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Autism and ADHD: the intelligent and the creative child!

ResearchBlogging.org
A new study by Ruthsatz and Urbach is doing the rounds nowadays. That study has nothing to do with Autism or ADHD per se. The study focuses on child prodigies and finds that they have high levels of intelligence, enhanced working memory and that they pay attention to details.

What the study also found was high level of autistic relatives and high scores on Autism spectrum for the prodigies. The relation between autism and prodigiousness was mediated by the endo-phenotype ‘paying attention to detail’ and none of the other symptoms of ASD seemed to play a role.

Many savants also are high on ASD and have exception working as well as long term memory. There too they pay excessive attention to details and are fascinated by speical interests.

 

On the other hand there is gathering literature that suggests that the ADHD kid is basically on the creative side of the spectrum – restless, trying multiple strategies,  having diffused and peripheral attention, and to an extent novelty and sensation seeking.

Also, if one thinks about that for a minute, autism and ADHD seem to be opposed on a number of dimensions. The three basic features of ADHD are 1) inattentiveness and distractibility vs  too much focus and fascination for an object shown by Autistic kid 2) impulsiveness vs restricted and repetitive motions and interests of the autistic kid and finally 3) hyperactivity vs restrained interactions and communications of the autistic kid.

There is also some data from fly models that suggest that autism and ADHD are opposites in a sense.

I may even go ahead and stick my neck and say that while autism is primarily characterized by emotion of Interest/ fascination/ attention ; ADHD is characterized by emotion of Wonder/Awe/surprise.

One theory of autism suggests that the social and communicative difficulties arise as the child hides in a cocoon to prevent over-stimulation and sensory overload; a theory of ADHS says that the child is under-stimulated and needs stimulants like Ritalin to achieve baseline of activation and sensory stimulus.

Another popular theory of autism posits that it arises primarily due to ‘weak central coherence’, or inability to see the context/ gestalt/ ‘the big picture’. The ADHD kid on the other hand is hypothesized to use a lot of peripheral attention and daydreams missing what is being centrally taught in the classroom.

And that brings me to the root of the differences in my opinion; while the Autism spectrum is characterized by a local processing style, the ADHD-psychotic spectrum is characterized by a global  processing style.

Some clarifications are due here. I believe ADHD to fall on the psychotic spectrum and have been proposing the autism and psychosis as opposites on a continuum model for close to eternity.

Also, when I say global/local processing styles I dont restrict the application to perception alone, but extend it to include cognitive style too.

There is a lot of work that has been done on global/ local processing styles with respect to perception, using Navon letter tasks and it is fairly established that normally people lean towards the global processing style.

Forrester et al extend this to cover there GLOMOSYS system that posits two basic types of perceptual/cognitive style- global and local.

It is instructive to pause and note here that psychosis is associated with a global processing style while autism with attention to details.

It is also instructive to pause and note that similar to autism-psychosis continuum , it seems Intelligence and creativity are also in a sense opposed to each other. Also while creativity  is associated with broad cognitive style that is divergent; intelligence is conceived of as narrow and focused application of abilities.

That brings me to my final analogy: while autistic kids may have pockets of intelligence and savantism and may be driving the evolution of intelligence; it is the ADHD kids who are more likely to be creative and are driving the evolution of creativity.

The romantic notion that psychosis is the price for creativity may not be untrue.

Joanne Ruthsatz, & Jourdan B. Urbach (2012). Child prodigy: A novel cognitive profile places elevated general intelligence,
exceptional working memory and attention to detail at the root
of prodigiousness Intelligence DOI: 10.1016/j.intell.2012.06.002

Jens F¨orster, & Laura Dannenberg (2010). GLOMOsys: A Systems Account of Global Versus Local Processing Psychological Inquiry, DOI: 10.1080/1047840X.2010.487849

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Environmental factors like teacher quality and SES affect the full flowering of potential

ResearchBlogging.org

In some combinations of environments and genot...
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This should be a no-brainer: in an era when increasingly words like ‘gene-environment interaction‘ are bandied around, it would be self evident that for full flowering of a prototypical trait, the genotype has to get the right environmental inputs. In absence of the right environmental conditions, the genetic differences may be masked and the trait under consideration suffer from universal stunted growth, thus even different genotypes leading to same phenotype -that of survival trait. contrast this with the condition where the environment provides rich conditions for the flowering of the trait under consideration. Here the trait will be having maximal value and would be a thriving trait value. Here genotype differences , if any , would be accentuated and become visible as difference sin phenotype expression.

If the above is a bit abstract , take the concrete example of SES and the corresponding low environmental condition and its relation to IQ/brain/cognitive ability. In an impoverished environment differences due to genetics would be masked and everyone will have a low IQ/cognitive ability. As opposed to this in an enriched environment condition, not only the average IQ would be higher (a thriving condition), there would be more differences in the IQ of children/people concerned as the right environment will make it possible for genetic effects to come into play and determine the IQ/cognitive ability. The above is a bit paradoxical and counter-intuitive- one advocates environmental interventions only to see that effects of environment becoming less for the trait and effects due to genetics becoming more prominent as more and more conducive environment is provided. The rationale for providing the right enriched environment /high SES to all would thus be not to eliminate inequality (inequality would paradoxically be accentuated) ; but to raise the trait value to maximum possible under the right environmental and that perhaps is for the good of all.

