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

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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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SES and the developing brain

I have written about poverty/SES and its effects on brain development/IQ earlier too,and this new review article by Farah and Hackman in TICS is a very good introduction to anyone interested in the issue.

The article reviews the behavioral studies that show that SES is correlated with at least the two brain systems of executive function and language abilities.It also review physiological data that shows that even when behavioral outcomes do not differ ERP can show differential activation in the brains of people with low and middle SES , thus suggesting that differences that may not be detected on behavioral measures may still exist. They also review (f)MRI data that shows no structural differences in the brains of low and middle SES children, but definite functional differences.they also review experimental manipulation of social status in labarotaories, and show how those studies also indicate that SES and executive function are correlated.

They then turn to the million dollar question of the direction of causality and for this infer indirectly based on the SES-IQ causal linkages.

What is the cause of SES differences in brain function? Is it contextual priming? Is it social causation, reflecting the influence of SES on brain development? Alternatively, is it social selection, in which abilities inherited from parents lead to lower SES? Current research on SES and brain development is not designed to answer this question. However, research on SES and IQ is relevant and supports a substantial role of SES and its correlated experience as causal factors.

Slightly less than half of the SES-related IQ variability in adopted children is attributable to the SES of the adoptive family rather than the biological. This might underestimate environmental influences because the effects of prenatal and early postnatal environment are included in the estimates of genetic influence. Additional evidence comes from studies of when poverty was experienced in a child's life. Early poverty is a better predictor of later cognitive achievement than poverty in middle- or late-childhood, an effect that is difficult to explain by genetics. SES modifies the heritability of IQ, such that in the highest SES families, genes account for most of the variance in IQ because environmental influences are in effect at ceiling in this group, whereas in the lowest SES families, variance in IQ is overwhelmingly dominated by environmental influences because these are in effect the limiting factor in this group. In addition, a growing body of research indicates that cognitive performance is modified by epigenetic mechanisms, indicating that experience has a strong influence on gene expression and resultant phenotypic cognitive traits . Lastly, considerable evidence of brain plasticity in response to experience throughout development indicates that SES influences on brain development are plausible.

Differences in the quality and quantity of schooling is one plausible mechanism that has been proposed. However, many of the SES differences summarized in this article are present in young children with little or no experience of school , so differences in formal education cannot, on their own, account for all of the variance in cognition and brain development attributable to SES. The situation is analogous to that of SES disparities in health, which are only partly explained by differential access to medical services and for which other psychosocial mechanisms are important causal factors .

The last point is really important and can be extended. Access to health services for low SES people may be a reason why , for eg, more schizophrenia incidence is found in low SES neighbourhoods. which brings us to the same chicken-and-egg question of the drift theory of schizophrenia- whether people with schizophrenia drift into low SES or low SES is a risk factor in itself. Exactly this point was brought to my attention when I was interacting with a few budding psychiatrists recently, this Martha Farah theory about the SES leading to lower IQ/ cognitive abilities. It is important to acknowledge that low SES not only leads to left hypo-frontality (another symptom of schizophrenia), schizophrenia is supposed to be due to lessened mylienation and again nutritional factors may have a role to play; also access to health care, exposure to chronic stress and lesser subjective feelings of control may all be mediating afctors that lead low SS to lead to schizophrenia/ psychosis.Also remember that schizophrenia is sort of a devlopmenetal disorder.

Well, I digressed a bit, but the idea is that not only does low SES affect 'normal' cognitive abilities, it may even increase the risk for 'abnormal' cognitive abilities that may lead to psychosis, and his effect of SES on IQ/cognitive abilities/ risk of mental diseases is mediated by the effect of SES on the developing brain. I have already covered the putative mechanisms by which SES may affect brain development, but just to recap, here I quote from the paper:

Candidate causal pathways from environmental differences to differences in brain development include lead exposure, cognitive stimulation, nutrition, parenting styles and transient or chronic hierarchy effects. One particularly promising area for investigation is the effect of chronic stress. Lower-SES is associated with higher levels of stress in addition to changes in the function of physiological stress response systems in children and adults. Changes in such systems are likely candidates to mediate SES effects as they impact both cognitive performance and brain regions, such as the prefrontal cortex and hippocampus, in which there are SES differences.

We can only hope that the evil of low SES is recognized as soon as possible and if for nothing else, than for advancing science, some intervention studies are done that manipulate the SES variables in the right direction and thus ensure that the full cognitive potential of the children flowers.



HACKMAN, D., & FARAH, M. (2009). Socioeconomic status and the developing brain Trends in Cognitive Sciences, 13 (2), 65-73 DOI: 10.1016/j.tics.2008.11.003

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Neurological correlates of Poverty

While people generally do not squirm on reading a headline claiming neural correlates of religion, god, trust, consciousness, political/ sexual orientation etc, I am sure the title neural correlates of Poverty would have lead to some uneasy shuffling around. How can poverty that is clearly a result of economic opportunities/ capabilities be reduced to brain? Are we claiming that low inherent IQ and the neural correlates thereof define and lead to poverty? Or is the claim instead that poverty leads to definite changes in the brain, which may lead to manifestation of low IQ and the sustenance of the vicious circle of poverty? The regular readers of the blog will know which side of the fence I am sitting on!

The blogosphere is normally abuzz with controversial topics like atheism, meaninglessness of evolution and race and gender differences(for eg.  in IQ) and people defend these sacred dictum doggedly, claiming that 'is' and 'ought' need not be confused, especially in a cold, logical science which deals with all facts and should not be guided by values. Yet, the same blogosphere generally silently ignores, or does not take a stand , when the 'is' and 'ought' are in sync and something morally significant is also found to be scientifically valid. Rather the apology for such facts is made very cautiously, with the spirit of not offending the people who have a different, and in my view, an inferior moral system.

