attention

Attention

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Attention problems

Attention training

Older news items (pre-2010) brought over from the old website

Attention is more about reducing the noticeability of the unattended

No visual scene can be processed in one fell swoop — we piece it together from the bits we pay attention to (which explains why we sometimes miss objects completely, and can’t understood how we could have missed them when we finally notice them). We know that paying attention to something increases the firing rate of neurons tuned for that type of stimulus, and until a recent study we thought that was the main process underlying our improved perception when we focus on something. However a macaque study has found that the main cause — perhaps four times as important — is a reduction in the background noise, allowing the information coming in to be much more noticeable.

[1093] Mitchell, J. F., Sundberg K. A., & Reynolds J. H.
(2009).  Spatial Attention Decorrelates Intrinsic Activity Fluctuations in Macaque Area V4.
Neuron. 63(6), 879 - 888.

http://esciencenews.com/articles/2009/09/23/rising.above.din

Brainwaves regulate our searching

A long-standing question concerns how we search complex visual scenes. For example, when you enter a crowded room, how do you go about searching for your friends? Now a monkey study reveals that visual attention jumps sequentially from point to point, shifting focus around 25 times in a second. Intriguingly, and unexpectedly, it seems this timing is determined by brainwaves. The finding connects speed of thinking with the oscillation frequency of brainwaves, giving a new significance to brainwaves (whose function is rather mysterious, but of increasing interest to researchers), and also suggesting an innovative approach to improving attention.

[1118] Buschman, T. J., & Miller E. K.
(2009).  Serial, Covert Shifts of Attention during Visual Search Are Reflected by the Frontal Eye Fields and Correlated with Population Oscillations.
Neuron. 63(3), 386 - 396.

http://www.eurekalert.org/pub_releases/2009-08/miot-tme080609.php

Ability to ignore distraction most important for attention

Confirming an earlier study, a series of four experiments involving 84 students has found that students with high working memory capacity were noticeably better able to ignore distractions and stay focused on their tasks. The findings provide more evidence that the poor attentional capacity of individuals with low working memory capacity result from a reduced ability to ignore attentional capture (stimuli that involuntarily “capture” your attention, like a loud noise or a suddenly appearing object), rather than an inability to focus.

[828] Fukuda, K., & Vogel E. K.
(2009).  Human Variation in Overriding Attentional Capture.
J. Neurosci.. 29(27), 8726 - 8733.

http://www.eurekalert.org/pub_releases/2009-08/uoo-bbo080609.php

Stress disrupts task-switching, but the brain can bounce back

A new neuroimaging study involving 20 male M.D. candidates in the middle of preparing for their board exams has found that they had a harder time shifting their attention from one task to another after a month of stress than other healthy young men who were not under stress. The finding replicates what has been found in rat studies, and similarly correlates with impaired function in an area of the prefrontal cortex that is involved in attention. However, the brains recovered their function within a month of the end of the stressful period.

[829] Liston, C., McEwen B. S., & Casey B. J.
(2009).  Psychosocial stress reversibly disrupts prefrontal processing and attentional control.
Proceedings of the National Academy of Sciences. 106(3), 912 - 917.

Full text available at http://www.pnas.org/content/106/3/912.abstract
http://www.eurekalert.org/pub_releases/2009-01/ru-sdh012709.php

Attention, it’s all about connecting

An imaging study in which volunteers spent an hour identifying letters that flashed on a screen has shed light on what happens when our attention wanders. Reduced communication in the ventral fronto-parietal network, critical for attention, was found to predict slower response times 5-8 seconds before the letters were presented.

Daniel Weissman presented the results at the 38th annual meeting of the Society for Neuroscience, held Nov. 15 to 19 in Washington, DC.

http://www.newscientist.com/article/mg20026865.600-bored-your-brain-is-disconnecting.html

The importance of acetylcholine

A rat study suggests that acetylcholine, a neurotransmitter known to be important for attention, is critical for "feature binding"— the process by which our brain combines all of the specific features of an object and gives us a complete and unified picture of it. The findings may lead to improved therapies and treatments for a variety of attention and memory disorders.

[1265] Botly, L. C. P. [1], & De Rosa E.
(2008).  A Cross-Species Investigation of Acetylcholine, Attention, and Feature Binding.
Psychological Science. 19, 1185 - 1193.

http://www.eurekalert.org/pub_releases/2008-11/afps-bba111808.php

Attention grabbers snatch lion's share of visual memory

It’s long been thought that when we look at a visually "busy" scene, we are only able to store a very limited number of objects in our visual short-term or working memory. For some time, this figure was believed to be four or five objects, but a recent report suggested it could be as low as two. However, a new study reveals that although it might not be large, it’s more flexible than we thought. Rather than being restricted to a limited number of objects, it can be shared out across the whole image, with more memory allocated for objects of interest and less for background detail. What’s of interest might be something we’ve previously decided on (i.e., we’re searching for), or something that grabs our attention.  Eye movements also reveal how brief our visual memory is, and that what our eyes are looking at isn’t necessarily what we’re ‘seeing’ — when people were asked to look at objects in a particular sequence, but the final object disappeared before their eyes moved on to it, it was found that the observers could more accurately recall the location of the object that they were about to look at than the one that they had just been looking at.

[1398] Bays, P. M., & Husain M.
(2008).  Dynamic shifts of limited working memory resources in human vision.
Science (New York, N.Y.). 321(5890), 851 - 854.

http://www.physorg.com/news137337380.html

How Ritalin works to focus attention

Ritalin has been widely used for decades to treat attention deficit hyperactivity disorder (ADHD), but until now the mechanism of how it works hasn’t been well understood. Now a rat study has found that Ritalin, in low doses, fine-tunes the functioning of neurons in the prefrontal cortex, and has little effect elsewhere in the brain. It appears that Ritalin dramatically increases the sensitivity of neurons in the prefrontal cortex to signals coming from the hippocampus. However, in higher doses, PFC neurons stopped responding to incoming information, impairing cognition. Low doses also reinforced coordinated activity of neurons, and weakened activity that wasn't well coordinated. All of this suggests that Ritalin strengthens dominant and important signals within the PFC, while lessening weaker signals that may act as distractors.

