# Question
Title: What is the effect of ADD/ADHD on performance variability on cognitive tests over time?
I've read research reporting that lower IQ is correlated with higher variability in reaction time. But such research does not seem to disentangle the possible effect of ADD, in which case you might have a separate cluster that does not follow the general trend.

So, **if ADD/ADHD subjects are given a cognitive test on different days, will the variability in their outcomes be higher than that in controls?**

# Answer
### General thoughts on factors influencing test-retest correlations

From a theoretical perspective, it makes sense that groups that experience temporary states that lower state intelligence or lead to a poorer test taking orientation would have lower test-retest cognitive test correlations. By lower state intelligence, I'm referring to states of being such as are observed with extreme sleep deprivation, alcohol intoxication, or while on psychedelic drugs that would temporarily lower actual cognitive functioning. By test taking orientation I'm referring to motivation and concentration issues related to taking a test. Because such factors are transient, they may be present in one test session and not another, and as such would be a source of error in measurement and would reduce test-retest correlations. In groups where such factors are greater or more prevalent, then test-retest correlations should be reduced more than other groups.

A clinician that administers a high-quality intelligence test should be aware of these transient factors when administering the test. If a test-taker is not taking a test seriously, is distracted, and so on, then the scores may have to be disregarded. Some studies might use group testing protocols that don't pick up these issues. And perhaps sometimes teasing out what is transient and what is permanent may be difficult.

In general, latent ability on a task is conceptualised in terms of an individual applying reasonable and normal levels of effort, under normal and healthy conditions, in a normal and reasonable environment. Under such conditions, a person is not able to improve their performance (discounting practice and cheating effects), but there are many ways that performance can decline. Sackett et al (1988) talks about the difference between typical and maximal performance. Test performance is meant to be conducted under conditions of maximal performance, where the test taker is meant to be applying maximal effort to the task.

### Thoughts about ADHD

Research does show that in general individuals diagnosed with ADHD tend to score lower on intelligence tests. Frazier et al (2004) peformed a large-scale meta analysis and found that healthy participants scored on average 0.61 standard deviations (i.e., weighted d) higher on full scale IQ than ADHD participants.

Many of the symptoms of ADHD, such as being easily distracted, experiencing boredom on a task, and other problems related to sustained attention would make the administration of some cognitive tests difficult. It is possible for any measure of an individual's IQ to fall below what the person is capable, where such an effect can be due, in addition to random variation between testing sessions, to systematic variability in transient factors. Thus, it seems plausible that individuals with ADHD could more frequently experience such factors, and as such, experience lower test-retest correlations.

**Caveat**: ADHD research is not my area. I imagine there are some empirical studies that have compared test-retest correlations of tests between ADHD and other groups. I just haven't found them.

### References

* Frazier, T., Demaree, H., and Youngstrom, E. (2004). Meta-analysis of intellectual and neuropsychological test performance in attention-deficit/hyperactivity disorder. Neuropsychology, 18(3):543.
* Sackett, P. R., Zedeck, S., and Fogli, L. (1988). Relations between measures of typical and maximum job performance. Journal of Applied Psychology, 73:482-486.
> 7 votes

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Tags: performance, adhd, reliability
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# Question
Title: Effectiveness of recalling information in the same location it was learned
In an interview with Robert A. Bjork this article claims that it is easier to recall information where it occurred:

> Similarly, studying in only one location is great as long as you’ll only be required to recall the information in the same location. If you want information to be accessible outside your dorm room, or office, or nook on the second floor of the library, Bjork recommends varying your study location. And again, these tips generalize.

How much of an effect does this have with:

1. Memorizing random facts
2. Recalling the details of an event that occurred at the location

# Answer
> 5 votes
The phenomenon you are referring to is called situated learning.

Recall of information from memory is facilitated by retrieval cues; the more retrieval cues you have, the easier it is to recall an item from memory. Location (and pictorial information information in general) is perhaps such an effective cue because of the amount of information that it construes. You might say that a picture has higher entropy relative to, e.g., a sound, in that a picture can be easily recalled yet it conveys much more. A room contains numerous details-- the placement of furniture, the color of the walls, the books on the shelf-- all of which may act as retrieval cues.

The reason these seemingly meaningless details are such helpful retrieval cues can be linked to the idea of Hebbian learning, which is often summarized by the mnemonic, "cells that fire together, wire together". When you study in a room, neurons are firing in response to stimuli in the room, while other neurons fire in response to the content you are learning. Thus, when you are again tested in that same room, the stimuli in the room fire the same neurons, which act as retrieval cues. If you study in a different location, you would have no retrieval cues-- which is why Bjork suggests varying your study location.

I would say that this has an effect on both memorizing random facts as well as events that happened in a particular location. However, events tend to be more memorable for a number of other reasons. For instance, they are experienced (rather than simply read off a page). There is some evidence that autobiographical memories are treated differently than semantic memories, both at the cognitive and neural level.

# Answer
> 6 votes
According to the tradition expressed in a well documented (references at the end) popular science book about memory, Moonwalking with Einstein in which the author trains himself from zero to become the American memory champion, the memory palace technique (also known as the method of loci where loci is place in latin) is the one used by most competitive memory practitioners. It consists in recalling random facts (your question 1) by staging them into an imaginary location. Other aspects of the technique involve imagining an actor or agent in these places, but this element is used as a reminder for the type of information involved: the place itself is the key to memorising. Therefore the answer to question 2 is that details of an event are much easier to remember at the location where they occurred, when recalling this location.

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Tags: memory
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title: Are there axioms in the mind?

text:
# Question
Title: Are there axioms in the mind?
While studying artificial neural networks, I stumbled upon the following question:

**Are there any hard-wired axioms in the brain that must exist equally in every conscious mind?**

I could imagine that, since neuron can't change their principal behavior, simple signal-modulating functions should always work the same way.

By axioms I don't necessarily mean the axioms known from math, but functional units that do not rely on further microscopic elements. Like if you say neurons functioning is also influenced by proteins - ok than turn to protein level. At some point there should be a level reached where things (neurons, proteins, atoms) should always work the same.

But of course the mathematical axioms are a good way to prove that some functions evaluate to the same result in different brains.

For example if you teach addition to a child then you would first show the child one apple then two, then three, etc. Although abstract logical rules like the commutative property possibly reside in more complex structures, nobody needs to be taught in counting.

This possibly also fits in philosophy - I wasn't quite sure.

# Answer
I think we should first clarify the difference between simple mathematical operators and a complete formal axiomatic system. I think it is very clear that exponentiation, square root or multiplication operations are taught. They represent a higher level cognitive ability than simple addition and subtraction.

In the level of more innate abilities, we can still claim that some numeric tendencies do exist in the human infant. Moreover, these are shown to exist in primates, and even rodents (for instance, see Brannon & Terrace, 1998). It is argued that in infants, numbers are represented in two distinct systems; one is responsible for small numbers and their relations, and the second responsible for mass amounts of items (have a look at Feigenson, Dehaene, Spelke, 2004). Briefly speaking, infants can differentiate 2 items from 3 items or 1 item; on the other hand they can distinguish 40 items from 80 items better than they could 60 or 70 items. So this second system operates on a relatively macro level. Moreover, first system not only provides a comparison operator, but addition also. So, infants can understand that two individual objects make 2 objects. For instance, suppose you have a box and three dolls in it. If you remove two of them, they can understand that there is still one more in it. To summarize, numeric abilities at least partially seem to have innate roots.

This is not surprising at all. In evolutionary terms, it is not hard to see that counting objects can be essential, up to a certain point. For instance, it is very important that prey should recognize whether there is a single hunter or two of them. As another example, it is equally important to recognize whether there are 100 or 200 footsteps on the ground, but not that important whether there is 100 or 105 footsteps. There are surely cognitive abilities with innate roots; one other such ability is human face recognition for example. Infants can detect an upright human face right after they are born.

How is such information represented? It does not have to be in an axiomatic fashion. Neurons have a very simple structure themselves, but networks of neurons are capable of great things. They can be represented in states of a dynamical system, like associative memory. Or they can be represented by simple feedforward structures. It is very hard to distinguish that. My primitive claim is that assuming innate formal axiomatic systems is against the rule of parsimony.
> 6 votes

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Tags: intelligence, neuroscience
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# Question
Title: By learning to read and write upside down, what did I do to myself?
If you've ever read Encyclopedia Brown books, you'll be familiar with the backwards writing in the back of the book that explains the solution to the case.

When I was in my mid-late teens (I don't remember the age exactly) I decided to teach myself to read/write upside down (and backwards?). I can read most text fairly well when the page is upside down (and have no problems with the upside down unicode text people post on Facebook), and I am able to write upside down with relative ease.

However, since developing that skill I have fairly extreme difficulties remembering how to write lowercase b's and d's, and I've had to use a trick I've heard dyslexics use and I remind myself that a 'b' points towards the end of a word.

I have no problems reading words with the characters, or typing them on the computer when I want a 'b' or a 'd', only when I'm writing it.

Did I give myself a very limited form of dyslexia?

# Answer
### Negative Transfer

A common scientific term to describe what you are talking about is called negative transfer. I.e., where learning one skill actually results in lower performance on another skill. This is contrasted with positive transfer, when learning one skill facilitates performance on another skill. In general (although I don't have refs on hands) positive transfer is generally a greater effect than negative transfer. Negative transfer tends to be localised to particular aspects of the task (e.g., b's and d's).

