The Olfactory Determinants of Culture

First direct evidence that babies react to taste and smell in the womb

Oct 2022, phys.org

https://medicalxpress.com/news/2022-09-evidence-babies-react-womb.html

4D ultrasound -- Fetuses exposed to carrot showed more "laughter-face" responses while those exposed to kale showed more "cry-face" responses.

via Durham University: Flavour Sensing in Utero and Emerging Discriminative Behaviours in the Human Fetus, Psychological Science (2022). DOI: 10.1177/09567976221105460

https://dx.doi.org/10.1177/09567976221105460

Ancient humans had same sense of smell, but different sensitivities

Jan 2023, phys.org

https://phys.org/news/2023-01-ancient-humans-sensitivities.html

"We had the odorant receptor genomes from Neanderthal and Denisovan individuals and we could compare them with today's humans and determine if they resulted in a different protein."

So then they tested the responses of 30 lab-grown olfactory receptors from each hominin against a battery of smells to measure how sensitive each kind of receptor was to a particular fragrance.

The laboratory tests showed the modern and ancient human receptors were essentially detecting the same odors, but their sensitivities differed.

Denisovans -- less sensitive to floral, better at sulfur, balsamic, and honey

Neanderthals -- less responsive to green, floral and spicy scents

via Duke University: Claire A. de March et al, Genetic and functional odorant receptor variation in the Homo lineage, iScience (2022). DOI: 10.1016/j.isci.2022.105908

https://dx.doi.org/10.1016/j.isci.2022.105908

Image credit: AI Art - Photograph of a Neanderthal Nikon 85mm DSLR - 2023.jpeg

Reminder that "mummy fever" was a thing back in the 1800's and it was a big deal to break out the mummy meat for your esteemed guests, because nothing identifies the top tier of society like eating small fragrant bits of ancient humans:

Teasing out the secret recipes for mummification in ancient Egypt

Feb 2023, Ars Technica

https://arstechnica.com/science/2023/02/teasing-out-the-secret-recipes-for-mummification-in-ancient-egypt/

The results: “We could identify a large diversity of substances which were used by the embalmers,” co-author Maxime Rageot of the University of Tübingen told New Scientist. Those substances included oils or tars from juniper, cypress, or cedar; various resins, including some from Pistacia trees; and animal fats, beeswax, and plant oils. Most of those have been found before in mummies, but two resins—dammar and elemi—have not been previously identified anywhere in Egypt before. They also found bitumen from the Dead Sea. -via New Scientist

On Hedonic Consultation

AKA The Evolution of the Autobiographical Odor Encyclopedia 

This study copied below measures how fast we detect good smells vs bad smells (spoiler, bad smells are detected faster). 

But while reading through this, consider that bad smells can become good over a series of exposures matched with good feelings. Aged cheese, fermented cabbage, and burned cannabis are pretty well known examples of this. There's also people, who smell, each with our own odor fingerprint, although we may not realize it at times, as it might be below our limit of detection.

And then there's the reverse, where things (or people) that once smelled good, all of the sudden smell bad, such as with changes in birth control, or pregnancy, or after a viral infection like Covid (see the parosmia triggers study). In those cases, the whole olfactory system is rewritten, a kind of blank slate re-learning, where smells with strong odor components (like individually unique body odors, or coffee) are perceived as if for the first time, with the bad stuff up front. And all you can focus on is the bad, since you have to "re-learn" the smell, and how the good integrates with the bad to produce something that is neither good, nor bad, nor even identifiable by semantic description, but only by the name of the person. 

Nonetheless, there seem to be some good millisecond metrics here:

Seeing how odor is processed in the brain

Jun 2022, phys.org

https://medicalxpress.com/news/2022-05-odor-brain.html

• Detection occurred before the odor was consciously perceived by the participant
• Odor information in the brain is unrelated to perception during the early stages of being processed
• Later, unpleasant odors were processed more quickly than pleasant odors

The participants wore an EEG cap while having smells shot at their face, and so that researchers could see when and where odors are processed in the brain.

"We were surprised that we could detect signals from presented odors from very early EEG responses, as quickly as 100 milliseconds after odor onset, suggesting that representation of odor information in the brain occurs rapidly"

Remember that the olfactory system has only a few synapse-steps, making it the most direct sensory system we have.

