Posts Tagged: Brain
The process of learning requires the sophisticated ability to constantly update our expectations of future rewards so we may make accurate predictions about those rewards in the face of a changing environment. Although exactly how the brain orchestrates this process remains unclear, a new study by researchers at the California Institute of Technology (Caltech) suggests that a combination of two distinct learning strategies guides our behavior.
One accepted learning strategy, called model-free learning, relies on trial-and-error comparisons between the reward we expect in a given situation and the reward we actually get. The result of this comparison is the generation of a “reward prediction error,” which corresponds to that difference. For example, a reward prediction error might correspond to the difference between the projected monetary return on a financial investment and our real earnings.
In the second mechanism, called model-based learning, the brain generates a cognitive map of the environment that describes the relationship between different situations. “Model-based learning is associated with the generation of a ‘state prediction error,’ which represents the brain’s level of surprise in a new situation given its current estimate of the environment,” says Jan Gläscher, a postdoctoral scholar at Caltech and the lead author of the study.
Eighteen participants were scanned using functional magnetic resonance imaging as they learned the task. The brain scans showed the distinctive, previously characterized neural signature of reward prediction error — generated during model-free learning — in an area in the middle of the brain called the ventral striatum. During model-based learning, however, the neural signature of a state prediction error appeared in two different areas on the surface of the brain in the cerebral cortex: the intraparietal sulcus and the lateral prefrontal cortex.
These observations suggest that two unique types of error signals are computed in the human brain, occur in different brain regions, and may represent separate computational strategies for guiding behavior. “A model-free system operates very effectively in situations that are highly automated and repetitive — for example, if I regularly take the same route home from work,” Gläscher says, “whereas a model-based system, although requiring much greater brain-processing power, is able to adapt flexibly to novel situations, such as needing to find a new route following a roadblock.”
For those interested, the actual paper is:
"States versus Rewards: Dissociable Neural Prediction Error Signals Underlying Model-Based and Model-Free Reinforcement Learning", by Jan P. Glascher, Nathaniel Daw, Peter Dayan and John P. O’Doherty.
Neuroscientists have found several ways in which the brains of top-notch athletes seem to function better than those of regular folks.
The qualities that set a great athlete apart from the rest of us lie not just in the muscles and the lungs but also between the ears. That’s because athletes need to make complicated decisions in a flash. [...]
In recent years neuroscientists have begun to catalog some fascinating differences between average brains and the brains of great athletes. By understanding what goes on in athletic heads, researchers hope to understand more about the workings of all brains—those of sports legends and couch potatoes alike.
[A]n athlete’s actions are much more than a set of automatic responses; they are part of a dynamic strategy to deal with an ever-changing mix of intricate challenges.
Good genes may account for some of the differences in ability, but even the most genetically well-endowed prodigy clearly needs practice—lots of it—to develop the brain of an athlete.
Some (but not all) of the research involved, for those interested…
- A computational neuroanatomy for motor control, by Reza Shadmehr of Johns Hopkins University and John Krakauer of Columbia University
- “Neural efficiency” of athletes’ brain for upright standing: A high-resolution EEG study, by Claudio Del Percio of Sapienza University in Rome, Claudio Babiloni, Nicola Marzano, Marco Iacoboni, Francesco Infarinato, Fabrizio Vecchio, Roberta Lizio, Pierluigi Aschieri, Antonio Fiore, Giancarlo Toràn, Michele Gallamini, Marta Baratto and Fabrizio Eusebi
- Neuroplasticity: Changes in grey matter induced by training, by Bogdan Draganski, Christian Gaser, Volker Busch, Gerhard Schuierer, Ulrich Bogdahn & Arne May
- How do world-class cricket batsmen anticipate a bowler’s intention?, by Müller S, Abernethy B, Farrow D.
There’s an interesting paper out titled, "Evolutionary Divergence in Brain Size between Migratory and Resident Birds". It’s interesting in that it is an example of a case where, big brains are not always better. Here’s what ScienceDaily has to say about it….
Scientists have known for some time that migratory birds have smaller brains than their resident relatives. Now a new study looks into the reasons and concludes that the act of migrating leads to a reduced brain size. Authors point to the fact that the causes could be due to a need to reduce energetic, metabolic and cognitive costs.