For a extended encyclopedia entry see Perkins and Salomon's (1992) article on transfer of learning.

### Research on Mirror Reading and Writing

There's even research specifically on reading and writing upside down and back to front (e.g., see google scholarfor 'mirror writing' and 'mirror reading'). I haven't read much of this literature.

To take but one article, Gottfried, Sancar, and Chatterjee (2003) discuss acquired mirror writing.

They note, perhaps relevant to your question, that:

> Young children, developmental dyslexics, and some healthy left-handed adults may produce mirror text.

I assume that in the above cases (as opposed to your case where it is acquired with deliberate practice), writing in reverse is involuntary, in which case I suppose you could describe writing a few letters back to front involuntarily as representing a symptom shared with some developmental dyslexics.

As an interesting historical aside, they note that

> Perhaps the most celebrated mirror writer, Leonardo da Vinci, generated over 5000 notebook pages in reverse.

So, you're in good company.

### References

* Gottfried, J., Sancar, F., and Chatterjee, A. (2003). Acquired mirror writing and reading: evidence for reflected graphemic representations. Neuropsychologia, 41(1):96-107. FREE PDF
* Perkins, D. and Salomon, G. (1992). Transfer of learning. International encyclopedia of education. FREE LINK
> 53 votes

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Tags: cognitive-psychology, learning, motor, transfer
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title: What are the benefits of dyslexia?

text:
# Question
Title: What are the benefits of dyslexia?
Reading the Wikipedia article on Dyslexia, it sounds very negative. The article defines Dyslexia as a learning disability, which I understand it is classified as. However, knowing individuals with dyslexia, they do not consider themselves to have a disability, they say that *their brains process information differently*. Not *worse*, just *differently*. And I can attest to the fact that some of the people I know who are Dyslexic far exceed my abilities in certain areas, namely music, picture / visual / spacial skills, memory, timing, etc.

The Wikipedia article mentions no *benefits* to dyslexia.

* Have any studies been done on benefits of dyslexia?
* If so, what were the findings ?

# Answer
This is not my area, but I did a little research and found a few things:

Silverman (2000) has an interesting article discussing children who are gifted but also have a learning disability. The article makes a number of interesting points about how other skills are often developed in order to compensate for a given disability (e.g., like how people who are vision impaired are better able to use their remaining senses).

In relation to dyslexia, Silverman summarises the work of Ron Davis. Specifically, Silverman stated that:

> Ron Davis (1994), in The Gift of Dyslexia, describes the benefits of dyslexia. He lists the basic abilities that all dyslexics share:
>
> 1. They can utilize the brain’s ability to alter and create perceptions (the primary ability).
> 2. They are highly aware of the environment.
> 3. They are more curious than average.
> 4. They think mainly in pictures instead of words.
> 5. They are highly intuitive and insightful.
> 6. They think and perceive multi-dimensionally (using all the senses).
> 7. They can experience thought as reality.
> 8. They have vivid imaginations. (p. 5)

That said, I haven't read the book, and I'm unclear on the degree of empirical support for these claims.

### References

* Davis, R. D. (with E. M. Braun). (1994). The gift of dyslexia. Burlingame, CA: Ability Workshop Press.
* Silverman, L. (2000). The two-edged sword of compensation: How the gifted cope with learning disabilities. In Uniquely gifted: Identifying and meeting the needs of twice exceptional learners, pages 153-159. FREE PDF
> 10 votes

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Tags: intelligence
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# Question
Title: How does task difficulty schedule affect the rate and efficiency of perceptual learning?
In perceptual learning (line length/orientation discrimination, visual target detection, tone frequency discrimination, etc), when training people or animals to perform a task better, is there an optimal schedule for controlling task difficulty, to acheive maximal improvement?

At least 3 reasonable schedules come to (my) mind:

1. Easy to difficult - In education it is the custom to begin with easy examples or problems, and gradually progress to more difficult ones. Maybe this should work in perceptual learning as well.
2. Interleave different difficulties
3. Always the most difficult possible

Has there been a rigorous comparison of such schedules and their effect on the rate of learning, and the maximal performance level achieved at the end of training?

# Answer
### General pedagogical ideas around optimal difficulty

Many theories of instruction suggest that learning is optimal when an appropriate level of challenge is maintained. If a task is too easy, there's little to learn. If a task is too difficult, the learner can be overwhelmed. The implication for practice is that task difficulty should increase in conjunction with the increased skill of the learner. Of course, we can think of exceptions to these ideas, such as the value of over-learning, and the general idea of exposing people to challenging problems to motivate further learning. This also still leaves open the question of precisely how fast difficulty should increase for a given learner.

Academically, I've seen these ideas expressed in several places:

**Deliberate practice**: This is a concept drawn from the expertise literature. It is posited that amount of deliberate practice (rather than amount of simple experience) is one of the key factors that discriminates experts from non-experts (see Ericsson et al 2006). A key aspect of task difficulty relates to the sequencing of task difficulty with practice. Ericsson et al (2006) writes that

> The core assumption of deliberate practice ... is that expert performance is acquired gradually and that effective improvement of performance requires the opportunity to find suitable training tasks that the performer can master sequentially... Deliberate practice presents performers with tasks that are initially outside their current realm of reliable performance, yet can be mastered within hours of practice by concentrating on critical aspects and by gradually refining performance through repetitions after feedback.

**Challenge point in motor learning**: Guadagnoli and Lee present a framework for thinking about optimal difficulty in motor learning. Their framework suggests that optimal learning is achieved by maintaining task difficulty at the point where it provides optimal information (where learning is possible, but it is not too difficult as to be overwhelming).

### Research on task difficulty and perceptual learning

In a review article Ahissar and Hochstein (2004) also suggest easy to difficult is the most effective regime for perceptual learning:

> As found in our studies, training is more effective if subjects start with easy conditions and gradually move to more difficult con- ditions. The importance of beginning training with easy conditions was first found by Pavlov. When Pavlov reinforced a dog’s salivation following its seeing an ellipse but not following its seeing a circle (Figure I, rightmost pair of stimuli), the dog could not avoid generalization and salivated at sight of the circle, as well. Only by using very elongated ellipses, and training along the continuum, from left to right, was it able to achieve good performance for small circle/ellipse differences. This phenomenon was subsequently termed ‘transfer along a continuum’ (of different degrees of difficulty).

### References

* Ahissar, M. and Hochstein, S. (2004). The reverse hierarchy theory of visual perceptual learning. Trends in cognitive sciences, 8(10):457-464. FREE PDF
* Ericsson, K. et al. (2006). The influence of experience and deliberate practice on the development of superior expert performance. The Cambridge handbook of expertise and expert performance, 10(3):683-703. FREE PDF
* Guadagnoli, M. and Lee, T. (2004). Challenge point: a framework for conceptualizing the effects of various practice conditions in motor learning. Journal of Motor Behavior, 36(2):212-224. FREE PDF
> 8 votes

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Tags: perception, perceptual-learning, training
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# Question
Title: What are different ways to determine centroids of fMRI activation, their drawbacks and perks?
I'm reading an older article on bilingualism (Kim, Relkin, Lee, & Hirsch, 1997) for a seminar. They were interested in the spatial separation of two languages in early and late bilinguals. They determined activated regions and their overlap in two conditions and across two groups.

To determine these areas, they first excluded all voxels where BOLD response difference to baseline was not above their empirically derived false positive rate (p\<0.0005) and then used all these voxels as if they had the same activation to determine the "centre-of-mass".

They then calculated the distance between the centroids in the two conditions to measure spatial separation and counted the overlapping voxels.

I have some issues with this method:

* it seems as if they're throwing away a lot of information by discarding all voxels where activity was not above chance level and maybe more importantly by treating all significantly activated voxels as the same even though there may be significant differences in their activation level
* minor: it's volumetric, not mass, so it should be centre of volume, right? It may be a misunderstanding of the workings of fMRI on my part.

They also demonstrated that the derived distance held up well over two orders of magnitude in significance level, but still they didn't differentiate between highly and not-so-highly-but-still-significantly activated voxels. Is this still state-of-the-art?

### My questions:

* What are different ways to calculate centroids without discarding so much information but still backing up the conclusion inferentially (some sort of spatial confidence area?)?
* What are their drawbacks (my lecturer says they all have some, so you can just as well use the above-mentioned multistage method, but this seems like the easy way out)?
* What is a well-tested method to test spatial separation of activity for two conditions? I'm not asking about demonstrating functional separation, but once you don't treat voxels as on-and-off anymore, calculating degree of overlap becomes less trivial.

### References

* Kim, K.H.S. and Relkin, N.R. and Lee, K.M. and Hirsch, J. (1997). Distinct cortical areas associated with native and second languages, *Nature*. FREE PDF

# Answer
This question gets close to something that might alternatively be posted to stats.stackoverflow.com. Personally, I've always felt that application of Null-Hypothesis Significance Testing (NHST) methods to neuroimaging data does a particularly good job of highlighting their scientific deficiencies.