[663] Devilbiss, D. M., & Berridge C. W.
(2008).  Cognition-Enhancing Doses of Methylphenidate Preferentially Increase Prefrontal Cortex Neuronal Responsiveness.
Biological Psychiatry. 64(7), 626 - 635.

http://www.eurekalert.org/pub_releases/2008-06/uow-suh062408.php

Disentangling attention

A new study provides more evidence that the ability to deliberately focus your attention is physically separate in the brain from the part that helps you filter out distraction. The study trained monkeys to take attention tests on a video screen in return for a treat of apple juice. When the monkeys voluntarily concentrated (‘top-down’ attention), the prefrontal cortex was active, but when something distracting grabbed their attention (‘bottom-up’ attention), the parietal cortex became active. The electrical activity in these two areas vibrated in synchrony as they signaled each other, but top-down attention involved synchrony that was stronger in the lower-frequencies and bottom-up attention involved higher frequencies. These findings may help us develop treatments for attention disorders.

[1071] Buschman, T. J., & Miller E. K.
(2007).  Top-Down Versus Bottom-Up Control of Attention in the Prefrontal and Posterior Parietal Cortices.
Science. 315(5820), 1860 - 1862.

http://dsc.discovery.com/news/2007/03/29/attention_hea.html?category=health

Asymmetrical brains let fish multitask

A fish study provides support for a theory that lateralized brains allow animals to better handle multiple activities, explaining why vertebrate brains evolved to function asymmetrically. The minnow study found that nonlateralized minnows were as good as those bred to be lateralized (enabling it to favor one or other eye) at catching shrimp. However, when the minnows also had to look out for a sunfish (a minnow predator), the nonlateralized minnows took nearly twice as long to catch 10 shrimp as the lateralized fish.

[737] Dadda, M., & Bisazza A.
(2006).  Does brain asymmetry allow efficient performance of simultaneous tasks?.
Animal Behaviour. 72(3), 523 - 529.

http://sciencenow.sciencemag.org/cgi/content/full/2006/623/2?etoc

Why are uniforms uniform? Because color helps us track objects

Laboratory tests have revealed that humans can pay attention to only 3 objects at a time. Yet there are instances in the real world — for example, in watching a soccer match — when we certainly think we are paying attention to more than 3 objects. Are we wrong? No. Anew study shows how we do it — it’s all in the color coding. People can focus on more than three items at a time if those items share a common color. But, logically enough, no more than 3 color sets.

[927] Halberda, J., Sires S. F., & Feigenson L.
(2006).  Multiple spatially overlapping sets can be enumerated in parallel.
Psychological Science: A Journal of the American Psychological Society / APS. 17(7), 572 - 576.

http://www.eurekalert.org/pub_releases/2006-06/jhu-wau062106.php

An advantage of age

A study comparing the ability of young and older adults to indicate which direction a set of bars moved across a computer screen has found that although younger participants were faster when the bars were small or low in contrast, when the bars were large and high in contrast, the older people were faster. The results suggest that the ability of one neuron to inhibit another is reduced as we age (inhibition helps us find objects within clutter, but makes it hard to see the clutter itself). The loss of inhibition as we age has previously been seen in connection with cognition and speech studies, and is reflected in our greater inability to tune out distraction as we age. Now we see the same process in vision.

[1356] Betts, L. R., Taylor C. P., Sekuler A. B., & Bennett P. J.
(2005).  Aging Reduces Center-Surround Antagonism in Visual Motion Processing.
Neuron. 45(3), 361 - 366.

http://psychology.plebius.org/article.htm?article=739
http://www.eurekalert.org/pub_releases/2005-02/mu-opg020305.php

We weren't made to multitask

A new imaging study supports the view that we can’t perform two tasks at once, rather, the tasks must wait their turn — queuing up for their turn at processing.

[1070] Jiang, Y., Saxe R., & Kanwisher N.
(2004).  Functional magnetic resonance imaging provides new constraints on theories of the psychological refractory period.
Psychological Science: A Journal of the American Psychological Society / APS. 15(6), 390 - 396.

http://www.eurekalert.org/pub_releases/2004-06/aps-wwm060704.php

More light shed on memory encoding

Anything we perceive contains a huge amount of sensory information. How do we decide what bits to process? New research has identified brain cells that streamline and simplify sensory information, markedly reducing the brain's workload. The study found that when monkeys were taught to remember clip art pictures, their brains reduced the level of detail by sorting the pictures into categories for recall, such as images that contained "people," "buildings," "flowers," and "animals." The categorizing cells were found in the hippocampus. As humans do, different monkeys categorized items in different ways, selecting different aspects of the same stimulus image, most likely reflecting different histories, strategies, and expectations residing within individual hippocampal networks.

[662] Hampson, R. E., Pons T. P., Stanford T. R., & Deadwyler S. A.
(2004).  Categorization in the monkey hippocampus: A possible mechanism for encoding information into memory.
Proceedings of the National Academy of Sciences of the United States of America. 101(9), 3184 - 3189.

http://www.eurekalert.org/pub_releases/2004-02/wfub-nfo022604.php

Neural circuits that control eye movements play crucial role in visual attention

Everyone agrees that to improve your memory it is important to “pay attention”. Unfortunately, noone really knows how to improve our ability to “pay attention”. An important step in telling us how visual attention works was recently made in a study that looked at the brain circuits that control eye movements. It appears that those brain circuits that program eye movements also govern whether the myriad signals that pour in from the locations where the eyes could move should be amplified or suppressed. It appears that the very act of preparing to move the eye to a particular location can cause an amplification (or suppression) of signals from that area. This is possible because humans and primates can attend to something without moving their eyes to that object.

[741] Moore, T., & Armstrong K. M.
(2003).  Selective gating of visual signals by microstimulation of frontal cortex.
Nature. 421(6921), 370 - 373.

http://www.eurekalert.org/pub_releases/2003-01/pu-ssh012303.php

Different aspects of attention located in different parts of the brain

We all know attention is important, but we’ve never been sure exactly what it is. Recent research suggests there’s good reason for this – attention appears to be multi-faceted, far less simple than originally conceived. Patients with specific lesions in the frontal lobes and other parts of the brain have provided evidence that different types of attentional problems are associated with injuries in different parts of the brain, suggesting that attention is not, as has been thought, a global process. The researchers have found evidence for at least three distinct processes, each located in different parts of the frontal lobes. These are: (1) a system that helps us maintain a general state of readiness to respond, in the superior medial frontal regions; (2) a system that sets our threshold for responding to an external stimulus, in the left dorsolateral region; and (3) a system that helps us selectively attend to appropriate stimuli, in the right dorsolateral region.

