“The word ‘onomatopoeia’ is also an onomatopoeia because it’s derived from the sound produced when the word is spoken aloud.” – Ken M
Imagine ringing up a caveman. Utter astonishment on both ends of the line aside, what would you talk about? What could you even talk about? Without the accompanied pantomime it may even be impossible to convey a message, made up out of regular words, that is received in full fidelity in this situation. It’s hard holding a conversation that transcends time and space solely through the medium of sound. Certain sounds, however, are and will consist of similar sound waves regardless of whether they are perceived here and now or during the construction of the pyramids of Giza. Likely you would use sounds mimicking sounds from nature like thunder or bird calls. Some onomatopoeia might be understood as they resemble a sound but not quite.
Fart jokes are considered low hanging fruit to some. But in my opinion, their universality and timelessness are unmatched. From the Japanese in the Edo period to Mozart and Aristophanes, fart jokes are understood by everyone (at least humans) throughout all ages. Reportedly Mozart was obsessed with farts and even wrote songs about them. Using sound to convey a sound is what makes a fart joke so easy to understand. If you wrote down a joke it wouldn’t make people laugh that can’t read the joke. Sound is powerful in that regard. Although maybe not as powerful as the visual medium. The Arecibo message is a message meant to be received by other intelligent life forms. It was transmitted through radio waves but carries a visual message. Can we use sound to convey a visual message in other ways?
Wolfgang Kohler observed exactly this when he performed an experiment where he presented people with two figures, one jagged, one rounded, and two names: Takete and Baluba. He then asked which name belonged to which figure. A strong preference for calling the rounded shape Baluba and the jagged shape Takete was found. This seems to indicate that humans have to ability to convert a sound into a visual idea.
In 2001 a similar experiment was repeated under Tamil speakers in India. The Tamil language uses many ideophones, words that elicit an idea in sound, on a daily basis. In the case of onomatopoeia, a sound evokes the idea of another sound. In Tamil, these ideas can also be different perceptions like visual ideas. Ideophones are uncommon in western languages, but we do have a few such as zigzag or blob. The shape of the letters may have something to do with this, but I ain’t no linguist.
Another ideophone is the conceptualisation of a whole personality when hearing a name. ‘Recently’ this has become a meme in the form of Karen. Karen is someone that ate a whole burger and decided not to like it and now wants to speak to the manager for a refund. Everyone knows a person like this and many people think that the name Karen fits the bill. This concept isn’t completely new. The name Kevin in Germany is associated with a low-achieving person, usually from a lower-class background. It’s a vicious cycle where Kevins, for whom this is not true, are discriminated against and are met with low expectations. It is possible for a Kevin to not be a ‘Kevin’ but the prophecy tends to fulfill itself for these reasons. This is called Kevinismus.
Do some names better suit particular faces than others? Do people have a preference? In order to test this, I propose the following two surveys. In the first survey, people are shown 10 faces, 5 male and 5 female, of non-existent people and are asked to come up with a name for each face. These names are then compiled into a list per face and a list of all male and all female names to sample from later. In the second survey, people are shown the same 10 faces with underneath each a list of 5 names of which 4 are randomly sampled from the list of all names and one is the most frequent name from the first survey. If some names really belong to certain faces we should see that participants choose the most frequent name more often than random chance.
This is exactly what I did. Posted my survey on r/samplesize and r/namenerds and celebrated a premature Burns night. A few hours later I checked in on the form and it had more than 200 respondents! Eventually, the survey would have an astonishing 1108 responses before I stopped taking entries to make survey number 2 (probably should not have done that during Burns night). After cleaning the data I quickly realised that I had a problem: Michael and Mark. Two faces had those names as their most frequent names. I might just mess this up later, I decided that each face would get the most frequent unique name instead. Let me introduce you to the names of the faces and the results!
- Sarah 58
- Emily 57
- Emma 32
- Hannah 25
- Alice 22
- Sarah 34%
- Carrie 23.1%
- Evelyn 19.9%
- Lynn 11.5%
- Jennifer 11.5%
Honourable mention: Lily Guardian of the Forest. In this case the data seems to confirm the hypothesis!
- Michael 47
- Mark 47
- Robert 31
- David 31
- John 22
- David 39.6%
- Michael 30.1%
- Enrique 17.7%
- Keith 7.9%
- Jack 4.7%
Honourable mentions: Dan glancer of surreal gallery, Skebep Bernardo. Here the top choice came second.
- Jessica 40
- Ashley 38
- Karen 31
- Sarah 27
- Brittany 25
- Brittany 39.4%
- Jessica 30.1%
- Caitlyn 21.7%
- Linda 6%
- Mary 2.9%
Honourable mention: Baby Karen. Here the fifth place was randomly sampled and overtook the top choice. This face has some elements commonly associated with ‘Karen’ mainly the hair-do, but not quite everything. Nevertheless Karen was the third most frequent entry.