Personally, these days I'd use Generalized Additive Modelling (GAM) to analyze data from an individual's scan and if aggregating multiple spatially normalized scans I'd use Generalized Additive Mixed effects Modelling (GAMM). In both approaches, I'd use an AIC-adjusted likelihood ratio to evaluate the strength of evidence for differences amongst groups/conditions in the activity patterns discerned by GAM. *A posteriori* investigation of any evidence-supported differences can be achieved by visualization and parametric bootstrapping of confidence intervals.

If you *really* want to *explicitly* evaluate evidence for a group difference in the distance between activation areas, then one approach is to assume an a priori model for the activation, say a mixture of two trivariate gaussians with a parameter that specifies their separation, then use something like hierarchical Bayesian modelling to establish credible intervals on the difference between groups on the separation parameter.
> 5 votes

# Answer
Great question!

To start, you are absolutely right-- a 1997 article on fMRI is considered very old as far as the field is concerned. Statistical techniques have improved dramatically since then so, while I haven't read the article in question, it's safe to assume that there are probably lots of things they would do differently if the study was conducted today. In fact, there are often quite a few ways of analyzing the same data; some of my answers below reflect my own experience using AFNI (SPM users may use different techniques).

> it seems as if they're throwing away a lot of information by discarding all voxels where activity is not above chance level and maybe more importantly by treating all significantly activated voxels as the same even though there may be significant differences in their activation level

Following the standard mass-univariate approach to fMRI, there is not much you can do about throwing away voxels that aren't below alpha level. Not doing so would inflate your Type I error rate. There are alternatives to the mass-univariate approach, such as multi-voxel pattern analysis (MVPA), but each technique has its own strengths and weaknesses.

As far as treating all significant voxels the same-- that's not quite true. They usually are treated the same qualitatively-- that's just how classical hypothesis testing works. A p-value less than alpha means you can reject the null, otherwise you can't. There's no middle ground. Besides, voxel activations are all relative anyway (using the cognitive subtraction technique), so absolute activation levels are not always informative. However, you can use activation levels to determine the centroid! So let me jump to that:

> What are different ways to calculate centroids without discarding so much information but still backing up the conclusion inferentially (some sort of spatial confidence area?)?

After performing a mass GLM to determine significant voxels, voxels can be clustered. In AFNI, this is done using a k-neighbors approach; e.g. if I set my cluster size to k=20, all voxels that don't belong to a cluster of minimum 20 contiguous, significant voxels will be eliminated.

The center of each cluster is most often reported as the *peak* activation in the cluster (see? not all activations are treated equally). Sometimes, it is reported as the geometric center of mass-- and yes, that's volume, not real mass. Peak is usually more informative because it's more likely that the task your measuring is functionally localized at the peak, rather than the center of mass. However, center of mass may give you a better idea of the spatial extent of your cluster. There are probably better justifications, but that's what jumps to mind...

> What is a good method to test spatial separation of activity for two conditions? I'm not asking about demonstrating functional separation, but once you don't treat voxels as on-and-off anymore, calculating degree of overlap becomes less trivial.

In general, spatial separation is usually not important. Some functional regions are much larger than others-- there may be 'little' functional difference between voxels that are largely separated in the motor cortex. But the same spatial separation between voxels of two different cytoarchitectonic structures may have radically different functions. I may be missing the point here though, if so, please clarify and I'll try to help...
> 4 votes

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Tags: neuroscience, language, neuroimaging, fmri
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# Question
Title: Is variation in human brain size related to mental functioning?
It seems that the head size of human beings has evolved to be relatively similar, yet there is still some amount of variation. While I'm sure the correlation is not absolute, I read that larger heads do imply larger brains.

Thus, my questions:

* What is the correlation between head size and brain size?
* What is the relationship between brain size and mental processing ability (e.g., intelligence, emotional responses to certain stimuli, reaction time, etc)?
* Why might these correlations exist or not exist?

# Answer
> 15 votes
One relatively recent review on this topic is Rushton & Ankney (2009). They report that there have been a large number of studies with varying results:

* 28 studies, covering a total of 1,389 subjects, used brain imaging techniques to estimate the size of the brain. Correlations with general mental ability (GMA) ranged from 0.04 to 0.69, with an unweighted mean of 0.40. Weighting the correlations with the sample size produces a mean of 0.38.
* 59 studies, covering a total of 63,403 subjects, used external head measurements to estimate the size of the brain. Correlations with general mental ability ranged from 0.02 to 0.55, with an unweighted mean of 0.21. Weighting the correlations with the sample sizes produces a mean of 0.20.
* 6 studies used Jensen's method of uncorrelated vectors to distill *g*, getting a mean correlation of 0.63.

The last point probably needs some explaining. The *g-factor*, or *g* for short, is the quantity that IQ tests are designed to measure. The name "g factor" comes from the fact that it is a common, general factor which all kinds of intelligence draw upon. For instance, Deary (2001) analyzed an American standardization sample of the WAIS-III intelligence test, and built a model where performance on the 13 subtests was primarily influenced by four group factors, or components of intelligence: verbal comprehension, perceptual organization, working memory, and processing speed. In addition, there was a common *g* factor that strongly influenced all four. The model indicated that the variance in *g* was responsible for 74% of the variance in verbal comprehension, 88% of the variance in perceptual organization, 83% of the variance in working memory, and 61% of the variance in processing speed. In other words, if a person has a lot of *g*, then (s)he is likely to also get a high score on tests measuring all subtypes of intelligence. This is possibly because *g* is something in the brain that all the subtypes of intelligence benefit from. But note the word likely - the correlation is not perfect, so it's still possible to have a high *g* but be worse than average in some subtype of intelligence. It's just less likely than the converse.

Now experts might be cringing at that previous paragraph, because I talked about an individual having a lot of *g*. Technically, *g* is something that is computed from the correlations between various test scores in a given sample, and there's no such thing as the *g* of any specific individual. The technique doesn't even guarantee that *g* actually corresponds with any physical quantity, as opposed to something that the method just happened to produce by accident. So when you want to measure someone's intelligence, you make a lot of people take tests that are known to be strongly *g-loaded*. That means that the performance on the tests is strongly correlated with *g*. Then you take their raw scores and standardize them to produce an IQ score, so that if e.g. only 10% of the test-takers got a raw score of X, then anyone getting the raw score of X is assigned an IQ indicating that they're in the top 10% of the population. And although this still doesn't tell us what an individual's *g* score is, it gives us a score that's closely correlated with *g*. IQ also seems to predict a large number of things like life outcomes and performance in a variety of tasks and so forth. See e.g. Jensen (1998) for (much) more on this.

So now that I've explained all that, what does the "6 studies used Jensen's method of uncorrelated vectors to distill *g*" bit mean? Well, if I'm understanding the paper right, then the 28 + 59 other studies calculated the correlation between brain size and IQ. In other words, between brain size and performance on some particular intelligence test, that ultimately measures performance on one or more *subtypes* of intelligence. Subtypes which are closely, but not perfectly, correlated with *g*. The 6 studies were different in that they attempted to calculate the actual correlation between *g* and brain size, which is different from the correlation between IQ and brain size. Which one is better? Here I must admit that my expertise isn't sufficient to answer the question confidently, but I'd suppose that it depends on what you're trying to measure. If you want to know the extent to which the common factor influencing all the subtypes of intelligence varies between brains, then you want to use the estimate where *g* has been extracted. If you want to know how much brain size seems to correlate with mental ability in practice, you might want to use the correlation with IQ. Neither is more correct than the other.

Other findings from the paper:

* The brain volume–GMA correlation is equally strong in males and females.
* It is also found in people of East Asian, East Indian, European, Turkish, African, South American, and Amerindian descent.
* Studies using a narrow age range of younger or older samples show the same magnitude of correlation.
* Many studies appear to show that the size effects are manifest throughout the brain and not specific to any particular region; however, other studies show GMA centered in the frontal brain regions. Two studies found support for both positions - the more *g*-loaded subtests were distributed throughout the brain but concentrated most in the frontal lobes.
* The correlation between head size and GMA is found both within families and between families. From the paper: "the within-family finding is of special interest because it controls for most of the sources of variance that distinguish families, such as social class, styles of child rearing, and general nutrition, that differ between families."
* Body size and brain size are correlated. The average correlation is 0.2 for MRI studies, or 0.3-0.4 for studies that use skull measurements. But body size and GMA are also correlated, at around 0.20-0.25. There is disagreement about whether the size of the body should be controlled for when estimating brain size/GMA correlations, but a correlation is found regardless of whether or not body size is controlled for. In other words, both relative *and* absolute brain size are correlated with IQ.
* The brain size/intelligence correlation is also found in non-human animals, both within a species (e.g. rats) and between different species (e.g. species of birds, species of primates).
* Brain size is also environmentally sensitive: for instance, rats raised in complex environments have thicker cortices and larger brains than rats reared in impoverished environments.
* The paper also reviews results showing that brain size and GMA are correlated with age, socioeconomic position, sex, and population group differences, but those aren't relevant for the question so I won't go into them here.

**References:**

Deary, I.J. (2001) Human intelligence differences: a recent history. *Trends in Cognitive Sciences*, 5, 127–130.

Jensen, A.J. (1998) *The g Factor – The Science of Mental Ability*. Praeger.

