How the brain works

Right-Brain/Left-Brain

Are you right-brained or left-brained?

One of the dumber questions around.

I think it’s safe to say that if you only had one hemisphere of your brain, you wouldn’t be functioning.

Of course, that’s not the point. But the real point is little more sensible. The whole idea of right brain vs left brain did come out of scientific research, but as is so often the case, the myth that developed is light years away from the considerably duller scientific truths that spawned it.

It is true that, for most of us, language is processed predominantly in the left hemisphere. But what is becoming increasingly more evident is that even the most specialized tasks activate areas across the brain.

In any case, I don’t think the real meaning behind this simplistic dichotomy of right-brain / left-brain has much to do with the physical nature of the brain. People hope by rooting the concept in something that is physically real, that they will thereby make the concept real. Well, I’m sorry, but the supposed scientific foundation for the concept doesn’t exist. However, what we can ask is, is the concept valid? Are some people logical, analytical, sequential thinkers? Are others holistic, intuitive, creative thinkers?

Yes, of course. This is news?

But I don’t like dichotomies. It should never be forgotten that people aren’t either/or. Attributes invariably belong on a continuum, and we are all capable of responding in ways that differ as a function of the task we are confronted with, and the context in which it appears (especially, for example, the way something is phrased). Rather than saying a person is an analytical thinker, we should say, does a person tend to approach most problems in an analytical manner? This is not simply a matter of semantics; there’s an important distinction here.

But there are other personal attributes of importance in learning and problem-solving. For example, working memory capacity, imagery ability, anxiety level, extraversion / introversion, self-esteem (in this case, meaning assessment of one’s own abilities), field-dependence / field-independence (field dependence represents the tendency to perceive and adhere to an existing, externally imposed framework while field independence represents the tendency to restructure perceived information into a different framework). Which attributes are most important? Is this in fact a meaningful question?

The fact is, different personal attributes interact with different task and situational variables in different ways. While it’s probably always good to have a high working memory capacity (the capacity to hold more items in conscious memory at one time), it’s more important in some situations than others. To be a “high-imagery” person may sound a good thing, but if you realize it’s measured on a verbal-imagery continuum, you can see that it’s a trade-off. Personally, I’ve never found being high-verbal, low-imagery a drawback!

The point is, of course, that different styles lend themselves to different tasks (by which I mean, different ways of doing different tasks). It’s not so much what you are, as that you recognize what your strengths and weaknesses are, and realize, too, the pluses and minuses of those abilities / conditions.

For example, a study of 13-year olds investigated the question of interaction between working memory capacity and cognitive style, measured on two dimensions, Wholist-Analytic, and Verbaliser - Imager. They found working memory capacity made a marked difference for Analytics but had little effect for Wholists, and similarly, Verbalisers were affected but not Imagers [1].

Thus, if your working memory capacity is low, in demanding tasks you might find yourself better to approach it holistically – looking at the big picture, rather than focusing on the details.

Once you recognize your strengths and weaknesses, you can consciously apply strategies that work for you, and approach tasks in ways that are better for you. You can also work on your weaknesses. An interesting recent study that I believe has wider applicability than the elderly population who participated in it, found elderly people who draw on both sides of the brain seem to do better at some mental tasks than those who use just one side [2].

Web resources

Cognitive style

There’s an article about cognitive style from a business perspective:
http://www.elsinnet.org.uk/abstracts/aom/sad-aom.htm

If you’re really interested in cognitive style, the Wholist-Analytic, Verbal-Imager inventory was constructed by R.J. Riding, and he’s written a, fairly scholarly, book, entitled “Cognitive Styles and Learning Strategies: Understanding Style Differences in Learning and Behaviour”
http://tinyurl.com/6gpu8

Left-brain / Right-brain

You can also read an essay by William H. Calvin, an affiliate professor at the University of Washington School of Medicine in Seattle, Washington: Left Brain, Right Brain: Science or the New Phrenology?
http://williamcalvin.com/bk2/bk2ch10.htm

And an article first published in the New Scientist on 'Right Brain' or 'Left Brain' - Myth Or Reality? by John McCrone.
http://www.rense.com/general2/rb.htm

This article originally appeared in the January 2005 newsletter.

