By ALISON GOPNIK
Published: August 15, 2009
Your Baby Is Smarter Than You Think
GENERATIONS of psychologists and philosophers have believed that babies and young children were basically defective adults — irrational, egocentric and unable to think logically. The philosopher John Locke saw a baby’s mind as a blank slate, and the psychologist William James thought they lived in a “blooming, buzzing confusion.”
Even today, a cursory look at babies and young children leads many to conclude that there is not much going on.
New studies, however, demonstrate that babies and very young children know, observe, explore, imagine and learn more than we would ever have thought possible. In some ways, they are smarter than adults.
Three recent experiments show that even the youngest children have sophisticated and powerful learning abilities. Last year, Fei Xu and Vashti Garcia at the University of British Columbia proved that babies could understand probabilities. Eight-month-old babies were shown a box full of mixed-up Ping-Pong balls: mostly white but with some red ones mixed in. The babies were more surprised, and looked longer and more intently at the experimenter when four red balls and one white ball were taken out of the box — a possible, yet improbable outcome — than when four white balls and a red one were produced.
In 2007, Laura Schulz and Elizabeth Baraff Bonawitz at M.I.T. demonstrated that when young children play, they are also exploring cause and effect. Preschoolers were introduced to a toy that had two levers and a duck and a puppet that popped up. One group was shown that when you pressed one lever, the duck appeared and when you pressed the other, the puppet popped up. The second group observed that when you pressed both levers at once, both objects popped up, but they never got a chance to see what the levers did separately, which left mysterious the causal relation between the levers and the pop-up objects.
Then the experimenter gave the children the toys to play with. The children in the first group played with the toy much less than the children in the second group did. When the children already knew how the toy worked, they were less interested in exploring it. But the children in the second group spontaneously played with the toy, and just by playing around, they figured out how it worked.
In 2007 in my lab at Berkeley, Tamar Kushnir and I discovered that preschoolers can use probabilities to learn how things work and that this lets them imagine new possibilities.
We put a yellow block and a blue block on a machine repeatedly. The blocks were likely but not certain to make the machine light up. The yellow block made the machine light up two out of three times; the blue block made it light up only two out of six times.
Then we gave the children the blocks and asked them to light up the machine. These children, who couldn’t yet add or subtract, were more likely to put the high-probability yellow block, rather than the blue one, on the machine.
We also did the same experiment, but instead of putting the high-probability block on the machine, we held it up over the machine and the machine lit up.
Children had never seen a block act this way, and at the start of the experiment, they didn’t think it could. But after seeing good evidence, they were able to imagine the peculiar possibility that blocks have remote powers. These astonishing capacities for statistical reasoning, experimental discovery and probabilistic logic allow babies to rapidly learn all about the particular objects and people surrounding them.
Sadly, some parents are likely to take the wrong lessons from these experiments and conclude that they need programs and products that will make their babies even smarter. Many think that babies, like adults, should learn in a focused, planned way. So parents put their young children in academic-enrichment classes or use flashcards to get them to recognize the alphabet. Government programs like No Child Left Behind urge preschools to be more like schools, with instruction in specific skills.
But babies’ intelligence, the research shows, is very different from that of adults and from the kind of intelligence we usually cultivate in school.
Schoolwork revolves around focus and planning. We set objectives and goals for children, with an emphasis on skills they should acquire or information they should know. Children take tests to prove that they have absorbed a specific set of skills and facts and have not been distracted by other possibilities.
This approach may work for children over the age of 5 or so. But babies and very young children are terrible at planning and aiming for precise goals. When we say that preschoolers can’t pay attention, we really mean that they can’t not pay attention: they have trouble focusing on just one event and shutting out all the rest. This has led us to underestimate babies in the past. But the new research tells us that babies can be rational without being goal-oriented.
Babies are captivated by the most unexpected events. Adults, on the other hand, focus on the outcomes that are the most relevant to their goals. In a well-known experiment, adults saw a video of several people tossing a ball to one another. The experimenter told them to count how many passes particular people made. In the midst of this, a person in a gorilla suit walked slowly through the middle of the video. A surprising number of adults, intent on counting, didn’t even seem to notice the unexpected gorilla.
Adults focus on objects that will be most useful to them. But as the lever study demonstrated, children play with the objects that will teach them the most. In our study, 4-year-olds imagined new possibilities based on just a little data. Adults rely more on what they already know. Babies aren’t trying to learn one particular skill or set of facts; instead, they are drawn to anything new, unexpected or informative.
