Over the past few decades, neuroscientists have made much progress in mapping the brain by deciphering the functions of individual neurons that perform very specific tasks, such as recognizing the location or color of an object. However, there are many neurons, especially in brain regions that perform sophisticated functions such as thinking and planning, that don’t fit into this pattern. Instead of responding exclusively to one stimulus or task, these neurons react in different ways to a wide variety of things. MIT neuroscientist Earl Miller first noticed these unusual activity patterns about 20 years ago, while recording the electrical activity of neurons in animals that were trained to perform complex tasks.
Miller and other neuroscientists who first identified this neuronal activity observed that while the patterns were difficult to predict, they were not random. “In the same context, the neurons always behave the same way. It’s just that they may convey one message in one task, and a totally different message in another task,” Miller says.
For example, a neuron might distinguish between colors during one task, but issue a motor command under different conditions.
Miller and colleagues proposed that this type of neuronal flexibility is key to cognitive flexibility, including the brain’s ability to learn so many new things on the fly. “You have a bunch of neurons that can be recruited for a whole bunch of different things, and what they do just changes depending on the task demands,” he says.
During this task, the flexible neurons, known as “mixed selectivity neurons,” exhibited a great deal of nonlinear activity — meaning that their responses to a combination of factors cannot be predicted based on their response to each individual factor (such as one image).
Fusi’s computer model revealed that these mixed selectivity neurons are critical to building a brain that can perform many complex tasks. When the computer model includes only neurons that perform one function, the brain can only learn very simple tasks. However, when the flexible neurons are added to the model, “everything becomes so much easier and you can create a neural system that can perform very complex tasks,” Fusi says.
The flexible neurons also greatly expand the brain’s capacity to perform tasks. In the computer model, neural networks without mixed selectivity neurons could learn about 100 tasks before running out of capacity. That capacity greatly expanded to tens of millions of tasks as mixed selectivity neurons were added to the model. When mixed selectivity neurons reached about 30 percent of the total, the network’s capacity became “virtually unlimited,” Miller says — just like a human brain.
Mixed selectivity neurons are especially dominant in the prefrontal cortex, where most thought, learning and planning takes place. This study demonstrates how these mixed selectivity neurons greatly increase the number of tasks that this kind of neural network can perform, says John Duncan, a professor of neuroscience at Cambridge University.
“Especially for higher-order regions, the data that have often been taken as a complicating nuisance may be critical in allowing the system actually to work,” says Duncan, who was not part of the research team.
Miller is now trying to figure out how the brain sorts through all of this activity to create coherent messages. There is some evidence suggesting that these neurons communicate with the correct targets by synchronizing their activity with oscillations of a particular brainwave frequency.
“The idea is that neurons can send different messages to different targets by virtue of which other neurons they are synchronized with,” Miller says. “It provides a way of essentially opening up these special channels of communications so the preferred message gets to the preferred neurons and doesn’t go to neurons that don’t need to hear it.” — Complex brain function depends on flexibility - MIT News Office
Turns out, that old “practice makes perfect” adage may be overblown. New research led by Michigan State University’s Zach Hambrick finds that a copious amount of practice is not enough to explain why people differ in level of skill in two widely studied activities, chess and music. In other words, it takes more than hard work to become an expert. Hambrick, writing in the research journal Intelligence, said natural talent and other factors likely play a role in mastering a complicated activity.
“The evidence is quite clear,” he writes, “that some people do reach an elite level of performance without copious practice, while other people fail to do so despite copious practice.” Hambrick and colleagues analyzed 14 studies of chess players and musicians, looking specifically at how practice was related to differences in performance. Practice, they found, accounted for only about one-third of the differences in skill in both music and chess. So what made up the rest of the difference? Based on existing research, Hambrick said it could be explained by factors such as intelligence or innate ability, and the age at which people start the particular activity. A previous study of Hambrick’s suggested that working memory capacity – which is closely related to general intelligence – may sometimes be the deciding factor between being good and great. — Practice makes perfect? Not so much
“When most people think about differences between languages, they think they use different words and they have different grammars, but at their base languages use different sounds,” Lotto said. “One of the reasons it sounds different when you hear someone speaking a different language is because the actual sounds they use are different; they have a sound code that’s specific to that language,” he said. “One of the reasons someone might sound like they have an accent if they learn Spanish first is because their ‘pa’ is like an English ‘ba,’ so when they say a word with ‘pa,’ it will sound like a ‘ba’ to an English monolingual.”
“What this showed is that when you put people in English mode, they actually would act like English speakers, and then if you put them in Spanish mode, they would switch to acting like Spanish speakers,” Lotto said. “These bilinguals, hearing the exact same ‘ba’s and ‘pa’s would label them differently depending on the context.” When the study was repeated with 32 English monolinguals, participants did not show the same shift in perception; they labeled ‘ba’ and ‘pa’ sounds the same way regardless of which language they were told they were hearing. It was that lack of an effect for monolinguals that provided the strongest evidence for two sound systems in bilinguals. “Up until this point we haven’t had a good answer to whether bilinguals actually learn two different codes—so a ‘ba-pa’ English code and a ‘ba-pa’ Spanish code—or whether they learn something that’s sort of in the middle,” Lotto said. “This is one of the first clear demonstrations that bilinguals really do have two different sounds systems and that they can switch between one language and the other and then use that sound system.”
This is true primarily for those who learn two languages very young, Lotto said. “If you learn a second language later in life, you usually have a dominant language and then you try to use that sounds system for the other language, which is why you end up having an accent,” he said.
“This raises the possibility that bilinguals can perceive speech like a native speaker in both languages,” said Gonzales. — Study shows how bilinguals switch between languages