Rushton, J.P. & Ankney, C.D. (2009) Whole Brain Size and General Mental Ability: A Review. *International Journal of Neuroscience*, 119(5), 692-732.

# Answer
> -2 votes
Kinda : p

The overwhelming consensus is that it has more to do with brain/body mass ratios. If you are an elephant, you need a lot of wiring to control everything, and that all takes room. Humans have the best ratios by far.

That being said, think about a child prodigy vs an adult. One clearly outmatches the other in terms of brain size. In some brain structures, size or density **does** increase with expertise. But overall, you need to understand that these are really complex networks and size/space is only one factor affecting their performance.

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Tags: intelligence, neuroscience
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# Question
Title: What are the considerations for using wide-reaching messages and training over individual-focused efforts for changing behavior?
In most places that I've worked, when some sort of problem arose or corrective action needs to be taken, one of the first responses is to send mass emails at an organization or team level and set up mandatory group training sessions. Sometimes, this is in place of an effort to correct individual actions that led to the problem on a personal level.

**Does the use of broad messages (email, group training, individual training mandated for all individuals) have an impact on behavior that is equal to or greater than the use of individually-focused efforts?**

I realize that in some cases, the occurrence of a problem could indicate a lack of training that does need to be addressed at a larger level to minimize future occurrences. I also realize that that some incidents might be accidental - the person realized that they did or didn't do something and personally took corrective action to prevent it in the future. However, I'm especially interested in cases where an individual performed some type of action (or inaction) that led to the occurrence of a problem and did not take corrective action on their own and if group-oriented messages have a chance of changing their behavior.

# Answer
> 3 votes
The first thing that comes to mind when reading this is diffusion of responsibility. Because responsibility has not been explicitly assigned, as it would be in a one-on-one meeting, people tend to feel like they do not need to do anything.

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Tags: io-psychology, performance, training, communication
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# Question
Title: How does raw IQ change over the lifespan at the group-level?
In the classic review of intelligence research (*Intelligence: Knowns and Unknowns*), Neisser et al (1996) write the following

> **Stability**. Intelligence test scores are fairly stable during development. When Jones and Bayley (1941) tested a sample of children annually throughout childhood and adolescence, for example, scores obtained at age 18 were correlated r = .77 with scores that had been obtained at age 6 and r = .89 with scores from age 12. When scores were averaged across several successive tests to remove short-term fluctuations, the correlations were even higher. The mean for ages 17 and 18 was correlated r = .86 with the mean for ages 5, 6, and 7, and r = .96 with the mean for ages 11, 12, and 13. (For comparable findings in a more recent study, see Moffitt, Caspi, Harkness, & Silva, 1993.) Nevertheless, IQ scores do change over time. In the same study (Jones & Bayley, 1941), the average change between age 12 and age 17 was 7.1 IQ points; some individuals changed as much as 18 points.

Typically, IQ scores are standardised for age and thus remove age related differences.

My question is

**What is the relationship between age and raw intelligence test score at the group-level from around age 5 and into adultyhood?**

From what I've read, I understand that at the group-level intelligence increases monotonically with age during childhood and that it levels-off at some point (perhaps around 18?). Also, below a certain age (e.g., in infancy), completely different tests are required to measured intelligence. I also understand that individuals may experience various trajectories (e.g., some might plateau earlier, etc.), but I'm interested in the group-level. Thus, an ideal answer would probably provide a sense of the functional form of the increase in intelligence over childhood and adolescence.

My first thought for an answer was to look at the test manual for a standardised child intelligence test, such as the WISC, but I don't currently have easy access to that.

### References

* Jones, H. E., and Bayley, N. (1941). The Berkeley Growth Study. Child Development, 12, 167-173.
* Moffitt, T. E., Gabrielli, W. F., Mednick, S. A., & Schulsinger, E (1981). Socioeconomic status, IQ, and delinquency. Journal of Abnormal Psychology, 90, 152-156.
* Neisser, U., Boodoo, G., Bouchard, T. J., Boykin, A. W., Brody, N., Ceci, S. J., Halpern, D. F., Loehlin, J. C., Perloff, R., Sternberg, R. J., and Urbina, S. (1996). Intelligence: Knowns and unknowns. American Psychologist, 51:77-101. FREE PDF

# Answer
> 6 votes
In general, the introduction section of McArdle et al (2002) provides a good literature review on the topic (see link to PDF in references).

### Early research by Jones and Conrad (1933)

McArdle et al (2002) extract a quote from Jones and Conrad's (1933) summary of the literature based on large cross-sectional studies and also the classic Army alpha tests:

> "The chief characteristic of the curve may be summarized as involving a linear growth to about 16 years, and a negative acceleration beyond 16 to a peak between the ages of 18 and 21. A decline follows, which is much more gradual than the curve of growth, but which by the age of 55 involves a recession to the 14-year level. (p. 239)"

### Cattell on Crystalised and Fluid Intelligence

Also, see Figure 1 in McArdle et al (2002) which extracts a diagram from Cattell (1987) that shows Cattell's theorised relationship between age and two types of intelligence, crystalised and fluid. The theory suggests that fluid intelligence increases at a faster rate and peaks at around 18 and then shows a steady decline, whereas crystalised intelligence increases throughout the life span, with the rate of learning decreasing over time, and arguably approaching a plateau around 50 before commencing a very gradual decline around 60.

### References

* Cattell, R. B. (1987). Intelligence: Its structure, growth and action. Amsterdam: North-Holland.
* Jones, H. E., & Conrad, H. S. (1933). The growth and decline of intelli- gence: A study of a homogeneous group between the ages of ten and sixty. Genetic Psychology Monographs, 13(1), 223–275
* McArdle, J., Ferrer-Caja, E., Hamagami, F., and Woodcock, R. (2002). Comparative longitudinal structural analyses of the growth and decline of multiple intellectual abilities over the life span. Developmental Psychology, 38(1):115. FREE PDF

*NOTE: After posting the question, I did a little research and found the above research. I've self-awarded the answer for sake of closure, but I'm happy to award to someone else with a better answer.*

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Tags: intelligence, developmental-psychology, aging, iq
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# Question
Title: Is it possible to improve reading speed and visual comprehension by doing exercises?
### Background

I'm trying to capture detailed information from the images in my visual memory, mainly text. My daily life requires reading many documents on varying topics. I want to increase my reading speed and comprehension so that I can see text blocks as a whole; that is I see them not as meaningful text, but an image to be processed later. Perhaps then I would be able to iterate over the images in memory faster than I can with my eyes.

However, when I need to remember the details of a topic, I can visually remember the location of the text in the document, but I can't recall details such as whole sentences.

I'm trying to improve my visual memory by this simple exercise:

* chose a random image from flickr
* have a look at it for a few seconds
* eyes closed, try to rebuild the image from memory

but normally I can not remember many of the details from the image, even if it's not very detailed.

When I try this exercise with text blocks, I mostly end up "reading the block". It's hard to treat a text block as an image without reading it. So I normally fail at visualizing it in my memory.

### Questions

* Is it possible to improve reading speed, reading comprehension, and visual comprehension through visualisation exercises like those above?
* If not with the above exercises, are there other exercises that can improve such skills?

# Answer
There's a general discussion of speed reading methods on Wikipedia, but let's look at some specific articles and see what they say about reading speed and comprehension.

### Bell's review of the reading speed and comprehension literature

In Bell's (2001) review of the empirical literature, he makes a number of points, which seem reasonable to me:

* A few studies have shown that you can increase reading speed with various "speed reading techniques" (e.g., Hill, 1981, Richard, 1982).
* However, "techniques ... employed on speed reading courses ... tend to cause readers to suffer lower levels of reading comprehension..." This is a within-person phenomena. People who read particularly slowly often have low comprehension, but this is not relevant to the question of whether an individual should increase or slow down their reading speed.
* Several authors recommend reading at the right speed, neither too fast nor too slow (e.g., Coady, 1979).
* Part of being a skilled reader is about adapting reading strategies, particularly with regards to the speed / comprehension trade-off, to the nature of the text and the reading goals in the situation.

### Discussion of reading expertise

There is a massive literature on reading comprehension. For example, take Pearson et al's (1992) list that states that expert readers:

> * Search for connections between what they know and the new information they encounter
> * Monitor the adequacy of their models of text meaning
> * Take steps to repair faulty comprehension once they realize they have failed to understand something
> * Learn early on to distinguish important from less important ideas in text they read
> * Are adept at synthesising information within and across texts and reading experiences
> * Draw inferences during and after reading to achieve a full, integrated understanding of what they read
> * Sometimes consciously, and almost always unconsciously, ask questions of themselves, the authors they encounter, and the texts they read.

The key feature of this list is that expert readers employ strategies related to constructing meaning. They don't pertain to exact strategies for moving the eyes over the page and the process of visualising text, and so on.

### Implications for increasing reading speed

On the basis of the above, I don't think trying to integrate whole blocks of text visually would be a useful training exercise for helping a person achieve his or her reading goals.

When it comes to reading, I think it's more important to think about your goals than to focus on reading speed. Why do you need to read the text? What are you hoping to get out of the text? How will this information be used?