References: 
  1. Riding. R.J., Grimley, M., Dahraei, H. & Banner, G. 2003. Cognitive style, working memory and learning behaviour and attainment in school subjects. British Journal of Educational Psychology, 73 (2), 149–169.
  2. Cabeza, R., Anderson, N.D., Locantore, J.K. & McIntosh, A.R. 2002. Aging Gracefully: Compensatory Brain Activity in High-Performing Older Adults. NeuroImage, 17(3), 1394-1402.

The role of consolidation in memory

"Consolidation" is a term that is bandied about a lot in recent memory research. Here's my take on what it means.

Becoming a memory

Initially, information is thought to be encoded as patterns of neural activity — cells "talking" to each other. Later, the information is coded in more persistent molecular or structural formats (e.g., the formation of new synapses). It has been assumed that once this occurs, the memory is "fixed" — a permanent, unchanging, representation.

With new techniques, it has indeed become possible to observe these changes (you can see videos here). Researchers found that the changes to a cell that occurred in response to an initial stimulation lasted some three to five minutes and disappeared within five to 10 minutes. If the cell was stimulated four times over the course of an hour, however, the synapse would actually split and new synapses would form, producing a (presumably) permanent change.

Memory consolidation theory

The hypothesis that new memories consolidate slowly over time was proposed 100 years ago, and continues to guide memory research. In modern consolidation theory, it is assumed that new memories are initially 'labile' and sensitive to disruption before undergoing a series of processes (e.g., glutamate release, protein synthesis, neural growth and rearrangement) that render the memory representations progressively more stable. It is these processes that are generally referred to as “consolidation”.

Recently, however, the idea has been gaining support that stable representations can revert to a labile state on reactivation.

Memory as reconstruction

In a way, this is not surprising. We already have ample evidence that retrieval is a dynamic process during which new information merges with and modifies the existing representation — memory is now seen as reconstructive, rather than a simple replaying of stored information

Reconsolidation of memories

Researchers who have found evidence that supposedly stable representations have become labile again after reactivation, have called the process “reconsolidation”, and suggest that consolidation, rather than being a one-time event, occurs repeatedly every time the representation is activated.

This raises the question: does reconsolidation involve replacing the previously stable representation, or the establishment of a new representation, that coexists with the old?

Whether reconsolidation is the creating of a new representation, or the modifying of an old, is this something other than the reconstruction of memories as they are retrieved? In other words, is this recent research telling us something about consolidation (part of the encoding process), or something about reconstruction (part of the retrieval process)?

Hippocampus involved in memory consolidation

The principal player in memory consolidation research, in terms of brain regions, is the hippocampus. The hippocampus is involved in the recognition of place and the consolidation of contextual memories, and is part of a region called the medial temporal lobe (MTL), that also includes the perirhinal, parahippocampal,and entorhinal cortices. Lesions in the medial temporal lobe typically produce amnesia characterized by the disproportionate loss of recently acquired memories. This has been interpreted as evidence for a memory consolidation process.

Some research suggests that the hippocampus may participate only in consolidation processes lasting a few years. The entorhinal cortex, on the other hand, gives evidence of temporally graded changes extending up to 20 years, suggesting that it is this region that participates in memory consolidation over decades. The entorhinal cortex is damaged in the early stages of Alzheimer’s disease.

There is, however, some evidence that the hippocampus can be involved in older memories — perhaps when they are particularly vivid.

A recent idea that has been floated suggests that the entorhinal cortex, through which all information passes on its way to the hippocampus, handles “incremental learning” — learning that requires repeated experiences. “Episodic learning” — memories that are stored after only one occurrence — might be mainly stored in the hippocampus.

This may help explain the persistence of some vivid memories in the hippocampus. Memories of emotionally arousing events tend to be more vivid and to persist longer than do memories of neutral or trivial events, and are, moreover, more likely to require only a single experience.

