Colexify My Insides

 

Comparison of universal colexification networks of emotion concepts with Austronesian and Indo-European language families. Credit: T. H. Henry

How do you know that a 12-inch ruler is in fact 12 inches long? You don't. You trust. I don't know who you trust, if it's the ruler manufacturer, or the society you live in, or who else. But you don't actually know how long that ruler really is.

How do ruler manufacturers know how long 12 inches is? They use a ruler, of course. And where does that ruler come from?

I work in a field where we have to take very precise and accurate measurements of environmental conditions, such as nanogram-concentrations of mercury vapor in the air. If your equipment thinks it's pulling 0.2 liters per minute of air instead of 0.3, then what happens after 8 hours worth of minutes? You get a very distorted sense of how much mercury is in the air (96 vs 144 liters to begin with).

This is why we calibrate our equipment, using another piece of equipment to make sure ours is doing what it says it does. Sure we could talk about The Kilogram, which until last year was used to calibrate every other kilogram-measuring thing ever, and was protected in multiple nested glass encasements in a vault in the basement of a nondescript building in the remote countryside of France.

But instead, we're going talk about language. Because there's no Kilogram for language.

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In the same way that we don't know how long any particular ruler is if we don't have an ur-ruler, how do I know that your meaning of a word is the same as mine? This is like asking if the red you see is the same red I see. Or if the pain you feel is the same pain I feel. Language, like feelings in general, is subjective. How can we calibrate something that has no universal standard?

Language, unlike feelings, does offer a metric by which we can compare and even measure it's meaning to different people. It's not a surprise; words are the way we measure language. But not until now, with the era of Big Data fully upon us, can we can put all the words in the world into one database and compare their meanings across all languages, using the database itself as the closest thing to a universal measuring rod that we can get.

This is called colexification, where we draw lines between all the words in that database, and find common denominators and groupings of words. The goal is to create a universal structure of emotional language that can be used to calibrate and understand these words and especially the people who use them. These are called "emotion colexification networks," and they show us for example how in Austronesian languages, "surprise" is  associated with "fear," whereas Tai-Kadai languages associate "surprise" with the concepts "hope" and "want." (Take a look at the top image in this post.)

In other words, we can now see that if you say you're surprised, but you're saying that in an Austronesian language, then you're probably not so happy, although in English, the word surprise represents something more like happiness.

The researchers working with this ultimate cross-lingual lexicon found significant variations on the positioning of words in the network – the meaning of words changes a lot as you go from one language to another, even if those words are translated as equal with each other.

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In closing, this is interesting research for the world of olfaction, which is another one of those severely subjective phenomena. In fact, the researchers in this study use the same two data points as for olfactory studies, those being valence and intensity. It should be obvious, because the limbic system is the common denominator between the two. The limbic system is the domain of our emotions and of olfactory experience.

Post-Script

Also like in the very recent olfactory research, this study is made possible because of an advance in the database used. CLICS is a database of colexifications involving 2474 languages from around the world; only a few years ago this database had only 300 languages in it.

Notes

Emotion semantics show cultural variation and universal structure across languages of the world.

J.C. Jackson el al. Science (2019).

The meaning of emotion - Cultural and biological evolution impact how humans feel feelings

Dec 2019, phys.org

Roses Really Do

OutKast – Roses – 2003

video link

It’s true. Roses really do smell like the end product of our metabolism. Two major constituents of the smell of roses are Skatole and Indole, which are good olfactory representatives of excrement. They’re also used extensively in perfume.

This may or may not explain why many air fresheners (at least as far as I know since the dawn of aerosolized air fresheners) smell like roses. Regardless of whether shit and flowers go together like peanut butter and jelly, it was used a lot as an air freshener scent, at least as far as the early 90’s. (Anyone like to weigh-in here on the history of air fresheners?)