I believe whenever people discuss poverty/SES, they have either of the two moral systems: first, the world is unfair and poor people are poor because of some external factors/ circumstances; addressing them may solve/ eliminate the problem of poverty;  and second: the world is fair (like an idealized free market) and if someone is poor they are due to either inherent internal flaws (bad genes) or maybe bad choices (they want to be poor/ are lazy and unindustrious etc); so the problem of poverty cannot/ should not be solved.  I subscribe to the first moral system and believe in interventions to solve the problem of poverty. I am glad to have scientific facts to my side and have been addressing these issues in a series of posts .

The latest impetus to write on the topic comes form reading Lehrer's post titled Poverty and the brain at the Frontal cortex and I am glad to have found a fellow blogger who doesn't mind speaking on such controversial topics and take a stand for 'is' that is in sync with 'ought'. It is an excellent post regarding how early interventions can help alleviate poverty and how a poor person suffers from the viscous circle of poverty by the mediating influence of brain and IQ.

Lehrer also mentions the work of Martha Farah (of Visual Agnosia fame whose earlier work was on vision) on the same and I recommend reading at least this article by Martha and colleagues, although many other invaluable gems are present on her site.

The article begins with an anecdotal reference to how Martha first became aware of the gravity of the issue, when she saw her babysitters / maids steeped in poverty and the low IQ and SES viscous circle. this resonates with me and I can easily relate to this as my child enjoys a lot of toys while our maid's children are faced with lack.

I would now quote extensively from the aforementioned article:

It seemed to me that children’s experience of the world is very different in low and middle SES environments. Most middle SES children have abundant opportunities to explore the world, literally, in terms of people met and places seen, and figuratively, in terms of the world of ideas. In contrast, low SES children generally have fewer interactions with the wider world and much of what they do experience is stressful. Basic research with animals has established the powerful effects of both environmental impoverishment and stress on the developing brain.

She then goes on to make out the case for NCC of poverty:

For the sake of exploring the cognitive neuroscience perspective on transgenerational poverty, and discovering what, if anything, it can contribute to correcting socioeconomic inequality, the first order of business is to ask whether socioeconomic status bears any straightforward relation to brain development. On the face of things it might seem unlikely that characteristics such as income, education and job status, which are typically used to estimate SES, would bear any systematic relationship to physiological processes such as those involved in brain development. It is, however, well established that SES affects physical health through a number of different causal pathways (Adler et al. 1994), many of which could play a role in brain development. It is also clear that poverty is associated with differences in brain function on the basis of the differences in standardized test performance cited earlier, as cognitive tests reflect the function of the brain. However, for a cognitive neuroscience approach to be helpful, the relations between socioeconomic status and the brain must be relatively straightforward and generalizable. The first question that my collaborators and I addressed was therefore: Can we generalize about the neurocognitive correlates of socioeconomic status? Once we have established the neurocognitive profile of childhood poverty, we can begin to test more specific hypotheses about causal mechanisms.

I will now digress a little from the main topic and introduce the five neurocognitive systems that Martha and colleagues have identified and how they tested some children from low and middle SES for finding their capabilities in these systems.

The children were tested on a battery of tasks adapted from the cognitive neuroscience literature, designed to assess the functioning of five key neurocognitive systems. These systems are described briefly here.

• The Prefrontal/Executive system enables flexible responding in situations where the appropriate response may not be the most routine or attractive one, or where it requires maintenance or updating of information concerning recent events. It is dependent on prefrontal cortex, a late-maturing brain region that is disproportionately developed in humans.

• The Left perisylvian/Language system is a complex, distributed system encompassing semantic, syntactic and phonological aspects of language and dependent predominantly on the temporal and frontal areas of the left hemisphere that surround the Sylvian fissure.

• The Medial temporal/Memory system is responsible for one-trial learning, the ability to retain a representation of a stimulus after a single exposure to it (which contrasts with the ability to gradually strengthen a representation through conditioning-like mechanisms), and is dependent on the hippocampus and related structures of the medial temporal lobe.

• The Parietal/Spatial cognition system underlies our ability to mentally represent and manipulate the spatial relations among objects, and is primarily dependent upon posterior parietal cortex.

• The Occipitotemporal/Visual cognition system is responsible for pattern recognition and visual mental imagery, translating image format visual representations into more abstract representations of object shape and identity, and reciprocally translating visual memory knowledge into image format representations (mental images).

Not surprisingly, in view of the literature on SES and standardized cognitive tests, the middle SES children performed better than the low SES children on the battery of tasks as a whole. For some systems, most notably the Left perisylvian/Language system and the Prefrontal/Executive system, the disparity between low and middle SES kindergarteners was both large and statistically significant.

Thus, they found, in a small group of children , that Language and Executive systems' performance differed in low and middle SES children and they were able to replicate this finding with a larger group of children too. This time they broke executive function further into components and found a finer granularity of how SES affects the brain:

As before, the language system showed a highly significant relationship to SES, as did executive functions including Lateral prefrontal/Working memory and Anterior cingulate/Cognitive control components and the Parietal/Spatial cognition system. With a more demanding delay between exposure and test in the memory tasks, we also found a difference in the Medial temporal/Memory system. Performance on the Parietal/spatial system tests also differed as a function of SES.

They also did some studies with older children and to summarize the results of all these studies in their own words:

In sum, although the outcome of each study was different, there were also commonalities among them despite different tasks and different children tested at different ages. The most robust neurocognitive correlates of SES appear to involve the Left perisylvian/Language system, the Medial temporal/Memory system (insofar as SES effects were found in both studies that tested memory with an adequate delay) and the Prefrontal/Executive system, in particular its Lateral prefrontal/Working memory and Anterior cingulate/Cognitive control components. Children growing up in low SES environments perform less well on tests that tax the functioning of these specific systems.

Next they look at the causal versus correlational nature of findings and if causal, then the directions of causality. It is this paragraph , that amazed me, for they seem to be apologetic for the fact that their findings are also ethically good ones.

