Washington: Learning one’s own native language may seem easy. However, a recent study has revealed that language acquisition between birth and 18 years of age is a remarkable feat of cognition, rather than something humans are just hardwired to do.
The findings, published in the Journal of the Royal Society Open Science, challenge assumptions that human language acquisition happens effortlessly and that robots would have an easy time mastering it.
Researchers calculated that from infancy to young adulthood, learners absorb approximately 12.5 million bits of information about language- about two bits per minute- to fully acquire linguistic knowledge. If converted into binary code, the data would fill a 1.5 MB floppy disk, the study has found.
“Ours is the first study to put a number on the amount you have to learn to acquire language,” said Steven Piantadosi, study senior author. “It highlights that children and teens are remarkable learners, absorbing upwards of 1,000 bits of information each day,” he said.
For example, when presented with the word “turkey,” a young learner typically gathers bits of information by asking, “Is turkey a bird? Yes, or no? Does a turkey fly? Yes, or no?” and so on, until grasping the full meaning of the word “turkey.”
Piantadosi and study lead author Frank Mollica sought to gauge the amounts and different kinds of information that English speakers need to learn their native language.
They arrived at their results by running various calculations about language semantics and syntax through computational models. Notably, the study found that linguistic knowledge focuses mostly on the meaning of words, as opposed to the grammar of a language.
“A lot of research on language learning focuses on syntax, like word order,” Piantadosi said. “But our study shows that syntax represents just a tiny piece of language learning and that the main difficulty has got to be in learning what so many words mean,” he added.
That focus on semantics versus syntax distinguishes humans from robots, including voice-controlled digital helpers such as Alexa, Siri and Google Assistant.
“This really highlights a difference between machine learners and human learners. Machines know what words go together and where they go in sentences, but know very little about the meaning of words,” said Piantadosi
As for the question of whether bilingual people must store twice as many bits of information, Piantadosi said this is unlikely in the case of word meanings, many of which are shared across languages.
“The meanings of many common nouns like ‘mother’ will be similar across languages, and so you won’t need to learn all the information about their meanings twice,” he said.