His research, if he is to use lofty adjectives (largely meaningless in my experience) such as 'innate' to describe social structures and desires, must encompass a wider range of societies.
Happiness ratings are highest in the socialist societies, while lowest in right wing authoritarian societies. This list could be extended.

Why, then, do right wing partisans ignore this evidence and continue to support policies that are patently dysfunctional? I believe it is because, having stated a position, based on either their own family values or those dictated by their religion, they are loathe to change their minds and declare that they have been wrong.
Right wing positions are more frequently associated with Protestant evangelicals and with traditional (Reagan) Catholics. Often the leaders of these groups (e.g. television evangelists, sinning priests) epitomize the opposite of the stated values. But both of these groups embrace forgiveness, absolution, being born again. Other groups—atheists, non-fundamentalist Jews and non-fundamentalist Protestants—do not have the option of absolution; they make firmer demands on themselves
Once you set up the adjectives in the form of operationally defined personality traits and cognitive styles, it's easy to collect the data to support them. The flaw is in the characterization process itself.
Only some professional philosophers, jurists, scientists and academics believe that the principal point of political argument (or most any argument) is, or ought to be, truth rather than persuasion, and that an argument's principal appeal should be reason rather than passion.
Like other biological systems, moral intuition consists of an imperfect community of jerry-rigged faculties. Societies further combine these universal ingredients in creatively different ways.
a rational analysis of voting suggests that the core act of modern democratic government makes absolutely no sense. Economists would literally call voting "irrational" because it violates the preferences of the people who engage in it. For some reason, people decide to vote even though they would not buy a lottery ticket with identical odds, cost, and payoff. Economists typically think that people who vote are making a mistake, or there are other benefits to voting that we have not considered. For example, early scholars noted that people might vote in order to fulfill a sense of civic duty or to preserve the right to vote. Later scholars have also pointed out that people might vote because they enjoy expressing themselves in the same way they enjoy expressing themselves when they cheer for their favorite team at a ballgame. But these explanations beg the question, "Why?" It is a tautology to say that people vote because they feel like voting.
the rational analysis of voting overlooks important psychological features of human social networks that we have known about for some time.
the media does not reach the masses directly. Instead, a group of "opinion leaders"—a coinage they may have invented—usually acts as intermediary,
the more polarized we become by befriending only people with similar ideologies, the greater incentive we have to participate in politics.
cascades are primarily local phenomena, occurring in a smaller part of the population closely connected to an individual. As it turns out, this is exactly what we have been finding in our other studies of the spread of obesity, smoking, and happiness. These phenomena can spread to our friends (1 degree of separation), our friends' friends (2 degrees), and our friends' friends' friends (3 degrees), but not much further. This "3 Degree Rule" suggests that the power of one individual to influence many is limited by the effect of competing waves of influence that emanate from everyone else in the network.
In other words, one very important reason why people vote Republican is because their parents did. However, other studies have shown that the decision to affiliate with any political party and the strength of this attachment are significantly influenced by genes.