Do these associations reflect the effects of SES on brain development, or the opposite direction of causality? Perhaps families with higher innate language, executive and memory abilities tend to acquire and maintain a higher SES. Such a mechanism seems likely, a priori, as it would be surprising if genetic influences on cognitive ability did not, in the aggregate, contribute to individual and family SES. However, it seems also seems likely that causality operates in the opposite direction as well, with SES influencing cognitive ability through childhood environment. Note that the direction of causality is an empirical issue, not an ethical one. The issue of whether and to what extent SES differences cause neurocognitive differences or visa versa should not be confused with the issue of whether we have an obligation to help children of any background become educated, productive citizens.

Then, quite important from this blog's point of view, they review the literature that supports SES to IQ direction of causality.

Cross-fostering studies of within- and between -SES adoption suggest that roughly half the IQ disparity in children is experiential (Capron & Duyme, 1989; Schiff & Lewontin, 1986). If anything, these studies are likely to err in the direction of underestimating the influence of environment because the effects of prenatal and early postnatal environment are included in the estimates of genetic influences in adoption studies. A recent twin study by Turkheimer and colleagues (2003) showed that, within low SES families, IQ variation is far less genetic than environmental in origin. Additional evidence comes from studies of when, in a child’s life, poverty was experienced. Within a given family that experiences a period of poverty, the effects are greater on siblings who were young during that period (Duncan et al. 1994), an effect that cannot be explained by genetics. In sum, multiple sources of evidence indicate that SES does indeed have an effect on cognitive development, although its role in the specific types of neurocognitive system development investigated here is not yet known.

Next they tried to tease out what specific SES related factors can affect the different neurocognitive systems. They list both physical and psychological factors that have been hypothesized and researched on in relation to SES and IQ.

Potential causes, physical and psychological

What aspects of the environment might be responsible for the differences in neurocognitive development between low and middle SES children? A large set of possibilities exist, some affecting brain development by their direct effects on the body and some by less direct psychological mechanisms. Three somatic factors have been identified as significant risk factors for low cognitive achievement by the Center for Children and Poverty (1997): inadequate nutrition, substance abuse (particularly prenatal exposure), and lead exposure.

As with potential physical causes, the set of potential psychological causes for the SES gap in cognitive achievement is large, and the causes are likely to exert their effects synergistically. Here we will review research on differences in cognitive stimulation and stress.

They then discuss the psychological factors, which they then investigated, in more detail.

One difference between low and middle SES families that seems predictable, even in the absence of any other information, is that low SES children are likely to receive less cognitive stimulation than middle SES children. Their economic status alone predicts that they will have fewer toys and books and less exposure to zoos, museums and other cultural institutions because of the expense of such items and activities. This is indeed the case (Bradley et al. 2001a) and has been identified as a mediator between SES and measures of cognitive achievement (Bradley and Corwyn 1999; Brooks-Gunn and Duncan 1997; McLoyd 1998). Such a mediating role is consistent with the results of neuroscience research with animals. Starting many decades ago (e.g., Volkmar & Greenough, 1972) researchers began to observe the powerful effects of environmental stimulation on brain development. Animals reared in barren laboratory cages showed less well developed brains by a number of different anatomical and physiological measures, compared with those reared in more complex environments with opportunities to climb, burrow and socialize (see van Praag et al 2000 for a review).

The lives of low SES individuals tend to be more stressful for a variety of reasons, some of which are obvious: concern about providing for basic family needs, dangerous neighborhoods, and little control over one’s work life. Again, research bears out this intuition: Turner and Avison (2003) confirmed that lower SES is associated with more stressful life events by a number of different measures. The same appears to be true for children as well as adults, and is apparent in salivary levels of the stress hormone cortisol (Lupien et al. 2001).

Why is stress an important consideration for neurocognitive development? Psychological stress causes the secretion of cortisol and other stress hormones, which affect the brain in numerous ways (McEwen 2000). The immature brain is particularly sensitive to these effects. In basic research studies of rat brain development, rat pups are subjected to the severe stress of prolonged separation from the mother and stress hormone levels predictably climb. The later anatomy and function of the brain is altered by this early neuroendocrine phenomenon. The brain area most affected is the medial temporal area needed for memory, although prefrontal systems involved in the regulation of the stress response are also impacted (Meaney et al. 1996).

They then go on to discuss how this information can be used to formulate mechanisms that mediate the effect of low SES on diffrent neurocognitive systems.

The latest phase of our research is an attempt to make use of the description of the SES disparities in neurocognitive development in testing hypotheses about the causal pathways. Drawing on our previous research that identified three neurocognitive systems as having the most robust differences as a function of SES (Perisylvian/Language, Medial temporal/Memory, and Prefrontal/Executive), we are now testing hypotheses concerning the determinants of individual differences in the development of these systems in children of low SES. Specifically, we are investigating the role of childhood cognitive stimulation and social/emotional nurturance (Farah et al. 2005; Childhood experience and neurocognitive development: Dissociation of cognitive and emotional influences).

They then describe an observational study of interaction between children and parents and how they assess the cognitive simulation an social/emotional nurturance using HOME assessment battery. What they found follows:

Children’s performance on the tests of Left perisylvian/Language was predicted by average cognitive stimulation. This was the sole factor identified as predicting language ability by forward stepwise regression, and one of three factors identified by backwards stepwise regression, along with the child’s gender and the mother’s IQ. In contrast, performance on tests of Medial temporal/Memory ability was predicted by average social/emotional nurturance. This was the sole factor identified as predicting memory ability by forward stepwise regression and one of three factors identified by backwards stepwise regression, along with the child’s age and cognitive stimulation. The relation between memory and early emotional experience is consistent with the animal research cited earlier, showing a deleterious effect of stress hormones on hippocampal development. Our analyses did not reveal any systematic relation of the predictor variables considered here to Lateral prefrontal/Working memory or Anterior cingulate/Cognitive control function. In conclusion, different aspects of early experience affect different systems of the developing brain. Cognitive stimulation influences the development of language, whereas social/emotional nurturance affects the development of memory but not language.

