Does the Dual n-back increase intelligence?

Some evidence indicates that playing the videogame Dual n-back increases working memory and fluid intelligence.

A group of us would first take a memory test. Next, a randomly selected subgroup would play the Duel n-back a few hours a week for, say, a month. Then, both groups would take another memory test. Next, we would wait, say, two months with no one playing the game. Finally, the two groups would again take a memory test. We could probably still learn a lot by omitting the control group.

Here is a free version of the game.

Here's an example, which I will review the comments from and use to develop a sort of standard structure, which I will then incorporate in the top level post for future. So please comment both on the idea and my expression of it, and make suggestions for what basic info should be included either in response to this or in response to the primary article.

Area: Evolutionary Psychology

Topic: Genetic versus "cultural" influence on mate attraction.

Specific problem: There's a belief that various aspects of physical attraction are genetically determined. It is difficult to separate genetic effects from cultural effects. This is an attempt to try to control for that, to see how (in)substantial the effects of culture are. The underlying idea is that, while different cultures also have different genetic makeups, different times in the same geographic area may see different cultures with much more related genetic makeups.

The actual experiment: Sample a group of people (possibly just one sex per experiment) and obtain their views on physical attractiveness. Show them images of people, or drawings, or ask questions about what physical qualities they would find desirable in a mate. (e.g. An attractive member of the opposite sex would be taller than me - strongly agree, agree, disagree, strongly disagree). Then, and this is the expensive part, use the exact same survey on people's offspring at about the same age. It may be ideal to compare people with aunts and uncles rather than parents, as parents are likely to have a more direct non-genetic effect on preferences.

This is a rather general description, but it should be perfectly adequate for someone in the relevant field to design a very effective and insightful experiment. It could even easily be incorporated as part of a larger experiment tracking qualities between generations.

Things I'd like to see tested which may or may not have been tested before but I haven't seen in the literature.

1) There's a lot of evidence that people are wildly overconfident. The most classic version of this is how if you ask people to give a range for something such that they are 90% sure they got it right, and do it for a long list of things (like say the populations of various countries) they will get much lower than 90%. Will be people more properly calibrate when there is money at stake? (This is something that Mass Driver and I discussed a while back.) The way I'd test this is after they've given the various options see what bets they are willing to take about their being correct and how closely they match their estimated confidence.

2) Are people who have learned about cognitive biases less likely to be subject to them to any substantial degree? The one I'm most curious about is the conjunction fallacy. The obvious way to test this is to put people who have just finished a semester of intro psychology or something similar and see if they show less of a conjunction bias than students who have not done so.

3) Can training make one better at the color-word version of the Stroop interference test?

Field: Software Engineering. Issue: what are the determinants of efficiency in getting stuff done that entails writing software.

At the Paris LW meetup, I described to Alexandros the particular subtopic about which I noticed confusion (including my own) - people call it the "10x thesis". According to this, in a typical workgroup of software professionals (people paid to write code), there will be a ten to one ratio between productivities of the best and worst. According to a stronger version, these disparities are unrelated to experience.

The research in this area typically has the following setup: you get a group of N people in one room, and give them the same task to perform. Usually there is some experimental condition that you want to measure the effect of (for instance "using design patterns" vs "not using design patterns"), so you split them into subgroups accordingly. You then measure how long each takes to finish the task.

The "10x" result comes from interpreting the same kind of experimental data, but instead of looking at the effect of the experimental condition, you look at the variance itself. (Historically, this got noticed because it vexed researchers that the variance was almost always swamping out the effects of the experimental conditions.)

The issue that perplexes me is that taking a best-to-worst ratio in each group, in such cases, will give a measurement of variance that is composed of two things: first, how variable the time required to complete a task is intrinsically, and second, how different people in the relevant population (which is itself hard to define) differ in their effectiveness at completing tasks.

When I discussed this with Alexandros I brought up the "ideal experiment" I would want to use to measure the first component: take one person, give them a task, measure how long they take. Repeat N times.

However this experiment isn't valid, because remembering how you solved the task the first time around saves a huge amount of time in successive attempts.

So my "ideal" experiment has to be amended: the same, but you wipe the programmer's short-term memory each time, resetting them to the state they were in before the task. Now this is only an impossible experiment.

What surprised me was Alexandros' next remark: "You can measure the same thing by giving the same task to N programmers, instead".

This seems clearly wrong to me. There are two different probability distributions involved: one is within-subject, the other inter-subject. They do not necessarily have the same shape. What you measure when giving one task to N programmers is a joint probability distribution, the shape of which could be consistent with infinitely many hypotheses about the shape of the underlying distributions.

Thus, my question - what would be a good experimental setup and statistical tools to infer within-subject variation, which cannot be measured, from what we can measure?

Bonus question: am I totally confused about the matter?

Field: Electrical Engineering. No idea how practical this is though:

An important problem with increasing the number of cores on a chip is having enough bandwidth between the cores. Some people are working on in-silicone optical channels, which seems promising. Instead of this would it be possible for the different cores to communicate with each other wirelessly? This requires integrated transmitter and receivers, but I believe both exist.