### References

* Bell, T. (2001). Extensive reading: Speed and comprehension. The Reading Matrix, 1(1). PDF Coady, J. (1979). A psycholinguistic model of the ESL reader. In R. Mackay, B. Barkman, & R.R. Jordon (Eds), Reading in a second language (pp 5-12). Rowley, MA: Newbury House.
* Hill, J.K. (1981). Effective reading in a foreign language. English Language Teaching Journal, 35, (pp 270-281).
* Pearson, P., Roehler, L., Dole, J., and Duffy, G. (1992). Developing expertise in reading comprehension. What research has to say about reading instruction, 2:145-199. PDF
* Richard, W. (1982). Improving Reading Speed in Readers of English as a Second Language. JALT Journal, 4, (pp 89-95).
> 12 votes

# Answer
Not really. There is some evidence showing n-back improves working memory, but it's fairly weak.

What you are trying to do is speed-reading, which doesn't work either. Almost all speed reading techniques (suppressing inner voice, reading blocks of text at a time, etc) don't perform any better than skimming. Speed reading methods generally just trade speed for comprehension and retention.

The only trick that does help is Rapid Serial Visualization Presentation (RSVP). RSVP shows words sequentially, so you don't have to wait for your eye to move from word to word and back across lines of text. You might be able to double your reading speed, but it's pretty tiring and I believe all of the studies were rather scoped. I doubt that using RSVP all day would improve long-term retention that much, as you need to elaborate on what you are reading in order to retain it.
> 5 votes

# Answer
From the abstract Eye fixations predict reading comprehension: the relationships between reading skill, reading speed, and visual inspection:

> A discriminant function analysis showed that fixation duration was a successful predictor of reading comprehension, but that the number of fixations, regressive fixations, reading speed, and vocabulary were not reliable predictors. A multiple regression analysis revealed that reading speed was predicted by the number of fixations, the average fixation duration, and the duration of the final fixation upon the sentence, but there was no relationship with reading ability. Highly skilled readers are those who can extract information efficiently, but are not necessarily those who have fast overall reading rates.
> 2 votes

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Tags: memory, perception, reading, vision
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# Question
Title: How do animals recognize other animals of their own species?
My motivation for this question is dog-based, but I suppose it would apply equally well to humans. **How do animals recognize their own kind, particularly where there is large variation in appearance?**

The background is that I am reading "Discrimination of human and dog faces and inversion responses in domestic dogs (*Canis familiaris*)" by Racca et al. (2010; *Animal Cognition* 13: 525-533). These authors show that when dogs are presented upright objects, human faces, and dog faces, they spend more time gazing on novel objects and human faces than they do on familiar ones. However, they spend significantly more time gazing on familiar dog faces than they do on novel ones.

This got me thinking how a dog "knows" another face belongs to a dog. Acedotally (which I realize is worth approximately nil in science), my dog reacts very differently to seeing another dog in an adjacent car compared to a person. Given that there is so much phenotypic variation in dogs, how does it know that other animal is a dog, and not a cat or very large rodent? I could understand a lion recognizing another lion, because all lions look more or less the same.

# Answer
### The obvious: animals use a variety of senses

There's a wikipedia article on intra-species recognition. The article states:

> Different species may employ different methods, but all of them are based on one or more senses (after all, this is how the organism gather information about the environment). The recognition may happen by chemical signature (smell), by having a distinctive shape (sight), by emitting certain sounds (hearing), or even by behaviour patterns. Often a combination of these is used.... Intra-species recognition systems are often subtle... Sometimes, too, intra-species recognition is fallible

Of course, this is all fairly obvious, true, and not that helpful.

### A few articles of possible relevance

* Mateo (2004) has a review of recognition systems in animals if you want a more general theoretical framework.
* Pfennig (1998) discusses intra-species recognition and the relationship with mate selection.

### Musings on your particular question

In your particular example of two cars driving along with windows closed, we are probably excluding senses of smell and sound and requiring the dogs to rely exclusively on sight. I wonder whether prototype theory or some other human visual categorisation theory would apply to dogs also?

### References

* Mateo, J. (2004). Recognition systems and biological organization: the perception component of social recognition. In Annales Zoologici Fennici, volume 41, pages 729-745. PDF
* Pfenning (1988) PDF

*I'm way outside my expertise here. I tossed up whether to post this at all. I figured I'd post in the absence of another answer a week after posting. The above represents what I found from a little research.*
> 3 votes

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Tags: learning, animal-cognition, perception
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# Question
Title: What is the optimal length of a training session?
When a practice session is too long, there will presumably be a point where no further significant gains can be made without a break. At what point will this be?

**Update**: This question was originally about the length of an initial training session, but it has since been rescoped to cover training sessions in general.

# Answer
### Principles of optimal practice duration from the expertise literature

The expertise literature and its discussion of deliberate practice provides substantial guidance on the question of optimal practice duration. As Ericsson et al (2006) summarised:

> elite performers search continuously for optimal training activities, with the most effective duration and intensity, that will appropriately strain the targeted physiological system to induce further adaptation without causing overuse and injury.

With regards to optimal practice length, Ericsson et al (2006) stated (with key points bolded by me) that:

> Although the detailed nature of deliberate practice will differ across domains and as a function of attained skill, there appear to be limits on the daily duration of deliberate practice, and this limit seems to generalize across domains of expertise. **Expert performers from many domains engage in practice without rest for only around an hour**, and they prefer to practice early in the morning when their minds are fresh (Ericsson et al., 1993 ). Elite musicians (Ericsson, 2002) and athletes (Ericsson, 2001, 2003 c) report that the factor that limits their deliberate practice is primarily an inability to sustain the level of concentration that is necessary. Even more interestingly, **elite performers in many diverse domains have been found to practice, on the average, roughly the same amount every day, including weekends, and the amount of practice never consistently exceeds five hours per day** (Ericsson, 1996; Ericsson et al., 1993 ). The limit of four to five hours of daily deliberate practice or similarly demanding activities holds true for a wide range of elite performers in different domains, such as writing by famous authors (Cowley, 195 9; Plimpton, 1977), as does their **increased tendency to take recuperative naps**. Furthermore, unless the daily levels of practice are restricted, such that subsequent rest and nighttime sleep allow the individuals to restore their equilibrium, individuals often encounter overtraining injuries and, eventually, incapacitating “burnout.” In some domains of sports, such as gymnastics, sprinting, and weight lifting, the max- imal effort necessary for representative performance is so great that the amount of daily deliberate practice is even further limited by factors constraining the duration of produc- tion of maximal power and strength.

### Implications for the specifics of your question

Thus, based on the expertise literature, four or five one-hour sessions of deliberate practice in a day with plenty of rest between and at night would be optimal if the aim is to maximise learning on the focal task in the shortest period of time.

In general, I wouldn't define optimal duration of practice in terms of amount of the task learnt.

Also, in the real-world, the optimality criterion can vary between people. The expertise literature assumes that the goal is to maximise learning on the focal skill. However, in the real-world, a person may be learning a number of things, and also need to accomplish tangible outcomes at the same time.

### References

* Cowley, M. (Ed.) (195 9). Writers at work: The Paris review interviews. New York: Viking.
* Ericsson, K. A. (1996). The acquisition of expert performance: An introduction to some of the issues. In K. A. Ericsson (Ed.), The road to excellence: The acquisition of expert performance in the arts and sciences, sports, and games (pp. 1–5 0). Mahwah, NJ: Erlbaum.
* Ericsson, K. A. (2001). The path to expert performance: Insights from the masters on how to improve performance by deliberate practice. In P. Thomas (Ed.), Optimizing performance in golf (pp. 1–5 7). Brisbane, Australia: Australian Academic Press.
* Ericsson, K. A. (2002). Attaining excellence through deliberate practice: Insights from the study of expert performance. In M. Ferrari (Ed.), The pursuit of excellence in education (pp. 21–5 5 ). Hillsdale, NJ: Erlbaum.
* Ericsson, K. A. (2003c). The development of elite performance and deliberate practice: An update from the perspective of the expert-performance approach. In J. Starkes & K. A. Ericsson (Eds.), Expert performance in sport: Recent advances in research on sport exper- tise (pp. 49–81). Champaign, IL: Human Kinetics.
* Ericsson, K. A., Krampe, R. Th., & Tesch-Romer, C. (1993 ). The role of deliberate practice in the acquisition of expert performance. Psychological Review, 100, 3 63 –406. PDF
* Ericsson, K. et al. (2006). The influence of experience and deliberate practice on the development of superior expert performance. The Cambridge handbook of expertise and expert performance, 10(3):683-703. PDF
* Plimpton, G. (Ed.) (1977). Writers at work: The Paris review. Interviews, Second Series. New York: Penguin.
> 11 votes

# Answer
Experimental psychologists have really grappled with this question, because boundary conditions make a huge difference. One valiant effort is found in a recent paper by Rawson and Dunlosky at Kent State University, who put forth a "3 x 3" recommendation: Practice to your desired criterion three times, then repeat practice at 3 regularly spaced intervals.

### References

* Rawson, K. and Dunlosky, J. (2011). Optimizing schedules of retrieval practice for durable and efficient learning: How much is enough? Journal of Experimental Psychology: General, 140(3):283.
> 5 votes

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Tags: learning, training
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# Question
Title: Does learning one discipline improve performance in another discipline?
I have come across a lot of articles that suggest learning across disciplines would improve cognitive abilities. This is more often referred to as Integrative Learning. Also, nowadayas a lot of emphasis seems to be placed on Interdisciplinarity and cross-disciplinary research.

