, the writer, did not manage to actually score an interview with Gatto, which is disappointing, but supplements his article with thoughts and analyses to try to explain why Gatto stopped performing to go into construction. Fangone wrote about, or quoted some of Gatto’s words that struck me as circus folk:
By now, though, Gatto’s relationship with the juggling community had shifted. He no longer regularly attended conventions or entered competitions. Gatto didn’t want to impress other jugglers. “Nobody cares about good jugglers in the performance world,” he later wrote in an Internet forum. “They care about entertainers.”
Gatto’s frustration with young, Internet-native jugglers boiled over in 2008, when he got into a sort of arms race with Galchenko, the YouTube phenom. It began when Galchenko appeared on an NBC show called Celebrity Circus. He was there to set a record for doing as many five-club, five-up 360s as he could in one minute. He ended up doing the trick 21 straight times without dropping, breaking the previous record. After the show aired, Gatto posted a video of himself doing 24 five-ups in a minute, breaking the record Galchenko had just broken. Galchenko then posted footage of himself doing the trick 29 times in a minute. It went on like that for several more rounds.
A reporter for the Boston Globe called Gatto at the time and asked why Galchenko’s TV appearance had bothered him. Gatto praised Vova as a “great juggler,” but he also said of younger jugglers, “Until those kids grow a personality, they’re not going to wow anybody. The audience doesn’t care if you juggle 20 rings.” The reporter added, “Gatto now says he regrets getting involved in the 360s competition — though he says he can still go higher — because it sent the wrong message. The only way to judge a juggler, he says, is to watch him onstage, under the bright lights, over the course of a career.”
(Gatto) gave in. He grew to accept the necessity of kissing the ball and lobbing it gently into the crowd with a grin. He also learned to make hard tricks look hard, to pantomime the exertion and self-doubt of a man working at the edge of his ability even though his ability stretched on and on. He learned to entertain, because for some reason, even though we exist in a physical universe defined by the relative attractive powers of massive objects, the mere demonstration of a lush and lovely control of gravity is not enough. He labored to please an audience that could never appreciate his greatness. Then he got older and watched a new wave of jugglers abandon the stage for the flicker of computer screens, sneering at the bright-light mastery he’d worked so hard to gain.
It’s at times when reading things like these that makes a practitioner of a performing craft wonder: What am I, and what do I want to be? What do I want to achieve what I want with my craft?
Do I want to push the boundaries of technical circus? My own skill have been stagnating for a while — even though I’ve over 10 years of unicycling, poi/flag spinning/etc. under my belt, my skills have not leapt beyond those “Youtube whizzes” who pick up the sport mere months ago and are coming up with insanely technical and difficult Youtube videos on their progress. I picked up the pirouette on the unicycle maybe six years ago, and the pirouette is very flashy and crowd-pleasing, but I haven’t picked up much else since then. It was only recently, as of a year or two ago that I started to push myself to go beyond, and learn something that would please less the audience and more those in the know; circus folk.
But very often I wonder if it’s worth it, as Gatto might have, when the fancifulness of its difficulty is lost on the audience. I’m not sure Gatto ever fully achieved that stage, where he could reconcile his integrity as a practitioner of juggling and perform to the extent of his ability, with what the audience can see and understand.
The above image made its rounds on Reddit the other day. The question asks “If you choose an answer to this question at random, what is the chance you will be correct?” The options are:
Since the randomly choosing one out of four answers is a 25% chance, so it’s a)… and d)? So since there are two correct answers, out of four choices, that is 50%, which is b). But there’s only one b), it’s 25%, so it’s a) and d)… ad nauseam.
STOP. You’re doing this wrong. Let semantics easily (and hopefully painlessly) tell you how to solve this question.
Let’s look at the question again.
“If you choose an answer to this question at random”
Let’s break it down:
IF [You] [choose 1 answer randomly] to [this] question, [percentage answer=TRUE?]
The secret is in the word, “IF”. It summons a counterfactual version of you, that you are able to discuss things in an “if” world, while not being constrained to answer by “if” rules. Thus, [counterfactual You] is supposed to pick 1 answer to [this], where [this] is self-referential to a world that has 2 correct answers out of 4. The answer is 50% for you in this world, not the world [counterfactual You] inhabits.
Hence, in your reality, not the [counterfactual You] in the question, just answer the question that they asked about counterfactual you, simple as that. An equivalent question, substituting counterfactual you with a third person, is:
Kevin has to randomly pick 1 answer out of four. However, 2 of the answers are identical and correct. What is the percentage that Kevin will pick a right answer?
