Linguists discover 30 sounds that may have allowed communication before words existed.
- What did the first person who wanted to speak say?
- New research suggests that there are lots of sounds that everyone understands.
- These sounds may have allowed the first exchanges that gave birth to language.
As hard as it is sometimes to get a conversation started, imagine how difficult it must have been before words existed. Linguists have long wondered how verbal language began. Some form of communication must have been in place to get the whole thing going. Maybe it was gestures.
Now, a new study published in Scientific Reports by linguists at the University of Birmingham (UBir) in the UK and the Leibniz-Centre General Linguistics in Berlin proposes another idea: Verbal communication may have begun at least partly with "iconic" mouth-produced sounds whose meanings were inherently obvious to anyone who heard them. (The researchers use the word "iconic" to mean that these sounds represent things.)
The importance of these sounds may also extend beyond their role as the ultimate conversation starters, says co-author Marcus Perlman of UBir. "Our study fills in a crucial piece of the puzzle of language evolution, suggesting the possibility that all languages — spoken as well as signed — may have iconic origins."
30 iconic sounds
Credit: Alexander Pokusay /
The researchers have posted a few of these iconic sounds: "cut," "tiger," "water," and "good." (Note: These audio files won't play in Apple's Safari browser.) The study reveals that there are a lot more of these sounds than previously appreciated, and likely enough to form a bridge to language development.
Co-author UBir's Bodo Winter explains:
"Our findings challenge the often-cited idea that vocalizations have limited potential for iconic representation, demonstrating that in the absence of words people can use vocalizations to communicate a variety of meanings — serving effectively for cross-cultural communication when people lack a common language."
The researchers compiled a list of 30 iconic-sound candidates that likely would have been of use to the earliest speakers. These included mouth noises that could represent:
- animate beings — "child," "man," "woman," "snake," "tiger," "deer"
- inanimate objects — "fire," "rock," "meat," "water," "knife," "fruit"
- activities — "eat," "sleep," "cut," "cook," "gather," "hunt," "hide"
- descriptors — "good," "bad," "small," "big" "dull," "sharp"
- quantities — "one," "many"
- demonstrative words — "this," "that"
Was "nom, nom" the sound for eating?
Credit: Aleksandra Ćwiek, et al. / Scientific Reports
Making a list — and making noises — is one thing; finding out if anyone understands them is another. The researchers tested out their iconic sounds in two different experiments.
In an online experiment, speakers of 25 different languages were asked to match the meaning of iconic sounds to six written labels. They listened to three performances for each of the 30 candidates, 90 recordings in all.
Participants correctly identified the sounds' meaning roughly 65 percent of the time.
Some meanings were more readily understood than others. "Sleep" was correctly identified by almost 99 percent, as opposed to "that," understood by only 35 percent. The most often understood sounds were "eat," "child," "sleep," "tiger," and "water." The least? "That," "gather," "sharp," "dull," and "knife."
The researchers next conducted field experiments to capture the meaningfulness of the sounds in oral cultures with inconsistent literacy levels. For these people, researchers played twelve iconic sounds for animals and inanimate objects as listeners identified each from a grid of pictures. The volunteers correctly identified the sounds' meanings about 56 percent of the time, again above the level of chance.
The universal roots of language
In addition to being the sounds that facilitated the birth of language, the authors of the study wonder if such commonly understood sounds may also be a factor in the similarities that exist between different modern languages that don't share a common root language. They cite other research that found "vocalizations for 25 different emotions were identifiable across cultures with above-chance accuracy."
"The ability to use iconicity to create universally understandable vocalizations," says Perlman, "may underpin the vast semantic breadth of spoken languages, playing a role similar to representational gestures in the formation of signed languages."
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Digitized logbooks from the 1800s reveal a steep decline in strike rate for whalers.
Until someone works out a way to communicate with them, we can't really know how smart whales are. We do know they have the largest brains of any animals on the planet—of course, big is a thing they do really well altogether—and that their brains have more cortical convolutions than any other creature, including humans. There are indications that they're quite intelligent.
If that's so, however, why did 19th-century whalers in the North Pacific find it so easy to drive them to the edge of extinction? Didn't they see what was happening? New research published by the Royal Society in the U.K. apparently has an answer to that question, and it is "yes." An analysis of newly digitized whalers' log books finds that whalers' ability to harpoon sperm whales dropped precipitously after initial successes.
One possible explanation for the falloff would be that whalers' competence somehow degraded over time, but that doesn't seem especially logical. A more likely interpretation is that whales warned each other and modified their behavior to avoid the ships. If this is so, it suggests several thrilling things about the animals. First, they apparently shared information about the new predators, and second, they developed an effective evasive strategy.
A good look at mariners’ records
Credit: Aris Suwanmalee/Adobe Stock
The paper was written by cetacean experts Hal Whitehead of Dalhousie University in Halifax, Nova Scotia and Luke Rendell of University of St. Andrews in Scotland, along with data scientist Tim. D. Smith. Whitehead and Rendell are co-authors of "The Cultural Lives of Whales and Dolphins."
The researchers were working from the logbooks of American whalers operating between 10° and 50° in the North Pacific Ocean in the 19th century. The daily logs listed a ship's noon position, the number of sperm whales sighted, and how many whales were harpooned ("struck") or processed ("tried"). These records allowed the researchers to identify the date on which first contact with local whales occurred. From there, they were able to calculate the rate at which whales were encountered over the subsequent years.
The researchers found that about 2.4 years after first contact, whalers' strike rate fell by 58 percent.
