Brain Is a Co-Conspirator in a Vicious Stress Loop

If after a few months’ exposure to our David Lynch economy, in which housing markets spontaneously combust, coworkers mysteriously disappear and the stifled moans of dying 401(k) plans can be heard through the floorboards, you have the awful sensation that your body’s stress response has taken on a self-replicating and ultimately self-defeating life of its own, congratulations. You are very perceptive. It has.

As though it weren’t bad enough that chronic stress has been shown to raise blood pressure, stiffen arteries, suppress the immune system, heighten the risk of diabetes, depression and Alzheimer’s disease and make one a very undesirable dinner companion, now researchers have discovered that the sensation of being highly stressed can rewire the brain in ways that promote its sinister persistence.

Reporting earlier this summer in the journal Science, Nuno Sousa of the Life and Health Sciences Research Institute at the University of Minho in Portugal and his colleagues described experiments in which chronically stressed rats lost their elastic rat cunning and instead fell back on familiar routines and rote responses, like compulsively pressing a bar for food pellets they had no intention of eating.

Moreover, the rats’ behavioral perturbations were reflected by a pair of complementary changes in their underlying neural circuitry. On the one hand, regions of the brain associated with executive decision-making and goal-directed behaviors had shriveled, while, conversely, brain sectors linked to habit formation had bloomed.

In other words, the rodents were now cognitively predisposed to keep doing the same things over and over, to run laps in the same dead-ended rat race rather than seek a pipeline to greener sewers. “Behaviors become habitual faster in stressed animals than in the controls, and worse, the stressed animals can’t shift back to goal-directed behaviors when that would be the better approach,” Dr. Sousa said. “I call this a vicious circle.”

Robert Sapolsky, a neurobiologist who studies stress at Stanford University School of Medicine, said, “This is a great model for understanding why we end up in a rut, and then dig ourselves deeper and deeper into that rut.”

The truth is, Dr. Sapolsky said, “we’re lousy at recognizing when our normal coping mechanisms aren’t working. Our response is usually to do it five times more, instead of thinking, maybe it’s time to try something new.”

And though perseverance can be an admirable trait and is essential for all success in life, when taken too far it becomes perseveration — uncontrollable repetition — or simple perversity. “If I were to try to break into the world of modern dance, after the first few rejections the logical response might be, practice even more,” said Dr. Sapolsky, the author of “Why Zebras Don’t Get Ulcers,” among other books. “But after the 12,000th rejection, maybe I should realize this isn’t a viable career option.”

Happily, the stress-induced changes in behavior and brain appear to be reversible. To rattle the rats to the point where their stress response remained demonstrably hyperactive, the researchers exposed the animals to four weeks of varying stressors: moderate electric shocks, being encaged with dominant rats, prolonged dunks in water. Those chronically stressed animals were then compared with nonstressed peers. The stressed rats had no trouble learning a task like pressing a bar to get a food pellet or a squirt of sugar water, but they had difficulty deciding when to stop pressing the bar, as normal rats easily did.

But with only four weeks’ vacation in a supportive setting free of bullies and Tasers, the formerly stressed rats looked just like the controls, able to innovate, discriminate and lay off the bar. Atrophied synaptic connections in the decisive regions of the prefrontal cortex resprouted, while the overgrown dendritic vines of the habit-prone sensorimotor striatum retreated.

According to Bruce S. McEwen, head of the neuroendocrinology laboratory at Rockefeller University, the new findings offer a particularly elegant demonstration of a principle that researchers have just begun to grasp. “The brain is a very resilient and plastic organ,” he said. “Dendrites and synapses retract and reform, and reversible remodeling can occur throughout life.”

Stress may be most readily associated with the attosecond pace of postindustrial society, but the body’s stress response is one of our oldest possessions. Its basic architecture, its linked network of neural and endocrine organs that spit out stimulatory and inhibitory hormones and other factors as needed, looks pretty much the same in a goldfish or a red-spotted newt as it does in us.

The stress response is essential for maneuvering through a dynamic world — for dodging a predator or chasing down prey, swinging through the trees or fighting off disease — and it is itself dynamic. As we go about our days, Dr. McEwen said, the biochemical mediators of the stress response rise and fall, flutter and flare. “Cortisol and adrenaline go up and down,” he said. “Our inflammatory cytokines go up and down.”

