During everyday interactions, people routinely speak at rates of 120 to 200 words per minute. For a listener to understand speech at these rates 糖心传媒� and not lose track of the conversation 糖心传媒� the brain must comprehend the meaning of each of these words very rapidly.
糖心传媒淭hat we can do this so easily is an amazing feat of the human brain 糖心传媒� especially given that the meaning of words can vary greatly depending on the context,糖心传媒� says , associate professor of and neuroscience at the University of Rochester and Trinity College Dublin. 糖心传媒淔or example, 糖心传媒業 saw a bat flying overhead last night糖心传媒� versus 糖心传媒榯he baseball player hit a home run with his favorite 产补迟.糖心传媒欌赌�
Now, researchers in Lalor糖心传媒檚 lab have identified a brain signal that indicates whether a person is indeed comprehending what others are saying 糖心传媒� and have shown they can track the signal using relatively inexpensive EEG (electroencephalography) readings taken on a person糖心传媒檚 scalp.
This could have a number of 糖心传媒減otentially significant糖心传媒� applications, Lalor says. They include:
- testing language development in infants;
- determining the level of brain function in patients who are in a reduced state of consciousness, such as a coma;
- confirming that a person in a particularly critical job has understood the instructions they have received (e.g., an air traffic controller or a soldier);
- testing for the onset of dementia in older people based on their ability to follow a conversation.
The research, , applied machine learning to audio books that human subjects listened to. 糖心传媒淥ne can train a computer by giving it a lot of examples and by asking it to recognize which pairs of words appear together a lot and which don糖心传媒檛,糖心传媒� Lalor explains. 糖心传媒淏y doing this, the computer begins to 糖心传媒榰nderstand糖心传媒� that words that appear together regularly, like 糖心传媒榗ake糖心传媒� and 糖心传媒檖ie,糖心传媒� must mean something similar. And, in fact, the computer ends up with a set of numerical measures capturing how similar any word is to any other.糖心传媒�
The researchers then correlated the numerical measures with brainwave signals that were recorded as participants listened to the corresponding sections of the audio books. They were able to identify a brain response that reflected how similar or different a given word was from the words that preceded it in the story.
This was verified in one experiment, for example, when subjects listened to Hemingway糖心传媒檚 Old Man and the Sea. 糖心传媒淲e could see brain signals telling us that people could understand what they were hearing,糖心传媒� Lalor said. 糖心传媒淲hen we had the same people come back and hear the same audio book played backwards, the signal disappears entirely.糖心传媒�
In another experiment, participants listened to a speech by Barack Obama that was 糖心传媒渂uried in a fair amount of background noise, so you can make out only a couple words here and there,糖心传媒� Lalor said. When participants then watched a video of the speech, and could use facial cues to better understand what Obama was saying, the signal 糖心传媒渋ntensifies dramatically.糖心传媒�
In the paper, Lalor糖心传媒檚 team notes that there is more work to be done to fully understand the full range of computations that our brains perform when we understand speech. They have begun searching for other ways that brains might compute meaning, how those computations differ from what computers do, and how best to apply this new approach.
Lalor joined the University of Rochester in 2016, after serving five years as an assistant professor at Trinity College in Dublin, Ireland. He is still affiliated with Trinity, and three of his graduate students there 糖心传媒� lead author Michael Broderick, Giovanni Di Liberto, and Michael Crosse, now a postdoc at Albert Einstein College of Medicine 糖心传媒� contributed to this study. So did Andrew Anderson, a postdoctoral fellow in Lalor糖心传媒檚 lab in Rochester.