Here is what they conclude:

What are the implications for society of a more mechanistic understanding of the effects of childhood poverty on brain development? To different degrees, and in different ways, we regard children as the responsibility of both parents and society. Parents’ responsibility begins before birth and encompasses virtually every aspect of the child’s life. Society’s responsibility is more circumscribed. In the United States, for example, society’s contribution to the cognitive development of children begins at age 5 or 6, depending on whether public kindergarten is offered. The physical health and safety of all infants and children is a social imperative, however, well before school age. Laws requiring lead abatement in homes occupied by children exemplify our societal commitment to protect them from the neurological damage caused by this neurotoxin. Research on the effects of early life stress and limited cognitive stimulation has begun to show that these concomitants of poverty have negative effects on neurological development too, by mechanisms no less concrete and real. Thus, neuroscience may recast the disadvantages of childhood poverty as a bioethical issue rather than merely one of economic opportunity.

In my view the societal implications are far reaching, if low SES leads to lowered cognitive functioning, it becomes our duty to provide more cognitive stimulation and ensure that all children get sufficient social/ emotional nurturance so that their IQ can flower to its full potential.

I would have liked to end on this note, but cant help pointing that the five neurocognitive systems Martha has identified, to me seems to follow in stages, with the later systems maturing later :

1) Occipital/ visual : describe/ perceive the world/ self
2) Parietal/ spatial:explain the world/self (may be involved in consciousness)
3) Temporal/ Memory: predict the world/self
4) Frontal/ executive: control the world/ self
5) Sylvian/ Language: improve the world/ self


We all know that language abilities develop the oldest and vision is more or less developed at birth; also the fact that SES should affect the latter stages of neurocognitive systems also gels in. the fact that cognitive stimulation affects language and emotional/social nurturance affects memory to me also fits in.

Anyway whatever the implication sof this research for stage theories, they have far reaching and imprortanat implications for social policy and education.

Farah, M.J.,Noble, K.G. and Hurt, H. (2005). Poverty, privilege and brain development: Emprical findings and ethical implications. In J. Illes (Ed.) Neuroethics in the 21st Century. New York: Oxford University Press.

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IQ and Religion: is the relation mediated by wealth and feelings of control?

Last week, on the blog action day, I re posted one of my earlier posts that questioned Kanazawa's assertion that IQ causes Longevity (and implicitly that low IQ causes Poverty and not the other way round) and that SES has no effect on longevity net of IQ.  That has been thoroughly dealt with earlier and I will not readdress the issue; 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. This Low IQ that is a result of Low SES also gets correlated to longevity; again which would be largely explained by the low SES of the person. But as Low SES leads to less longevity and less IQ , a correlation between IQ and Longevity would also be expected. 

A similar issue has cropped up , this time with respect to religion or belief in God. It has been claimed that high IQ  causes atheism and that low IQ leads to superstition and belief in God. The result, this time by Lynn's team is again correlational in nature and just like Kanazawa's study partially relies on Macro-data i.e. mean IQ of a country and its mean religious belief scores.

The abstract of the paper goes like:

Evidence is reviewed pointing to a negative relationship between intelligence and religious belief in the United States and Europe. It is shown that intelligence measured as psychometric g is negatively related to religious belief. We also examine whether this negative relationship between intelligence and religious belief is present between nations. We find that in a sample of 137 countries the correlation between national IQ and disbelief in God is 0.60.

Now, BHA science group , has written a  very good rebuttal to this proposition and I urge readers to go and read the discussion there in full. 

For the sake of completeness, let me summarize the case against the hypothesis that high IQ causes atheism.

Problems with the macro data on which this analysis is made:  for countries that have about 100 (average) mean IQ, the correlation does not hold. The correlation is mainly an artifact of the fact that low mean IQ countries also have high religious belief (see accompanying figure) . We can, in my opinion, thus restrict the discussion to low (mean) IQ countries and try to explain whether its the Low mean IQ of their people that causes religiosity; or that high religiosity somehow leads to low IQ (a very counter-intuitive though indeed); or more plausibly that some other factor like SES/ feelings of control may be the underlying reason for both low IQ and high religiosity. 

Now, I have shown elsewhere that low SES causes low IQ and not the other way round; what remains to be shown is that low SES also causes religious faith. 

The latter part I'll like to break in two parts: first , I believe that it is intuitive and there would be wealth of data showing that poverty or Low SES leads to fellings of helplessness or feelings of loss of control. Thus , the first assertion is that low mean SES in these countries, leads to the average person feeling less in control of his/her life and thus to feelings of loss of control.

The second part of the argument is that low feelings of control lead to religiosity/ superstition. Again I too have touched this before, but would right now like to point to this recent study that found that feelings of loss of control, lead to magical thinking/ superstitious belief and by extension (I am indeed taking a leap here) propensity towards religiosity. Of course we all know that religion is the opium of the masses (which are usaully poor) and rightly subdues the pwoerlessness and lack of control feelings that are otherwise unbearable.

 

Thus, I rest my case,  claiming that it is the low SES that leads to low IQ and high religious beliefs; the effect being mediated by nutritional/ enriched environmental factors in the former (IQ) case, while that of religion being mediated by feelings of control in the latter case. The actual correlation observed between IQ and religious faith , on the basis of low SES data , is at best spurious and due to the underlying low SES effects. 

  

 

Richard Lynn, John Harvey, Helmuth Nyborg (2008). Average Intelligence Predicts Atheism Rates across 137 Nations Intelligence


J. A. Whitson, A. D. Galinsky (2008). Lacking Control Increases Illusory Pattern Perception Science, 322 (5898), 115-117 DOI: 10.1126/science.1159845

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Blog Action Day: Poverty and IQ: from the archives

Well, today is blog action day 2008, and the topic for today is Poverty.