Part of the explanation for these differing approaches can be found in the brain. The young brain is remarkably plastic and flexible. Brains work because neurons are connected to one another, allowing them to communicate. Baby brains have many more neural connections than adult brains. But they are much less efficient. Over time, we prune away the connections we don’t use, and the remaining ones become faster and more automatic. Moreover, the prefrontal cortex, the part of the brain that controls the directed, planned, focused kind of intelligence, is exceptionally late to mature, and may not take its final shape until our early 20s.
In fact, our mature brain seems to be programmed by our childhood experiences — we plan based on what we’ve learned as children. Very young children imagine and explore a vast array of possibilities. As they grow older and absorb more evidence, certain possibilities become much more likely and more useful. They then make decisions based on this selective information and become increasingly reluctant to give those ideas up and try something new.
Computer scientists talk about the difference between exploring and exploiting — a system will learn more if it explores many possibilities, but it will be more effective if it simply acts on the most likely one. Babies explore; adults exploit.
Each kind of intelligence has benefits and drawbacks. Focus and planning get you to your goal more quickly but may also lock in what you already know, closing you off to alternative possibilities. We need both blue-sky speculation and hard-nosed planning. Babies and young children are designed to explore, and they should be encouraged to do so.
The learning that babies and young children do on their own, when they carefully watch an unexpected outcome and draw new conclusions from it, ceaselessly manipulate a new toy or imagine different ways that the world might be, is very different from schoolwork.
Babies and young children can learn about the world around them through all sorts of real-world objects and safe replicas, from dolls to cardboard boxes to mixing bowls, and even toy cellphones and computers. Babies can learn a great deal just by exploring the ways bowls fit together or by imitating a parent talking on the phone.
(Imagine how much money we can save on “enriching” toys and DVDs!)
But what children observe most closely, explore most obsessively and imagine most vividly are the people around them. There are no perfect toys; there is no magic formula. Parents and other caregivers teach young children by paying attention and interacting with them naturally and, most of all, by just allowing them to play.
OK HERE WE ARE BACK TO MARIA SO WHAT ARE WE TO DO NOW?
IT IS EVEN MORE VITAL THEN WE EVER THOUGHT TO REBUILD EARLY LEARNING INTO EASY LEARNING SO LET US PLAY ON PURPOSE
Single cells in the monkey brain encode abstract mathematical concepts
Posted on: January 21, 2010 11:50 AM, by Mo
OUR ability to use and manipulate numbers is integral to everyday life - we use them to label, rank, count and measure almost everything we encounter. It was long thought that numerical competence is dependent on language and, therefore, that numerosity is restricted to our species. Although the symbolic representation of numbers, using numerals and words, is indeed unique to humans, we now know that animals are also capable of manipulating numerical information.
One study published in 1998, for example, showed that rhesus monkeys can form spontaneous representations of small numbers and use them to choose containers with more pieces of fruit. More recently, it was found that monkeys can perform basic arithmetic on a par with college students. Now, German researchers report that not only do rhesus monkeys understand simple mathematical rules, but also that these rules are encoded by single neurons in the rhesus prefrontal.
Animal experiments and neuroimaging studies performed with humans have implicated the prefrontal cortex (PFC) in the processing and execution of numerical operations. In humans, this part of the brain is engaged during tasks involving mathematical rules, and it has long been known that damage to the PFC can lead to impaired quantitative reasoning. Sylvia Bongard and Andreas Nieder of the Institute of Neurobiology at the University of Tubingen therefore hypothesized that PFC neurons are involved in encoding aspects of numerosity, and designed a numerical task based on simple numerical rules to test this.
Two rhesus monkeys were shown pairs of visual stimuli consisting of sets of dots and trained to compare them by applying two simple mathematical rules. In each trial, they were shown a sample set of dots followed, after a short delay, by a test set with a different number of dots. The 'greater than' rule required the monkeys to release a lever if the test set contained more dots than the sample set, whereas the 'less than' rule required them to release the lever if it contained fewer dots. During the interval between each pair of stimuli, a cue was presented, indicating which of the two rules should be applied.