“For birds that travel a lot, exploring their surroundings produces more costs than benefits since the information which is useful in one place is not necessarily so in another. It also exposes them to more dangers. For these reasons we believe that for these species, their innate behaviour can be more useful than learned behaviour.”
I don’t find it a surprising result though. What is “better” depends on what your goals are. And I don’t see why more intelligence would necessarily be the “better” for reaching every possible goal, no matter what that goal is.
There’s an interesting paper called "Distributed neural system for general intelligence revealed by lesion mapping". (It’s open access, so anyone can read it online.)
The paper claims to have found the regions of the brain associated with general intelligence.
Here’s an excepts from ScienceDaily….
A collaborative team of neuroscientists at the California Institute of Technology (Caltech), the University of Iowa, the University of Southern California (USC), and the Autonomous University of Madrid have mapped the brain structures that affect general intelligence.
The study, to be published the week of February 22  in the early edition of the Proceedings of the National Academy of Sciences, adds new insight to a highly controversial question: What is intelligence, and how can we measure it?
[The Scientists] examine[d] a uniquely large data set of 241 brain-lesion patients who all had taken IQ tests. The researchers mapped the location of each patient’s lesion in their brains, and correlated that with each patient’s IQ score to produce a map of the brain regions that influence intelligence.
The researchers found that, rather than residing in a single structure, general intelligence is determined by a network of regions across both sides of the brain.
“One of the main findings that really struck us was that there was a distributed system here. Several brain regions, and the connections between them, were what was most important to general intelligence,” explains Gläscher.
“It might have turned out that general intelligence doesn’t depend on specific brain areas at all, and just has to do with how the whole brain functions,” adds Adolphs. “But that’s not what we found. In fact, the particular regions and connections we found are quite in line with an existing theory about intelligence called the ‘parieto-frontal integration theory.’ It says that general intelligence depends on the brain’s ability to integrate — to pull together — several different kinds of processing, such as working memory.”
Or in the words of the authors of the paper….
Distributed neural system for general intelligence revealed by lesion mapping
J. Gläschera, D. Rudraufc, R. Colome, L. K. Paula, D. Tranelc, H. Damasiof, and R. Adolphsa
General intelligence (g) captures the performance variance shared across cognitive tasks and correlates with real-world success. Yet it remains debated whether g reflects the combined performance of brain systems involved in these tasks or draws on specialized systems mediating their interactions. Here we investigated the neural substrates of g in 241 patients with focal brain damage using voxel-based lesion–symptom mapping. A hierarchical factor analysis across multiple cognitive tasks was used to derive a robust measure of g. Statistically significant associations were found between g and damage to a remarkably circumscribed albeit distributed network in frontal and parietal cortex, critically including white matter association tracts and frontopolar cortex. We suggest that general intelligence draws on connections between regions that integrate verbal, visuospatial, working memory, and executive processes.
It looks like that by measuring 3 structures of a person’s brain, you can predict how well they will perform at video games.
Remember that correlation is not causation, but it would not be surprising that different kinds of brains, and I’d assume different kinds of intellectual abilities, would make one better at various kinds of video games. Although my impression is that certain kinds of mental “activities” can build/develop your “mental muscles”. But that some people will get more out of these “activities” than others.
The MSNBC article says…
Does playing video games improve your brain? Or do bigger brains make it easier to learn video games?
My guess would be that people are born with certain kinds of brains. And that certain kinds of brains can give certain natural mental talents. But that playing video games can help develop their mental talents even further. (Similar to how people can be born mesomorphs and naturally have muscles and be able to put on muscles easily. But weight lifting at the gym can make their muscles even bigger and stronger.)
The article goes on to say…
Psychologists say they can predict how well you’ll do on a video game by looking at the size of just three little structures inside your brain. If those structures are bigger, you’ll probably catch on more quickly and do better.
But don’t start bragging about how gamers are naturally brainier just yet. The psychologists have more puzzles to solve before they level up.
“We’re really at the tip of the iceberg in understanding how all this gets put together,” said the University of Pittsburgh’s Kirk Erickson, the study’s principal author.
The 3 structures in the brain thy are talking about are the caudate nucleus, putamen, and nucleus accumbens. (The hippocampus showed no linkage.)
The article later says…
Past research has shown that expert gamers tend to outperform novices on basic measures of attention and perception. Some studies have suggested that video-game training can help novices bridge the gap – while others indicated that the novices couldn’t catch up after more than 20 hours of training.