The target organs of stress hormones likewise dance to the beat: blood pressure climbs and drops, the heart races and slows, the intestines constrict and relax. This system of so-called allostasis, of maintaining control through constant change, stands in contrast to the mechanisms of homeostasis that keep the pH level and oxygen concentration in the blood within a narrow and invariant range.

Unfortunately, the dynamism of our stress response makes it vulnerable to disruption, especially when the system is treated too roughly and not according to instructions. In most animals, a serious threat provokes a serious activation of the stimulatory, sympathetic, “fight or flight” side of the stress response. But when the danger has passed, the calming parasympathetic circuitry tamps everything back down to baseline flickering.

In humans, though, the brain can think too much, extracting phantom threats from every staff meeting or high school dance, and over time the constant hyperactivation of the stress response can unbalance the entire feedback loop. Reactions that are desirable in limited, targeted quantities become hazardous in promiscuous excess. You need a spike in blood pressure if you’re going to run, to speedily deliver oxygen to your muscles. But chronically elevated blood pressure is a source of multiple medical miseries.

Why should the stressed brain be prone to habit formation? Perhaps to help shunt as many behaviors as possible over to automatic pilot, the better to focus on the crisis at hand. Yet habits can become ruts, and as the novelist Ellen Glasgow observed, “The only difference between a rut and a grave are the dimensions.”

It’s still August. Time to relax, rewind and remodel the brain.

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Source:

http://www.nytimes.com/2009/08/18/science/18angier.html?_r=1

Tags:

Bruce S. McEwen, neuroendocrinology laboratory, Rockefeller University, Dendrites, synapses, Ellen Glasgow, neural, endocrine organs, stimulatory, inhibitory hormone, chronic stress, brain research,

Posted via email from Global Business News

High Frequency Trading: The Rise of the Machines

As a professional trader, you are confronted daily with all kinds of dynamics and situations that require a flexible and adaptive mind. You are faced with multiple variables constantly interacting with each other and your task is to process ever-changing information quickly and profitably. Valuations arbitrage, reflexive supply-and-demand dynamics, and structural changes are recurrent landmines in the typical day of traders and money managers.

We accept this “dangerous” line of work for only two reasons: monetary compensation and pride in being part of capital markets, that transmission mechanism without which innovation and creativity would be prisoners of their own ethereal state.

As a society, we are ready to strike compromises in return for a system that will allow the ethereal state of our creativity to turn into reality. We allow market insiders like market makers, broker-dealers, and others to have small advantages over us mortal investors in order to have them create the positive externalities that help us build a more sophisticated economic system. We give market makers and specialists a privileged look at the order flow (the supply and demand of stocks) in exchange for their commitment to maintaining orderly markets whenever an imbalance occurs.

We give systemic firms like JP Morgan and Goldman Sachs privileged access to liquidity via the Federal Reserve so that the banking system and capital markets can continue to serve us in our quest to invent, produce, and distribute new products. But sometimes things turn out more like a bad inland casino rather than a better market… We may still be reeling from the systemic economic collapse of last year, but new structural changes with potential negative externalities are already at our door.

For months I have witnessed strange dynamics in the way markets behaved: liquidity issues, intra-day volatility, and a constant disconnection between technical, sentiment and fundamental inputs. Markets often go through periods of irrationality, but this time it felt different.

As a professional trader and an educator on markets, my sensitivity level is higher than normal and I immediately began conducting research to make sense of my discomfort. This process pointed consistently to one element: high frequency trading or as I like to call it “the rise of the machines.”

What is High Frequency Trading?

High frequency trading (HFT) was, until recently, a topic confined to Wall Street insiders. Only in the last few weeks has it become a mainstream subject of debate via articles on theNew York Times, the Washington Post, and interviews on CNBC (yes even CNBC’s clueless anchors can now spell HFT).

The reason for this foray into the mainstream media is the potential negative ramifications HFT can have for all of us: investors, entrepreneurs, and just plain hopeful citizens.

But first, let’s define HFT as it is a very technical classification that, nonetheless, encompasses many different things. Generally speaking, HFT is high velocity trading based on mathematical algorithms that create huge daily volume on different electronic exchanges and platforms. It is machine against machine—endless trading in order to capture fractions of pennies in profits. But, so far so good: the machines provide liquidity to all of us. The owners of the machines (financial institutions) make an all-American profit and the liquidity aggregators (electronic exchanges) provide competition to other exchanges in the most capitalistic way.