While the monkeys performed this task, microelectrodes were used to record the activity of approximately 500 individual and randomly selected PFC neurons. The response of each cell was determined during four different time periods in each trial: the time during which the sample set of dots was displayed, the delay between the sample and the cue indicating which rule to apply, the time during which thecue was displayed, and the delay between presentation of the rule-related cue and the monkeys' response to it.
Significantly, the monkeys immediately applied the mathematical rules to all the stimuli pairs they were shown, even when the sample sets contained numerosities that had not been previously presented.
Selective responses were recorded during the interval between the cue and the response. 90 rule-selective neurons (~19% of the total from which recordings were made) were detected, which fired independently of the number of dots presented or the sensory properties of the rule-related cue. Of these, 50 fired exclusively when the monkeys produced 'greater than' responses, and the remaining 40 fired exclusively when they produced 'less than' responses. Rule selectivity was not encoded immediately, but emerged in the cells after a short period of time.
Across hundreds of trials, the monkeys had a minimum success rate of 83%. The researchers compared the neuronal responses of individual rule-selective neurons during trials in which the monkeys gave correct responses with trials in which they made errors. The firing rates were found to decrease significantly when the monkeys made the wrong choices. The selectivity of the responses also enabled the reearchers to predict which rule the monkeys were applying during each trial, from the cellular activity they recorded.
Thus, single neurons in the lateral PFC of the rhesus monkey can flexibly encode abstract mathematical rules which guide greater than/ less than decisions.
Each session involved large numbers of unique trials, so it was impossible for the monkeys to solve the task by learning. Instead, they were required to understand relationships between numerosities in each pair of stimuli, and to apply these principles to make their decisions. These findings are consistent with a model which proposes that the PFC contains a network of distinct rule-coding neuron clusters, each of which receives input from a corresponding internal memory cluster and sends its output to a dedicated downstream cluster.
The findings also add to a body of evidence suggesting that humans and other primates process numbers using common cognitive skills with a shared evolutionary origin.
Bongard, S. & Nieder, A. (2010). Basic mathematical rules are encoded by primate prefrontal cortex neurons Proc. Nat. Acad. Sci. DOI: 10.1073/pnas.0909180107.
Cantlon, J. F. & Brannon, E. M. (2006). Basic math in monkeys and college students [Full text]
Hauser M. D., et al. (2000). Spontaneous number representation in semifree-ranging rhesus monkeys. Proc. R. Soc. Lond. B Biol. Sci. 267:829-33 [PDF]
PROOVING Natural brain abilities and THE VALUE OF EARLY EDUCATION
Human Brain Uses a Grid to Represent Space
ScienceDaily (Jan. 25, 2010) —
'Grid cells' that act like a spatial map in the brain
have been identified for the first time in humans, according to new research by UCL scientists which may help to explain how we create internal maps of new environments.
The study is by a team from the UCL Institute of Cognitive Neuroscience and was funded by the Medical Research Council and the European Union.
Published in Nature, it uses brain imaging and virtual reality techniques to try to identify grid cells in the human brain. These specialised neurons are thought to be involved in spatial memory and have previously been identified in rodent brains, but evidence of them in humans has not been documented until now.
Grid cells represent where an animal is located within its environment, which the researchers liken to having a satnav in the brain. They fire in patterns that show up as geometrically regular, triangular grids when plotted on a map of a navigated surface. They were discovered by a Norwegian lab in 2005 whose research suggested that rats create virtual grids to help them orient themselves in their surroundings, and remember new locations in unfamiliar territory.
Study co-author Dr Caswell Barry said: "It is as if grid cells provide a cognitive map of space. In fact, these cells are very much like the longitude and latitude lines we're all familiar with on normal maps, but instead of using square grid lines it seems the brain uses triangles.
Lead author Dr Christian Doeller added: "Although we can't see the grid cells directly in the brain scanner, we can pick up the regular six-fold symmetry that is a signature of this type of firing pattern.
Interestingly, the study participants with the clearest signs of grid cells were those who performed best in the virtual reality spatial memory task, suggesting that the grid cells help us to remember the locations of objects."
Professor Neil Burgess, who leads the team, commented: "The parts of the brain which show signs of grid cells -- the hippocampal formation and associated brain areas -- are already known to help us navigate our environment and are also critical for autobiographical memory.
This means that grid cells may help us to find our way to the right memory as well as finding our way through our environment.
These brain areas are also amongst the first to be affected by Alzheimer's disease which may explain why getting lost is one of the most common early symptoms of this disease."
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