[260] Stuss, D. T., Binns M. A., Murphy K. J., & Alexander M. P.
(2002).  Dissociations within the anterior attentional system: effects of task complexity and irrelevant information on reaction time speed and accuracy.
Neuropsychology. 16(4), 500 - 513.

http://www.eurekalert.org/pub_releases/2002-10/apa-pda100702.php

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Running faster changes brain rhythms associated with learning

September, 2011

A mouse study finds that gamma waves in the hippocampus, critically involved in learning, grow stronger as mice run faster.

I’ve always felt that better thinking was associated with my brain working ‘in a higher gear’ — literally working at a faster rhythm. So I was particularly intrigued by the findings of a recent mouse study that found that brainwaves associated with learning became stronger as the mice ran faster.

In the study, 12 male mice were implanted with microelectrodes that monitored gamma waves in the hippocampus, then trained to run back and forth on a linear track for a food reward. Gamma waves are thought to help synchronize neural activity in various cognitive functions, including attention, learning, temporal binding, and awareness.

We know that the hippocampus has specialized ‘place cells’ that record where we are and help us navigate. But to navigate the world, to create a map of where things are, we need to also know how fast we are moving. Having the same cells encode both speed and position could be problematic, so researchers set out to find how speed was being encoded. To their surprise and excitement, they found that the strength of the gamma rhythm grew substantially as the mice ran faster.

The results also confirmed recent claims that the gamma rhythm, which oscillates between 30 and 120 times a second, can be divided into slow and fast signals (20-45 Hz vs 45-120 Hz for mice, consistent with the 30-55 Hz vs 45-120 Hz bands found in rats) that originate from separate parts of the brain. The slow gamma waves in the CA1 region of the hippocampus were synchronized with slow gamma waves in CA3, while the fast gamma in CA1 were synchronized with fast gamma waves in the entorhinal cortex.

The two signals became increasingly separated with increasing speed, because the two bands were differentially affected by speed. While the slow waves increased linearly, the fast waves increased logarithmically. This differential effect could have to do with mechanisms in the source regions (CA3 and the medial entorhinal cortex, respectively), or to mechanisms in the different regions in CA1 where the inputs terminate (the waves coming from CA3 and the entorhinal cortex enter CA1 in different places).

In the hippocampus, gamma waves are known to interact with theta waves. Further analysis of the data revealed that the effects of speed on gamma rhythm only occurred within a narrow range of theta phases — but this ‘preferred’ theta phase also changed with running speed, more so for the slow gamma waves than the fast gamma waves (which is not inconsistent with the fact that slow gamma waves are more affected by running speed than fast gamma waves). Thus, while slow and fast gamma rhythms preferred similar phases of theta at low speeds, the two rhythms became increasingly phase-separated with increasing running speed.

What’s all this mean? Previous research has shown that if inputs from CA3 and the entorhinal cortex enter CA1 at the same time, the kind of long-term changes at the synapses that bring about learning are stronger and more likely in CA1. So at low speeds, synchronous inputs from CA3 and the entorhinal cortex at similar theta phases make them more effective at activating CA1 and inducing learning. But the faster you move, the more quickly you need to process information. The stronger gamma waves may help you do that. Moreover, the theta phase separation of slow and fast gamma that increases with running speed means that activity in CA3 (slow gamma source) increasingly anticipates activity in the medial entorhinal cortex (fast gamma source).

What does this mean at the practical level? Well at this point it can only be speculation that moving / exercising can affect learning and attention, but I personally am taking this on board. Most of us think better when we walk. This suggests that if you’re having trouble focusing and don’t have time for that, maybe walking down the hall or even jogging on the spot will help bring your brain cells into order!

Pushing speculation even further, I note that meditation by expert meditators has been associated with changes in gamma and theta rhythms. And in an intriguing comparison of the effect of spoken versus sung presentation on learning and remembering word lists, the group that sang showed greater coherence in both gamma and theta rhythms (in the frontal lobes, admittedly, but they weren’t looking elsewhere).

So, while we’re a long way from pinning any of this down, it may be that all of these — movement, meditation, music — can be useful in synchronizing your brain rhythms in a way that helps attention and learning. This exciting discovery will hopefully be the start of an exploration of these possibilities.

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The durability and specificity of perceptual learning

September, 2011

Increasing evidence shows that perception is nowhere near the simple bottom-up process we once thought. Two recent perception studies add to the evidence.

Previous research has found practice improves your ability at distinguishing visual images that vary along one dimension, and that this learning is specific to the visual images you train on and quite durable. A new study extends the finding to more natural stimuli that vary on multiple dimensions.

In the small study, 9 participants learned to identify faces and 6 participants learned to identify “textures” (noise patterns) over the course of two hour-long sessions of 840 trials (consecutive days). Faces were cropped to show only internal features and only shown briefly, so this was not a particularly easy task. Participants were then tested over a year later (range: 10-18 months; average 13 and 15 months, respectively).

On the test, participants were shown both images from training and new images that closely resembled them. While accuracy rates were high for the original images, they plummeted for the very similar new images, indicating that despite the length of time since they had seen the original images, they still retained much of the memory of them.

Although practice improved performance across nearly all items and for all people, there were significant differences between both participants and individual stimuli. More interestingly, individual differences (in both stimuli and people) were stable across sessions (e.g., if you were third-best on day 1, you were probably third-best on day 2 too, even though you were doing better). In other words, learning didn’t produce any qualitative changes in the representations of different items — practice had nearly the same effect on all; differences were rooted in initial difficulty of discriminating the pattern.

However, while it’s true that individual differences were stable, that doesn’t mean that every person improved their performance the exact same amount with the same amount of practice. Interestingly (and this is just from my eye-ball examination of the graphs), it looks like there was more individual variation among the group looking at noise patterns. This isn’t surprising. We all have a lot of experience discriminating faces; we’re all experts. This isn’t the case with the textures. For these, people had to ‘catch on’ to the features that were useful in discriminating patterns. You would expect more variability between people in how long it takes to work out a strategy, and how good that strategy is. Interestingly, three of the six people in the texture group actually performed better on the test than they had done on the second day of training, over a year ago. For the other three, and all nine of those in the face group, test performance was worse than it had been on the second day of training (but decidedly better than the first day).

The durability and specificity of this perceptual learning, the researchers point out, resembles that found in implicit memory and some types of sensory adaptation. It also indicates that such perceptual learning is not limited, as has been thought, to changes early in the visual pathway, but produces changes in a wider network of cortical neurons, particularly in the inferior temporal cortex.