But what happens if we scratch the surface? Like Michel de Montaigne, the famed Renaissance scholar, once said: “There is no man so good, who, were he to submit all his thoughts and actions to the law would not deserve hanging ten times over.”

High frequency algorithmic trading is ridden with issues:

Volume. Machine-driven trading is over 60% of trading volume on a daily basis and in some confined cases it can be as high as 90%.

Adaptability. Machines are unthinking units that do not adapt to human reactions. HFT algorithms are based on correlations and historical relationships, which are great guidelines for trading and investing but by no means they can be used blindly (see: 1987 portfolio insurance, long-term capital management 1998, credit default swaps 2008, mortgage-backed securities 2008…the list of quant-related disasters is a sad one).

Exclusivity. HFT can only work by using incredibly fast and powerful computers that also must be placed in the exchanges as proximity helps the speed. Few people can afford the computers and/or the co-location fees charged by the electronic exchanges.

Flash quotes. Some brokers have access to quotes of orders before anyone else. By exploiting the speed of their machines, they can either arbitrage price differentials or potentially front-run clients. Another abuse of flash quotes (called flash because they last one–to-three milliseconds) is that they can be used as teaser quotes to gauge supply and demand without the risk of being hit due to their quickness.

Rebates. Many high frequency traders trade not for profit but for rebates paid by the electronic platforms to attract liquidity. This escamotage incentivizes useless and toxic volume.

While these are only the most immediate concerns about HFT, they have a potentially disproportionate influence on the cost of running our capital markets. The HFT lobby pushes the argument that they create positive externalities by exploiting improving technology—but there is a difference between volume and liquidity.

If over 60% of trading is toxic, it will go away in a nanosecond and most likely it will dissipate right when investors and money managers need it the most. This could cause a huge liquidity vacuum and a 1987-type of event. Liquidity is created by market players with a stake in the game, not by casino-like machines. Flash quotes and “predatory algorithms” also raise the cost of execution for the necessarily slower institutions like pension funds and mutual funds. Additionally, the surreal tempo of machine trading makes trading for all more expensive as we now have to prepare for the irrational moves and volatility of markets when executing our trades.

I love this business and I love technology, but checks and balances are needed to preserve our capital markets. Little adjustments can be made to reduce systemic risk, like re-instating circuit breakers that cut off program trading when price changes accelerate beyond certain parameters, like investigation or stopping flash quotes that drive front running, like making good on teaser quotes for longer than just three milliseconds, and so on. In the end, we need to understand that capital markets are here not to destabilize our economy, but to serve us as a society and help us make better lives.

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The Future of Search: Social Relevancy Rank

FriendFeed has recently launched a search feature, and so Facebook search must be coming soon.

Real-time Web search (of streams of activities) is a hot topic right now. Everyone, including Google and Microsoft, recognizes the value of using trusted contacts as filters. What was once called social search is now called real-time search, but this time it will really happen. First, it will be applied to streams and then to the Web in general.

What we are about to get is a Social Relevancy Rank. Whenever you search streams of activity, the results will be ordered not chronologically but by how relevant each is to you based on your social graph. That is, people who matter more to you will bubble up. How does this work? Well, there will be a formula, just as there is a formula for Page Rank.

Solution 101: Rank by Friends and People You Follow

Here is an idea so obvious that it is surprising Twitter has not implemented it already: front-load search results with people you follow. When you search for, say, "Wilco" on Twitter today, the results are in the chronological order. That is not really relevant because you do not know who most of these people are. But if instead you could see people you follow, the search results would be much more useful.

This is not possible on Twitter today, but it already works great on FriendFeed. There, results are filtered or ranked based your social graph. This is not difficult for FriendFeed to do because, on the one hand, it knows who you care about and, on the other, it applies its advanced feed search technology to your social graph:

This sounds awesome, but there is a problem. "Wilco" works well as a query because the band has just released a new album, but many other queries would return no results. Simply put, your friends on Facebook and people you follow on Twitter can't possibly have an opinion on every topic you may be interested in. This is a problem of sparse data: trusted opinions are scarce.

Small Worlds and Taste Neighbors

To solve the problem of sparse data, we need more data... obviously. One possible solution is to incorporate other sources that you trust (i.e. broaden your social graph). As a next step, search results could rank people you may not be directly following but who are being followed by people you follow. Or in Facebook-speak, friends of friends. You could argue that you are not familiar with their opinions and so cannot yet trust them, but given the small world phenomenon, their contributions are often just as valuable.