These initial twin studies suggested political ideas are heritable, but they said little about political behavior. That changed this year when we published a new study in the American Political Science Review that examined the heritability of voter participation.
research to link specific genes to political phenotypes, we established a direct association between voter turnout and monoamine oxidase A (MAOA) and a gene-environment interaction between turnout and the serotonin transporter (5HTT) gene among those who frequently participated in religious activities. In other research we have also found an association between voter turnout and a dopamine receptor (DRD2) gene that is mediated by a significant association between that gene and the tendency to affiliate with a political party. Thus we are beginning to find specific genes that can help us to predict who will vote and who won't.
The philosophical framework of liberalism makes it hard for Democrats to articulate the intuitions that most people share. Caring for a particular, individual baby, even a "special needs" baby, and being part of a particular, individual family, even a complex, messy family, are intrinsic human goods. Politics should help people achieve them successfully.
belief make tacit claims about normativity: claims not merely about how we human beings think and behave, but about how we should think and behave. Factual beliefs like "water is two parts hydrogen and one part oxygen" and ethical beliefs like "cruelty is wrong" are not expressions of mere preference. To really believe a proposition (whether about facts or values) is also to believe that one has accepted it for legitimate reasons.
Despite the remonstrations of people like Jonathan Haidt and Richard Shweder, science has long been in the values business. Scientific validity is not the result of scientists abstaining from making value judgments; it is the result of scientists making their best effort to value principles of reasoning that reliably link their beliefs to reality, through valid chains of evidence and argument.
This reliable failure of human reasoning is just that—a failure of reasoning. It does not suggest that there isn't a single correct answer to the Monty Hall problem. While it might seem the height of arrogance to say it, the people who actually understand the Monty Hall problem really do hold the "logical high ground."
Haidt appears to consider it an intellectual virtue to adopt, uncritically, the moral categories of his subjects. But where is it written that everything that people do or decide in the name of "morality" deserves to be considered part its subject matter?
counties of
the US that produce the wealth and innovation voted overwhelmingly
Democratic and the counties of the US that depend on government
subsidy or that simply underperform economically voted overwhelmingly
Republican.
the Republican success of the last
generation, since Nixon and Reagan cracked the code, has been to
exploit irrelevant (to national policy) anxieties. We are at the
point where the national maneuvering for office has nothing to do with
argument (so much for folks who say that "the economy should be
Obama's best argument") and everything to do with positioning a
message between now and election day so that pulling the lever or
pushing the button or punching the chad for one candidate makes you
feel morally satisfied, which is to say, less anxious and guilty and
ashamed.

the
humanistic culture of the orator from Demosthenes to Martin Luther
King Jr. is decisively gone. We don't fully understand what's
replacing it, but it's happening all around us—you might even call
it a third culture...

It is common to make the assumption that people are thinking when they vote and they are making reasoned choices. I harbor no such illusion. No argument I have ever gotten into with these people, (despite avoiding talking to them, I sometimes can't resist saying something true) has ever convinced anyone of anything. They are not reasoning, nor do they want to try. They simply believe what they believe.
It is all very nice to come up with complex analyses of what is going on. As is often the case, the real answer is quite simple. Most people can't think very well. They were taught not to think by religion and by a school system that teaches that knowledge of state capitals and quadratic equations is what education is all about and that well reasoned argument and original ideas will not help on a multiple choice test.
they are not equipped to think about politics and, in my mind, they are not equipped to vote. The fact that we let them vote while failing to encourage them to think for themselves is a real problem for our society.

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Seed: Standing on the Shoulders of Giants

Tags: computer, education, video_game, Spore, complexity, emergence, brain, cell, immuno, evolution on 2008-09-07 and saved by4 people -All Annotations (4) -About

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How the Large Hadron Collider Might Change the Web: Scientific American

Ironically, CERN's next great contribution to the Internet could be all but transparent to the end user. In a perfect world, Globus or its successors would simply make everything on a given grid straightforwardly and transparently accessible from any computer. "If Globus is a success," Bader said, "then you won't hear about it."

Tags: data, information, computer, particle_accelerator, LHC on 2008-09-05 -All Annotations (0) -About

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Sugata Mitra shows how kids teach themselves | Video on TED.com

descriptive, intriguing but not a rock-solid argument

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Google opens up new front in browser wars with Chrome

see http://www.kottke.org/08/09/google-chrome-google-browser

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Hoover Institution - Education Next - How Do We Transform Our Schools?

Tags: education, school, learning, computer, innovation, children, software on 2008-09-01 and saved by20 people -All Annotations (0) -About