The second, unrelated, study also bears on this issue of specificity.

We look at a scene and extract the general features — a crowd of people, violently riotous or riotously happy? — or we look at a scene and extract specific features that over time we use to build patterns about what goes with what. The first is called “statistical summary perception”; the second “statistical learning”.

A study designed to disentangle these two processes found that you can only do one or other; you can’t derive both types of information at the same time. Thus, when people were shown grids of lines slanted to varying degrees, they could either assess whether the lines were generally leaning to the left or right, or they could learn to recognize pairs of lines that had been hidden repeatedly in the grids — but they couldn’t do both.

The fact that each of these tasks interfered with the other suggests that the two processes are fundamentally related.

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Meditation's cognitive benefits

A critical part of attention (and working memory capacity) is being able to ignore distraction. There has been growing evidence that meditation training (in particular mindfulness meditation) helps develop attentional control, and that this can start to happen very quickly.

For example:

  • after an eight-week course that included up to 30 minutes of daily meditation, novices improved their ability to quickly and accurately move and focus attention.
  • three months of rigorous training in Vipassana meditation improved attentional control.
  • after eight weeks of Mindfulness Training, Marine reservists during pre-deployment showed increased working memory capacity and decreased negative mood (this training also included concrete applications for the operational environment and information and skills about stress, trauma and resilience in the body).
  • after a mere four sessions of 20 minutes, students produced a significant improvement in critical cognitive skills — and a dramatic improvement when conditions became more stressful (provided by increasingly challenging time-constraints).

There seem to be several factors involved in these improvements: better control of brainwaves; increased gray matter density in some brain regions; improved white-matter connectivity.

Thus, after ten weeks of Transcendental Meditation (TM) practice, students showed significant changes in brainwave patterns during meditation compared to eyes-closed rest for the controls. These changes reflected greater coherence and power in brainwave activity in areas that overlap with the default mode network (the brain’s ‘resting state’). Similarly, after an eight-week mindfulness meditation program, participants had better control of alpha brainwaves. Relatedly, perhaps, experienced Zen meditators have shown that, after interruptions designed to mimic spontaneous thoughts, they could bring activity in most regions of the default mode network back to baseline faster than non-meditators.

Thus, after an 8-week mindfulness meditation program, participants showed increased grey-matter density in the left hippocampus , posterior cingulate cortex, temporo-parietal junction , and cerebellum , as well as decreased grey-matter density in the amygdala . Similarly, another study found experienced meditators showed significantly larger volumes of the right hippocampus and the right orbitofrontal cortex, and to a lesser extent the right thalamus and the left inferior temporal gyrus.

These areas of the brain are all closely linked to emotion, and may explain meditators' improved ability in regulating their emotions.

Thus, long-term meditators showed pronounced differences in white-matter connectivity between their brains and those of age-matched controls, meaning that meditators’ brains were better able to quickly relay electrical signals. The brain regions linked by these white-matter tracts include many of those mentioned as showing increased gray matter density. Another study found that a mere 11 hours of meditation training (IBMT) produced measurable changes in the integrity and efficiency of white matter in the corona radiata (which links to the anterior cingulate cortex, an area where attention and emotion are thought to be integrated).

It’s an interesting question, the extent to which poor attentional control is a reflection of poor emotional regulation. Obviously there is more to distractability than that, but emotion and attention are clearly inextricably entwined. So, for example, a pilot study involving 10 middle school students with ADHD found that those who participated in twice-daily 10 minute sessions of Transcendental Meditation for three months showed a dramatic reduction in stress and anxiety and improvements in ADHD symptoms and executive function.

The effects of emotion regulation are of course wider than the effects on attention. Another domain they impact is that of decision-making. A study involving experienced Buddhist meditators found that they used different brain regions than controls when making decisions in a ‘fairness’ game. The differences reflected less input from emotional reactions and more emphasis on the actual benefits.

Similarly, brain scans taken while experienced and novice meditators meditated found that periodic bursts of disturbing noise had less effect on brain areas involved in emotion and decision-making for experienced meditators compared to novices — and very experienced meditators (at least 40,000 hours of experience) showed hardly any activity in these areas at all.

Attention is also entwined with perception, so it’s also interesting to observe that several studies have found improved visual perception attendant on meditation training and/or experience. Thus, participants attending a three-month meditation retreat, showed significant improvements in making fine visual distinctions, and ability to sustain attention.

But such benefits may depend on the style of meditation. A study involving experienced practitioners of two styles of meditation (Deity Yoga (DY) and Open Presence (OP)) found that DY meditators were dramatically better at mental rotation and visual memory tasks compared to OP practitioners and controls (and only if they were given the tasks immediately after meditating). Similarly, a study involving Tibetan Buddhist monks found that, during "one-point" meditation, monks were significantly better at maintaining their focus on one image, when two different images were presented to each eye. This superior attentional control was not found during compassion-oriented meditation. However, even under normal conditions the monks showed longer stable perception compared to meditation-naïve control subjects. And three months of intense training in Vipassana meditation produced an improvement in the ability of participants to detect the second of two visual signals half a second apart (the size of the improvement was linked to reduced brain activity to the first target — which was still detected with the same level of accuracy). Similarly, three months of intensive meditation training reduced variability in attentional processing of target tones.

References

You can read about these studies below in more detail. Three studies were mentioned here without having appeared in the news reports:

Lutz, A., Slagter, H. A., Rawlings, N. B., Francis, A. D., Greischar, L. L., & Davidson, R. J. (2009). Mental Training Enhances Attentional Stability: Neural and Behavioral Evidence. J. Neurosci., 29(42), 13418-13427. doi:10.1523/JNEUROSCI.1614-09.2009

Tang, Y.-Y., Lu, Q., Geng, X., Stein, E. A., Yang, Y., & Posner, M. I. (2010). Short-term meditation induces white matter changes in the anterior cingulate. Proceedings of the National Academy of Sciences, 107(35), 15649 -15652. doi:10.1073/pnas.1011043107

Travis, F., Haaga, D., Hagelin, J., Tanner, M., Arenander, A., Nidich, S., Gaylord-King, C., et al. (2010). A self-referential default brain state: patterns of coherence, power, and eLORETA sources during eyes-closed rest and Transcendental Meditation practice. Cognitive Processing, 11(1), 21-30. doi:10.1007/s10339-009-0343-2

Older news items (pre-2010) brought over from the old website

More on how meditation can improve attention

Another study adds to research showing meditation training helps people improve their ability to focus and ignore distraction. The new study shows that three months of rigorous training in Vipassana meditation improved people's ability to stabilize attention on target tones, when presented with tones in both ears and instructed to respond only to specific tones in one ear. Marked variability in response time is characteristic of those with ADHD.