I have debated this issue earlier in my low IQ and SES series of posts, but thought will comment on the same in light of two articles that I cam across recently. The first article is a bit old, but has the devastating effect of waking one form ones slumber as one realizes that low SES leads to brain effects in low SES children that are akin to those faced by normal children/people who suffer brain damage due to stroke etc. I came across this via this science daily release tweeted by someone today (forgot the source).

The second study is a brand new one , published just today (and I have just read the extract and accompanying Sci Am article). The study, using identical and fraternal twin studies, in essence found that children’s reading ability was largely genetic (82 % genetic component), but that teacher quality mattered a lot. The genotype was able to flower fully when teacher was high h=quality- not only the reading ability was better; differences were accentuated. In contrast, when teacher quality was low, environmental had a much stronger effect by leveling everyone to a smaller value. To quote from the article:

“When children receive more effective instruction, they will tend to develop at their optimal trajectory,” said study lead author Jeanette Taylor in a prepared statement. “When instruction is less effective, then children’s learning potential is not optimized and genetic differences are left unrealized.”

The researchers found that good instruction promoted stronger reading development. Without it, children were less likely to reach the potential conferred by their genes. “When teacher quality is very low, genetic variance is constricted, whereas, when teacher quality is very high, genetic variance blooms,” they report. While teacher quality appears to be an important contributor, other classroom factors, such as classmates and resources, might also influence reading ability, the researchers noted.

To me, the results are important, though self-evident. Hopefully they will seal the endless confusion on the matter and allow a more reasoned dialogue and intervention to happen where IQ and SES/enriched environment is concerned.

Taylor, J., Roehrig, A., Hensler, B., Connor, C., & Schatschneider, C. (2010). Teacher Quality Moderates the Genetic Effects on Early Reading Science, 328 (5977), 512-514 DOI: 10.1126/science.1186149
Kishiyama, M., Boyce, W., Jimenez, A., Perry, L., & Knight, R. (2009). Socioeconomic Disparities Affect Prefrontal Function in Children Journal of Cognitive Neuroscience, 21 (6), 1106-1115 DOI: 10.1162/jocn.2009.21101

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Sibling-correlation-422

IQ,SES and heritability

ResearchBlogging.org
A reader of this blog wrote to me recently regarding a series of posts I have written regarding IQ,SES and heritability, and I thought it would be good to share the comments with the rest of the mouse trap community and to delineate my position on the matter (and what I believe the studies show the relation is). First I’ll like to quote extensively from the comment (private mail):

Let me start with my belief that I think we share the same idea about IQ and its origins.
That is, genetics endows each and every person with a maximum IQ that can be achieved if and only if the environment is perfect for the development of this IQ.

Agreed!

Consequently, in identical twins, environment is the only cause of differences in IQ; IQ differences between persons that are not identical twins must be related to both environment and genes.

Sibling-correlation-422There are subtle nuances here. (no I’m not being pedantic, the importance of these will become clear in the course of this post). First if MZ twins are raised in the same family, they share the same genotype (A), they share the same ‘shared environment’ (C), so the difference is due to the non-shared environmental factor (E) only. In case of DZ twins raised in the same family, they share half the genotype (A), they share the same shared environment (C) and the difference in say IQ, is due to both genotype (A) and non-shared environmental factors (E). Twin studies with MZ and DZ twins (and in some cases siblings, half-siblings etc ) raised in the same family are used to tease apart the contribution of shared environmental factor, as opposed to genetics and non-shared environmental factors and can be used to find at a broad level if the trait is highly ‘genetically’ heritable (correlation between MZ>>DZ>>siblings), has high ‘shared environmental’ factors operating (MZ~DZ~sibling , but correlation still high) or is largely controlled by random non-shared environmental influences (low correlation in DZ/MZ/siblings). Please see accompanying Wikipedia figure.

Adoption studies are another method that is used to tease apart the shared environmental factors from genetic factors in calculating the heritability of a trait. Thus, even if correlation in MZ twin IQ is high, the effect could be due to shared environment factors (if say both MZ and DZ twin show similar correlation) , or it may be largely genetic (if MZ>>DZ when it comes to correlation between the trait in affected twins.

So it is necessary to qualify your statement: In identical twins, raised in the same family, , non-shared environment is the only cause of differences in IQ. IQ differences between persons that are not identical twins must be related to both shared environment , non-shared environment and genes.

I fully back up this citation:
“suffice it to say that I believe (and think that I have evidence on my side) that shows that in low SES conditions, a Low SES does not lead to full flowering of genetic Intelligence potential and is thus a leading cause of low IQ amongst low SES populations.”
In this case, I think that the problem starts only with your following comments; while you write “a leading cause”, I think that, by yourself, you mean “way more important than genetics”. If this is the case, would you explain to me why you think that?