I am afraid I will be posting one of my old posts today: a post relating Poverty and SES to IQ and I am also publishing some relevant comments as the comment length generally exceeded the article length:-):

The post, comments and my response to comments are as follows; I would love to rekindle debate on SES/Poverty and IQ again and am looking for more discussions. Also please check out this earlier post on the simillar poverty and IQ topic:

Original Post: Is low IQ the cause of income inequality and low life expectancy or is it the other way round?

As per this post from the BPS research digest, Kanazawa of LSE has made a controversial claim that economic inequality is not the cause of low life expectancy, but that both low life expectancy and economic inequality are a result of the low IQ of the poor people. The self-righteous reasoning is that people with low IQ are not able to adapt successfully to the stresses presented by modern civilization and hence perish. He thinks he has data on his side when he claims that IQ is eight times more strongly related to life expectancy, than is socioeconomic status. What he forgets to mention(or deliberately ignores) is growing evidence that IQ is very much determinant on the socioeconomic environment of its full flowering and a low IQ is because of two components- a low genetic IQ of parent plus a stunted growth of IQ/intelligence due to impoverished environment available because of the low socio-economic status of the parents.

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. Also discussed there, is the fact that the modern civilization as a whole has been successful in archiving the sate of socioeconomic prosperity that is sufficient for the full flowering of inherent genetic IQ of a child and as such the increments in IQ as we progress in years and achieve more and more prosperity (the Flynn effect) has started to become less prominent. This fact also explains the Kanazawa finding that in 'uncivilized' sub-Saharan countries the IQ is not related to life expectancy, but socio-economic status is. although, he puts his own spin on this data, a more parsimonious ( and accurate) reason for this is that in the sub-Saharan countries, even the well -of don't have the proper socio-economic conditions necessary for the full flowering of IQ and thus the IQ of both the well-off and poor parents in these countries is stunted equally. Thus, the well-off (which are not really that well-off in comparison to their counterparts in the western countries) are not able to be in any more advantageous position (with respect to IQ) than the poor in these countries. The resultant life expectancy effect is thus limited to that directly due to economic inequality and the IQ mediated effect of economic inequality is not visible.

What Kanazawa deduces from the same data and how he chooses to present these findings just goes on to show the self-righteous WASP attitude that many of the economists assume. After reading Freakonomics, and discovering how the authors twist facts and present statistics in a biased manner to push their idiosyncratic theories and agendas, it hardly seems surprising that another economist has resorted to similar dishonest tactics - shocking people by supposedly providing hard data to prove how conventional wisdom is wrong. Surprisingly, his own highlighting of sub-Saharan counties data that shows that life-expectancy is highly dependent on socio-economic conditions in these countries is highly suggestive of the fact that in cultures where the effects og economic inequality are not mediated via the IQ effects, economic inequality is the strongest predictor of low life expectancy.

Instead of just blaming the people for their genes/ stupidity, it would be better to address the reasons that lead to low IQs and when they are tackled, directly address the social inequality problem , as in the author's own findings, when IQ is not to blame for the low life expectancy, the blame falls squarely on economic inequality (as in the sub-Saharan countries data) .

7 comments:

Asterion said...

First of all, I beg you pardon for my limited english.
I find quite interesting your findings. But there could be an issue which limits the reasoning: how the IQ is meassured? or what does it really meassures? Does it really defines how smart or clever a person is?
I think there must be a lot of denounces about it. So, I think it's important to recognize the limits of this aproach based on IQ meassurment limitants. Of course, there could be a reference in your and Kanazawa's articles (I have not seen none of them).
All of this is beacuse I have met childs quite smarts living in the poorest zones of my city (Bogotá,
Colombia), I would say all of them seems to be quite smart, at least form my point if view. They are all really quick undertanding abstract problems and linking things. I think they have a strong capability to analize any situation. So, if you are able to meassure their IQ using problems wich need, for instance, to apply Phitagora's theorem, surelly they will be in trouble. So I think education could explain better economic inequalities and, thus, low life expentacy.
I never have explored this issue, so I would thank you refering me to some relevant literature related. Even telling me if I am quite wrong or not.

Always learning...


Sandy G said...

Hi Julian,

I appreciate your thoughtful comments. It is true that intelligence consists of a number of factors (as large as 8-10 broad factors), and is also differentiated as crystallized(Gc) and fluid (Gf); but for most analysis a concept of a general underlying common factor , spearman's g, is taken as reflective of intelligence and measured by the IQ scores.

In this sense, IQ/g does reflect how clever or smart a person is, but success/outcome in life is affected by other factors like motivation, effort, creativity etc.

I agree that many children in impoverished environments are quite smart, but you would be surprised to discover how providing an enriched environment to them, at their critical developmental periods,would have resulted in lasting intelligence gains. They are smart, but could have been smarter, if they had the right socioeconomic environment. On the other hand, an average child from well-to-do family would be able to maximally develop its inherent capabilities and thus stand a stronger chance than the poor smart child, whose capabilities haven't flowered fully.

Cultural bias in IQ measures have been found in the past, but the field has vastly improved now and these biases are fast disappearing leading to more accurate and valid cross-cultural comparisons.

The key to remember here is that poor socio-economic condition affects longevity via multiple pathways- one of them is direct by limiting access to good health care and nutrition, but there are also indirect effects mediated by , as you rightly pointed, education (poor people get less education and not vice versa) and also intelligence.

Garett Jones said...

Two words: East Asia.

If bad social and economic outcomes were the key driver of low IQ, then we'd expect East Asians to have had low IQ's back when they were poor--say, back in the 50's and 60's. Check out Table 4 of my paper (page 28) to see if that's the case...

http://mason.gmu.edu/~atabarro/iqprodprelim.pdf

Guess not. So, East Asians have been beating Causasians on IQ tests (on average) for as far back as we have data. You can get more historical data along these lines from Lynn's (2006) book, Race Difference in Intelligence.

And one can go even further back if you look at brain size, which correlates about 0.4 with IQ. Asian brains have been well-known to be larger than Caucasian brains for as long as folks have been measuring both of them. Hard to fit that in with WASP-driven science...