Don’t sweat the counterfactuals, just stick with this reality. The right answer is B.
(No need to read the below if you don’t want technical explanations)
If you want a really convoluted discussion about semantics and counterfactuals and why we can discuss counterfactuals without being constrained by counterfactual rules, it’s simple. In counterfactual semantics we often discuss the death of Aristotle (or was it Plato?), such as “Aristotle might not have been a philosopher if he had died as a kid.” This relates to the topic of indices and what names refer to, largely researched and discussed by many linguists and philosophers, such as Kripke.
A quick answer, without going too in-depth, is that if we are bound by the indices of the counterfactuals we refer to, we will be unable to talk or respond because the counterfactuals are in an infinite loop. Thus, we can talk about Aristotle’s death without having to go back in time to kill him, or talk about what would happen at the end of the world without destroying the world to be able to talk about it. Take the following multiple self-indexed sentence.
If I were you, I would kill me
There are two people involved in the conversation, “you” and “me”, yet to our minds there seems to be a conventional understanding of what the sentence means. It means that “I am such a terrible person that if there were another person, and that person were talking to me, he would hate me so much that he would kill me.” For such a short sentence, it takes such a long sentence to elaborate. Thank goodness for indices! This is how the above sentence works with indices:
IF [counterfactual I][sees]me, [counterfactual I][wants][kill] me.
There you go.
Some time ago, Instagram user jumppingjack posted the above image of a note she left to her mum. She said that her brother secretly added extra strokes to the characters in the note. The result is interesting though: even though extra strokes were added, the note is still readable to most competent Chinese speakers. This phenomenon is very similar to one not too long ago in English, coined “Typoglycemia,” a portmanteau of “typo” and “glycemia” and a pun on “hypoglycaemia,” where as long as the first and the last letter of the word is preserved, the middle can be scrambled and the words are still understandable.
This is an interesting case in what I call persistence of comprehension, where comprehension of words persists despite efforts to thwart it.
Unlike English, which uses the alphabetic system where each letter is a phoneme, or Japanese, a syllabary system where each character is a mora, Chinese uses a logographic system, using “pictures,” or logographs to represent words. So unlike the other two systems where there are things to scramble, it is hard to “scramble” a picture, and scrambling a picture is no different from adding or subtracting strokes from a character, which is what jumppingjack‘s brother did.
Before I go further, let me type out what the note intends to say:
谢谢 🙂 （我明天应该有吃午餐）
Mum, for tomorrow’s lunch because
there aren’t enough people, they have
changed this activity to next Monday.
(But next Saturday afternoon my
company has a lunch event)
Thanks 🙂 (I should be eating lunch tomorrow)
So how does persistence of comprehension occur in Chinese? I shall illustrate some of the characters that are easily understood despite the scrambling and the ones that threw me off (and my friends) the most. (Also, note that the person mis-wrote the character for 期 where he switched the 月 and 其 around, not of her brother’s doing. But the brother added an extra radical as well)
The image above sorts some of the words in the note in order of persistence of comprehensibility from top to bottom, with top being easiest to understand despite scrambling and the bottom being the hardest. The scrambled word is on the left and the proper word is on the right. Note that the bottom four scrambled words are all actual Chinese words, which I will talk about shortly.
The scrambling of the 的 character is one of the easiest to understand, because despite the additional stroke, it still mostly resembles its original character, and does not resemble any other words in the language. The added stroke is a not a radical, a graphical component of a word that is often semantic, unlike the scrambling of the character 明 (tomorrow). Similarly for 因, the added stroke turns the 大 in the 因 into a 太, but on the overall the word is not a real word and mostly resembles its original.
Now we look at the addition of a stroke in 明, turning the 日 (sun) radical, usually used for weather-related words, into a 目 (eye) radical, usually used for vision-related words. The resultant scrambled word is still not a word, but the morphing of a semantically-relevant radical into another makes one pause when reading the sentence. Also, the addition of a stroke to the 月 (moon) component turns it into a 用 (use) character, making comprehension even more difficult.
One step after the 明 character is the 们 character, where not a stroke but an entire 中 (middle) word has been inserted in the middle (haha) of 们. Some of my friends disagree that it is harder than the scrambling of 明, and I’m inclined to agree, and I’d put it as a toss-up between the two. However, I feel that the insertion of an entire word as opposed to a stroke or radical morphs the word enough to the point that it becomes alien enough not to even resemble its original, but does not resemble any other word in Chinese.