And then watch how they pretty much rehearse Proust's deep cookie immersion:

When unpleasant odors (such as rotten and rancid smells) were administered, participants' brains could differentiate them from neutral or pleasant odors as early as 300 milliseconds after onset. However, representation of pleasant odors (such as floral and fruity smells) in the brain didn't occur until 500 milliseconds onwards, around the same time as when the quality of the odor was also represented. From 600–850 milliseconds after odor onset, significant areas of the brain involved in emotional, semantic (language) and memory processing then became most involved.

via University of Tokyo: Mugihiko Kato et al, Spatiotemporal dynamics of odor representations in the human brain revealed by EEG decoding, Proceedings of the National Academy of Sciences (2022). DOI: 10.1073/pnas.211496611

https://doi.org/10.1073/pnas.2114966119

Post Script:

Professor Robert Sapolsky Stanford Lecture - On Recognizing Relatives (with smell)

https://www.youtube.com/watch?v=P388gUPSq_I

Learning to Smell: Olfactory Perception from Neurobiology to Behavior, by Donald Alan Wilson and Richard J. Stevenson, Johns Hopkins University Press (2006)

https://www.press.jhu.edu/books/title/8484/learning-smell

The Evolving Artificial Organism

A taste of things to come, researchers are finally firing-up an artificial organism to record how it evolves from primitive unicellular origins to hyper-plexed associative memory network.

The artificial organism unfolds in a virtual world at over ten-thousand generations per hour (kind of hard to do in real life). We can then see how higher beings develop the ability to create associations, and eventually use this knowledge to build more intelligent robots.

Good thing olfaction is the prototypical primordial sensory system, because that’s why this new research is being posted right here. But think about this for a moment – there is no artificial nose. We already have the seeing retina, the hearing cochlea, and even a hand that feels. The nose however, has not been reverse-engineered.

There is a true challenge in replicating the sense of smell, and that is because our sense of smell is programmed by our autobiography. Smells don't mean much to us outside of our subjective experience with them. You just can't upload a dictionary of smells into an electronic nose and expect it to recognize random odors in its environment.

The only way you could do that is if you had a robot that grew up, just like a little kid, with multimodal experiences, social integration, and existential episodes, all associated together and built together into the tangled ball of nerve fibers that we call Self.

Your robot would then have its own limbic system, programmed by a childhood of interaction with the world. It would have to develop a life of its own, an autobiography. This self-identity would then be the substrate upon which the odor network is built. It could then recognize odors, as they would stimulate physiological and emotional responses and associative episodic memories.

Because smell is so tied to our limbic system, it requires a body in order to work. A cerebral organoid isn't a body per se. And neither is an artificially intelligent neural network. And neither is a robot that “comes to life” as a fully-formed adult, all booted-up and ready to go. Humans don’t do it like that. You can’t have a self without a history. (See Patient HM for more on that, however.)

What this new research now reminds us, is that not only does an artificial intelligentity need a body in order to smell, it also needs a lifetime of learning as well.

Notes:

Evolution of learning is key to better artificial intelligence

Sep 2019, phys.org

Anselmo Pontes et al. The Evolutionary Origin of Associative Learning, The American Naturalist (2019). DOI: 10.1086/706252

Post Script:

Finally seeing someone recognize the utility of studying olfaction in the context of machine learning artificial intelligence:

"Srinivasan says he will focus on how noise or variability in odor coding determines the balance between discrimination and learning, explaining that the variability the duo is finding in their work might be a mechanism for distinguishing odors, which could be applied to making better machine learning or AI systems."

How mammals' brains evolved to distinguish odors is nothing to sniff at

July 2019, phys.org

Cracking the Black Box

New technique elucidates the inner workings of neural networks trained on visual data

Jul 2017, phys.org

"Deep learning" and "neural networks" are terms that have become firmly planted in our popular lexicon. They all refer to the same thing, which is an artificial brain-like thing that teaches itself via feedback loops. I talked about this in Hidden Scents because the way our brain decodes olfactory information is a lot like the way these deep learning networks process their own big data.

This deep learning approach is way more effective than traditional computing for lots of problems like facial recognition or natural language translation. They're also really good at handling Big Data, you  know, like all that stuff cybercriminals keep stealing for ransom? Thing is, once these networks 'figure out' how to do whatever it is that they do, we have no idea how they did it.

Usually, with traditional programming, we write the code, so we know what it does and how. With this, the network essentially writes its own program, and since it seems to know what it's doing, we don't ask how. We just take the results.

Until now. This is one of the researchers, quoted in phys.org:

"We catalogued 1,100 visual concepts—things like the color green, or a swirly texture, or wood material, or a human face, or a bicycle wheel, or a snowy mountaintop," says David Bau, an MIT graduate student in electrical engineering and computer science and one of the paper's two first authors. "We drew on several data sets that other people had developed, and merged them into a broadly and densely labeled data set of visual concepts. It's got many, many labels, and for each label we know which pixels in which image correspond to that label."

Here is the link to their work.

image source