[1500] Lutz, A., Slagter H. A., Rawlings N. B., Francis A. D., Greischar L. L., & Davidson R. J.
(2009).  Mental Training Enhances Attentional Stability: Neural and Behavioral Evidence.
J. Neurosci.. 29(42), 13418 - 13427.

http://www.physorg.com/news177347438.html

Meditation may increase gray matter

Adding to the increasing evidence for the cognitive benefits of meditation, a new imaging study of 22 experienced meditators and 22 controls has revealed that meditators showed significantly larger volumes of the right hippocampus and the right orbitofrontal cortex, and to a lesser extent the right thalamus and the left inferior temporal gyrus. There were no regions where controls had significantly more gray matter than meditators. These areas of the brain are all closely linked to emotion, and may explain meditators' improved ability in regulating their emotions.

[1055] Luders, E., Toga A. W., Lepore N., & Gaser C.
(2009).  The underlying anatomical correlates of long-term meditation: Larger hippocampal and frontal volumes of gray matter.
NeuroImage. 45(3), 672 - 678.

http://www.eurekalert.org/pub_releases/2009-05/uoc--htb051209.php

Meditation technique can temporarily improve visuospatial abilities

And continuing on the subject of visual short-term memory, a study involving experienced practitioners of two styles of meditation: Deity Yoga (DY) and Open Presence (OP) has found that, although meditators performed similarly to nonmeditators on two types of visuospatial tasks (mental rotation and visual memory), when they did the tasks immediately after meditating for 20 minutes (while the nonmeditators rested or did something else), practitioners of the DY style of meditation showed a dramatic improvement compared to OP practitioners and controls. In other words, although the claim that regular meditation practice can increase your short-term memory capacity was not confirmed, it does appear that some forms of meditation can temporarily (and dramatically) improve it. Since the form of meditation that had this effect was one that emphasizes visual imagery, it does support the idea that you can improve your imagery and visual memory skills (even if you do need to ‘warm up’ before the improvement is evident).

[860] Kozhevnikov, M., Louchakova O., Josipovic Z., & Motes M. A.
(2009).  The enhancement of visuospatial processing efficiency through Buddhist Deity meditation.
Psychological Science: A Journal of the American Psychological Society / APS. 20(5), 645 - 653.

http://www.sciencedaily.com/releases/2009/04/090427131315.htm
http://www.eurekalert.org/pub_releases/2009-04/afps-ssb042709.php

Transcendental Meditation reduces ADHD symptoms among students

A pilot study involving 10 middle school students with ADHD has found that those who participated in twice-daily 10 minute sessions of Trancendental Meditation for three months showed a dramatic reduction in stress and anxiety and improvements in ADHD symptoms and executive function. The effect was much greater than expected. ADHD children have a reduced ability to cope with stress.
A second, recently completed study has also found that three months practice of the technique resulted in significant positive changes in brain functioning during visual-motor skills, especially in the circuitry of the brain associated with attention and distractibility. After six months practice, measurements of distractibility moved into the normal range.

Grosswald, S. J., Stixrud, W. R., Travis, F., & Bateh, M. A. (2008, December). Use of the Transcendental Meditation technique to reduce symptoms of Attention Deficit Hyperactivity Disorder (ADHD) by reducing stress and anxiety: An exploratory study. Current Issues in Education [On-line], 10(2). Available: http://cie.ed.asu.edu/volume10/number2/

http://www.eurekalert.org/pub_releases/2008-12/muom-tmr122408.php

Meditation speeds the mind's return after distraction

Another study comparing brain activity in experienced meditators and novices has looked at what happens when people meditating were interrupted by stimuli designed to mimic the appearance of spontaneous thoughts. The study compared 12 people with more than three years of daily practice in Zen meditation with 12 others who had never practiced meditation. It was found that, after interruption, experienced meditators were able to bring activity in most regions of the default mode network (especially the angular gyrus, a region important for processing language) back to baseline faster than non-meditators. The default mode network is associated with the occurrence of spontaneous thoughts and mind-wandering during wakeful rest. The findings indicate not only the attentional benefits of meditation, but also suggest a value for disorders characterized by excessive rumination or an abnormal production of task-unrelated thoughts, such as obsessive-compulsive disorder, anxiety disorder and major depression.

[910] Pagnoni, G., Cekic M., & Guo Y.
(2008).  “Thinking about Not-Thinking”: Neural Correlates of Conceptual Processing during Zen Meditation.
PLoS ONE. 3(9), e3083 - e3083.

Full text available at http://dx.plos.org/10.1371/journal.pone.0003083
http://www.eurekalert.org/pub_releases/2008-09/eu-zts082908.php

Improved attention with mindfulness training

More evidence of the benefits of meditation for attention comes from a study looking at the performance of novices taking part in an eight-week course that included up to 30 minutes of daily meditation, and experienced meditators who attended an intensive full-time, one-month retreat. Initially, the experienced participants demonstrated better executive functioning skills, the cognitive ability to voluntarily focus, manage tasks and prioritize goals. After the eight-week training, the novices had improved their ability to quickly and accurately move and focus attention, while the experienced participants, after their one-month intensive retreat, also improved their ability to keep attention "at the ready."

[329] Jha, A. P., Krompinger J., & Baime M. J.
(2007).  Mindfulness training modifies subsystems of attention.
Cognitive, Affective & Behavioral Neuroscience. 7(2), 109 - 119.

http://www.eurekalert.org/pub_releases/2007-06/uop-mtc062507.php

Brain scans show how meditation affects the brain

An imaging study comparing novice and experienced meditators found that experienced meditators showed greater activity in brain circuits involved in paying attention. But the most experienced meditators with at least 40,000 hours of experience showed a brief increase in activity as they started meditating, and then a drop to baseline, as if they were able to concentrate in an effortless way. Moreover, while the subjects meditated inside the MRI, the researchers periodically blasted them with disturbing noises. Among the experienced meditators, the noise had less effect on the brain areas involved in emotion and decision-making than among novice meditators. Among meditators with more than 40,000 hours of lifetime practice, these areas were hardly affected at all. The attention circuits affected by meditation are also involved in attention deficit hyperactivity disorder.