So simple reverse causality surely plays some role, but it can't explain East Asia.....

Sandy G said...

Hi Garret,

Thanks for dropping by and commenting.

I guess we agree on more things, than we disagree on. For example, in section IID of your paper, you concur with my explanation of Flynn effect that it is most probably due to the increase in living conditions and due to environmental factors enabling the full flowering of potential. Environment can and does have a strong disruptive negative effect, though it only has a limited positive enabling effect (no amount of good environment can give you an intelligence that is disproportionate to what your genes endow on you; but even minor lack of right environmental inputs or toxins, can lead to dramatic stunted achievement of that potential intelligence).

Also, it is heartening to note, that early on in your paper you take the position that your paper will not settle genetic vs environmental debate on IQ, but would only provide evidence that national IQ is a good indicator of ntaional productivity.

I have no issue with the same and agree that if one disregards the process by which adult stable IQs are archived, then the stable adult IQ that has been archived would be a very good predictor of productivity and economic status (in a free market environment where other conditions re not adversely affecting success). There is no qualms with the causal relation between a better IQ leading to better SES, in a fair world.

What I do strongly disagree with is the assumption that low IQ is solely dependent on genetic factors. Bad socio-economic factors are the key drivers of low IQ- especially in situations where the socio-economic status is so low that it does'nt guarantee access to basic amenities of life like proper nutrition/ health care.

It is interesting to note that poor SES would cause stunted growth of IQ, and due to the causal relation between IQ and SES would lead to less productivity and lower income, thus maintaining or even aggravating the low SES. This is the downward vicious cycle from which it is very hard to emerge. This type of economy and culture would definitly have lower IQ than what could have been achieved in the right conditions. The sub-saharan countries that Kanazawa used in his study, match this pattern and some of the African countries National IQ (as per data appendix in your paper) viz. Kenya: 72, south afric: 72, ghana : 71 confirms to this pattern).

The opposite observation, that a spiraling economy should radically lead to high IQs is not reasonable, as the circle is vicious only in the downward direction. Monumental leaps in SES would not lead to dramatic effects in IQ, if the earlier SES levels were just sufficient to ensure that no negative effects of environment come into play. The Flynn effect is a tribute to the fact that high jumps in SES (above the base level) only lead to small incremental changes in IQ.

Another thing to keep in mind is that when the SES to low IQ causal link is suggested it is only for the achievement of the stable adult IQ and instrumental during the critical childhood developmental periods. Although, environmental toxins do have the capability to adversely affect IQ during adulthood, and there is emerging evidence for plasticity and neurogenesis in adulthood, a simpler and reasonably model is whereby adult IQ is stable and not much affected by SES changes (either up or down) once it has been stabilized. Thus, even if some positive effects of rising SES have to be observed, they would be observable only in children exposed to that SES and not in the IQ of the rest of the adult population, that has already acheived a stable IQ.

Thus, I do not agree with your explanation of the east Asian example. To me the data set appears to be very limited ( no IQ results before the 1950's; no data sets for the same country or population over time) and even if we assume that only after the 1980s the SES of these countries rose above the minimal needed SES, we still do not have the data for the IQ of children born under theses SES condition, to proclaim that ther eis no rise in IQ.

Further, it is quite plausible that productivity is dependent on many other factors than IQ, some of which are directly related to SES independent of IQ. Given a base level of SES, in which the East Asians had managed to develop their inherent genetic IQ to the fullest, the SES may still not be good enough to convert that IQ advantage to productivity. For example, a given household that has sufficient SES to provide good nutrition and health care, and thus ensure that its children archive their full IQ potentiality, may still not have enough resources to send them to a good school (or any school for that matter), may lack access to basic infrastructure support which handicaps the utilization of its intelligence and so on. Thus despite having the human capital, lack of the more prosaic monetary capital, may prevent them from archiving their full productivity. Thus, IQ may increase first to the maximal achievable level and only then SES increase dramatically.

It would be interesting to turn the East Asian example on its head and beg the question that if IQ is the definitive causal relation leading to SES , how do you explain the anomaly that despite high IQ's in 1950s (or for that matter Asian big brain since time immemorial) he East Asian countries did not have the corresponding productivity levels or SES. You might counter by saying that IQ -> SES causal link is mediated by factors like free markets, reforms etc to ensure that proper economic conditions are in place etc etc and only if these ideal market conditions are in place then only IQ predicts SES.

To that my simple counter-argument would be that SES -> IQ causal link also works but only in conditions when the SES is below the base level and that SES would not predict IQ absolutely. Given the same optimal SES in differnet countries, different cultures (which have different genetic pools) will have different IQ levels based on their inherent genetic capabilities.
As per this the IQ of east asians can be explained as either arising from the fact that they have already archived the SES required for full flowering; or that they still have to archive their highest IQ levels and their IQ levels are genetically vastly superior and may show more rise in future.

From Anecdotal evidence I can tell you that an average Indian has far more intelligence and creativity potential that the average IQ of 82 would suggest; most of the high SES families that have archived that high IQ migrate to US/ west and archive high SES there.
What brings down the national average is the sad fact that still a lot of Indians live below the poverty line - living in sub-optimal SES conditions that leads them to have low IQ' than what their genes or genetic makeup would suggest.

Looking forward to a fruitful discussion.
PS: Despite the tone of my original mail, I have high regards for economists in general and people like Amartya sen, Kahnman and Traversky in particular.

12:31 PM
Anonymous said...

Interesting blog entry. Has the author of it actually read the paper he is criticizing? I noticed that it costs $15 online. If not, is the author of the blog certain that the statistical methods employed by Kanazawa do not take his complaints into account implicitly? One hopes that the author is not criticizing a peer-reviewed scientific paper without having read it.

Sandy G said...