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At first glance there appears to be little non-consumption of education in the United States since students are required to receive schooling. Looking deeper, however, reveals many pockets of non-consumption where students would be delighted with computer-based learning rather than the alternative, nothing at all.
When a new approach or technology substitutes for the old, the pace of substitution almost always follows an S-curve, as depicted on the left side of Figure 3. The initial adoption is very slow, and then at some point the world flips and the substitution proceeds rapidly. The problem is that the S-curves are sometimes steep and other times gradual, so it is hard to know when the rapid adoption will begin. But there is a way to forecast the flip. First, one must plot the percentage of market shares held by the new, divided by the old (if each has 50 percent, the ratio will be 1.0) on the vertical axis. Second, the vertical axis needs to be arrayed on a logarithmic scale—so that .0001, .001, .01, .1, 1.0, and 10.0 are all equidistant. When plotted in this way, if disruption is truly happening and there is an S-curve developing, the data will fall on a straight line. Sometimes the line slopes upward steeply, and sometimes it is more gradual. But it is always straight. The reason is that the mathematics linearizes the S-curve. When the pace of substitution is plotted in this way, one typically can tell before the new approach accounts for 2 to 3 percent of the total what the slope of the line is. That makes it easy, then, to extend the line into the future to obtain a sense of when the new innovation will account for 25 percent, 50 percent, and 90 percent of the total. We call this line a “substitution curve.”

MIT’s Introduction to Algorithms, Lectures 1 and 2: Analysis of Algorithms - good coders code, great reuse

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Neurophilosophy : "Robot with rat brain" claims are exaggerated

use diigo fxn to monitor

Tags: brain, neuro, computer, robot, memory, artificial_intelligence on 2008-08-18 -All Annotations (0) -About

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Evolving Thoughts: Instruction and information

Tags: biology, genetics, systems_biology, model, theory, computer, information, philosophy on 2008-08-17 -All Annotations (0) -About

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one of the oldest and most widely used rhetorical tropes regarding genes, and latterly DNA, is that it/they are instructions. They cause the body to be built according to a program. Other metaphors include recipes, blueprints, code, and so on, but they all rely on this basic notion of instruction: DNA is a process of imparting information to be used in the construction of the organism.
information, which we all think we have a handle on, is what the instructions comprise
every use of the term information in respect of genes can either be replaced with the notion of causality, or can be ignored as adding nothing to the debate, or worse, as confusing and unhelpful.
hylomorphism, or form-substance dualism, requires that the properties of things are at least partly not due to the stuff of which it is made. Atomism requires that the properties of the parts fix all the properties of the wholes.

In modern debate terms, this is very much like - but not identical with - the reductionism/holism, or more recently, the reductionism/emergence dichotomy.
we are intentional actors, and so we conceive of the world in those terms.
the biological world, is not composed of formless gunk that gets its properties only when information is pressed into it. The parts of the system give the system its properties. Genes cause processes to occur, and not alone - they are one causal element of the entire process of being alive. They do not instruct us how to live and grow. They do not impart information. They cause developmental properties to occur

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Neurophilosophy : Robot controlled by "brain" in a culture dish

Tags: brain, neuro, computer, robot, memory, video, artificial_intelligence on 2008-08-15 -All Annotations (0) -About

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Seed: A New State of Mind

review from Mind Hacks - "One thing I notice a little of in the quotes from Montague, which is incredibly common in discussion of dopamine and reward, is a kind of 'reward system dogma'." http://www.mindhacks.com/blog/2008/08/the_best_is_yet_to_c.html

Tags: brain, anatomy, neuro, behavior, learning, dopamine, addiction, fmri, computer, application, prediction, science_is_a_method, model, theory, publishing, risk, social_network on 2008-08-15 and saved by13 people -All Annotations (8) -About