At first, it seems the whales didn't quite know what to do about the whalers and responded to them similarly to the manner in which they defend themselves against the only predator they'd known up to that point: orcas. They formed defensive circles, their powerful tails pointed out to fend off their attackers. Unfortunately, this provided no defense against harpoons and likely made whaler's jobs easier by gathering groups of whales together where they could be easily killed.
Soon however, the leviathan strategy shifted and whales took to swimming upwind away from whalers' ships, an effective evasive maneuver that kept them ahead of the wind-driven boats. As White tells The Guardian, "This was cultural evolution, much too fast for genetic evolution."
Whale social learning and strategy
Spectrogram of a humpback whale song
Credit: Spyrogumas/Wikimedia Commons
While there remains debate over whether whale communities exhibit characteristics we'd recognize as culture, examples of what seems to be social learning support the idea that it does exist.
Whales are known to communicate with each other over large distances through their haunting—and mysterious to us—songs. These songs provide some hard-to-argue-with evidence for social leaning among whales: They evolve over time, and as they change, those changes are reflected by entire local whale populations. "We don't have to do anything but observe it to know that there's no explanation other than learning from others that can account for this," wrote Whitehead and Rendell to NPR in 2015.
Rendell wrote in Science in 2013 about what seems to be an innovation that was shared among whales: the spread of a particular type of feeding, "lobtailing," that seems to have spread from one humpback whale in 1980 to hundreds in a wider area over the next few decades.
There are also examples of cetaceans clearly using strategy, such as the manner in which orcas hunt together for Weddell seals, described by NOAA scientist Bob Pitman. The seals attempt to evade the orcas by remaining out of the water on ice floes. The orcas synchronize their flukes to create waves that either knock a seal off of a floe, or break the ice apart. Once the seal is in the water, the orcas blow bubbles under the water and apparently using their tails to create enough turbulence that the seal finds it harder to get back on the ice. If it does get out to safety, the orcas do it all over again until, according to Pitman, by about the fourth attempt, they usually have their prey, which they share.
And then there's the whales' evasive tactics for dealing with 19th-century whaling ships.
Back to the present and future
Unfortunately, modern vessels , equipment, and strategies were not as easy to evade, and whale populations were severely depleted in the 20 century. And while that threat is hopefully diminishing, modern fishing tactics such a long-line fishing that hooks whales, the intrusion of human noise in the oceans, plastics and other floating waste, and climate change means that today's seas are just as challenging as ever to whales. Maybe moreso. And nobody can outswim climate change.
How our brains interpret computer code could impact how we teach it.
- Computer coding is a relatively new skill, so our brains can't have specialized areas for it from birth.
- The question of how we process computer code, as a language or as math, could impact how we teach the subject.
- A new MIT study suggests our brains treat it as its own special topic.
The comparison between computers and the human brain is hard to get away from. It is often a useful analogy, but sometimes conflicts with how our brains actually work.
One of the continuing questions about how our brains are similar or dissimilar to computers is how they process code. Do we process it as if it is a language or a series of math problems? This question is important for a number of reasons. From an educational standpoint, knowing how our brains work when dealing with coding problems could provide insights into how to teach it.
Some schools are beginning to allow students to select coding languages for a "foreign" language credit and are approaching the subject in the same way they might teach French. This might be a decent way to get more students into coding but could backfire if reliance on language learning techniques is misplaced, for example. Likewise, attempts to teach coding as math might be equally mistaken.
To help settle the debate, a new study analyzed the brain activity of computer programmers while they read code.
"Programs must be written for people to read, and only incidentally for machines to execute." - Harold Abelson.
The study, carried out by a team from MIT and Tufts University, had two dozen participants read code, English, and logic puzzles while in an fMRI machine. By seeing which parts of the brain lit up while doing these tasks, the researchers could determine how our brains process coding languages.
If the areas of the brain associated with language processing were to light up, then we treat code like we treat languages. The same would go for the math parts. The control tasks, reading either a real sentence or a nonsense one and memorizing the location of colored squares, demonstrated the baseline activation levels for these systems in each subject.
The coding languages used in the study were Python, a language considered highly readable by many, and ScratchJr, a symbolic picture code designed for children.
An example of the code and puzzles that might be seen in the experiment.
When the subjects were in the machine, they were asked to work through the code and predict the output. The brain scans showed only limited responses in the brain's language processing centers, but a considerable amount in the multiple demand (MD) system, which often handles math, logic, and executive tasks.
While this may sound like a win for the "coding is math" argument, it isn't quite the slam dunk you might think it is. This system handles most of our "difficult" thinking and is useful for many things. Logic and math typically cause the left half of it to fire up while the right half handles abstract thinking.
Working with Python caused both sides of the system to activate. ScractchJr worked the right side a little more than the left.
What does this mean?
These findings suggest that the brain handles coding as a unique and complex process. As lead author Anna Ivanova put it: "Understanding computer code seems to be its own thing. It's not the same as language, and it's not the same as math and logic."
The authors note that this does not rule out the possibility that very experienced programmers might have specially dedicated areas of the brain for coding. It also doesn't settle what the right way to learn the subject is; it could be the case that learning it requires elements from both pedagogues.
Are there any limits to the study?
This study was very small, it only involved about twenty people, and all of them had knowledge of the coding language they were tested with. The codes used are noted for their readability, and the results may differ if future test subjects without coding knowledge are trying to decipher something like Piet.
Despite these limitations, the study does provide helpful information about how the brain handles coding languages. It will undoubtedly be the first of many investigations into this topic.
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