[1364] Brefczynski-Lewis, J. A., Lutz A., Schaefer H. S., Levinson D. B., & Davidson R. J.
(2007).  Neural correlates of attentional expertise in long-term meditation practitioners.
Proceedings of the National Academy of Sciences. 104(27), 11483 - 11488.

Full text is available at http://tinyurl.com/3d6wx4
http://www.physorg.com/news102179695.html

Meditation may improve attentional control

Paying attention to one thing can keep you from noticing something else. When people are shown two visual signals half a second apart, they often miss the second one — this effect is called the attentional blink. In a study involving 40 participants being trained in Vipassana meditation (designed to reduce mental distraction and improve sensory awareness), one group of 17 attended a 3 month retreat during which they meditated for 10–12 hours a day (practitioner group), and 23 simply received a 1-hour meditation class and were asked to meditate for 20 minutes daily for 1 week prior to each testing session (control group). The three months of intense training resulted in a smaller attentional blink and reduced brain activity to the first target (which was still detected with the same level of accuracy. Individuals with the most reduction in activity generally showed the most reduction in attentional blink size. The study demonstrates that mental training can result in increased attentional control.

[1153] Slagter, H. A., Lutz A., Greischar L. L., Francis A. D., Nieuwenhuis S., Davis J. M., et al.
(2007).  Mental Training Affects Distribution of Limited Brain Resources.
PLoS Biol. 5(6), e138 - e138.

Full text available at http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0050138 
http://www.physorg.com/news97825611.html
http://www.eurekalert.org/pub_releases/2007-05/uow-mmf050407.php

Meditation skills of Buddhist monks yield clues to brain's regulation of attention

Recent research has suggested that skilled meditation can alter certain aspects of the brain's neural activity. A new study has now found evidence that certain types of trained meditative practice can influence the conscious experience of visual perceptual rivalry, a phenomenon thought to involve brain mechanisms that regulate attention and conscious awareness. Perceptual rivalry arises normally when two different images are presented to each eye, and it is manifested as a fluctuation in the "dominant" image that is consciously perceived. The study involved 76 Tibetan Buddhist monks with training ranging from 5 to 54 years. Tested during the practice of two types of meditation: a "compassion"-oriented meditation (contemplation of suffering within the world), and "one-point" meditation (involving the maintained focus of attention on a single object or thought). Major increases in the durations of perceptual dominance were experienced by monks practicing one-point meditation, but not during compassion-oriented meditation. Additionally, under normal conditions the monks showed longer stable perception (average 4.1 seconds compared to 2.6 seconds for meditation-naïve control subjects). The findings suggest that processes particularly associated with one-point meditation can considerably alter the normal fluctuations in conscious state that are induced by perceptual rivalry.

[350] Carter, O., Presti D., Callistemon C., Ungerer Y., Liu G., & Pettigrew J.
(2005).  Meditation alters perceptual rivalry in Tibetan Buddhist monks.
Current Biology. 15(11), R412-R413 - R412-R413.

http://www.eurekalert.org/pub_releases/2005-06/cp-mso060205.php

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Bilingualism helps early development of executive control

August, 2011

A study of Korean preschoolers demonstrates that at least some of the cognitive benefits of bilingualism are due to learning two languages, not because of a more diligent culture or a more enriched environment.

An increasing number of studies have been showing the benefits of bilingualism, both for children and in old age. However, there’s debate over whether the apparent benefits for children are real, or a product of cultural (“Asians work harder!” or more seriously, are taught more behavioral control from an early age) or environmental factors (such as socioeconomic status).

A new study aimed to disentangle these complicating factors, by choosing 56 4-year-olds with college-educated parents, from middle-class neighborhoods, and comparing English-speaking U.S. children, Korean-speaking children in the U.S. and in Korea, and Korean-English bilingual children in the U.S.

The children were tested on a computer-game-like activity designed to assess the alerting, orienting, and executive control components of executive attention (a child version of the Attention Network Test). They were also given a vocabulary test (the Peabody Picture Vocabulary Test-III) in their own language, if monolingual, or in English for the bilinguals.

As expected, given their young age, English monolinguals scored well above bilinguals (learning more than one language slows the acquisition of vocabulary in the short-term). Interestingly, however, while Korean monolinguals in Korea performed at a comparable level to the English monolinguals, Korean monolinguals in the U.S. performed at the level of the bilinguals. In other words, the monolinguals living in a country where their language is a majority language have comparable language skills, and those living in a country in which their primary language is a minority language have similar, and worse, language skills.

That’s interesting, but the primary purpose of the study was to look at executive control. And here the bilingual children shone over the monolinguals. Specifically, the bilingual children were significantly more accurate on the attention test than the monolingual Koreans in the U.S. (whether they spoke Korean or English). Although their performance in terms of accuracy was not significantly different from that of the monolingual children in Korea, these children obtained their high accuracy at the expense of speed. The bilinguals were both accurate and fast, suggesting a different mechanism is at work.

The findings confirm earlier research indicating that bilingualism, independent of culture, helps develop executive attention, and points to how early this advantage begins.

The Korean-only and bilingual children from the United States had first generation native Korean parents. The bilingual children had about 11 months of formal exposure to English through a bilingual daycare program, resulting in them spending roughly 45% of their time using Korean (at home and in the community) and 55% of their time using English (at daycare). The children in Korea belonged to a daycare center that did offer a weekly 15-minute session during which they were exposed to English through educational DVDs, but their understanding of English was minimal. Similarly, the Korean-only children in the U.S. would have had some exposure to English, but it was insufficient to allow them to understand English instructions. The researchers’ informal observation of the Korean daycare center and the ones in the U.S. was that the programs were quite similar, and neither was more enriching.

Reference: 

[2351] Yang, S., Yang H., & Lust B.
(2011).  Early Childhood Bilingualism Leads to Advances in Executive Attention: Dissociating Culture and Language.
Bilingualism: Language and Cognition. 14(03), 412 - 422.

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Negative gossip sharpens attention

July, 2011

Faces of people about whom something negative was known were perceived more quickly than faces of people about whom nothing, or something positive or neutral, was known.

Here’s a perception study with an intriguing twist. In my recent round-up of perception news I spoke of how images with people in them were more memorable, and of how some images ‘jump out’ at you. This study showed different images to each participant’s left and right eye at the same time, creating a contest between them. The amount of time it takes the participant to report seeing each image indicates the relative priority granted by the brain.