Here goes. Consider a large sample of children in say low SES populations. IQ may be represented by a formula IQ= aA+cC+eE; where A reflects genotype, C shared environment and E non-shared environment. Here we are assuming no interaction of IQ with SES, so this equation (given values of a, c, e ) should hold for all SES data (both high as well as low SES cohort) . Unfortunately, life is not that simple, and one can not fit the same equation to low SES as well as high SES data set without changing the slopes of variables involved. Thus, Turkheimer and colleagues in two sets of studies have shown that there is an interaction of IQ and SES and there is direct affcet (SES mediated by s) and indirect interaction effects mediated via effects on A(a’), C(c’) and E(e’). Thus our equation becomes
IQ= sSES+(a+a’SES)A+(c+c’SES)C+(e+e’SES)E.
This equation, with suitable values of s, a, a’, c, c’, e and e’ now holds for all values of SES and IQ and the data fits nicely and can be interpreted. Remember that a+a’SES is sort of indicative of contribution of genetic factor to IQ and the proportion of variance due to genetic factor(at any given SES) can be found by squaring this and dividing this by sum of all other variances .
Var(A) =SQR(a+a?SES)
Similarly heritability or proportion of variance due to A :SQR(h)=SQR(a+a?SES)/(SQR(a+a?SES)*SQR(c+c?SES)*SQR(e+e?SES))

Turkheimer have plotted nice plot of their data which sows clearly that in LOW SES situations, the proportion of variance in IQ is largely due to shared environmental factors (C) , while in HIGH SES situations, the proportion of variance in IQ is largely due to genetic factors (A). the figures (in the free PDF available at Turkheimer’s side) is a must see to grasp the significance of this. I am quoting a bit from the paper:

Figure 3 shows the FSIQ variance accounted for by the three components, with 95% confidence intervals. In the most impoverished families, the modeled heritability of FSIQ is essentially 0, and C accounts for almost 60% of the variability; in the most affluent families, virtually all of the modeled variability in IQ is attributable to A.

Let us pause here and reflect on what this means. This means that in low SES families, IQ is independent of genotype and is mostly dependent on the SES status. Let us take some concrete examples. Say the mean IQ of low SES sample (income as a proxy for SES ranging from 1000-5000 rs p.a.; mean 2500 and variance 250)) is 80 with mean variance of 20. Thus, in this sample a typical child has IQ in range 80 +-20 or between 60 and 100. Suppose further that there are 5 alleles that confer differential advantage for IQ on a locus thus representing 5 genotypes, then having either of the genotype will essentially give us no predictive power to say whether the IQ of a particular sample is 80 or 60 or 100. Also, let us assume that there are 5 classes of C and they are highly correlated with SES. First class of C (is 1000-1500 SES range) and so on and so forth. Then knowing whjat kind of family (C) the child grew up in we could easily predict his IQ (if he is of class C where SES ranges from 1000-1500), his IQ is most probably 60. This is what I mean when I say that in low SES environments IQ is largely determined by environment and not by genetics. Now , I have taken a jump here and equated C with SES, but that is a justified leap in my opinion (more about that later).

What this also means is that given the right type of environment (say class C with SES in upper range of 3500-5000 rs p.a.) , all children (irrespective of their genotype (any 5 variants of genotype) can still achieve an IQ in the upper range , say 100 as the environment is the only predominant factor operating at this level and the impact of genetics is still not felt. Thus, if we do increase SES and provide the right C, then every child in this group can have mean IQ of 100.

Contrast this with the case at the upper end of the strata (SES). Here most of the variation in IQ is predominantly due to genetics (A) and shared environment C does not seem to play a big factor. Thus, knowing a genotype of a child has greater predictive power in this sample, than knowing his C (or family income or SES). Thus, evenif we provide a very enriched environment to all children (increase their C to the highest percentile), it would have no effect on increasing the mean IQ of the sample as now the IQ is mostly under genetic control.

This in a nutshell, is what I mean when I claim that low SES is the leading cause of low IQ in low SES families.

Before I rest, some objections might be readily apparent to a keen observer. First is the assumption inherent that SES and C are the same. I would like to propose here a new shared and universal sub-threshold environmental factor and would like to elaborate with a couple of examples. Let us say that those below poverty level do not have access to iodized salt and are thus prone to goiter and also mysteriously to low IQ as there is a module of brain (5 diff alleles at a particular locus leading to differences in abilities using this module) that needs iodine for its flowering and in absence of iodine, none of the alleles have any effect whatsoever- the module itself does not develop, so there are no questions of differences in ability or IQ due to differences in genotype etc. Now, given this state of affairs and also the fact that low SES families do not have access to iodine, when IQ is measured (then because of absence of this factor X), all children in this proband will have an IQ that does not measure abilities of this module (say this module adds 20 points of IQ) and thus all of them will have an IQ less by 20 points than was actually possible.Say the mean IQ measured is 80. Given the fact that some of the higher SES within this low SES group may have partial and sporadic access to iodine , the variance will be entirely environmental and no genetic variance would be found with some people having IQ close to 100 , who are in relatively upper start and have decent access to iodine. Contrast this the higher SES proband all of whom have access to iodized salt and thus can use their additional 20 points advantage on IQ tests. It would not be surprising if most of the variation here was genetic based on factor X allele) rather than due to income level or SES.

Another example to ruminate on is another universal and shared sub-threshold factor like having a golf course in the house. Let us assume that within higher SES group, this environmental enrichment factor plays a role, with some lower strata of higher SES (the middle class) not able to afford a golf course, while the higher higher SES strata (the upper class) abvle to afford a golf course and expose their children to them . Further, suppose that there is a module in the brains and genes switched on only if exposure to golf course takes place. Then within this higher SES group, what we will observe is that though the genetics plays a good role (due to factor X-iodine: remember, which is available to all in this group) ; still there would also be variation due to environment (golf course exposure) and that a full 20 points more can be added to all people of this group (with mean IQ 100 raising their IQ to 120), if all were exposed to a golf course and a intelligence-module-dependent-on-golf-course-exposure was allowed to develop. And on the higher end of IQ (and SES) what we would find is that most of the variance now is genetic (due to this golf-course module coming into play), while at the lower end, most of the variance is still environmental within this ‘high’ SES group.