Another step could be to include people with similar tastes, so-called taste neighbors. This approach is common among vertical social networks such as Last.fm, Flixster, and Goodreads. These networks have ideas about which people, other than your friends, are like you. However, this is a costly calculation and takes time. In order for Twitter to do something like this, it would have to compare people based on links or perform semantic analyses of tweets over time. Yet even though this is a difficult problem, it will be solved in time.

The Influencers and the Crowd

Aside from using the "second degree" of your social graph or taste neighbors, a Social Relevancy Rank could front-load influencers. In the absence of any other metric, someone who is followed by hundreds of thousands of users is likely more relevant to you than someone you don't know at all. Using number of followers as a weight might be a good way to order the rest of the activity stream.

In general, combing through countless tweets from strangers is not terribly useful anyway. Just as people have stopped looking at anything beyond the first page of results on Google, sifting through pages of tweets in chronological order gets tedious quickly. What needs to be incorporated into the Social Relevancy Rank is the aggregate sentiment of the crowd: a score that tells you yay or nay and gives you an opportunity to drill into more results if you choose.

The Quest for the Perfect Filter

There is no such thing as a perfect formula. Even Page Rank isn't perfect. Yet we all use it and find it useful. Much as Page Rank has been adapted and tuned to search the web, Social Relevancy Rank will evolve over time to help us make sense of endless streams of activity. This ranking will have a profound impact on how we tap into our friends' opinions.

It will change the face of general Web searches in time, too. Today, results are automatically ranked by relevancy and freshness. Once Social Relevancy Rank is factored in, search results will be re-ordered based on social relevancy.

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Tags:

FriendFeed search feature, Facebook search, real-time Web search, Google, Twitter, Twitter search, PageRank, Microsoft, trusted contacts as filters, social search, Global IT News, Social Relevancy Rank, metrics,

Posted via email from Global Business News

Disorderly Genius: How Chaos Drives The Brain

HAVE you ever experienced that eerie feeling of a thought popping into your head as if from nowhere, with no clue as to why you had that particular idea at that particular time? You may think that such fleeting thoughts, however random they seem, must be the product of predictable and rational processes. After all, the brain cannot be random, can it? Surely it processes information using ordered, logical operations, like a powerful computer?

Actually, no. In reality, your brain operates on the edge of chaos. Though much of the time it runs in an orderly and stable way, every now and again it suddenly and unpredictably lurches into a blizzard of noise.

Neuroscientists have long suspected as much. Only recently, however, have they come up with proof that brains work this way. Now they are trying to work out why. Some believe that near-chaotic states may be crucial to memory, and could explain why some people are smarter than others.

In technical terms, systems on the edge of chaos are said to be in a state of "self-organised criticality". These systems are right on the boundary between stable, orderly behaviour - such as a swinging pendulum - and the unpredictable world of chaos, as exemplified by turbulence.

The quintessential example of self-organised criticality is a growing sand pile. As grains build up, the pile grows in a predictable way until, suddenly and without warning, it hits a critical point and collapses. These "sand avalanches" occur spontaneously and are almost impossible to predict, so the system is said to be both critical and self-organising. Earthquakes, avalanches and wildfires are also thought to behave like this, with periods of stability followed by catastrophic periods of instability that rearrange the system into a new, temporarily stable state.

Self-organised criticality has another defining feature: even though individual sand avalanches are impossible to predict, their overall distribution is regular. The avalanches are "scale invariant", which means that avalanches of all possible sizes occur. They also follow a "power law" distribution, which means bigger avalanches happen less often than smaller avalanches, according to a strict mathematical ratio. Earthquakes offer the best real-world example. Quakes of magnitude 5.0 on the Richter scale happen 10 times as often as quakes of magnitude 6.0, and 100 times as often as quakes of magnitude 7.0.

These are purely physical systems, but the brain has much in common with them. Networks of brain cells alternate between periods of calm and periods of instability - "avalanches" of electrical activity that cascade through the neurons. Like real avalanches, exactly how these cascades occur and the resulting state of the brain are unpredictable.

It might seem precarious to have a brain that plunges randomly into periods of instability, but the disorder is actually essential to the brain's ability to transmit information and solve problems. "Lying at the critical point allows the brain to rapidly adapt to new circumstances," says Andreas Meyer-Lindenberg from the Central Institute of Mental Health in Mannheim, Germany.