Dear Anonymous,

It would be better if, after having read the paper (otherwise by your own high standards you wouldn't have defended an article without having read it first), you would be kind enough to tell the readers of this blog how Kanazawa has taken the effects of low SES-low IQ developmentally mediated effect in consideration in his study.

You are correct in guessing that I haven't read the article (I believe in free access; so neither publish nor read material that is not freely available). I'll welcome if you or someone else could mail me the relevant portions or post them on this blog (under fair use).

As for invoking authority covertly by referring to peer-review in a prestigious journal, I would like to disclose that I haven't taken a single course or class in psychology- either in school or college- so if authority is the determinant: you can stick to reading articles in scholarly journals by those who have doctoral degrees. Blogs are not for you. Otherwise, if you believe more in open discussions and logical arguments, lets argue on facts and study method weaknesses etc and rely more on public-review to catch any discrepancies.

What I could gather from the abstract was that "The macro-level analyses show that income inequality and economic development have no effect on life expectancy at birth, infant mortality and age-specific mortality net of average intelligence quotient (IQ) in 126 countries". I take this to mean, that SES has no effect on longevity , if the effects of IQ are factored out. the 'if' is very important. This a very perverse position. This assumes that longevity is due to IQ and if IQ mediated difference in longevity data is factored out, the effcets on longevity of SES are negligible. This depends on an a priori assumption that longevity is primarily explained by IQ; and only after taking its effects into consideration, we need to look for an effect of SES on longevity.

What prevents the other, more valid and real interpretation : that SES predicts longevity and that there is little effect of IQ on longevity net of SES. Here the variation in longevity is explained by SES and after taking that into account, it would be found that, independent of IQ as a consequent of SES, IQ by itself would have little effect on longevity. the same set of data leads to this interpretation, because IQ and SES are related to a great degree and both are also related to longevity. It is just a matter of interpretation, that which is the primary cause and which an effect.


To take an absurd position, I can argue that longevity predicts/ causes both SES and IQ and reverse the causal link altogether. One can take a theoretical stand, that if people live longer , we have more labor force, blah, blah,blah... so more prodcutivity so better SES; further longevity menas that there are more wise old folks in the society and as IQ is mostly deterinmed by social influences (I do not subscribe to this, I am just taking an absurd position to show the absurdity of Kanazawa position), hence longevity of the population(more wise men) causes high IQs.

Also, please note that the above conclusion is only for the macro data he has. That interpretation is independent of his micro level data that found that self-reported health was more predicted by IQ than by SES. That micro data has nothing to do with the interpretation of the macro data. Again I don't know where he got the micro data, but I'm sure that would be a developed world population sample.
I am somewhat familiar with the macro data on which he is basing such claims, and there I do not see any reason to prefer his interpretation over other more realistic interpretations.

In the future, lets discuss merits of arguments, and not resort to ad hominem attacks over whether someone is qualified to make an argument or not. (in my opinion, by reading an abstract too, one can form a reasonable idea of what the arguments and methodologies employed are, and is thus eligible to comment)

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After Social Maturity, Emotional Maturity or EI/ EQ

My last two posts have dealt with the Social Maturity theory of the developmental psychologist Robert Kegan. This post is about emotional maturity as reflected in Emotional quotient (EQ) / Emotional Intelligence (EI).

I presume that everybody is familiar with the term Emotional Intelligence, thanks to Daniel Goleman. It can be defined as:

Emotional Intelligence (EI), often measured as an Emotional Intelligence Quotient (EQ), describes an ability, capacity, skill or (in the case of the trait EI model) a self-perceived ability, to identify, assess, and manage the emotions of one's self, of others, and of groups.

As per Goleman, a person has many emotional competencies, related and measured by the above EQ, and these fall in five broad domains.

The Five Components of Emotional Intelligence

Self-awareness. The ability to recognize and understand personal moods and emotions and drives, as well as their effect on others. Hallmarks* of self-awareness include self-confidence, realistic self-assessment, and a self-deprecating sense of humor. Self-awareness depend on one's ability to monitor one's own emotion state and to correctly identify and name one's emotions.

Self-regulation.The ability to control or redirect disruptive impulses and moods, and the propensity to suspend judgment and to think before acting. Hallmarks include trustworthiness and integrity; comfort with ambiguity; and openness to change.

Motivation. A passion to work for reasons that go beyond money and status, which are external rewards. A propensity to pursue goals with energy and persistence. Hallmarks include a strong drive to achieve, optimism even in the face of failure, and organizational commitment.

Empathy. The ability to understand the emotional makeup of other people. A skill in treating people according to their emotional reactions. Hallmarks include expertise in building and retaining talent, cross-cultural sensitivity, and service to clients and customers. (In an educational context, empathy is often thought to include, or lead to, sympathy, which implies concern, or care or a wish to soften negative emotions or experiences in others.) See also Mirror Neurons.

It is important to note that empathy does not necessarily imply compassion. Empathy can be 'used' for compassionate or cruel behavior. Serial killers who marry and kill many partners in a row tend to have great emphatic skills!

Social skills. Proficiency in managing relationships and building networks, and an ability to find common ground and build rapport. Hallmarks of social skills include effectiveness in leading change, persuasiveness, and expertise building and leading teams.

These can easily be related to the Big five traits (although I am not aware of any research that does so). Below I try to correlate them to the Big five. Some of the material is taken from this source.

I) SELF-AWARENESS:

• Emotional Awareness:recognizing one's emotions and their effect
• Accurate Self-assessment: knowing one's strengths and limits
• Self-confidence: A strong sense of one's self-worth and capabilities

One can easily relate this to Neuroticism as I believe that N underlies the awareness of emotions for the first time in the child.

II) SELF-REGULATION

• Self-control: Keeping disruptive emotions and impulses in check
• Trustworthiness: Maintaining standards of honesty and integrity
• Conscientiousness: Taking responsibility for personal performance
• Adaptability: Flexibility in handling change
• Innovation: Being comfortable with novel ideas, approaches and new information

Introduction of Conscientiousness as a sub-competency in this domain makes it easy to correlate this with Conscientiousness . Also note the emphasis on impulses.