If the above seems far fetched this is exactly what Turkheiemrs et al found in their follow up study focusing on mid to high SES children. I quote from it (again the pdf has beautiful figures and you should see them) :

Figure 2 illustrates the relations between income and genetic and shared environmental proportions of variance, as implied by the parameters estimated in Model 3. Genetic influences accounted for about 55% of the variance in adolescents’ cognitive aptitude and shared environmental influences about 35% among higher income families. Among lower income families, the proportions were in the reverse direction, 39% genetic and 45% shared environment. Although the shared environmental proportion of variance decreased with income, shared environmental variance per se did not decrease. The interactive effect was driven entirely by the increase in genetic variance. Genetic variance in cognitive aptitude nearly doubled from 4.41 in families earning less than $5000 annually to 8.29 in families earning more than $25,000 annually.

Our investigation supports our hypothesis that the magnitude of genetic influences on cognitive aptitude varies with socioeconomic status. This partially replicates the results presented by Turkheimer et al. (2003); however, no shared environmental interaction effects were demonstrable in the current study. Genetic influences accounted for about 55% of the variance in adolescents’ cognitive aptitude and shared environmental influences about 35% among higher income families. Among lower income families, the proportions were in the reverse direction, 39% genetic and 45% shared environment. This pattern is similar to the pattern seen in Turkheimer et al. (2003), although less marked.

So, I want you to pause here and grasp the significance of this- at every level of IQ-SES, there may be threshold factor that giverns whether IQ modules flower to full potential and this is the putative mechanism that leads to SES causing low or high IQ directional and causal relation. At each level, as the threshold factors become available,. more and more IQ starts coming under genetic control, but , and this is important, for jumps in IQ to take place , increasing SES (removing the sub-threshold conditions) is VERY important.

I mean “not following up on the ‘a leading cause’”, because in a later post, you write:
“Now, I have shown elsewhere that low SES causes low IQ”
Here, there is no mention of any other possible cause besides the environment anymore.

Yes, because as shown very strongly by Turkeihems and team , at low SES, shared environment/SES is the putative mechanism and genetics has no/negligible role to play. So for low SES, low SES causes low IQ. period.

in another post, you write
“A series of studies that I have discussed earlier, clearly indicate that in the absence of good socioeconomic conditions, IQ can be stunted by as large as 20 IQ points. ”
This same post also contained this citation “Children of well-off biological parents reared by poor/well -off adopted parents have Average IQ about 16 point higher than children of poor biological parents”
In my opinion, the latter would indicate the approximate range of genetic IQ differences for the samples in this study, while the former would indicate the approximate maximal environmental gain that can be hoped for in the environments that were encountered in these studies.

No they don’t. They talk about different SES groups, so as shown findings from one cannot be extrapolated to the other. In the low SES group, there is no genetic variation. We can thus not conclude that that (16 points diff.) is the ‘average’ genetic component taken the entire sample together. what one can say is that if mean IQ of high SES children was 100, the mean IQ of low SES children was 84 . Period. The difference is likely due to the fact, that the module X has not developed in low SES people (more later) .

Regarding the former, yes I agree that that is the maximum gain that one can hope for if all children of low SES were given the right environment (raised to high SES). Put another way, if mean IQ of poor/low SES children is 84 , then given the right conditions the mean of the low SES children can be raised to 104 (greater than high SES children’s mean :-).

As both of them do cover the same IQ range (10-20), the logical consequence for a broad statement on IQ and genetics seems therefore to be, that these studies may say that overall, IQ changes can be expected to be determined to approximately equal parts by genetics and environment, with environment being responsible for a typically larger part in low SES families, and genetics playing a relatively larger part in high SES families.

Agreed partially, but that glosses over the fact of sub-threshold universal shared environments and the fact that the role of genetic and environmental component varies with SES, an therefore an ideal statement would be IQ is under gentic controltolarge extent, but that gentics needs threshold environments to flower and thus the importance of environment component- not in explaining variance , but by its direct effect on IQ enabling/flowering.

This same post also contained this citation “Children of well-off biological parents reared by poor/well -off adopted parents have Average IQ about 16 point higher than children of poor biological parents”
In my opinion, the latter would indicate the approximate range of genetic IQ differences for the samples in this study, while the former would indicate the approximate maximal environmental gain that can be hoped for in the environments that were encountered in these studies.
As both of them do cover the same IQ range (10-20), the logical consequence for a broad statement on IQ and genetics seems therefore to be, that these studies may say that overall, IQ changes can be expected to be determined to approximately equal parts by genetics and environment, with environment being responsible for a typically larger part in low SES families, and genetics playing a relatively larger part in high SES families.
There also is this citation:
“The normal observation that identical twins belonging to well-off/middle class families have IQ rates similar as compared to fraternal twins, thus indicates that for children from well-off background (biological/adopted), the IQ (observed phenotype) is mostly due to genetic factors (underlying genotype) and environmental factors are not a big determinant.

The paradoxical observation that identical twins belonging to poor families have IQ rates as varying as compared to fraternal twins, should indicate that for children from poor background (biological/adopted), the IQ (observed phenotype) is mostly due to environmental factors and genetic factors (the underlying genotype ) are not a big determinant.”