Thus (putting it plainly) if a economist learns psychology, his brain can draw inferences from psychology that could help his cognitive abilities in dealing with economics.

* Is this true and supported by facts?
* If this is true, does the brain automatically interrelates concepts or we need to train the brain in a particular manner?
* Does this depend on age?

# Answer
> 23 votes
The answer to this question will depend on how you construe 'cognitive abilities.' For instance, for certain formulations the answer is trivially yes: an economist who learns psychology may come away with psychological knowledge that could help his economics, like how to space out his studying to maximally improve retention; or how to deal with the cognitive dissonance of causing widespread financial collapse. Demonstrations of transfer in the other direction -- from economics to psychology -- are even easier to demonstrate. Our department, for example, has in recent years seen an influx of psychology graduate students whose backgrounds are in much more mathematically sophisticated disciplines. These students are able to leverage their mathematical skills to great advantage in what has, in past years, sometimes been a field with excessively 'fluffy' methodology. But in both these instances -- psych --\> economics and economics --\> psych -- we're talking not so much about 'transfer' of knowledge as of literal re-use. If you buy paper plates for a picnic, can you also use them inside your house? Of course you can.

The question gets harder when we zoom in further. If we accept as given that the techniques of economics would be useful in the field of psychology, what about the atomic cognitive units which collectively constitute the edifice of economics knowledge? Does the way of viewing the world one adopts when one has knowledge of economics, or the process of abstract thinking and symbolic manipulation one must acquire in the course of it, help with other disciplines in ways that do not directly relate to full-blown techniques one learns in economics?

The short answer is that nobody knows, so far as I'm aware. Which doesn't mean that people don't have strongly held beliefs. For many years students spent years learning Latin because it was thought to have various beneficial effects, though these have never been demonstrated to my knowledge. Coming back to the economics example, the issue is that nobody seems to know what it even means to know economics. Aside from the specific ability to solve economics problems, what kind of cognitive capacity can an economics expert demonstrate? We have only begun to be able to answer this question with regards to mathematics (c.f. Ansari et al, 2005.) or to much of anything else. Our sense of what mental representations are -- of what knowledge is built from -- is extraordinarily primitive.

However, there are substantive reasons to continue to believe in the possibility of non-trivial transfer. The most compelling of these is the study of metaphor, for which the canonical work is probably Lakoff & Johnson's (1980) phenomenal and very readable book (see also Bowdle & Gentner, 2005; Holyoak & Thagard 1997 for more technical treatments.) The idea is that knowledge structures representing one thing can sometimes be mapped onto knowledge structures representing another thing. When such a mapping can be found, one is able to make additional inferences on the second thing that correspond to inferences based on the first thing. According to these theorists, the ability to make inferences about the world that flow from these kinds of metaphorical transfer account for a large part of our intellectual ability.

An example might be useful. A prominant metaphor, according to Lakoff and Johnson, is "An argument is a war." According to this metaphor, we might understand a destination domain (argument) in terms of a source domain (war.) This mapping between the two domains allows us to structure the former in terms of the latter. Wars have winners and losers, for instance; wars end when one side surrenders or is destroyed; one succeeds in war by hurting one's enemy; and so on. By extension, then, an argument has a winner and loser; an argument ends in surrender or destruction; and in an argument the goal is to hurt your opponent.

This construction of what it means to have argument, and of how one should conduct onself in the course of an argument, would lead to very different results than, say, the metaphor "An argument is a dance." It's clear, from this formulation, how consequential is the choice of metaphor in constructing one's attitude to and behaviors during an argument. Metaphors are not simply colorful language (the argument goes) they are instructions for how we might leverage certain knowledge structures and behaviors (those involving war, in this case) for application in a completely unrelated domain (an argument, in this case.)

The most powerful idea latent in this metaphorical construction of cognition is that metaphorical extension is not only (or even mostly) about language -- any knowledge representation can potentially be aligned with another, and the tools and circumstances governing the use of the former could (potentially) be used to gain 'experience' in the latter automatically, for free. (See Durso et al., (1994) for a nice example of how this might culminate in an 'insight' experience.) And in fact, DARPA has been very interested in this kind of 'bootstrap learning' looking for ways where experience in a relatively cheap (in dollars or risk) domain might be leveraged to benefit a more expensive domain, although the results of this work have not yet lit the world on fire.

Concerning the automaticity of this process, the answer seems to be both yes and no. Insofar as people do use some kind of metaphorical extension to understand the world, and to apply knowledge across situations, then the process occurs automatically, at least to some extent. (Note that the details of this process are still very much in question.) But even if it is automatic, Chrysikou (2006) showed that the cross-pollination between disparate types of knowledge can be improved by conscious effort. In her study, subjects who spent ten minutes performing the "creative uses" task (in which they generated novel and unusual applications for various objects; for example, a creative use for a toothpick might be as a javelin for a grasshopper) solved significantly more insight problems than subjects who did not perform the CUT beforehand. One theory for these results is that the process of coming up with creative uses energized the machinery used to create ad hoc categories (see Barsalou 1983), and that the ability to blend disparate forms of knowledge in these dynamic bundles is an important component in problem-solving.

This seems relevant to the original question in that whatever benefit is automatically gained simply by possessing a varied store of cross-discipline knowledge, seems to be enhanced by purposeful engagement and cross-linkage between these cognitive components, an enhancement that manifests concretely in increased problem-solving performance.

All that being said, in my opinion the extent and mechanisms of cross-domain transfer are still largely undetermined. Despite the considerable theoretical literature in support of it, at this point the proponents of deep transfer are running on faith as much as anything.

EDIT: Note that an earlier question on this site explores some of these same ideas.

References:

Ansari, D., Garcia, N., Lucas, E., Hamon, K., & Dhital, B. (2005). Neural correlates of symbolic number processing in children and adults. NeuroReport, 16(16), 1769.

Barsalou, L. W. (1983). Ad hoc categories. Memory & Cognition, 11(3), 211–227.

Bowdle, B. F., & Gentner, D. (2005). The career of metaphor. Psychological Review, 112(1), 193–216.

Chrysikou, E. G. (2006). When Shoes Become Hammers: Goal-Derived Categorization Training Enhances Problem-Solving Performance. Journal of Experimental Psychology: Learning, Memory, and Cognition, 32(4), 935–942.

Durso, F. T., Rea, C. B., & Dayton, T. (1994). Graph-Theoretic Confirmation of Restructuring during Insight. Psychological science, 5(2), 94–98. Association for Psychological Science.

Holyoak, K. J., & Thagard, P. (1997). The analogical mind. The American psychologist, 52(1), 35–44.

Lakoff, G., & Johnson, M. (1980). Metaphors We Live By. The University of Chicago Press.

# Answer
> 12 votes
There are two types of learning - knowledge and skills. Knowledge can be used in many different contexts, but skills are extremely narrow and precise. For example, subjects trained in memorizing numbers do not have any better ability at memorizing lists of words than untrained individuals.

> We tend to think that practicing mental tasks must also have some sort of general benefits beyond the particular tasks being practiced. At present, however, one of the few well-supported conclusions of cognitive psychology is that this simply does not happen, however much we would like to believe it does (Woodworth & Schlosberg, 1954); baron, 1985c). The specificity of practice effects has been known to psychologists (and ignored by everyone else) since Thorndike and Woodworth first discovered it in 1901.

On the other hand, knowledge learning develops a different sort of skill,

> An expert in a certain field can develop all sorts of ways to represent complex ideas in terms of a word or two, a mathematical symbol, or a visual image such as a graph.

that could transfer in the narrow circumstance that the "condensed" representation would be useful to the individual - for example, as in your question, in using psychology to extend or understand economics.

Quotes from Jonathan Baron's book, *Thinking and Deciding, second edition*, 1994, New York: Cambridge University Press.

references in quote:

Baron, J. (1985c). What kinds of intelligence components are fundamental? In S F Chipman, J W Segal, & R Glaser (Eds), *Thinking and learning skills. Vol 2: Research and open questions. Hillsdale, NJ: Erlbaum.*

*Woodworth, R S & Schlosberg, H. (1954). Experimental psychology*. New York: Holt.

# Answer
> 3 votes
There can be real isights from cross-disciplinary study, because the patterns of thinking and problem solving differs between differet disciplines. So an issue that someone cannot see an answer to might be very clear to someone from a different discipline, because they view problems in a different way. I am sure that some of the physicists who have made breakthroughs in the last 50 years have had insights from other disciplines, but I cannot find examples at the moment.

So, in your example, if an economist learns psychology, he can put his psychological approach to issues to use in his economics, and vv, meaning that he might be able to do well at both of them. A lot of "performance" at a discipline is about seeing things in different ways, and so seeing resolutions to issues.

# Answer
> 0 votes
In my personal experience, your questions are not easy to answer. I try to answer your questions by my best effort:

### 1.Is this true and supported by facts?

Yes, I would says that in your example(economist learns psychology), in the first sight it seems not related too much, but in deep feeling, any discipline will strong connect to human. It exist a very big system that include all the disciplines, so any discipline just a part of system. For example, economist needs to know the money works in the society. When he just thinking about that question, he may get the first spirit layer of answers that someone really needs an object so he use money to buy it. And in some opportunities, he learn psychology and find that many people buy things not for really needs but for desires or other people encourage him. In this concept, it shows that when you learn more and have more deeper feelings, you can see more details of the same thing,and it will really help you lift your origin thinking level.