III) MOTIVATION

• Achievement drive: Striving to improve or meet a standard of excellence
• Commitment: Aligning with the goals of the group or organization
• Initiative: Readiness to act on opportunities
• Optimism: Persistence in pursuing goals despite obstacles and setbacks

This can be related to Positive emotionality or Extarversion as the emphasis seems to be on developmental of positive emotions and general energy and motivation level.

IV) EMPATHY

• Understanding others: sensing others' feelings and perspectives, taking an active interest in their concerns
• Developing others: Sensing others development needs and bolstering their abilities
• Service orientation: Anticipating, recognizing, and meeting customers' needs
• Leveraging diversity: Cultivating opportunities through different kinds of people
• Political Awareness: Reading a group's emotional currents and power relationships

This also by being named Empathy , is clearly reflective of Agreeableness. The focus for the first time shifts from self to others.

V) SOCIAL SKILLS

• Influence: Wielding effective tactics for persuasion
• Communication: Listening openly and sending convincing messages
• Conflict management: Negotiating and resolving disagreements
• Leadership: Inspiring and guiding individuals and groups
• Change Catalyst: Initiating or managing change
• Building bonds: Nurturing instrumental relationships
• Collaboration and cooperation: Working with others toward shared goals
• Team capabilities: creating group synergy in pursuing collective goals

This can be stretched to correlate to Rebelliousness-conformity/ openness/ intellect. It reflects how one uses the acquired emotional knowledge about others emotional states to advantage.

Please note that while the first three domains refer to individual's self-reflective behavior, the last tow are focused on how individual relates with others. I believe it is possible to move a notch higher and add three more domains to this - one that relate to how groups themselves function effectively in emotional settings. Note that the definition of EI contains references to how groups behave wisely, but that is not captured in above analysis by Goleman, which is confined to individuals self-reflective or other-oriented behavior, but does not cover group dynamics.

Now, many people have dismissed Goleman as Pop science, So I would like to move beyond Goleman to other people working in the same field like Mayor and Salovey and Heins. Mayor and Salovey have defined EI as :

The Four branches of EI:

1. Perception Appraisal and Expression of Emotion
2. Emotional Facilitation of Thinking
3. Understanding and Analyzing Emotions; Employing Emotional Knowledge
4. Reflective Regulation of Emotions to Promote Emotional and Intellectual Growth

Perception, Appraisal and Expression of Emotion

• Ability to identify emotion in one's physical states, feelings, and thoughts.
• Ability to identify emotions in other people, designs, artwork, etc. through language, sound, appearance, and behavior.
• Ability to express emotions accurately, and to express needs related to those feelings.
• Ability to discriminate between accurate and inaccurate, or honest vs. dishonest expressions of feeling.

Emotional Facilitation of Thinking

• Emotions prioritize thinking by directing attention to important information.
• Emotions are sufficiently vivid and available that they can be generated as aids to judgment and memory concerning feelings.
• Emotional mood swings change the individual's perspective from optimistic to pessimistic, encouraging consideration of multiple points of view.
• Emotional states differentially encourage specific problem-solving approaches such as when happiness facilitates inductive reasoning and creativity.

Understanding and Analyzing Emotions; Employing Emotional Knowledge

• Ability to label emotions and recognize relations among the words and the emotions themselves, such as the relation between liking and loving.
• Ability to interpret the meanings that emotions convey regarding relationships, such as that sadness often accompanies a loss.
• Ability to understand complex feelings: simultaneous feelings of love and hate or blends such as awe as a combination of fear and surprise.
• Ability to recognize likely transitions among emotions, such as the transition from anger to satisfaction or from anger to shame.

Reflective Regulation of Emotion to Promote Emotional and Intellectual Growth

• Ability to stay open to feelings, both those that are pleasant and those that are unpleasant.
• Ability to reflectively engage or detach from an emotion depending upon its judged informativeness or utility.
• Ability to reflectively monitor emotions in relation to oneself and others, such as recognizing how clear, typical, influential or reasonable they are.
• Ability to manage emotion in oneself and others by moderating negative emotions and enhancing pleasant ones, without repressing or exaggerating information they may convey.

I would like to modify and extend the Mayor and Salovey breakup of EI into the following eight components. It is also my thesis that they occur in the following order:

1. Emotional self-Awareness: people can differ in how much aware are they of their own internal emotional states.
2. Emotional tone/ vivacity : people can differ in how much emotion they feel for the same external / internal triggers. some may have vivid emotions while some may have bland emotions.
3. Emotional understanding/analysis/ knowledge/ monitoring : people can differ in how they interpret ones emotional states- which states they deem as close, positive, negative etc and whether they identify the states correctly.
4. Emotional self-regulation: people can differ in their abilities to regulate their emotional states: some states may be more desirable and some need to be replaced with other depending on external exigences.
5. Emotional Maturity/development/ refinement: people may differ in the extent to which they let their lives be defined by a prominent emotional/ mood state. Some may devlope their primary emotion to be Joy while others may define them primarily by sad emotions.
6. Emotional others-awareness or empathy: while the discussion till now was focused on the individual's emotions, it now moves to others' emotions. People may differ in their ability to perceive and feel the correct emotional state of others
7. Emotional communication/ labeling/ expression: People may differ in their ability to communicate their emotions to others, to label them correctly in such verbal/ non-verbal communication.
8. Emotional Integrity/ holism : people may differ in their ability to feel contradictory emotions within themselves and integrate in an overarching integral framework. they may also differ in their ability to judge the honesty or trustworthiness of others' expressed/ subtle emotions.

To me this seems a promising framework using one which could investigate the EQ/ EI conundrum. However, the above is juts a hypothesis; I believe it is testable and generates many predictions that can, and should, be tested and the theory verified or rejected accordingly. I also belive that these competencies develop in stages and follow a distinct developmental pattern. this too can be verified or rejected.

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