Whether or not the hippocampus may retain some older memories, the evidence that some memories might be held in the hippocampus for several years, only to move on, as it were, to another region, is another challenge to a simple consolidation theory.

Memory more complex than we thought

So where does all this leave us? What is consolidation? Do memories reach a fixed state?

My own feeling is that, no, memories don't reach this fabled "cast in stone" state. Memories are subject to change every time they are activated (such activation doesn't have to bring the memory to your conscious awareness). But consolidation traditionally (and logically) refers to encoding processes. It is reasonable, and useful, to distinguish between:

  • the initial encoding, the "working memory" state, when new information is held precariously in shifting patterns of neural activity,
  • the later encoding processes, when the information is consolidated into a more permanent form with the growth of new connections between nerve cells,
  • the (possibly much) later retrieval processes, when the information is retrieved in, most probably, a new context, and is activated anew

I think that "reconsolidation" is a retrieval process rather than part of the encoding processes, but of course, if you admit retrieval as involving a return to the active state and a modification of the original representation in line with new associations, then the differences between retrieval and encoding become less evident.

When you add to this the possibility that memories might "move" from one area of the brain to another after a certain period of time (although it is likely that the triggering factor is not time per se), then you cast into disarray the whole concept of memories becoming stable.

Perhaps our best approach is to see memory as a series of processes, and consolidation as an agreed-upon (and possibly arbitrary) subset of those processes.

References: 
  • Frankland, P.W., O'Brien, C., Ohno, M., Kirkwood, A. & Silva, A.J. 2001. -CaMKII-dependent plasticity in the cortex is required for permanent memory. Nature, 411, 309-313.
  • Gluck, M.A., Meeter, M. & Myers, C.E. 2003. Computational models of the hippocampal region: linking incremental learning and episodic memory. Trends in Cognitive Sciences, 7 (6), 269-276.
  • Haist, F., Gore, J.B. & Mao, H. 2001. Consolidation of human memory over decades revealed by functional magnetic resonance imaging. Nature neuroscience, 4 (11), 1139-1145.
  • Kang, H., Sun, L.D., Atkins, C.M., Soderling, T.R., Wilson, M.A. & Tonegawa, S. (2001). An Important Role of Neural Activity-Dependent CaMKIV Signaling in the Consolidation of Long-Term Memory. Cell, 106, 771-783.
  • Lopez, J.C. 2000. Shaky memories in indelible ink. Nature Reviews Neuroscience, 1, 6-7.
  • Miller, R.R. & Matzel, L.D. 2000. Memory involves far more than 'consolidation'. Nature Reviews Neuroscience, 1, 214-216.
  • Slotnick, S.D., Moo, L.R., Kraut, M.A., Lesser, R.P. & Hart, J. Jr. 2002. Interactions between thalamic and cortical rhythms during semantic memory recall in human. Proc. Natl. Acad. Sci. U.S.A., 99, 6440-6443.
  • Spinney, L. 2002. Memory debate focuses on hippocampal role. BioMedNet News, 18 March 2002.
  • Wirth, S., Yanike, M., Frank, L.M., Smith, A.C., Brown, E.N. & Suzuki, W.A. 2003. Single Neurons in the Monkey Hippocampus and Learning of New Associations. Science, 300, 1578-1581.
  • Zeineh, M.M., Engel, S.A., Thompson, P.M. & Bookheimer, S.Y. 2003. Dynamics of the Hippocampus During Encoding and Retrieval of Face-Name Pairs, Science, 299, 577-580.

Gray matter

Brain tissue is made up of cell bodies ("gray matter") and the filaments that extend from the cell bodies ("white matter").

The density of cells (volume of gray matter) in a particular region of the brain appears to correlate positively with various abilities and skills.

The density of cells is determined by both genes and environmental factors, such as experience.

The speed with which we can process information is governed by the white matter.

Brain tissue is divided into two types: gray matter and white matter. These names derive very simply from their appearance to the naked eye. Gray matter is made up of the cell bodies of nerve cells. White matter is made up of the long filaments that extend from the cell bodies - the "telephone wires" of the neuronal network, transmitting the electrical signals that carry the messages between neurons.