But this fact does explain why I, among others I’m sure, hallucinate excrement when smelling roses – you expect it to be there. This is called redintegration, a kind of hallucination, and it’s explained in this clip from Hidden Scents:

Part of a smell can carry with it the co-occurring odor molecules around them in the memory, and it will later be used to substitute for the whole. Strains of cannabis, aside from the strong skunk-like smell, can have significant amounts of limonene in them. Through redintegration, the potent smells of such cannabis become so tied together that upon smelling an orange (almost entirely limonene), a frequent user might hallucinate the other odors of cannabis along with the orange. This phenomenon represents an apparition superimposed in order to satisfy the nose-brains’ insistence on predicting an odor based on limited or partial information – a behavior that is not limited to olfactory perception.

Post Script

The scent of “musk” comes from the neither region of the musk deer. Just saying. Today you wouldn’t know that, because that scent of musk is now more associated with fresh laundry. Musk is a very big molecule, for a smell, and it sticks really well to your clothes even as they’re being washed, so it’s the main ingredient in laundry detergents.

When you smell “clean laundry,” you don’t think you’re smelling musk, but you are. Funny how today we associate clean with a thing that ultimately comes from an animal’s butt. (Please note that today, most if not all musk comes from a laboratory and not an animal.)

Corpus of Hedonics

Looking at this paper today:

The Emotional and Chromatic Layers of Urban Smells. Daniele Quercia, Luca Maria Aiello, Rossano Schifanella. 2016.

https://arxiv.org/pdf/1605.06721.pdf

The chart above shows the relation between a smell-producing location, and the pleasantness of the words used on social media near that place. Most positive words are used near the Food category, and less near Waste. Nature has less happy words with it than Food, perhaps because of the happy activities people are doing in these areas, i.e., eating and being with friends. Regardless, this chart seems to make sense, and can serve as proof that this odor-hedonics data can be predictive of people's emotions.

Who cares? Someone like an urban planner can use this to get an idea of how to better arrange municipal facilities. Someone who wants to update local land use regulations could use this data to see which areas of town work and which ones don't. The study-authors mention the matching of this data to 'most optimal route' data to give the 'most pleasurable route' through a city.

They used a lot of semantic-hedonic smell knowledge from a previous study (Henshaw 2013) which organizes two lists of pleasant and unpleasant smells, and I'd like to just copy it here:

Pleasant smells

bread, baked, baked goods, coffee, coffees, aftershave, cut grass, grass, grassy, floral, flower, flowers, flowershop, flowery, lavender, lilies, lily, magnolia, rose, rosey, tulip, tulips, violet, violets, baby, babies, child, children, sea, seaside, countryside, cedar, cedarwood, conifer, dry grass, earth, earthy, eucalyptus, ground, leafy, leaves, old wood, pine, sandalwood, soil, tree, trees, wood, woodlands, woody, petrol, diesel, fuel, gasoline, soap powder, soap

Unpleasant smells

flatulence, fart, vomit, dog shit, dogshit, excrement, faeces, farts, feces, manure, shit, cigarette smoke, cigarette, cigarettes, cigar, cigars, smoker, tabacco, tobacco, pee, piss, ammonia, urine, public toilet, public toilets, toilet, toilets, urinal, urinals, gone-off milk, fish, rotten fish, rotten food, rotten, rotten fruit, rotten fruits, putrid, bus, buses, car, cars, exhaust, traffic, fume, fumes, body odour, body odor, sweat, sweaty, dirty clothes

  Henshaw, V. 2013. Urban Smellscapes: Understanding and Designing City Smell Environments. Routledge.

Post Script:

This is how they got to the bottom of their smellscape emotion chart:

"We set out to study the relationship between the smellscape and emotions on our data. To do so, we need to have a lexicon of emotion words. We use two of them: the “Linguistic Inquiry Word Count” (LIWC) (Pennebaker 2013), that classifies words into positive and negative emotions, and the “EmoLex” word-emotion lexicon (Mohammad and Turney 2013), that classifies words into eight primary emotions based on Plutchik’s psycho-evolutionary theory (Plutchik 1991) (i.e., anger, fear, anticipation, trust, surprise, sadness, joy, and disgust).

  Pennebaker, J. 2013. The Secret Life of Pronouns: What Our Words Say About Us. Bloomsbury.

  Plutchik, R. 1991. The emotions. University Press of America.

  Mohammad, S. M., and Turney, P. D. 2013. Crowdsourcing a word–emotion association lexicon. Computational Intelligence 29(3):436–465.