- Mark 92
- John 59
- David 42
- Robert 41
- Michael 35
- Doug 35.7%
- Michael 29.8%
- Rick 27.7%
- Reiner 5.1%
- Kajim 1.6%
Honourable mention: Garret imposer of dreams. I messed up. I should’ve included the top mention in the second survey here, instead I accidentally entered Michael instead… Nevertheless ‘Michael’ does very well in the second survey. One explanation as to why ‘Mark’ was chosen so many is times is that this face may resemble Mark Cuban a little (maybe/probably I’m very wrong).
- Mark 55
- John 45
- Peter 35
- David 34
- Michael 31
- Robert 36.7%
- Patrick 27%
- John 25.6%
- Ilgar 8%
- Bob 2.7%
Honourable mentions: Jean Pierre commiter of war crimes, goodwill Jared Kushner. Here I went with John as the name among random samples. ‘John’ was outranked by ‘Robert’ and ‘Patrick’.
- Michelle 31
- Amy 25
- Jennifer 23
- Lisa 23
- Kim 21
- Michelle 52.5%
- Mindy 17.3%
- Helen 13.6%
- Hannah 10.2%
- Sandy 6.4%
Honourable mentions: Supreme leader Annette of the Czech Republic, Mandy Microkrediet. ‘Michelle’ hit it out of the park.
- Gordon 38
- John 27
- Bob 27
- Paul 26
- Robert 24
- Gordon 32.1%
- Peter 29.4%
- Richard 24.2%
- Jason 8.7%
- Joey 5.7%
Honourable mentions: Bob teller of dad jokes, generic chef #5. ‘Gordon’ was a big succes. As the last honourable mention alludes to, this face may resemble chef Gordon Ramsey.
- Michael 31
- James 20
- Mark
- Marcus
- George
- Marcus 46.5%
- Eric 22.5%
- James 17.6%
- Seth 10.6%
- Jim 2.8%
Honourable mention: Guptar possesor of worldly riches. Here I went with ‘James’, which didn’t do so well. ‘Marcus’, the 4th name in the first survey seems to be more fitting.
- Susan 53
- Mary 48
- Linda 43
- Margaret 41
- Barbara 26
- Susan 62.9%
- Gillian 17.4%
- Harriet 13%
- Claire 5.7%
- Savannah 1%
Honourable mention: grandma Deborah conveyer of bed time tales. ‘Susan’ is an enormous succes!
- Margaret 42
- Susan 40
- Karen 35
- Mary 34
- Elizabeth 28
- Margaret 48.6%
- Marianne 34.8
- Monica 11.3%
- Megan 3.9%
- Hayley 1.3%
Honourable mention: grandma Daisy baker of cookies. Margaret matched up very well. Susan was a good second and Karen made another appearance.
Bonus rounds
A whopping 89.1% ascribed the name Takete to the jagged shape, reaffirming the original hypothesis. 6.2% found that both names were equally fitting and 4.7% found Maluma a better fit.
This is the image that all started it off for me. The fisherman hat, the weird glasses, long sunkissed hair, and inebriated gaze. He strikes me as a ‘the Dude’ type of personality, but I didn’t have a name. From some comments on Reddit, I gathered that he bears a resemblance to the professional boxers Jake and Logan Paul. Personally, I’ve settled on Steve.
- Jake 39
- Logan 35
- Chris 23
- Kyle 23
- Steve 19
Honourable mentions: Geoff but insists it’s pronounced phonetically, Master Leaf, Floombeard, Broderick, Slappy Whiskers, he calls himself Jagster, Shaggy from Scooby Doo, Broccolingus, Dude McBro the Alpha Surfer of Florida, Skane Skurr, Kyle’s midlife crisis, Lil Soggy, Jimbob, Child Boulder,
- Damien 52
- Kevin 29
- Sid 17
- Kyle 13
- Damian 11
Honourable mentions: Shadowfax, Malachi Badson, Tony Pajamas, xX_69gamerjuice69_Xx, Helvetica, Smob, Discount Pennywise
Apparently Damian is a kid and son of the devil in movie Omen. Sid is also famously an evil kid in Toy Story.
- Jason 24
- Seth 23
- James 20
- Chris 16
- Jack 16
6 people correctly found my real name: Rumpelstiltskin. Honourable mentions: Llanfairpwllgwyngyllgogerychwyrndrobwllllantysiliogogogoch, “I’m not mad, I’m just disappointed”, 5/10, “…you […] actually look less real than the other people…”, X Æ 12 ẞ, Facey McFaceFace, Cletus
Thanks, everyone for participating everyone! The blog is still a work in progress, the story is a bit all over the place and I probably abused a lot of jargon. The study could have been conducted better as some pointed out. I agree. I didn’t expect to get enough responses for even one name to be repeated twice. It can luckily easily be set up again. Many seemed to enjoy filling out the names nevertheless. If you have any suggestions either email me or shoot me a message on Reddit.