[R]esearchers behind the latest study stress that brain structures aren’t set in stone. “We know that’s not true for a lot of nuclei in the brain,” Kramer said. “We know that exercise can increase the volume of the nuclei.”
I’d assume some people will get more out of exercise than others.
I guess my mother may have been correct in claiming that video games help exercise kids minds, and build their “mental muscles”.
It’s interesting how much mind reading techniques have been progressing….
Two hundred years ago, archaeologists used the Rosetta Stone to understand the ancient Egyptian scrolls. Now, a team of Carnegie Mellon University scientists has discovered the beginnings of a neural Rosetta Stone. By combining brain imaging and machine learning techniques, neuroscientists Marcel Just and Vladimir Cherkassky and computer scientists Tom Mitchell and Sandesh Aryal determined how the brain arranges noun representations. Understanding how the brain codes nouns is important for treating psychiatric and neurological illnesses.
“In effect, we discovered how the brain’s dictionary is organized,” said Just, the D.O. Hebb Professor of Psychology and director of the Center for Cognitive Brain Imaging. “It isn’t alphabetical or ordered by the sizes of objects or their colors. It’s through the three basic features that the brain uses to define common nouns like apartment, hammer and carrot.”
[...] the three codes or factors concern basic human fundamentals:
1. how you physically interact with the object (how you hold it, kick it, twist it, etc.);
2. how it is related to eating (biting, sipping, tasting, swallowing); and
3. how it is related to shelter or enclosure.
The three factors, each coded in three to five different locations in the brain [...]
In the case of hammer, the motor cortex was the brain area activated to code the physical interaction. “To the brain, a key part of the meaning of hammer is how you hold it, and it is the sensory-motor cortex that represents ‘hammer holding,’” [...]
One of the most devastating types of mental illness could be a by-product of the evolution of human beings’ uniquely sophisticated intelligence, a new genetic study has suggested.
Scientists have discovered that a common version of a particular gene appears both to enhance a key thinking circuit in the brain, and to be linked to a raised risk of schizophrenia.
In the study, the NIMH team examined a common variant of a gene called DARPP-32. Three quarters of the subjects studied had inherited at least one copy of the variant.
This common version of the gene appears to make the brain’s most sophisticated thinking region more efficient, the researchers found. It improves the way that information is exchanged between the striatum, a brain region that processes reward, and the prefrontal cortex, the brain’s executive hub that manages thoughts and actions.
When this circuit works efficiently, the normal outcome is more flexible thinking and improved working memory. As a result, genes such as DARPP-32 that enhance it have probably been favoured by evolution.
The same circuit, however, has also been linked to brain functions that go wrong in patients with schizophrenia. [...]
An interesting are of study in human genetics is the notion of introgression. That modern homo sapiens may have genes may have genes from other archaic human groups. Like Neanderthals.
Evidence that the adaptive allele of the brain size gene microcephalin introgressed into Homo sapiens from an archaic Homo lineage
At the center of the debate on the emergence of modern humans and their spread throughout the globe is the question of whether archaic Homo lineages contributed to the modern human gene pool, and more importantly, whether such contributions impacted the evolutionary adaptation of our species. A major obstacle to answering this question is that low levels of admixture with archaic lineages are not expected to leave extensive traces in the modern human gene pool because of genetic drift. Loci that have undergone strong positive selection, however, offer a unique opportunity to identify low-level admixture with archaic lineages, provided that the introgressed archaic allele has risen to high frequency under positive selection. The gene microcephalin (MCPH1) regulates brain size during development and has experienced positive selection in the lineage leading to Homo sapiens. Within modern humans, a group of closely related haplotypes at this locus, known as haplogroup D, rose from a single copy ≈37,000 years ago and swept to exceptionally high frequency (≈70% worldwide today) because of positive selection. Here, we examine the origin of haplogroup D. By using the interhaplogroup divergence test, we show that haplogroup D likely originated from a lineage separated from modern humans ≈1.1 million years ago and introgressed into humans by ≈37,000 years ago. This finding supports the possibility of admixture between modern humans and archaic Homo populations (Neanderthals being one possibility). Furthermore, it buttresses the important notion that, through such adminture, our species has benefited evolutionarily by gaining new advantageous alleles. The interhaplogroup divergence test developed here may be broadly applicable to the detection of introgression at other loci in the human genome or in genomes of other species.