Disorder is essential to the brain's ability to transmit information and solve problems

The idea that the brain might be fundamentally disordered in some way first emerged in the late 1980s, when physicists working on chaos theory - then a relatively new branch of science - suggested it might help explain how the brain works.

The focus at that time was something called deterministic chaos, in which a small perturbation can lead to a huge change in the system - the famous "butterfly effect". That would make the brain unpredictable but not actually random, because the butterfly effect is a phenomenon of physical laws that do not depend on chance. Researchers built elaborate computational models to test the idea, but unfortunately they did not behave like real brains. "Although the results were beautiful and elegant, models based on deterministic chaos just didn't seem applicable when looking at the human brain," says Karl Friston, a neuroscientist at University College London. In the 1990s, it emerged that the brain generates random noise, and hence cannot be described by deterministic chaos. When neuroscientists incorporated this randomness into their models, they found that it created systems on the border between order and disorder - self-organised criticality.

More recently, experiments have confirmed that these models accurately describe what real brain tissue does. They build on the observation that when a single neuron fires, it can trigger its neighbours to fire too, causing a cascade or avalanche of activity that can propagate across small networks of brain cells. This results in alternating periods of quiescence and activity - remarkably like the build-up and collapse of a sand pile.

Neural avalanches

In 2003, John Beggs of Indiana University in Bloomington began investigating spontaneous electrical activity in thin slices of rat brain tissue. He found that these neural avalanches are scale invariant and that their size obeys a power law. Importantly, the ratio of large to small avalanches fit the predictions of the computational models that had first suggested that the brain might be in a state of self-organised criticality (The Journal of Neuroscience, vol 23, p 11167).

To investigate further, Beggs's team measured how many other neurons a single cell in a slice of rat brain activates, on average, when it fires. They followed this line of enquiry because another property of self-organised criticality is that each event, on average, triggers only one other. In forest fires, for example, each burning tree sets alight one other tree on average - that's why fires keep going, but also why whole forests don't catch fire all at once.

Sure enough, the team found that each neuron triggered on average only one other. A value much greater than one would lead to a chaotic system, because any small perturbations in the electrical activity would soon be amplified, as in the butterfly effect. "It would be the equivalent of an epileptic seizure," says Beggs. If the value was much lower than one, on the other hand, the avalanche would soon die out.

Beggs's work provides good evidence that self-organised criticality is important on the level of small networks of neurons. But what about on a larger scale? More recently, it has become clear that brain activity also shows signs of self-organised criticality on a larger scale.

As it processes information, the brain often synchronises large groups of neurons to fire at the same frequency, a process called "phase-locking". Like broadcasting different radio stations at different frequencies, this allows different "task forces" of neurons to communicate among themselves without interference from others.

The brain also constantly reorganises its task forces, so the stable periods of phase-locking are interspersed with unstable periods in which the neurons fire out of sync in a blizzard of activity. This, again, is reminiscent of a sand pile. Could it be another example of self-organised criticality in the brain?

In 2006, Meyer-Lindenberg and his team made the first stab at answering that question. They used brain scans to map the connections between regions of the human brain and discovered that they form a "small-world network" - exactly the right architecture to support self-organised criticality.

Small-world networks lie somewhere between regular networks, where each node is connected to its nearest neighbours, and random networks, which have no regular structure but many long-distance connections between nodes at opposite sides of the network (see diagram). Small-world networks take the most useful aspects of both systems. In places, the nodes have many connections with their neighbours, but the network also contains random and often long links between nodes that are very far away from one another.

For the brain, it's the perfect compromise. One of the characteristics of small-world networks is that you can communicate to any other part of the network through just a few nodes - the "six degrees of separation" reputed to link any two people in the world. In the brain, the number is 13.

Meyer-Lindenberg created a computer simulation of a small-world network with 13 degrees of separation. Each node was represented by an electrical oscillator that approximated a neuron's activity. The results confirmed that the brain has just the right architecture for its activity to sit on the tipping point between order and disorder, although the team didn't measure neural activity itself (Proceedings of the National Academy of Sciences, vol 103, p 19518).

That clinching evidence arrived earlier this year, when Ed Bullmore of the University of Cambridge and his team used brain scanners to record neural activity in 19 human volunteers. They looked at the entire range of brainwave frequencies, from 0.05 hertz all the way up to 125 hertz, across 200 different regions of the brain.