### 2.If this is true, does the brain automatically interrelates concepts or we need to train the brain in a particular manner?

Both are right, because of different motivation. First, you can try to think this: When you see something A, you may just see at a glance. But in later maybe some objects or cases that let you remember A, in this time,your brain just collect them together automatically. What I want to say is : It just a glance maybe have many impacts. If you try to focus to learn something, they will carve more deep in your mind, and get more opportunities to get it out for your work.

Second, Focus,calm down,...etc, such of ways that you are really familiar with. So you may use these method,but you haven't aware of that because it is not so hard for forming a habit. In these particular manners, you may get more chances to learn more and deep.

### 3.Does this depend on age?

Actually, it is not dependent on age but depends on:

1. Do you focus on learning?
2. For how long can you learn without taking a break?
3. How familiar are you with the subject you are learning? ...etc

If you feel I haven't answer your question totally, feel free to ask more questions.

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Tags: learning, performance, transfer
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# Question
Title: Why does neuroplasticity decrease in adults?
Although adult brains are malleable and even undergo limited neuorgenesis, the extent of the neuroplasticiy is much lower than in children. This is most obvious in language acquisition, and recovery from brain trauma.

**Are there formal models (computational or mathematical) that explain *why* our brains so drastically reduce in plasticity with age?**

If a highly malleable brain is supposed to help us adapt and deal with a constantly changing environment, then naively one would expect it to be advantageous to maintain a malleable brain for your whole life.

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### Background from critical-period in language acquisition

*This is an example of formal models that I am already familiar with that answer a related question (critical-period in language acquisition). I am interested in answers in this spirit, but that can address not just language-acquisition but the general decrease in neuroplasticity.*

In the case of the critical period for language acquisition there are evolutionary models by Hurford (1991) and Komarova & Nowak (2001). However, neither model generalizes easily to the case of neuralplasticity.

Hurford's model uses neutral drift to explain the upper bound on the critical period of language acquisition because of the need for second language acquisition in life is largely unnecessary. However, the need to adapt to your environment is necessary throughout life, so plasticity should not be under neutral drift.

In the case of Komarova & Nowak, the upper bound is due to a trade-off between the cost of learning (driving critical period down) and the importance of learning a language accurately (driving critical period up). This balances out and allows for an ESS due to dimishing returns: once you've learned a language pretty-well it becomes more costly to invest in learning further than the returns from better learning. However, adapting to a constantly changing environment is not a single static task, and thus it is not clear why your returns would diminish. Further, it is not clear how keeping high plasticity is more costly than maintaining lower plasticity.

### Notes

* This is a question of *why*, not *how*. Although it is very interesting to know *how* the plasticity of adult brains decreases, in this question I am interested in *why* this is the case over the hypothetical "keep as malleable as a baby" alternative.
* Both Hurford (1991) and Komarova & Nowak (2001) provide formal evolutionary models that I do not describe in detail. I am interested in formal models like this, although they need not be evolutionary. An answer on the level of rhetoric (especially if it is evolutionary rhetoric) is not nearly as interesting to me as a formal model.
* Hurford (1991) and Komarova & Nowak (2001) are meant as examples of work that answer the potentially easier question of critical period of language-acquisition. I am interested in the more general question of decrease in neuroplasticity.

### References

Hurford, J. R. (1991). The evolution of critical period for language acquisition. Cognition, 40, 159-201. FREE PDF

Komarova, N. L. & Nowak, M. A. (2001). Natural selection of the critical period for language acquisition. Proc. R. Soc. London. B, 268(1472), 1189-1196. FREE PDF

# Answer
> 7 votes
Another reason for reduced plasticity in adults is that learning something different in the presence of an existing knowledge structure is more difficult than learning from a "blank slate". In a sense, you get interference from the known language (for example). One person who has developed this argument computationally is Jay McClelland in the context of native Japanese speakers learning the English /r/-/l/ distinction (e.g., McCandliss et al., 2002), though I think the principle may apply more generally.

The idea is that "reduced plasticity" is just a consequence of interference from the established knowledge rather than a fundamental change in plasticity. Part of the argument is that you only see reduced plasticity when there is a conflict between the (for example) first language and second language; aspects of the second language that match the first language are learned very quickly.

# Answer
> 0 votes
What you mean is that you want teenage existentialist angst to last for life? The neuroplasticity of younger children comes at a cost, which is not really knowing ones place in the world, because the world model you are building is constantly changing. Getting a world model broadly settled then enables you to move on in life and do more.

If we retained the same level of neuroplasticity for life, we would never actually do much, because we would never have a clear model of life to build on. The result of the moulding and structuring of a young persons brain is to produce something adapted for the rest of their life. The flexibility is important because their precise situation may require different adaptions to other people.

The real issue is that, even 100 years ago, once a person was adapted for life, their world would not change substantially in their lifetime. Today that is not the case ( Alvin Tofler, Future Shock, is a classic explaining this ). But keeping the plasticity for longer would, if anything, probably make the situation worse, not better, as the influential 20-somethings built a world around their own ever changing perspective.

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Tags: neuroscience, developmental-psychology, computational-modeling, evolution, plasticity
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# Question
Title: What skills are required to build simulations of the human brain?
I want to build a system that has the ability to gather data from the internet in order to build a cognitive model of the human brain. The model should be able to answer the questions required by a robot. Also, I would like to apply an AI inference engine to get more important information to the robot.

**If I want to do build such a model, what "skill list" do I need? What skills would be most important? What "opensource project" might be helpful?**

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added:

Thank everyone who encourage me. You let me have more brave to build a human brain mind model(not brain structure).

Let me introduce myself. I have some CS and EE background and now research on robotics area. I love cognitive science so much so I want to figure out how to let robot really have the mind like human to achieve goal more clever. Thank the hint of @Jeromy Anglim, I give a video youtube example:James and Rosie Preparing Popcorn and Sandwiches

That video shows two robot cooperate together to make a sandwich with cognitive architecture(CRAM or Cogito) based on ROS(Robot OS,ROS map,ROS Navigation Video) system. After reading their papers I found that they just using database or downloading web page instructions or using very well structured pages which are for human (ex.CYC,OpenMind Indoor Common Sense database,Semantic web,ORO,SUMO,Wikipedia,google images,germandeli).

Also I found that many free resources,any article on WWW should be most important database. The most important thing is how to build a system that can read natural language and have a model (maybe based on CRAM cognitive architecture) to understand it,because CRAM has a very good knowledge processor(KNOWROB,M. Tenorth and M. Beetz, “KnowRob — Knowledge Processing for Autonomous Personal Robots,” in IEEE/RSJ International Conference on Intelligent RObots and Systems., 2009.). If perhaps, I want to collect all linux opencourse libraries to make it more fast and robust. I also begin to take online machine learning course on stanford.

My goal is to build a human mind model can learn,have some innovation,and find intelligent solutions to dedicate itself to human society.But my first step is to find a way that robot can try to understand all kinds of data on the WWW (any data that it can't understand just warn the first 10,and ignore others), and structure them as an intelligent interaction system which can answer knowledge or skill questions which can be search on google. The most important of that system is try to use all information on the web clever with robust libraries( I don't want to reinvent wheels).

I know it is a long term project, but I loving and willing to achieve in my whole life. Because this is an integration project and any success in that will make a big change in the world just as ROS in robotics also an integration project and very active on most top robotics research centers and in the beginning on industry now. I know My own energy is not enough for this purpose, but I can gather all the power of opensource and volunteers in the world to do it together~^^ (Now is the day which techs are really enough for this project like computer speed,WWW,advanced research papers,opensourse libraries,free opencourseware lessons,..., what a lack is a group of excellent people volunteer to make it become true to help anyone who use this project.)

# Answer
> 7 votes
Schroedingers Cat nailed a couple of important points and I need to expand it further.

I believe the important roadblock in the creation of an absolutely intelligent machine is the fact that human brain is still not decrypted. There isn't a holistic cognitive model that describes how the brain functions, reacts or take decisions. Even in case such a model is inferred, it still cannot be put into real use since human decision is based on knowledge and the environment (popularly called culture). So a machine being rational is hard to prove since human themselves cannot prove their rationality.

Technically, there are three problems

1. Language - Almost all programs use one or the other form of Context Free Grammar. Humans always use context while speaking.
2. Computationally it isn't feasible to do a simulation model similar to brain. Just assume a choice of 2 items among a set of 10 items. You have a possible 2^10 combinations. Think of 100 conditions and your computer would go into limbo.
3. Most of the AI engines built today are a far cry from those proposed by their forefathers. You must have read the AI: A modern approach. They are mostly built for a particular domain and aren't absolutely intelligent

And regarding your robot searching Google for the answers, its already possible by using the Google API's to search for answers but it won't fulfill your requirement. Google uses the PageRank technology for its results, which is more of a machine learning technique and is still far from true semantic search.