So, 66 college students were shown faces of people, and told something ‘gossipy’ about each one. The gossip could be negative, positive or neutral — for example, the person “threw a chair at a classmate”; “helped an elderly woman with her groceries”; “passed a man on the street.” These faces were then shown to one eye while the other eye saw a picture of a house.

The students had to press one button when they could see a face and another when they saw a house. As a control, some faces were used that the students had never seen. The students took the same length of time to register seeing the unknown faces and those about which they had been told neutral or positive information, but pictures of people about whom they had heard negative information registered around half a second quicker, and were looked at for longer.

A second experiment confirmed the findings and showed that subjects saw the faces linked to negative gossip for longer periods than faces about whom they had heard about upsetting personal experiences.

Reference: 

[2283] Anderson, E., Siegel E. H., Bliss-Moreau E., & Barrett L F.
(2011).  The Visual Impact of Gossip.
Science. 332(6036), 1446 - 1448.

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Visual perception - a round-up of recent news

July, 2011

Memory begins with perception. Here's a round-up of recent research into visual perception.

Memory begins with perception. We can’t remember what we don’t perceive, and our memory of things is influenced by how we perceive them.

Our ability to process visual scenes has been the subject of considerable research. How do we process so many objects? Some animals do it by severely limiting what they perceive, but humans can perceive a vast array of features. We need some other way of filtering the information. Moreover, it’s greatly to our advantage that we can process the environment extremely quickly. So that’s two questions: how do we process so much, and so fast?

Brain region behind the scene-facilitation effect identified

A critical factor, research suggests, is our preferential processing of interacting objects — we pick out interacting objects more quickly than unrelated objects. A new study has now identified the region of the brain responsible for this ‘scene-facilitation effect’. To distinguish between the two leading contenders, the lateral occipital cortex and the intraparietal sulcus, transcranial magnetic stimulation was used to temporarily shut down each region in turn, while volunteers viewed brief flashes of object pairs (half of which were interacting with each other) and decided whether these glimpsed objects matched the presented label. Half of the object pairs were shown as interacting.

The scene-facilitation effect was eliminated when the lateral occipital cortex was out of action, while the non-performance of the intraparietal sulcus made no difference.

The little we need to identify a scene

The scene-facilitation effect is an example of how we filter and condense the information in our visual field, but we also work in the opposite direction — we extrapolate.

When ten volunteers had their brains scanned while they viewed color photographs and line drawings of six categories of scenes (beaches, city streets, forests, highways, mountains and offices), brain activity was nearly identical, regardless of whether participants were looking at a color photo or a simple line drawing. That is, researchers could tell, with a fair amount of success, what category of scene the participant was looking at, just by looking at the pattern of brain activity in the ventral visual cortex — regardless of whether the picture was a color photo or a line drawing. When they made mistakes, the mistakes were similar for the photos and the drawings.

In other words, most of what the brain is responding to in the photo is also evident in the line drawing.

In order to determine what those features were, the researchers progressively removed some of the lines in the line drawings. Even when up to 75% of the pixels in a line drawing were removed, participants could still identify what the scene was 60% of the time — as long as the important lines were left in, that is, those showing the broad contours of the scene. If only the short lines, representing details like leaves or windows, were left, participants became dramatically less accurate.

The findings cast doubt on some models of human visual perception which argue that people need specific information that is found in photographs to classify a scene.

Consistent with previous research, activity in the parahippocampal place area and the retrosplenial cortex was of greatest importance.

The brain performs visual search near optimally

Visual search involves picking out a target in a sea of other objects, and it’s one of the most important visual tasks we do. It’s also (not surprisingly, considering its evolutionary importance) something we are very very good at. In fact, a new study reveals that we’re pretty near optimal.

Of course we make mistakes, and have failures. But these happen not because of our incompetence, but because of the complexity of the task.

In the study, participants were shown sets of lines that might or might not contain a line oriented in a particular way. Each screen was shown for only a fraction of a second, and the contrast of each line was randomly varied, making the target easier or more difficult to detect. The variation in contrast was designed as a model for an important variable in visual search — that of the reliability of the sensory information. Optimally, an observer would take into consideration the varying reliability of the items, giving the information different weights as a result of that perceived reliability. That weighted information would then be combined according to a specific integration rule. That had been calculated as the optimal process, and the performance of the participants matched that expectation.

The computer model that simulated this performance, and that matched the human performance, used groups of (simulated) neurons that responded differently to different line orientations.

In other words, it appears that we are able, very quickly, to integrate information coming from multiple locations, while taking into account the reliability of the different pieces of information, and we do this through the integration of information coming from different groups of neurons, each group of which is responding to different bits of information.

Another recent study into visual search has found that, when people are preparing themselves to look for very familiar object categories (people or cars) in natural scenes, activity in their visual cortex was very similar to that shown when they were actually looking at the objects in the scenes. Moreover, the precise activity in the object-selective cortex (OSC) predicted performance in detecting the target, while preparatory activity in the early visual cortex (V1) was actually negatively related to search performance. It seems that these two regions of the visual cortex are linked to different search strategies, with the OSC involved in relatively abstract search preparation and V1 to more specific imagery-like preparation. Activity in the medial prefrontal cortex also reflected later target detection performance, suggesting that this may be the source of top-down processing.

The findings demonstrate the role of preparatory and top-down processes in guiding visual search (and remind us that these processes can bias us against seeing what we’re looking for, just as easily as they help us).

'Rewarding' objects can't be ignored

Another aspect of visual search is that some objects just leap out at us and capture our attention. Loud noises and fast movement are the most obvious of the attributes that snag our gaze. These are potential threats, and so it’s no wonder we’ve evolved to pay attention to such things. We’re also drawn to potential rewards. Prospective mates; food; liquids.

What about rewards that are only temporarily rewarding? Do we move on easily, able to ignore previously rewarding items as soon as they lose their relevance?

In a recent study, people spent an hour searching for red or green circles in an array of many differently colored circles. The red and green circles were always followed by a monetary reward (10 cents for one color, and 1 cent for the other). Afterwards, participants were asked to search for particular shapes, and color was no longer relevant or rewarded. However, when, occasionally, one of the shapes was red or green, reaction times slowed, demonstrating that these were distracting (even though the participants had been told to ignore this if it happened).

This distraction persisted for weeks after the original learning session. Interestingly, people who scored highly on a questionnaire measuring impulsivity were more likely to be distracted by these no-longer-relevant items.