The volume of gray matter tissue - a measure you will see cited in various reports - is a measure of the density of brain cells in a particular region.

Recently, the most comprehensive structural brain-scan study of intelligence to date has supported an association between general intelligence and the volume of gray matter tissue in specific regions of the brain (you can see a picture of these areas here). These structures are the same ones implicated in memory, attention and language.

Previous research has shown the regional distribution of gray matter in humans is highly heritable. But it also clearly has a strong environmental influence. Recent studies have found:

  • an increased volume of gray matter in Broca's area of professional musicians, apparently reflecting, at least in part, the number of years devoted to musical training
  • an increased volume of gray matter in the anterior hippocampus of experienced London taxi drivers (a brain region involved in spatial navigation), with volume correlated with length of taxi-driving experience
  • an increase in the development of new brain cells in older adults who underwent an aerobic training program compared with those who did not

The brain-scan study also found age differences: in middle age, more of the frontal and parietal lobes were related to IQ; less frontal and more temporal areas were related to IQ in the younger adults.

Age differences have already been found to exist in gray matter volume and distribution.

Mapping of the progressive maturation of the human brain in childhood and adolescence has found an initial overproduction of synapses in the gray matter after birth, which is followed, for the most part just before puberty, with their systematic pruning. This process occurs in different regions at different times, with gray matter loss beginning first in the motor and sensory parts of the brain, and then slowly spreading downwards and forwards, to areas involved in spatial orientation, speech and language development, and attention (upper and lower parietal lobes), then to the areas involved in executive functioning, attention or motor coordination (frontal lobes), and finally to the areas that integrate these functions (temporal lobe). The sequence appears to agree with regionally relevant milestones in cognitive development.

Various learning and memory problems have been associated with decreased gray matter in particular regions of the brain:

  • children with selective problems in short term phonological memory and others diagnosed with specific language impairment had less gray matter in both sides of the cerebellum compared to controls
  • adolescents had less gray matter in an area in the left parietal lobe if they had a deficit in calculation ability, compared to those who had no such deficit

Gray matter is not the sole arbiter of ability and knowledge, of course. The number of neurons is clearly important, but so is the connectivity of the neuronal network. Interestingly, although gray matter declines steadily from adolescence, white matter keeps growing until our late forties. This is consistent with a large-scale study of mental abilities in adults, that found that mental faculties were unchanged until the mid-40s, when a marked decline began and continued at a constant rate. Accuracy did not seem to be affected, only speed. White matter governs the speed with which signals travel in the brain.

Adult Neurogenesis

Neurogenesis occurs in two main areas in the adult brain: the hippocampus and the olfactory bulb.

The transformation of a new cell into a neuron appears to crucially involve a specific protein called WnT3, that's released by support cells called astrocytes.

A chemical called BDNF also appears critical for the transformation into neurons.

Most recently, T-cells have also been revealed as important for neurogenesis to occur.

The extent and speed of neurogenesis can also be enhanced by various chemicals. Nerve growth factors appear to enhance the proliferation of precursor cells (cells with the potential to become neurons), and the prion protein that, damaged, causes mad cow disease, appears in its normal state to speed the rate of neurogenesis.

The integration of the new neuron into existing networks appears to need a brain chemical called GABA.

Indications are that moderate alcohol may enhance neurogenesis, but excess alcohol certainly has a negative effect. Most illegal drugs have a negative effect, but there is some suggestion cannabinoids may enhance neurogenesis. Antidepressants also seem to have a positive effect, while stress and anxiety reduce neurogenesis. However, positive social experiences, such as being of high status, can increase neurogenesis. Physical activity, mental stimulation, and learning, have all been shown to have a positive effect on neurogenesis.

What is neurogenesis?

Neurogenesis — the creation of new brain cells — occurs of course at a great rate in the very young. For a long time, it was not thought to occur in adult brains — once you were grown, it was thought, all you could do was watch your brain cells die!