These are extremely nice observations. I would be interested in the conclusions one might be tempted to draw from them. Reading the latter part of this sentence, one might come to the following conclusion (conclusion 1): “if in low-SES families the variations in IQ are largely determined by environmental factors, then providing a positive environment for the development of IQ would increase the IQ levels in these families impressively (up to 20 points; but, this is an up to value, means would be more interesting).”
While I completely agree with this thinking, one might also be tempted to draw the conclusion that (conclusion 2) “As IQ variations in low-SES families are largely due to environment, providing an IQ-stimulating environment in low-SES families might completely eliminate the IQ differences between low-SES families and high-SES families”
At the least, a non-cautious reader might understand your words as such. I am not sure whether you think that way or not. I would like to hear your opinion on that. I think that this citation “Children of well-off biological parents reared by poor/well -off adopted parents have Average IQ about 16 point higher than children of poor biological parents” provides an argument that precludes conclusion 2. It would rather say that (conclusion 3), ” providing a perfect IQ-stimulating environment for low-SES families as encountered in these studies, one should think that their offspring would achieve an IQ level that is 16 points lower than that of the offspring of high-SES families.”
I would like to hear your opinion on my conclusion 3.

I agree with conclusion 1. I also agree with conclusion 2 (not based on political correctness, but hard data). The paper on which these figures are based can be found here. The mean IQ of high SES persons is 113.5 and the mean IQ of low SES children is 98.00, thus a difference of ~16 points. The variation in IQ of high SES children raised in high SES families is 12.25; as shown this variance is likely due to genetics (say hundred percent is due to genetics); then changing the SES within the given range should have no effect on average IQ and it would remain 119 (for this high +/high+ group). On the other hand, the variance in low SES, reared by low SES families is 15.41 and mean is 92.40;thus if all were given enriched environment, their mean IQ would become 92.4+ 15.4 = ~ 108 . We still have a 10 point difference which can be accounted for by the fact that genetics had not come into play for low SES , low SES group yet and as genetics enters and increases the variance due to genetic flowering,, their IQ would be in the same league as high IQ/High IQ children.

So definitely the conclusion 3 is flawed- the difference would not be close to 16 points, but negligible, as the 16 points nowhere measures gentic difference in abilities, but reflects the genetic factor not yet active in low SES, due to improper environmental exposure.

I think that this is a rather important conclusion, as it tells us something about the differences in IQ that can be expected to exist between distinct population stratums (don’t know whether this is an appropriate word for what I try to say; I hope you understand what I mean).
If this is the ballpark of figures that we can expect between low-and high SES IQ differences, this would have important effects on future IQ-distributions. Population-wide stability of IQ-performance, if measured in a saturated environment (maximum stimulation of all members of society), can then only be achieved if all stratums of society have the same number of offspring per individuum. If low-SES families have more children, we have to expect that the 16-point lower IQ will decrease the whole-population IQ.
Here, the 16 points only apply to the sample as measured in your example; the true value of the saturated stratum-dependent-IQ together with stratum-dependent birthrates will determine the shift of the saturated IQ-distribution for the generations to come.

Do you agree with this point of view?

To use a very strong and negative connotation word, the above smacks of eugenics. And I wont comment further on this. Each according to his own philosophy, but beware that science does not support your conclusions. Instead of population controlling the poor, please try to elevate their vicious loop of undeserved poverty, low IQ and harmful stigma.

Turkheimer, E., Haley, A., Waldron, M., D’Onofrio, B., & Gottesman, I. (2003). Socioeconomic status modifies heritability of iq in young children Psychological Science, 14 (6), 623-628 DOI: 10.1046/j.0956-7976.2003.psci_1475.x

Harden, K., Turkheimer, E., & Loehlin, J. (2006). Genotype by Environment Interaction in Adolescents’ Cognitive Aptitude Behavior Genetics, 37 (2), 273-283 DOI: 10.1007/s10519-006-9113-4

Capron, C., & Duyme, M. (1989). Assessment of effects of socio-economic status on IQ in a full cross-fostering study Nature, 340 (6234), 552-554 DOI: 10.1038/340552a0

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Living on the edge of chaos; implications for autism and psychosis

SMI32-stained pyramidal neurons in cerebral co...Image via Wikipedia

I serendipitously came cross this article today about how our brains are self-organized criticality or systems living on the edge of chaos. There are many interesting ideas and gold nuggets in that article, and I’ll briefly quote from it.

In reality, your brain operates on the edge of chaos. Though much of the time it runs in an orderly and stable way, every now and again it suddenly and unpredictably lurches into a blizzard of noise.

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

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

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

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

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

Two of the power laws that are found in human brains relate to the phase shift and phase lock periods of EEG/fMRI or human brain systems etc. As per this PLOS comp biology paper:

Self-organized criticality is an attractive model for human brain dynamics, but there has been little direct evidence for its existence in large-scale systems measured by neuroimaging. In general, critical systems are associated with fractal or power law scaling, long-range correlations in space and time, and rapid reconfiguration in response to external inputs. Here, we consider two measures of phase synchronization: the phase-lock interval, or duration of coupling between a pair of (neurophysiological) processes, and the lability of global synchronization of a (brain functional) network. Using computational simulations of two mechanistically distinct systems displaying complex dynamics, the Ising model and the Kuramoto model, we show that both synchronization metrics have power law probability distributions specifically when these systems are in a critical state. We then demonstrate power law scaling of both pairwise and global synchronization metrics in functional MRI and magnetoencephalographic data recorded from normal volunteers under resting conditions. These results strongly suggest that human brain functional systems exist in an endogenous state of dynamical criticality, characterized by a greater than random probability of both prolonged periods of phase-locking and occurrence of large rapid changes in the state of global synchronization, analogous to the neuronal “avalanches” previously described in cellular systems. Moreover, evidence for critical dynamics was identified consistently in neurophysiological systems operating at frequency intervals ranging from 0.05–0.11 to 62.5–125 Hz, confirming that criticality is a property of human brain functional network organization at all frequency intervals in the brain’s physiological bandwidth.