The findings indicate that stimuli that have been previously associated with reward continue to capture attention regardless of their relevance to the task in hand, There are implications here that may help in the development of more effective treatments for drug addiction, obesity and ADHD.

People make an image memorable

What makes an image memorable? It’s always been assumed that visual memory is too subjective to allow a general answer to this question. But an internet study has found remarkable consistency among hundreds of people who viewed images from a collection of about 10,000 images, some of which were repeated, and decided whether or not they had seen the image before. The responses generated a memorability rating for each image. Once this had been collated, the researchers made "memorability maps" of each image by asking people to label all the objects in the images. These maps were then used to determine which objects make an image memorable.

In general, images with people in them were the most memorable, followed by images of human-scale space — such as the produce aisle of a grocery store — and close-ups of objects. Least memorable were natural landscapes, although those could be memorable if they featured an unexpected element, such as shrubbery trimmed into an unusual shape.

Computer modeling then allowed various features for each image (such as color, or the distribution of edges) to be correlated with the image's memorability. The end result was an algorithm that can predict memorability of images the computational model hasn't "seen" before.

The researchers are now doing a follow-up study to test longer-term memorability, as well as working on adding more detailed descriptions of image content.

Reference: 

[2291] Kim, J. G., Biederman I., & Juan C-H.
(2011).  The Benefit of Object Interactions Arises in the Lateral Occipital Cortex Independent of Attentional Modulation from the Intraparietal Sulcus: A Transcranial Magnetic Stimulation Study.
The Journal of Neuroscience. 31(22), 8320 - 8324.

[2303] Walther, D. B., Chai B., Caddigan E., Beck D. M., & Fei-Fei L.
(2011).  Simple line drawings suffice for functional MRI decoding of natural scene categories.
Proceedings of the National Academy of Sciences. 108(23), 9661 - 9666.

[2292] Ma, W J., Navalpakkam V., Beck J. M., van den Berg R., & Pouget A.
(2011).  Behavior and neural basis of near-optimal visual search.
Nat Neurosci. 14(6), 783 - 790.

[2323] Peelen, M. V., & Kastner S.
(2011).  A neural basis for real-world visual search in human occipitotemporal cortex.
Proceedings of the National Academy of Sciences. 108(29), 12125 - 12130.

[2318] Anderson, B. A., Laurent P. A., & Yantis S.
(2011).  Value-driven attentional capture.
Proceedings of the National Academy of Sciences. 108(25), 10367 - 10371.

Isola, P., Xiao, J., Oliva, A. & Torralba, A. 2011. What makes an image memorable? Paper presented at the IEEE Conference on Computer Vision and Pattern Recognition, June 20-25, Colorado Springs.

 

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Mindfulness meditation may help attention through better control of alpha rhythms

May, 2011

New research suggests that meditation can improve your ability to control alpha brainwaves, thus helping you block out distraction.

As I’ve discussed on many occasions, a critical part of attention (and working memory capacity) is being able to ignore distraction. There has been growing evidence that mindfulness meditation training helps develop attentional control. Now a new study helps fill out the picture of why it might do so.

The alpha rhythm is particularly active in neurons that process sensory information. When you expect a touch, sight or sound, the focusing of attention toward the expected stimulus induces a lower alpha wave height in neurons that would handle the expected sensation, making them more receptive to that information. At the same time the height of the alpha wave in neurons that would handle irrelevant or distracting information increases, making those cells less receptive to that information. In other words, alpha rhythm helps screen out distractions.

In this study, six participants who completed an eight-week mindfulness meditation program (MBSR) were found to generate larger alpha waves, and generate them faster, than the six in the control group. Alpha wave activity in the somatosensory cortex was measured while participants directed their attention to either their left hand or foot. This was done on three occasions: before training, at three weeks of the program, and after the program.

The MBSR program involves an initial two-and-a-half-hour training session, followed by daily 45-minute meditation sessions guided by a CD recording. The program is focused on training participants first to pay close attention to body sensations, then to focus on body sensations in a specific area, then being able to disengage and shifting the focus to another body area.

Apart from helping us understand why mindfulness meditation training seems to improve attention, the findings may also explain why this meditation can help sufferers of chronic pain.

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Why multitasking is more difficult with age

April, 2011

A new study reveals that older adults’ greater problems with multitasking stem from their impaired ability to disengage from an interrupting task and restore the original task.

Comparison of young adults (mean age 24.5) and older adults (mean age 69.1) in a visual memory test involving multitasking has pinpointed the greater problems older adults have with multitasking. The study involved participants viewing a natural scene and maintaining it in mind for 14.4 seconds. In the middle of the maintenance period, an image of a face popped up and participants were asked to determine its sex and age. They were then asked to recall the original scene.

As expected, older people had more difficulty with this. Brain scans revealed that, for both groups, the interruption caused their brains to disengage from the network maintaining the memory and reallocate resources to processing the face. But the younger adults had no trouble disengaging from that task as soon as it was completed and re-establishing connection with the memory maintenance network, while the older adults failed both to disengage from the interruption and to reestablish the network associated with the disrupted memory.

This finding adds to the evidence that an important (perhaps the most important) reason for cognitive decline in older adults is a growing inability to inhibit processing, and extends the processes to which that applies.

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Exercise improves executive function and math in sedentary children

February, 2011
  • A three-month trial comparing the effects of exercise programs on cognitive function in sedentary, overweight children, has found dose-related benefits of regular aerobic exercise.

A study involving 171 sedentary, overweight 7- to 11-year-old children has found that those who participated in an exercise program improved both executive function and math achievement. The children were randomly selected either to a group that got 20 minutes of aerobic exercise in an after-school program, one that got 40 minutes of exercise in a similar program, or a group that had no exercise program. Those who got the greater amount of exercise improved more. Brain scans also revealed increased activity in the prefrontal cortex and reduced activity in the posterior parietal cortex, for those in the exercise group.

The program lasted around 13 weeks. The researchers are now investigating the effects of continuing the program for a full year. Gender, race, socioeconomic factors or parental education did not change the impact of the exercise program.

The effects are consistent with other studies involving older adults. It should be emphasized that these were sedentary, overweight children. These findings are telling us what the lack of exercise is doing to young minds. I note the report just previous, about counteracting what we have regarded as “normal” brain atrophy in older adults by the simple action of walking for 40 minutes three times a week. Children and older adults might be regarded as our canaries in the coal mine, more vulnerable to many factors that can affect the brain. We should take heed.

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