Adult neurogenesis (the creation of new brain cells in adult brains) was first discovered in 1965, but only recently has it been accepted as a general phenomenon that occurs in many species, including humans (1998).

Where does adult neurogenesis occur?

It's now widely accepted that adult neurogenesis occurs in the subgranular zone of the dentate gyrus within the hippocampus and the subventricular zone (SVZ) lining the walls of the lateral ventricles within the forebrain. It occurs, indeed, at a quite frantic rate — some 9000 new cells are born in the dentate gyrus every day in young adult rat brains — but under normal circumstances, at least half of those new cells will die within one or two months.

The neurons produced in the SVZ are sent to the olfactory bulb, while those produced in the dentate gyrus are intended for the hippocampus.

Adult neurogenesis might occur in other regions, but this is not yet well-established. However, recent research has found that small, non-pyramidal, inhibitory interneurons are being created in the cortex and striatum. These new interneurons appear to arise from a previously unknown class of local precursor cells. These interneurons make and secrete GABA (see below for why GABA is important), and are thought to play a role in regulating larger types of neurons that make long-distance connections between brain regions.

How does neurogenesis occur?

New neurons are spawned from the division of neural precursor cells — cells that have the potential to become neurons or support cells. How do they decide whether to remain a stem cell, turn into a neuron, or a support cell (an astrocyte or oligodendrocyte)?

Observation that neuroblasts traveled to the olfactory bulb from the SVZ through tubes formed by astrocytes has led to an interest in the role of those support cells. It's now been found that astrocytes encourage both precursor cell proliferation and their maturation into neurons — precursor cells grown on glia divide about twice as fast as they do when grown on fibroblasts, and are about six times more likely to become neurons.

Adult astrocytes are only about half as effective as embryonic astrocytes in promoting neurogenesis.

It’s been suggested that the role of astrocytes may help explain why neurogenesis only occurs in certain parts of the brain — it may be that there’s something missing from the glial cells in those regions.

The latest research suggests that the astrocytes influence the decision through a protein that it secretes called Wnt3. When Wnt3 proteins were blocked in the brains of adult mice, neurogenesis decreased dramatically; when additional Wnt3 was introduced, neurogenesis increased.

How are these new neurons then integrated into existing networks? Mouse experiments have found that the brain chemical called GABA is critical. Normally, GABA inhibits neuronal signals, but it turns out that with new neurons, GABA has a different effect: it excites them, and prepares them for integration into the adult brain. Thus a constant flood of GABA is needed initially; the flood then shifts to a more targeted pulse that gives the new neuron specific connections that communicate using GABA; finally, the neuron receives connections that communicate via another chemical, glutamate. The neuron is now ready to function as an adult neuron, and will respond to glutamate and GABA as it should.

The creation and development of new neurons in the adult brain is very much a "hot" topic right now — it's still very much a work-in-progress. However, it is clear that other brain chemicals are also involved. An important one is BDNF (brain-derived neurotrophic factor), which seems to be needed during the proliferation of hippocampal precursor cells to trigger their transformation into neurons.

Other growth factors have been found to stimulate proliferation of hippocampal progenitor cells: FGF-2 (fibroblast growth factor-2) and EGF (epidermal growth factor).

Recently it has been discovered that the normal form of the prion protein which, when malformed, causes mad cow disease, is also involved in neurogenesis. These proteins, in their normal form, are found throughout our bodies, and particularly in our brains. Now it seems that the more of these prion proteins that are available, the faster neural precursor cells turn into neurons.

The immune system's T cells (which recognize brain proteins) are also critically involved in enabling neurogenesis to occur. Among mice given environmental enrichment, only those with healthy T-cells had their production of new neurons boosted.

Factors that influence neurogenesis

A number of factors have been found to affect the creation and survival of new neurons. For a start, damage to the brain (from a variety of causes) can provoke neurogenesis.

Moderate alcohol consumption over a relatively long period of time can also enhance the formation of new nerve cells in the adult brain (this may be related to alcohol's enhancement of GABA's function). Excess alcohol, however, has a detrimental effect on the formation of new neurons in the adult hippocampus. But although neurogenesis is inhibited during alcohol dependency, it does recover. A pronounced increase in new neuron formation in the hippocampus was found within four-to-five weeks of abstinence. This included a twofold burst in brain cell proliferation at day seven of abstinence.