Further, as per research by Thatcher et al, the EEG phase shift is larger in people with high IQ, while phase lock is smaller in the people with high IQ.

Phase shift duration (40–90 ms) was positively related to intelligence (P < .00001) and the phase lock duration (100–800 ms) was negatively related to intelligence (P < .00001). Phase reset in short interelectrode distances (6 cm) was more highly correlated to I.Q. (P < .0001) than in long distances (> 12 cm).

Further, in this paper , thatcher eta look at autistics and conclude that the people with autism show some deficits in phase shift and phase lock.

Results: In both short (6 cm) and long (21 – 24 cm) inter-electrode distances phase shift duration in ASD subjects was significantly shorter in all frequency bands but especially in the alpha-1 frequency band (8 – 10 Hz) (P < .0001). Phase lock duration was significantly longer in the alpha-2 frequencyband (10 – 12 Hz) in ASD subjects (P < .0001). An anatomical gradient was present with the occipitalparietal regions the most significant.
Conclusions: The findings in this study support the hypothesis that neural resource recruitment occurs in the lower frequency bands and especially the alpha-1 frequency band while neural resource allocation occurs in the alpha-2 frequency band. The results are consistent with a general GABA inhibitory neurotransmitter deficiency resulting in reduced number and/or strength of thalamo-cortical connections in autistic subjects 

It is interesting that in the original new scientist article , thatcher speculates that the pattern in schizophrenia may be reverse of what is seen in autism (exactly my thoughts, though the confounding of low IQ with autism may explain his autism results to an extent):

He found that the length of time the children’s brains spent in both the stable phase-locked states and the unstable phase-shifting states correlated with their IQ scores. For example, phase shifts typically last 55 milliseconds, but an additional 1 millisecond seemed to add as many as 20 points to the child’s IQ. A shorter time in the stable phase-locked state also corresponded with greater intelligence – with a difference of 1 millisecond adding 4.6 IQ points to a child’s score (NeuroImage, vol 42, p 1639). Thatcher says this is because a longer phase shift allows the brain to recruit many more neurons for the problem at hand. “It’s like casting a net and capturing as many neurons as possible at any one time,” he says. The result is a greater overall processing power that contributes to higher intelligence. Hovering on the edge of chaos provides brains with their amazing capacity to process information and rapidly adapt to our ever-changing environment, but what happens if we stray either side of the boundary? The most obvious assumption would be that all of us are a short step away from mental illness. Meyer-Lindenberg suggests that schizophrenia may be caused by parts of the brain straying away from the critical point. However, for now that is purely speculative. Thatcher, meanwhile, has found that certain regions in the brains of people with autism spend less time than average in the unstable, phase-shifting states. These abnormalities reduce the capacity to process information and, suggestively, are found only in the regions associated with social behaviour. “These regions have shifted from chaos to more stable activity,” he says. The work might also help us understand epilepsy better: in an epileptic fit, the brain has a tendency to suddenly fire synchronously, and deviation from the critical point could explain this. “They say it’s a fine line between genius and madness,” says Liley. “Maybe we’re finally beginning to understand the wisdom of this statement.”

Thus, it seems Autism and Psychosis are just two ways in which self-organized criticality can cease to do what it was designed to do- live on the edge , without falling on either side of order or chaos.

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Major conscious and unconcoscious processes in the brain: part 3: Robot minds

This article continues my series on major conscious and unconscious processes in the brain. In my last two posts I have talked about 8 major unconscious processes in the brain viz sensory, motor, learning , affective, cognitive (deliberative), modelling, communications and attentive systems. Today, I will not talk about brain in particular, but will approach the problem from a slightly different problem domain- that of modelling/implementing an artificial brain/ mind.

I am a computer scientist, so am vaguely aware of the varied approaches used to model/implement the brain. Many of these use computers , though not every approach assumes that the brain is a computer.

Before continuing I would briefly like to digress and link to one of my earlier posts regarding the different  traditions of psychological research in personality and how I think they fit an evolutionary stage model . That may serve as a background to the type of sweeping analysis and genralisation that I am going to do. To be fair it is also important to recall an Indian parable of how when asked to describe an elephant by a few blind man each described what he could lay his hands on and thus provided a partial and incorrect picture of the elephant. Some one who grabbed the tail, described it as snake-like and so forth.

With that in mind let us look at the major approaches to modelling/mplementing the brain/intelligence/mind. Also remember that I am most interested in unconscious brain processes till now and sincerely believe that all the unconscious processes can, and will be successfully implemented in machines.   I do not believe machines will become sentient (at least any time soon), but that question is for another day.

So, with due thanks to @wildcat2030, I came across this book today and could immediately see how the different major approaches to artificial robot brains are heavily influenced (and follow) the evolutionary first five stages and the first five unconscious processes in the brain.
The book in question is ‘Robot Brains: Circuits and Systems for Conscious Machines’ by Pentti O. Haikonen and although he is most interested in conscious machines I will restrict myself to intelligent but unconscious machines/robots.