Lastly, the last four words, 公，午，伞，and 下, have strokes and/or word components added to them, that they actually resemble other words in the language, 翁 (old man), 牛 (cow), 伞 (umbrella), and 卡 (card). With such resemblance to real words, little wonder people have difficult understanding the words as they read them.
How is it that we are able to understand the note with little difficulty?
In the English “Typoglycemia,” it has been suggested that we identify words not solely by letter position in a word, but by context, shape of the word, and position of word in the sentence. I’m going as far to suggest that in English seeing the individual letters of a scrambled word draws upon our stored memory of the word, further aiding comprehension of a scrambled word. Compare:
Example 1 is classic “typoglycemia” where persistence of comprehension is strong, example 2 removes one non-essential letter from each word, and persistence of comprehension is still relative strong. Example 3 removes what I consider an essential component to the memory of the word, which are usually consonants and not vowels. Take this example:
In English, vowels can be removed quite easily and the comprehension of the word is still possible. This suggests that consonants play a slightly more important part in the reading of words. In that aspect, comprehension of written English has some similarity to comprehension of written Arabic or Hebrew, where typically vowels are not included in the writing (in the way English does anyway). Thus, it is harder to understand “aroindg” as “according” because
How does this relate to Chinese? If we can say that we draw upon essential sequences of components in the comprehension of written English, perhaps there is an equivalent of that in the comprehension of Chinese. I believe that in reading Chinese, there is a stored visual memory of what the character looks like in general, and also an idea of what strokes the character should contain (“legal strokes”), and what it should not (“illegal strokes”).
First, we address whether modifying a Chinese character sets off alarm bells to the reader. Adding legal strokes to scrambled characters should stand out less to the reader, causing him to accept the character as a real word visually. We look at the following example where this is demonstrated:
In the note, a floating shuzhe (vertical-bend) stroke is added to the 够 character, and in the Chinese language there is no such occurrence of a floating shuzhe; they are always attached to other strokes, such as in 喝 (with some exceptions, like 断, which may or may not be attached). Being visually alerted that there is something wrong with the character, we immediately visually discount the scrambled 够, and are able to extract the original word. In the example of 他, the pie (leftward-slant) stroke is added on top of the 亻radical, creating a 彳(step) radical, which exists. Thus when reading the scrambled word of 他, it does not jump out at the reader visually as the shuzhe stroke in 够 does, and we are likely to gloss over it and accept it as it appears to us and are less likely to question whether the character is out of place contextually or not.
Next, adding a legal stroke to scrambled words causes more confusion when the stroke turns the original word into a semantically different word. There is extra confusion when the meaning of the new word does not fit in the context of the sentence, especially when the word has been accepted as it is, as explained in the previous paragraph. These can be seen in the following examples:
If my premises are right, in example 1, readers should be able to identify the error most easily and yet still read the sentence in its original context. In example 2, they should gloss over the wrong character, and since it still resembles very much like the original, is not a new or any word at all, persistence of comprehension should still be strong. In example 3, this is where comprehension begins to be thwarted, where 他能够卡去吃午餐 (He is able to card go eat lunch) and 他能够下去吃牛餐 (He is able to go down and eat cow meal) don’t make any sense as the scrambled words have both legal strokes and are real words, and the meaning of the scrambled words are contextually out of place in the sentence.
What I have coined “the persistence of comprehension” is a seemingly little-researched area in English, much less Chinese. I offered the following reasons explaining the persistence of comprehension in English “typoglycemia,” where through the combination of context, length of word, letter position, shape of word, word position in a sentence, and (what I have demonstrated with examples) identifying the letters, which draw upon phonetic representations of the word in our head, we are able to read English.
In the more interesting case of Chinese, which is logographic, I posited that there are legal and illegal strokes which can be added to a character. Legal strokes are less likely to be noticed than illegal ones. If the scrambled word is a real actual word, the effect of having legal strokes masks the fact that the word has been scrambled, and when we read it, the sentence doesn’t make sense because we do not suspect a character has been tampered with.
All in all, more extensive research must be done, than what this blog can provide. I don’t know if I will be able to do so, but if anyone wants to hear my notes on this topic, feel free to reach out to me at ws672[at]nyu[dot]edu.
Note: In the original version of this post, I wrote that jumppingjack was male, when she is female. Corrections have been made.