Most drugs of abuse such as nicotine, heroine, and cocaine suppress neurogenesis, but a new study suggests that cannabinoids also promote neurogenesis. The study involved a synthetic cannabinoid, which increased the proliferation of progenitor cells in the hippocampal dentate gyrus of mice, in a similar manner as some antidepressants have been shown to do. The cannabinoid also produced similar antidepressant effects. Further research is needed to confirm this early finding.

If antidepressants promote neurogenesis, it won't be surprising to find that chronic stress, anxiety and depression are associated with losing hippocampal neurons. A rat study has also found that stress in early life can permanently impair neurogenesis in the hippocampus.

Showing the other side of this picture, perhaps, an intriguing rat study found that status affected neurogenesis in the hippocampus, with high-status animals having around 30% more neurons in their hippocampus after being placed in a naturalistic setting with other rats.

Also, a study into the brains of songbirds found that birds living in large groups have more new neurons and probably a better memory than those living alone.

Both physical activity and environmental enrichment (“mental stimulation”) have been shown to affect both how many cells are born in the dentate gyrus of rats and how many survive. Learning that uses the hippocampus has also been shown to have a positive effect, although results here have been inconsistent.

Inconsistent results from studies looking at neurogenesis are, it is suggested, largely because of a confusion between proliferation and survival. Neurogenesis is measured in terms of these two factors, which researchers often fail to distinguish between: the generation of new brain cells, and their survival. But these are separate factors, that are independently affected by various factors.

The inconsistency found in the effects of learning may also be partly explained by the complex nature of the effects. For example, during the later phase of learning, when performance is starting to plateau, neurons created during the late phase were more likely to survive, but neurons created during the early phase of more rapid learning disappeared. It’s speculated that that this may be a “pruning” process by which cells that haven’t made synaptic connections are removed from the network.

And finally, rodent studies suggest a calorie-restricted diet may also be of benefit.

It's not all about growing new neurons

A few years ago, we were surprised by news that new neurons could be created in the adult brain. However, it’s remained a tenet that adult neurons don’t grow — this because researchers have found no sign that any structural remodelling takes place in an adult brain. Now a mouse study using new techniques has revealed that dramatic restructuring occurs in the less-known, less-accessible inhibitory interneurons. Dendrites (the branched projections of a nerve cell that conducts electrical stimulation to the cell body) show sometimes dramatic growth, and this growth is tied to use, supporting the idea that the more we use our minds, the better they will be.