There's hope though. I am just in the same situation (coincidentally I have subscribed for both machine learning and natural language processing, though all courses are indefinitely postponed). I could suggest learning the following

1. Artificial Neural Networks
2. Natural Language Processing
3. Probabilistic Models
4. Unsupervised machine learning techniques
5. Advanced pattern discovery

I also suggest a change in your original model. Instead of searching Google to find the answers, it would be better you download the stack exchange data dump and start analyzing the patterns since those answers are given by true humans.

Feel free to ask more questions. There are more things to be questioned than to be answered in this field.

# Answer
> 6 votes
"how to build a system that can read natural language and ... understand it"

I am not sure you appreciate what you are wanting here. Just this one piece requires an understanding of the processes of the mind that I don't believe we are at yet. There is a lot of material about how we MIGHT achieve this, but you would need to appreciate the whole cognative processing model ( or rather, find one of the models that you like best ), and be able to mirror it.

There is also, I think a requirement in here to automatically search the web and identify good quality answers. Well, if you do this, I am sure that Google will be very interested in you. Most people are poor at doing this reliably, so trying to enable a computer system to achieve this is probably not practical at this moment.

I think the most important thing you need is an entire research department. The requirement you have is so diverse that it needs many different people working on many different areas. You would need, I woudl say, PhDs in cogsci or similar, and AI, as a minimum.

This all sounds very negative, but actually, I think it is a good question, because it is important to understand the scope of the answer, for everyone who works in machine intellegence.

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Tags: methodology, cognitive-modeling, computational-modeling, nlp
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# Question
Title: What can we learn from the neural networks of C.elegans to understand human brains?
Recently I am reading some works about Caenorhabditis Elegans. A C.elegans has 302 neurons and we already know the function and connection of every one of their neurons so that we can exactly reconstruct it by computer programs. Also, C.elegans have a couple of sensory inputs, and a kind of intelligence (they can learn something).

The question is: what can we learn from C.elegans, so that we can understand Human brains?

To specify, I want to know:

1. What's the relationship between C.elegans and Human beings (or primates)? Is there any route in the terms of evolution?
2. Does the neural system of C.elegans still exist in human brains, as either a working or an idle part?

I am a computer scientist working on artificial intelligence. My purpose is to clarify the mechanisms of the Human brain. I hope C.elegans could help but I don't know much about biology.

# Answer
Caenorhabditis elegans is probably not an ancestor to Humans. As found in Sponge proteins are more similar to those of Homo sapiens than to Caenorhabditis elegans, certain sponges were found to have more similar protein structures to humans than C. elegans suggesting the sponges are the ancestor.

For your second point, it depends what you mean. The actual, exact sort of network in C. elegans doesn't exist as a "part" in Human brains nor would it even if humans had C elegans as a direct ancestor; the network would have evolved considerably.

The basic structure of the neural network as a connected net of neurons and the basic functionality has of course remained through all organisms with developed neural networks. Like all life, certain related developmental processes exist between humans and C. elegans, which is why studying them (and most any other animal) is often relevant to Human research of similar biological or psychological areas of interest.

C. elegans can even learn, showing it is a very complete model of a neural network, despite it's simplicity.

C. elegans is not studied because it is an ancestor or because it has any particular relation to the human mind, but because it has an **extremely simple, completely functional neural network**. There are complete maps of C elegans' neural network and even online tools to search the synapses of C elegans. There are whole websites dedicated to research on C. elegans.

Similar to fruit flies, C. elegans also provide a great vector for developmental research because they have a lifespan of a couple weeks instead of decades.

Some great further reading is the introductory Why use C. elegans to study the synapse ?
> 14 votes

# Answer
There are some important relationships between the c. elegans nervous system and the human nervous system that should be pointed out here:

* Neurons in both animals communicate with each other via synapses that use special molecules called neurotransmitters to convey activity. All major neurotransmitters used in humans are also used in c. elegans (Glutamate, GABA, Acetylcholine, Dopamine).
* Because of the genetic similarity of c. elegans to humans, you can splice human genes into their genomes and have them produce human proteins, which interact with c. elegans proteins and provide an isolated way to study their activity.
* The basic principles of excitable membranes formulated by Hodgkin and Huxley in 1952 are applicable to understanding and modeling the nervous systems of both creatures
* Because of these facts, the creation of a system to simulate the nervous system of the c. elegans represents a lower bound of complexity of a system needed to simulate the human nervous system.
> 12 votes

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Tags: neuroscience, neural-network, evolution
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# Question
Title: Why are mind maps recommended for study and recap?
In both my last year of high school and my freshmen year at the university, I got strong recommendations to study using mind maps - especially because this is apparently better to cope with **large amounts** of new information.

* Is mind mapping effective in learning and reflecting on information?
* If so, how does mind mapping help in learning and reflecting on information?

# Answer
> 9 votes
Concept mapping is a way of reviewing the material, depending on the study it may be significantly better than other forms of review, though not all studies I read found that superiority. The most popular theory for its claimed superiority is that it also organizes the information better for recall; most of the papers that found concept mapping better than the other forms of review tested had the subjects create their own concept maps.

Most of the reading I did was several months ago, and I don't have references readily available; but I was Googling metacognition recently and found a paper, that says concept maps work by improving metacognition, though they are only marginally better for that than rereading.

Joshua S. Redford, Keith W. Thiede, Jennifer Wiley, Thomas D. Griffin. Concept mapping improves metacomprehension accuracy among 7th graders. Learning and Instruction (2011), doi:10.1016/j.learninstruc.2011.10.007

# Answer
> 11 votes
An excellent question! A 2011 paper in *Science* by Karpicke and Blunt in the Cognition and Learning Lab at Purdue University gets at this issue. They offer that:

> "Concept mapping is considered an active learning task, and it serves as an elaborative study activity when students construct concept maps in the presence of the materials they are learning. Under these conditions, concept mapping bears the defining characteristics of an elaborative study method: It requires students to enrich the material they are studying and encode meaningful relationships among concepts within an organized knowledge structure."

They go on in their paper, however, to demonstrate that concept mapping is far less efficient than an even simpler technique: repeated retrieval (e.g., self-testing). Practicing retrieval, they argue, "enhances learning by retrieval-specific mechanisms rather than by elaborative study processes."

Reference:

Karpicke, J.D., & Blunt, J.R. (2011). Retrieval practice produces more learning than elaborative studying with concept mapping. *Science*, *331*, 772-775.

# Answer
> 4 votes
I think it's important to keep in mind the different effects that one might achieve using mind mapping, with regard to the way mind maps are being evaluated in the literature mentioned so far. While retrieval ability is easy to measure and certainly a useful metric that approximates one important aspect of learning in an academic setting, retrieval isn't everything.

My own experience (n=1) is that mind mapping is probably not a great use of time when preparing for the kinds of evaluations usually required of one in college. Mind mapping has, however, proved remarkably beneficial to my ability to index information, and to see relationships between different things. This is crucially different from the retrieval model described earlier: I am not trying to prepare myself to reproduce some given body of facts; rather, I want a tool to help integrate new information with what I already know, or else help explore the connections between disparate elements in my knowledge store. For these pursuits mind mapping has been a superb tool.

I suspect that the benefits of mind mapping may be related to the benefits of the Creative Uses Task described by Chrysikou (2006), for the same reasons I described in my answer to this question, namely, the elaboration and schematic connection between far-flung semantic contents. How is the industrial revolution like an old-fashioned donut? I have no idea, but framing a context that meaningfully interprets both things can elicit a more profound understanding of the logical path between them. Further, this process has more than once been the cause of an insight, or reorganization of conceptual knowledge, a la Durso et al. (1994); less personally, it's a flavor of combinatorial activity that features heavily in many models of creativity (c.f. Thagard & Stewart, 2011).

References:

Chrysikou, E. G. (2006). When Shoes Become Hammers: Goal-Derived Categorization Training Enhances Problem-Solving Performance. Journal of Experimental Psychology: Learning, Memory, and Cognition, 32(4), 935–942.

Durso, F. T., Rea, C. B., & Dayton, T. (1994). Graph-Theoretic Confirmation of Restructuring during Insight. Psychological science, 5(2), 94–98. Association for Psychological Science.

Thagard, P. (2011). The Aha! experience: Creativity through emergent binding in neural networks. Cognitive Science 35, 1-33.

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Tags: learning, educational-psychology
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# Question
Title: How can consumers  obtain an image and analysis of their own brain?
What resources are there for consumers who wish to get a detailed analysis of their personal brain function? (besides consumer EEG machines...)

That is, how do I get access to an fMRI scan? What's the current price range for such a thing? Do I have to be a grad student/researcher?

Furthermore, how do I get a detailed analysis of anything that could be interesting/out of the ordinary?

# Answer
The easiest way to get an fMRI (and many other brain) scan is to volunteer to be a participant at your local university, hospital, or research center. They will let you keep a picture of your brain, provide some interesting information at de-briefing, and usually even give a bit of monetary compensation for your time. Since the research group has to have an MD on board in order to do these tests, they will also notify you if they detect something abnormal or dangerous in your scan. The only caveat is that the selection criteria for being a participant are usually a little bit more involved than in a standard psychological study. Thus, you might have to volunteer for a few studies before you are selected.

If you want further analysis, you might consider companies like this. There you can submit your brainscans after you've received them for further analysis.
> 6 votes

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Tags: neuroimaging
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