Power laws again

The team found that the duration both of phase-locking and unstable resynchronisation periods followed a power-law distribution. Crucially, this was true at all frequencies, which means the phenomenon is scale invariant - the other key criterion for self-organised criticality.

What's more, when the team tried to reproduce the activity they saw in the volunteers' brains in computer models, they found that they could only do so if the models were in a state of self-organised criticality (PLoS Computational Biology, vol 5, p e1000314). "The models only showed similar patterns of synchronisation to the brain when they were in the critical state," says Bullmore.

The work of Bullmore's team is compelling evidence that self-organised criticality is an essential property of brain activity, says neuroscientist David Liley at Swinburne University of Technology in Melbourne, Australia, who has worked on computational models of chaos in the brain. But why should that be? Perhaps because self-organised criticality is the perfect starting point for many of the brain's functions.

The neuronal avalanches that Beggs investigated, for example, are perfect for transmitting information across the brain. If the brain was in a more stable state, these avalanches would die out before the message had been transmitted. If it was chaotic, each avalanche could swamp the brain.

At the critical point, however, you get maximum transmission with minimum risk of descending into chaos. "One of the advantages of self-organised criticality is that the avalanches can propagate over many links," says Beggs. "You can have very long chains that won't blow up on you."

Self-organised criticality also appears to allow the brain to adapt to new situations, by quickly rearranging which neurons are synchronised to a particular frequency. "The closer we get to the boundary of instability, the more quickly a particular stimulus will send the brain into a new state," says Liley.

It may also play a role in memory. Beggs's team noticed that certain chains of neurons would fire repeatedly in avalanches, sometimes over several hours (The Journal of Neuroscience, vol 24, p 5216). Because an entire chain can be triggered by the firing of one neuron, these chains could be the stuff of memory, argues Beggs: memories may come to mind unexpectedly because a neuron fires randomly or could be triggered unpredictably by a neuronal avalanche.

The balance between phase-locking and instability within the brain has also been linked to intelligence - at least, to IQ. Last year, Robert Thatcher from the University of South Florida in Tampa made EEG measurements of 17 children, aged between 5 and 17 years, who also performed an IQ test.

The balance between stability and instability in the brain has been linked with intelligence, at least as measured by scores on an IQ test

He found that the length of time the children's brains spent in both the stable phase-locked states and the unstable phase-shifting states correlated with their IQ scores. For example, phase shifts typically last 55 milliseconds, but an additional 1 millisecond seemed to add as many as 20 points to the child's IQ. A shorter time in the stable phase-locked state also corresponded with greater intelligence - with a difference of 1 millisecond adding 4.6 IQ points to a child's score (NeuroImage, vol 42, p 1639).

Thatcher says this is because a longer phase shift allows the brain to recruit many more neurons for the problem at hand. "It's like casting a net and capturing as many neurons as possible at any one time," he says. The result is a greater overall processing power that contributes to higher intelligence.

Hovering on the edge of chaos provides brains with their amazing capacity to process information and rapidly adapt to our ever-changing environment, but what happens if we stray either side of the boundary? The most obvious assumption would be that all of us are a short step away from mental illness. Meyer-Lindenberg suggests that schizophrenia may be caused by parts of the brain straying away from the critical point. However, for now that is purely speculative.

Thatcher, meanwhile, has found that certain regions in the brains of people with autism spend less time than average in the unstable, phase-shifting states. These abnormalities reduce the capacity to process information and, suggestively, are found only in the regions associated with social behaviour. "These regions have shifted from chaos to more stable activity," he says. The work might also help us understand epilepsy better: in an epileptic fit, the brain has a tendency to suddenly fire synchronously, and deviation from the critical point could explain this.

"They say it's a fine line between genius and madness," says Liley. "Maybe we're finally beginning to understand the wisdom of this statement."

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Tags:

phase-shifting, social behaviour, IQ, EEG measurements, Meyer-Lindenberg, Power laws, self-organised criticality, "power law" distribution, epilepsy, "phase-locking", Global Development News,

Source:

http://www.newscientist.com/article/mg20227141.200-disorderly-genius-how-chaos-drives-the-brain.html?full=true

Posted via email from Global Business News

Five More Search Tools You Should Know

Have you ever needed to see the search results for another city — maybe because you want to see what PPC ads are shown somewhere else?