References: 
  1. Aberg, E., Hofstetter, C., Olson, L. & Brené, S. 2005. Moderate ethanol consumption increases hippocampal cell proliferation and neurogenesis in the adult mouse. International Journal of Neuropsychopharmacology, 8(4), 557-567.
  2. Bull, N.D. & Bartlett, P.F. 2005. The Adult Mouse Hippocampal Progenitor Is Neurogenic But Not a Stem Cell. Journal of Neuroscience, 25, 10815-10821.
  3. Dayer, A.G., Cleaver, K.M., Abouantoun, T. & Cameron, H.A. 2005. New GABAergic interneurons in the adult neocortex and striatum are generated from different precursors. Journal of Cell Biology, 168, 415-427.
  4. Döbrössy, M.D., Drapeau, E., Aurousseau, C., Le Moal, M., Piazza, P.V. & Abrous, D.N. 2003. Differential effects of learning on neurogenesis: learning increases or decreases the number of newly born cells depending on their birth date. Molecular Psychiatry, 8, 974-982.
  5. Ge, S., Goh, E.L.K., Sailor, K.A., Kitabatake, Y., Ming, G-L. & Song, H. 2005. GABA regulates synaptic integration of newly generated neurons in the adult brain. Nature advance online publication; published online 11 December 2005
  6. Hairston, I.S., Little, M.T.M., Scanlon, M.D., Barakat, M.T., Palmer, T.D., Sapolsky, R.M. & Heller, H.C. 2005. Sleep Restriction Suppresses Neurogenesis Induced by Hippocampus-Dependent Learning. Journal of Neurophysiology, 94 (6), 4224-4233.
  7. Jiang, W. et al. 2005. Cannabinoids promote embryonic and adult hippocampus neurogenesis and produce anxiolytic- and antidepressant-like effects. Journal of Clinical Investigation, 115, 3104-3116.
  8. Johnson, R.A., Rhodes, J.S., Jeffrey, S.L., Garland, T. Jr., & Mitchell, G.S. 2003. Hippocampal brain-derived neurotrophic factor but not neurotrophin-3 increases more in mice selected for increased voluntary wheel running. Neuroscience, 121(1), 1-7.
  9. Karten, Y.J.G., Olariu, A. & Cameron, H.A. 2005. Stress in early life inhibits neurogenesis in adulthood. Trends in Neurosciences, 28 (4), 171-172.
  10. Kozorovitskiy, Y. & Gould, E.J. 2004. Dominance Hierarchy Influences Adult Neurogenesis in the Dentate Gyrus. The Journal of Neuroscience,24(30), 6755-6759.
  11. Lee, J., Duan, W., Long, J.M., Ingram, D.K. & Mattson, M.P. 2000. Dietary restriction increases the number of newly generated neural cells, and induces BDNF expression, in the dentate gyrus of rats. Journal of Molecular Neuroscience, 15(2), 99-108.
  12. Lie, D-C., Colamarino, S.A., Song, H-J., Désiré, L., Mira, H., Consiglio, A., Lein, E.S., Jessberger, S., Lansford, H., Dearie, A.R. & Gage, F.H. 2005. Wnt signalling regulates adult hippocampal neurogenesis. Nature, 437, 1370-1375.
  13. Lipkind, D., Nottebohm, F., Rado, R. & Barnea, A.2002. Social change affects the survival of new neurons in the forebrain of adult songbirds. Behavioural Brain Research, 133 (1), 31-43.
  14. Lombardino, A.J., Li, X-C., Hertel, M & Nottebohm, F. 2005. Replaceable neurons and neurodegenerative disease share depressed UCHL1 levels. PNAS, 102(22), 8036-8041.
  15. Nixon, K. & Crews, F.T. 2004. Temporally Specific Burst in Cell Proliferation Increases Hippocampal Neurogenesis in Protracted Abstinence from Alcohol. Journal of Neuroscience, 24, 9714-9722.
  16. Prickaerts, J., Koopmans, G., Blokland, A. & Scheepens, A. 2004. Learning and adult neurogenesis: Survival with or without proliferation? Neurobiology of Learning and Memory, 81, 1-11.
  17. Santarelli, L. et al. 2003. Requirement of Hippocampal Neurogenesis for the Behavioral Effects of Antidepressants. Science, 301(5634), 805-809.
  18. Song, H., Stevens, C.F. & Gage, F.H. 2002. Astroglia induce neurogenesis from adult neural stem cells. Nature, 417, 39-44.
  19. Steele, A.D., Emsley, J.G., Özdinler, P.H., Lindquist, S. & Macklis, J.D. 2006. Prion protein (PrPc) positively regulates neural precursor proliferation during developmental and adult mammalian neurogenesis. PNAS, 103, 3416-3421.
  20. Yoshimura, S. et al. 2003. FGF-2 regulates neurogenesis and degeneration in the dentate gyrus after traumatic brain injury in mice. Journal of Clinical Investigation, 112, 1202-1210.
  21. Ziv, Y., Ron, N., Butovsky, O., Landa, G., Sudai, E., Greenberg, N., Cohen, H., Kipnis, J. & Schwartz, M. 2006. Immune cells contribute to the maintenance of neurogenesis and spatial learning abilities in adulthood. Nature Neuroscience, 9, 268-275.

The role of sleep in memory

Why do we need sleep?

