July 2 (UPI) -- Children are remarkably efficient language learners -- they absorb new words, sentence structures and syntax much faster than teenagers and adults.
But why?
While most studies probing language learning have focused on differences in the brain, new research -- published Friday in the journal Psychological Science -- suggests children benefit not simply from neurological advantages, but also from the way adults talk to them.
"We have known for years that parents talk to children differently than to other adults in a lot of ways, for example simplifying their speech, reduplicating words and stretching out vowel sounds," study co-author Daniel Yurovsky said in a press release.
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"This stuff helps young kids get a toehold into language, but we didn't know whether parents change the way they talk as children are acquiring language, giving children language input that is 'just right' for learning the next thing," said Yurovsky, an assistant professor in psychology at Carnegie Mellon University.
When speaking to children, adults tend to talk slowly, raise the pitch of their voice and use simplified language structures. Adults also exaggerate enunciation and repeat words.
As the language fluency of young children improves, adults adapt their communication accordingly, using bigger words and more sophisticated sentence structures.
For most adults, these practices are fairly natural. Conscious or not, the progression recalls the more deliberate learning process deployed by math teachers.
"When you go to school, you start with algebra and then take plane geometry before moving onto calculus," Yurovsky said.
"People talk to kids using the same kind of structure without thinking about it. They are tracking how much their child knows about language and modifying how they speak so that for children understand them," Yurovsky said.
For the new study, Yurovsky and his colleagues developed a game to reveal the ways adults match their language to the speech development of young children.
The game asked parents to have children, aged 15 to 23 months, select a specific animal from a set of three.
Some participants selected from more familiar animals, like cows and cats, while others selected from animals likely to be unfamiliar to toddlers, like peacocks and leopards.
While the parents and children played the game in natural settings, researchers observed the ways parents talked about animals their children were familiar with versus animals their children were unfamiliar with.
"Parents have an incredibly precise knowledge of their child's language because they have witnessed them grow and learn," said Yurovsky. "These results show that parents leverage their knowledge of their children's language development to fine-tune the linguistic information they provide."
When talking about unfamiliar animals, researchers observed parents providing additional descriptors that the children could easily understand.
The researchers also observed that parents and caregivers adjusted their communication on the fly in response to level of understanding demonstrated by the toddler participants.
"This [research] approach lets us confirm experimentally ideas that we have developed based on observations of how children and parents engage in the home," Yurovsky said.
"We found that parents not only used what they already knew about their children's language knowledge before the study, but also that if they found out they wrong -- their child didn't actually know 'leopard' for example -- they changed the way they talked about that animal the next time around," Yurovsky said.
Though the racial diversity of the study's participants reflected the diverse composition of the United States population, authors of the new study acknowledge that the participating caregivers had a higher educational background than the average American.
Yurovsky and his research partners estimate their work is relevant not only for parents and teachers, but also for scientists working on machine learning algorithms.
"These results could help us understand how to think about machine learning language systems," Yurovsky said.
"Right now we train language models by giving them all of the language data we can get our hands on all at once. But we might do better if we could give them the right data at the right time, keeping it at just the right level of complexity that they are ready for," Yurovsky said.