Have you ever needed to see search results from a different country, or in a different language? Maybe you’re into real time search, and you’d love a place to find the latest photos and videos being shared on Twitter. Or perhaps you’re planning a vacation abroad, but you’re not sure when is the best time to visit Europe.

It’s time again for another roundup of the latest and greatest search tools and search engines, and in this article, I’ll share five such sites that will answer the above questions (and more). This is the fourth in my occasional series profiling under-the-radar search tools. Links to the previous three are at the end of this article.

SearchMuffin

Look, I don’t name ‘em, I just use ‘em and write about ‘em if they’re cool. And this one is SearchMuffin has a simple premise: Type in a keyword and choose a city from the dropdown menu, and it’ll show you the Google search results that match. Think of it as a sort of geo-targeted competitive research/PPC research tool. It’s about the easiest way I know of to see the PPC ads that appear in other cities.

And best of all, it’s not just limited to major U.S. cities; at the moment, there are 262 choices in the dropdown menu, including such non-metropolises as Roseville, California, and Arvada, Colorado. (No disrespect intended to Rosevillites and Arvadians.)

Glearch

Let’s expand our horizons beyond 262 U.S. cities. What if you needed to quickly see some search results from other countries and/or other languages? Glearch (again, I don’t name ‘em) is an international meta search engine that lets you search by country, by language, and/or by search engine. You can take those three options and customize each to build just the query you want.

Roooby

We’ve written a fair amount about real time search in the past few months, but we haven’t focused too much on the visual element — people posting photos and videos of what they’re doing now. Roooby is one of several real time search engines that capture media, but one of the few that surface both photos and videos. (Although, to be frank, Roooby could do a better job of finding videos by scanning sites such as Qik.com, TwitVid.io, and others that host live video.)

Roooby isn’t the only player in this space. TwitCaps, TwitPicGrid, Pingwire, and Twicsy offer similar real time image search engines.

Spezify

Speaking of media and images, here’s the most visual search tool I’ve ever seen: Spezify. The best way I can describe it is a sort of visual meta search engine. It pulls in results from Yahoo, Bing, Twitter, Flickr, YouTube, and even eBay and Amazon to create a fairly stunning search results page.

This is serious eye candy. There’s a settings page where you can choose the sources and types of content (images, text, video) you want included. But to be frank, the focus on visuals means the search results have no context whatsoever. You can move vertically and horizontally through the results, but you have no idea why you’re seeing what you’re seeing. It’s innovative to be sure, but for this searcher, it’s too lacking in functionality.

Joobili

Finally, here’s one for our readers in Europe, or for our readers traveling to Europe. It’s called Joobili, and it’s a travel/event search engine with a twist: Rather than telling the search engine what you want to do or where you want to go, you tell it when. There’s a cool date-based slider on the home page to get you started, and once you’re in the results, Joobili lets you see results based on categories (Arts, Sport, Nature, etc.), by country, or by keyword.

If you create an account, Joobili will let you save events to a wish list or a “went” list. You can also rank events to help other users make decisions on what to do and where to go. It’s a clever approach, but as I hinted above, it only covers Europe.

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http://globalblognetwork.blogspot.com/2009/07/26-people-who-mislead-you-on-twitter.html

http://globalblognetwork.blogspot.com/2009/07/ballmer-all-traditional-content-will-be.html

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http://globalblognetwork.blogspot.com/2009/07/google-unveils-sms-service-for-africa.html

http://globalblognetwork.blogspot.com/2009/07/yahoo-ceo-stop-comparing-us-to-google.html

http://globalblognetwork.blogspot.com/2009/06/future-of-facebook-usernames.html

http://globalblognetwork.blogspot.com/2009/06/googles-schmidt-rips-microsofts-bing.html

http://globalblognetwork.blogspot.com/2009/06/history-and-future-of-computer-memory.html

http://globalblognetwork.blogspot.com/2009/07/kosmix-tries-to-avoid-google-search.html

http://globalblognetwork.blogspot.com/2009/07/dispute-finder-intel-program-finds.html

Source:

http://searchengineland.com/five-more-search-tools-july09-22766

Tags:

TwitCaps, TwitPicGrid, Pingwire, Twicsy, real time image search engines, Spezify, SearchMuffin, Glearch, Joobili, Roooby, real-time Web search, Google, Twitter search, PageRank, Yahoo, Bing, Twitter, Flickr, YouTube, eBay, Amazon, Qik.com, TwitVid.io,

Posted via email from Global Business News