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That’s when Montague discovered the powers of dopamine, a neurotransmitter in the brain. His research on the singular chemical has drawn tantalizing connections between the peculiar habits of our neurons and the peculiar habits of real people, so that the various levels of psychological description — the macro and the micro, the behavioral and the cellular — no longer seem so distinct. What began as an investigation into a single neurotransmitter has morphed into an exploration of the social brain: Montague has pioneered research that allows him to link the obscure details of the cortex to all sorts of important phenomena, from stock market bubbles to cigarette addiction to the development of trust. “We are profoundly social animals,” he says. “You can’t really understand the brain until you understand how these social behaviors happen, or what happens when they go haywire.”
Add Sticky Note
- seductive
  posted by taryn930 on 2008-08-15
nucleus accumbens (NAcc), a part of the brain dense with dopamine neurons and involved with the processing of pleasurable rewards, like food and sex
temporal difference reinforcement learning (TDRL). Computer scientists Rich Sutton and Andrew Barto, who both worked on models of artificial intelligence, had pioneered the model. Sutton and Barto wanted to develop a “neuronlike” program that could learn simple rules and behaviors in order to achieve a goal. The basic premise is straightforward: The software makes predictions about what will happen
“The only reason we could see it so clearly,” Montague says, “is because we came at it from this theoretical angle. If you were an experimentalist seeing this data, it would have been extremely confusing.
It got bounced from every journal. I came this close to leaving the field. I realized that neuroscience just wasn’t ready for theory, even if the theory made sense.”
The crucial feature of these dopamine neurons, say Montague and Dayan, is that they are more concerned with predicting rewards than with the rewards themselves.
Our knowledge, in other words, emerges from our cellular mistakes. The brain learns how to be right by focusing on what it got wrong.
“A Neural Substrate of Prediction and Reward.” The paper has since been cited more than 1,200 times, and it remains the definitive explanation of how the brain parses reality into a set of accurate expectations, which are measured out in short bursts of dopamine.
Evolution essentially bootstrapped our penchant for intellectual concepts to the same reward circuits that govern our animal appetites. “The guy who’s on hunger strike for some political cause is still relying on his midbrain dopamine neurons, just like a monkey getting a treat,” Montague says. “His brain simply values the cause more than it values dinner.”
By representing everything in terms of neuron firing rates, the human brain is able to choose the abstract thought over the visceral reward, as long as the abstraction excites our cells more than apple juice. That’s what makes ideas so powerful: No matter how esoteric or ethereal they get, they are ultimately fed back into the same system that makes us want sex and sugar
Although dopamine neurons excelled at measuring the mismatch between their predictions of rewards and those that actually arrived — these errors provided the input for learning — they’d learn much quicker if they could also incorporate the prediction errors of others. Montague called this a “fictive error learning signal,” since the brain would be benefiting from hypothetical scenarios: “You’d be updating your expectations based not just on what happened, but on what might have happened if you’d done something differently.”
addiction is a disease of valuation: Dopamine cells have lost track of what’s really important.
The purpose of this particular experiment is to see how “one bad apple” can lead perfect strangers to also act badly. While Montague isn’t ready to share the results — he’s still gathering data — what he’s found so far is, he says, “stunning, shocking even…. For me the lesson has been that people act very badly in groups. And now we can see why.”

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Next Generation Tool For Visualizing Genomic Data Introduced

This new tool offers a Google Maps-like view of integrative genomic data

Tags: data, data_visualization, genetics, genome, computer, application, open_source on 2008-08-11 -All Annotations (0) -About

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Bubble Question Frames Design for Olympic Aquatics Center - NYTimes.com

see photo http://www.abc.net.au/reslib/200801/r219605_861517.jpg

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Petabyte Scale Data-Analysis and the Scientific Method

Tags: science, math, computer, science_is_a_method, information, google on 2008-07-07 -All Annotations (0) -About

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But the idea that massive scale data collection and computing renders the
scientific method obsolete? That we no longer need models, or theories, or experiments? That's blatant silliness.
poor understanding of really big numbers is exploited by creationists who try to convince people that things like evolution are impossible. The arguments are nonsense, but because the numbers are so big, so incomprehensible, that you can trick people.
The big problem with huge amounts of data is that it's easy to find false correlations. In a huge amount of data, you expect to find tons of patterns. Many of those patterns will be statistical coincidences. And it can be very difficult to identify the ones that are.
patterns in huge quantities of data - even seemingly incredibly unlikely patterns - become downright likely when you're searching at petabit scale. Our intuitions about what's likely to happen
as a result of randomness or coincidence are a total failure at massive scale.
If you try to do science without understanding - that is, all you do is look for patterns in data - then you're likely to "discover" a whole bunch of correlations that don't mean anything. If you don't try to understand those correlations, then you can't tell the real ones from the chance ones.
Mr. Anderson
is confusing the fact that we don't know what the result will be for a particular query with the idea that we don't know why our system works well. Web-search systems aren't based on any kind of randomness: they find relationships between webpages based on hypotheses about how links between pages can provide information about the subject and quality of the link target.
I thought that reminded me of a quote of Darwin's, and sure enough it did:

"About thirty years ago there was much talk that geologists ought only to observe and not theorize; and I well remember someone saying that at this rate a man might as well go into a gravel-pit and count the pebbles and describe the colours. How odd it is that anyone should not see that all observation must be for or against some view if it is to be of any service!"

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The End of Theory: The Data Deluge Makes the Scientific Method Obsolete

Tags: science, google, information, computer, math on 2008-07-05 and saved by70 people -All Annotations (30) -About

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Mind Hacks: Linguistic feathers ruffled by high tech new school

Some psychologists, for example, completely reject any scientific approach to thought and behaviour because they say it strips human experience of exactly what it means to be human - the lived subjective experience of life.

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Want to Remember Everything You'll Ever Learn? Surrender to This Algorithm

Tags: learning, memory, computer, cognition, psychology, DIY, evolution, prodigy on 2008-05-19 and saved by46 people -All Annotations (2) -About

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Given the chance to observe our behaviors, computers can run simulations, modeling different versions of our path through the world. By tuning these models for top performance, computers will give us rules to live by. They will be able to tell us when to wake, sleep, learn, and exercise; they will cue us to remember what we've read, help us track whom we've met, and remind us of our goals. Computers, in Wozniak's scheme, will increase our intellectual capacity and enhance our rational self-control.
clarify your goals, gain knowledge through spaced repetition, preserve health, work steadily, minimize stress, refuse interruption, and never resist sleep when tired.
By projecting the achievement of extreme memory back along the forgetting curve, by provably linking the distant future — when we will know so much — to the few minutes we devote to studying today, Wozniak has found a way to condition his temperament along with his memory.
When it comes to learning, what really matters is how things fit together. We master the stories, the schemas, the frameworks, the paradigms; we rehearse the lingo; we swim in the episteme.

The disadvantage of this comforting notion is that it's false
Much of the information does remain in our memory, though we cannot recall it. "To this day," Bjork says, "most people think about forgetting as decay, that memories are like footprints in the sand that gradually fade away. But that has been disproved by a lot of research.
Precisely those things that seem to signal we're learning well — easy performance on drills, fluency during a lesson, even the subjective feeling that we know something — are misleading when it comes to predicting whether we will remember it in the future. "The most motivated and innovative teachers, to the extent they take current performance as their guide, are going to do the wrong things,"
the user's sense of achievement is exactly what we should most distrust.
"We have an intellectually impressive group of theories, but history offers little confidence that they will provide any meaningful insight into natural behavior." Neisser encouraged psychologists to leave their labs and study memory in its natural environment, in the style of ecologists
The best time to study something is at the moment you are about to forget it. And yet — as Neisser might have predicted — that insight was useless in the real world. Determining the precise moment of forgetting is essentially impossible in day-to-day life.

Obviously, computers were the answer
techniques are designed to overcome steep learning curves through automated steps, like stairs on a hill. He calls it incremental reading, and it has come to dominate his intellectual life. Wozniak no longer wastes time worrying that he hasn't gotten to some article he wants to read; once it's loaded into the system, he trusts his algorithm to apportion it to his consciousness at the appropriate time.
"After four months," Biedalak says sadly, "you sometimes get a reply to some sentence in an email that has been scrambled in his incremental reading process."
"It was never a feel-good product," Murakowski says, and here he may be getting closer to the true conflict that lies at the heart of the struggle to optimize intelligence, a conflict that transcends design and touches on some curious facts about human nature. We are used to the idea that normal humans can perform challenging feats of athleticism. We all know someone who has run a marathon or ridden a bike cross-country. But getting significantly smarter — that seems to be different. We associate intelligence with pure talent, and academic learning with educational experiences dating far back in life. To master a diffic