The first chapter of the book (which has made to my reading list) is available at Wiley site in its entirety and I quote extensively from there:

Presently there are five main approaches to the modelling of cognition that could be used for the development of cognitive machines: the computational approach (artificial intelligence, AI), the artificial neural networks approach, the dynamical systems approach, the quantum approach and the cognitive approach. Neurobiological approaches exist, but these may be better suited for the eventual explanation of the workings of the biological brain.

The computational approach (also known as artificial intelligence, AI) towards thinking machines was initially worded by Turing (1950). A machine would be thinking if the results of the computation were indistinguishable from the results of human thinking. Later on Newell and Simon (1976) presented their Physical Symbol System Hypothesis, which maintained that general intelligent action can be achieved by a physical symbol system and that this system has all the necessary and sufficient means for this purpose. A physical symbol system was here the computer that operates with symbols (binary words) and attached rules that stipulate which symbols are to follow others. Newell and Simon believed that the computer would be able to reproduce human-like general intelligence, a feat that still remains to be seen. However, they realized that this hypothesis was only an empirical generalization and not a theorem that could be formally proven. Very little in the way of empirical proof for this hypothesis exists even today and in the 1970s the situation was not better. Therefore Newell and Simon pretended to see other kinds of proof that were in those days readily available. They proposed that the principal body of evidence for the symbol system hypothesis was negative evidence, namely the absence of specific competing hypotheses; how else could intelligent activity be accomplished by man or machine? However, the absence of evidence is by no means any evidence of absence. This kind of ‘proof by ignorance’ is too often available in large quantities, yet it is not a logically valid argument. Nevertheless, this issue has not yet been formally settled in one way or another. Today’s positive evidence is that it is possible to create world-class chess-playing programs and these can be called ‘artificial intelligence’. The negative evidence is that it appears to be next to impossible to create real general intelligence via preprogrammed commands and computations.

The original computational approach can be criticized for the lack of a cognitive foundation. Some recent approaches have tried to remedy this and consider systems that integrate the processes of perception, reaction, deliberation and reasoning (Franklin, 1995, 2003; Sloman, 2000). There is another argument against the computational view of the brain. It is known that the human brain is slow, yet it is possible to learn to play tennis and other activities that require instant responses. Computations take time. Tennis playing and the like would call for the fastest computers in existence. How could the slow brain manage this if it were to execute computations?

The artificial neural networks approach, also known as connectionism, had its beginnings in the early 1940s when McCulloch and Pitts (1943) proposed that the brain cells, neurons, could be modelled by a simple electronic circuit. This circuit would receive a number of signals, multiply their intensities by the so-called synaptic weight values and sum these modified values together. The circuit would give an output signal if the sum value exceeded a given threshold. It was realized that these artificial neurons could learn and execute basic logic operations if their synaptic weight values were adjusted properly. If these artificial neurons were realized as hardware circuits then no programs would be necessary and biologically plausible artificial replicas of the brain might be possible. Also, neural networks operate in parallel, doing many things simultaneously. Thus the overall operational speed could be fast even if the individual neurons were slow. However, problems with artificial neural learning led to complicated statistical learning algorithms, ones that could best be implemented as computer programs. Many of today’s artificial neural networks are statistical pattern recognition and classification circuits. Therefore they are rather removed from their original biologically inspired idea. Cognition is not mere classification and the human brain is hardly a computer that executes complicated synaptic weight-adjusting algorithms.

The human brain has some 10 to the power of 11 neurons and each neuron may have tens of thousands of synaptic inputs and input weights. Many artificial neural networks learn by tweaking the synaptic weight values against each other when thousands of training examples are presented. Where in the brain would reside the computing process that would execute synaptic weight adjusting algorithms? Where would these algorithms have come from? The evolutionary feasibility of these kinds of algorithms can be seriously doubted. Complicated algorithms do not evolve via trial and error either. Moreover, humans are able to learn with a few examples only, instead of having training sessions with thousands or hundreds of thousands of examples. It is obvious that the mainstream neural networks approach is not a very plausible candidate for machine cognition although the human brain is a neural network.

Dynamical systems were proposed as a model for cognition by Ashby (1952) already in the 1950s and have been developed further by contemporary researchers (for example Thelen and Smith, 1994; Gelder, 1998, 1999; Port, 2000; Wallace, 2005). According to this approach the brain is considered as a complex system with dynamical interactions with its environment. Gelder and Port (1995) define a dynamical system as a set of quantitative variables, which change simultaneously and interdependently over quantitative time in accordance with some set of equations. Obviously the brain is indeed a large system of neuron activity variables that change over time. Accordingly the brain can be modelled as a dynamical system if the neuron activity can be quantified and if a suitable set of, say, differential equations can be formulated. The dynamical hypothesis sees the brain as comparable to analog feedback control systems with continuous parameter values. No inner representations are assumed or even accepted. However, the dynamical systems approach seems to have problems in explaining phenomena like ‘inner speech’. A would-be designer of an artificial brain would find it difficult to see what kind of system dynamics would be necessary for a specific linguistically expressed thought. The dynamical systems approach has been criticized, for instance by Eliasmith (1996, 1997), who argues that the low dimensional systems of differential equations, which must rely on collective parameters, do not model cognition easily and the dynamicists have a difficult time keeping arbitrariness from permeating their models. Eliasmith laments that there seems to be no clear ways of justifying parameter settings, choosing equations, interpreting data or creating system boundaries. Furthermore, the collective parameter models make the interpretation of the dynamic system’s behaviour difficult, as it is not easy to see or determine the meaning of any particular parameter in the model. Obviously these issues would translate into engineering problems for a designer of dynamical systems.

