A while ago there was an article in Quantamagazine about neuroscience, “To Decode the Brain, Scientists Automate the Study of Behaviour“. It’s main gist is that scientists used machine learning to classify patterns in the behaviour of animals, or as the subtitle puts it to “capture and analyze the “language” of animal behavior”.
This quote puts it quite succinctly:
By studying animals’ behaviors more rigorously and quantitatively, researchers are hoping for deeper insights into the unobservable “drives,” or internal states, responsible for them. “We don’t know the possible states an animal can even be in,” wrote Adam Calhoun […]
Tracing those internal states back to specific activity in the brain’s complex neural circuitry presents a further hurdle. Although sophisticated tools can record from thousands of neurons at once, “we don’t understand the output of the brain,” Datta said. “Making sense of these dense neural codes is going to require access to a richer understanding of behavior.”
The article then goes on to describe how modern technology like motion tracking revolutionized the quantitative study of the behavior of animals by letting scientists track, collect and analyze movement patterns and so on.
The article then segues into deeper questions:
Because pose-tracking software has simplified data collection, “now we can think about other problems,” said Benjamin de Bivort, a behavioral biologist at Harvard University. Starting with: How do we define the building blocks of behavior, and how do we interpret them? […]
The zoologist Ilan Golani at Tel Aviv University has spent much of the past six decades in search of a less arbitrary way to describe and analyze behavior — one involving a fundamental unit of behavior akin to the atom in chemistry.
It goes on to describe a breakthrough that discovered minimal building blocks in the movements of mice.
The dynamics of the animals’ three-dimensional behavior seemed to segment naturally into small chunks that lasted for 300 milliseconds on average. “This is just in the data. I’m showing you raw data,” Datta said. “It’s just a fundamental feature of the mouse’s behavior.”
Those chunks, he thought, looked an awful lot like what you might expect a unit of behavior to look like — like syllables, strung together through a set of rules, or grammar.
This is of course only an analogy, a handy metaphor, because language is not behaviour.
Because of these advances “they’re starting to make the first connections to the brain and its internal states”.
Datta and his colleagues discovered that in the striatum, a brain region responsible for motor planning and other functions, different sets of neurons fire to represent the different syllables identified by MoSeq. So “we know that this grammar is directly regulated by the brain,” Datta said. “It’s not just an epiphenomenon, it’s an actual thing the brain controls.”
The article then ends with this inspirational passage
The scientists are careful to note that these techniques should enhance and complement traditional behavioral studies, not replace them. They also agree that much work needs to be done before core universal principles of behavior will start to emerge. Additional machine learning models will be needed, for example, to correlate the behavioral data with other complex types of information.
“This is very much a first step in terms of thinking about this problem,” Datta said. He has no doubt that “some kid is going to come up with a much better way of doing this.” Still, “what’s nice about this is that we’re getting away from the place where ethologists were, where people were arguing with each other and yelling at each other over whether my description is better than yours. Now we have a yardstick.”
“We are getting to a point where the methods are keeping up with our questions,” Murthy said. “That roadblock has just been lifted. So I think that the sky’s the limit. People can do what they want.”
Reading this article as a linguist leads to a mix of very diverse feelings. On one hand it is great to see these advances in the behaviour of animals and its links to neurological signals. On the other hand it is funny to see how the mere beginning of an understanding of patterns in behaviour is hailed as a breakthrough while a general classification is viewed as the holy grail that could bring cognitive science/neuroscience to unimaginable new depths.
We already do have a pretty advanced (comparatively) understanding of the behaviour (of a higher cognitive function even) of an animal: language in homo sapiens. We actually know of ‘syllables’ in the structure of language, and how they are strung together, almost as if we had an understanding of the ‘grammar’ of how language works. We are even so far that we can do theoretical debate about the structure of these patterns: what the actual minimal building blocks are or if some of them have to be broken up further, how these building blocks are related to each other, what the best framework is to talk about etc. That is, we are far beyond simply quantitatively collecting data. In this case it is even comparably easy to get access to these data.
For some reason, however, this does not make linguistics the most advanced cognitive science around in the eyes of the public, neighbouring fields or even some linguists themselves. Talking is not a cognitive function, is just stuff humans do, like filling out tax forms or knowing the rules of football. This is similar to the argument Norbert Hornstein has been making about the fact that if bee dances can earn you a Nobel prize in biology, linguists should be able to get them, too.
What’s even more sad, but on a different level, is the fact that even though we do have such a comparably deep understanding, this still doesn’t help us to achieve those Nobel prize-worthy discoveries in linking cognition to activity in the brain (which is the impression you get at the end of the article: “Just imagine what we could do now with the understanding of behaviour from in 50 years”).
So it’s still early days.
Before I forget, there have been some other articles with tangential links to linguistics in Quantamagazine in the last year:
One is about recent efforts and achievements in Natural Language Understanding, i.e. more from an engineering perspective than from a scientific one, but people like Tal Linzen and also colourful green ideas make an appearance.
The other is about discoveries about computational power inside single neurons, something that should make those happy who are fans of Gallistel & King & co.