The quantum approach maintains that the brain is ultimately governed by quantum processes, which execute nonalgorithmic computations or act as a mediator between the brain and an assumed more-or-less immaterial ‘self’ or even ‘conscious energy field’ (for example Herbert, 1993; Hameroff, 1994; Penrose, 1989; Eccles, 1994). The quantum approach is supposed to solve problems like the apparently nonalgorithmic nature of thought, free will, the coherence of conscious experience, telepathy, telekinesis, the immortality of the soul and others. From an engineering point of view even the most practical propositions of the quantum approach are presently highly impractical in terms of actual implementation. Then there are some proposals that are hardly distinguishable from wishful fabrications of fairy tales. Here the quantum approach is not pursued.

The cognitive approach maintains that conscious machines can be built because one example already exists, namely the human brain. Therefore a cognitive machine should emulate the cognitive processes of the brain and mind, instead of merely trying to reproduce the results of the thinking processes. Accordingly the results of neurosciences and cognitive psychology should be evaluated and implemented in the design if deemed essential. However, this approach does not necessarily involve the simulation or emulation of the biological neuron as such, instead, what is to be produced is the abstracted information processing function of the neuron.

A cognitive machine would be an embodied physical entity that would interact with the environment. Cognitive robots would be obvious applications of machine cognition and there have been some early attempts towards that direction. Holland seeks to provide robots with some kind of consciousness via internal models (Holland and Goodman, 2003; Holland, 2004). Kawamura has been developing a cognitive robot with a sense of self (Kawamura, 2005; Kawamura et al., 2005). There are also others. Grand presents an experimentalist’s approach towards cognitive robots in his book (Grand, 2003).

A cognitive machine would be a complete system with processes like perception, attention, inner speech, imagination, emotions as well as pain and pleasure. Various technical approaches can be envisioned, namely indirect ones with programs, hybrid systems that combine programs and neural networks, and direct ones that are based on dedicated neural cognitive architectures. The operation of these dedicated neural cognitive architectures would combine neural, symbolic and dynamic elements.

However, the neural elements here would not be those of the traditional neural networks; no statistical learning with thousands of examples would be implied, no backpropagation or other weight-adjusting algorithms are used. Instead the networks would be associative in a way that allows the symbolic use of the neural signal arrays (vectors). The ‘symbolic’ here does not refer to the meaning-free symbol manipulation system of AI; instead it refers to the human way of using symbols with meanings. It is assumed that these cognitive machines would eventually be conscious, or at least they would reproduce most of the folk psychology hallmarks of consciousness (Haikonen, 2003a, 2005a). The engineering aspects of the direct cognitive approach are pursued in this book.

Now to me these computational approaches are all unidimensional-

  1. The computational approach is suited for symbol-manipulation and information-represntation and might give good results when used in systems that have mostly ‘sensory’ features like forming a mental represntation of external world, a chess game etc. Here something (stimuli from world) is represented as something else (an internal symbolic represntation).
  2. The Dynamical Systems approach is guided by interactions with the environment and the principles of feedback control systems and also is prone to ‘arbitrariness’ or ‘randomness’. It is perfectly suited to implement the ‘motor system‘ of brain as one of the common features is apparent unpredictability (volition) despite being deterministic (chaos theory) .
  3. The Neural networks or connectionsim is well suited for implementing the ‘learning system’ of the brain and we can very well see that the best neural network based systems are those that can categorize and classify things just like ‘the learning system’ of the brain does.
  4. The quantum approach to brain, I haven’t studied enough to comment on, but the action-tendencies of ‘affective system’ seem all too similar to the superimposed,simultaneous states that exits in a wave function before it is collapsed. Being in an affective state just means having a set of many possible related and relevant actions simultaneously activated and then perhaps one of that decided upon somehow and actualized. I’m sure that if we could ever model emotion in machine sit would have to use quantum principles of wave functions, entanglemnets etc.
  5. The cognitive approach, again I haven’t go a hang of yet, but it seems that the proposal is to build some design into the machine that is based on actual brain and mind implemntations. Embodiment seems important and so does emulating the information processing functions of neurons. I would stick my neck out and predict that whatever this cognitive approach is it should be best able to model the reasoning and evaluative and decision-making functions of the brain. I am reminded of the computational modelling methods, used to functionally decompose a cognitive process, and are used in cognitive science (whether symbolic or subsymbolic modelling) which again aid in decision making / reasoning (see wikipedia entry)

Overall, I would say there is room for further improvement in the way we build more intelligent machines. They could be made such that they have two models of world – one deterministic , another chaotic and use the two models simulatenously (sixth stage of modelling); then they could communicate with other machines and thus learn language (some simulation methods for language abilities do involve agents communicating with each other using arbitrary tokens and later a language developing) (seventh stage) and then they could be implemented such that they have a spotlight of attention (eighth stage) whereby some coherent systems are amplified and others suppressed. Of course all this is easier said than done, we will need at least three more major approaches to modelling and implementing brain/intelligence before we can model every major unconscious process in the brain. To model consciousness and program sentience is an uphill task from there and would definitely require a leap in our understandings/ capabilities.

Do tell me if you find the above reasonable and do believe that these major approaches to artificial brain implementation are guided and constrained by the major unconscious processes in the brain and that we can learn much about brain from the study of these artificial approaches and vice versa.

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