A lot of theories have been thrown up over the years as to what we need sleep for (to keep us wandering out of our caves and being eaten by sabertooth tigers, is one of the more entertaining possibilities), but noone has yet been able to point to a specific function of the sleep state that would explain why we have it and why we need so much of it.

One of the things we do know is that young birds and mammals need as much as three times the amount of sleep as adult birds and mammals. It has been suspected that neuronal connections are remodeled during sleep, and this has recently been supported in a study using cats (Cats who were allowed to sleep for six hours after their vision was blocked in one eye for six hours, developed twice as many new or modified brain connections as those cats who were kept awake in a dark room for the six hours after the period of visual deprivation).

Certainly a number of studies have shown that animals and humans deprived of sleep do not perform well on memory tasks, and research has suggested that there may be a relationship between excessive daytime sleepiness (EDS) and cognitive deficits. A recent study has found that for seniors at least, EDS is an important risk factor for cognitive impairment.

The effect of sleep on memory and learning

Some memory tasks are more affected be sleep deprivation than others. A recent study, for example, found that recognition memory for faces was unaffected by people being deprived of sleep for 35 hours. However, while the sleep-deprived people remembered that the faces were familiar, they did have much more difficulty remembering in which of two sets of photos the faces had appeared. In other words, their memory for the context of the faces was significantly worse. (The selective effect of sleep on contextual memory is also supported in a recent mouse study – see below)

While large doses of caffeine reduced the feelings of sleepiness and improved the ability of the sleep-deprived subjects to remember which set the face had appeared in, the level of recall was still significantly below the level of the non-sleep-deprived subjects. (For you coffee addicts, no, the caffeine didn’t help the people who were not sleep-deprived).

Interestingly, sleep deprivation increased the subjects’ belief that they were right, especially when they were wrong. In this case, whether or not they had had caffeine made no difference.

In another series of experiments, the brains of sleep-deprived and rested participants were scanned while the participants performed complex cognitive tasks. In the first experiment, the task was an arithmetic task involving working memory. Sleep-deprived participants performed worse on this task, and the fMRI scan confirmed less activity in the prefrontal cortex for these participants. In the second experiment, the task involved verbal learning. Again, those sleep-deprived performed worse, but in this case, only a little, and the prefrontal areas of the brain remained active, while parietal lobe activity actually increased. However, activity in the left temporal lobe (a language-processing area) decreased. In the third study, participants were given a "divided-attention" task, in which they completed both an arithmetic and a verbal-learning task. Again, sleep-deprived participants showed poorer performance, depressed brain activation in the left temporal region and heightened activation in prefrontal and parietal regions. There was also increased activation in areas of the brain that are involved in sustained attention and error monitoring.

These results indicate that sleep deprivation affects different cognitive tasks in different ways, and also that parts of the brain are able to at least partially compensate for the effects of sleep deprivation.

Sleep deprivation mimics aging?

A report in the medical journal The Lancet, said that cutting back from the standard eight down to four hours of sleep each night produced striking changes in glucose tolerance and endocrine function that mimicked many of the hallmarks of aging. Dr Eve Van Cauter, professor of medicine at the University of Chicago and director of the study, said, "We suspect that chronic sleep loss may not only hasten the onset but could also increase the severity of age-related ailments such as diabetes, hypertension, obesity and memory loss."

Should we draw any conclusion from the finding that sleep deprivation increased the subjects’ belief that they were right, especially when they were wrong, and the finding that chronic sleep deprivation may mimic the hallmarks of aging? No, let us merely note that many people become more certain of their own opinions as they mature into wisdom.

Is sleep necessary to consolidate memories?

This is the big question, still being argued by the researchers. The weight of the evidence, however, seems to be coming down on the answer, yes, sleep is necessary to consolidate memories — although maybe for only some types of memory. Most of the research favoring sleep’s importance in consolidation has used procedural / skill memory — sequences of actions.

From this research, it does seem that it is the act of sleep itself, not simply the passage of time, that is critical to convert new memories into long-term memory codes.

Some of the debate in this area concerns the stage of sleep that may be necessary. The contenders are the deep "slow wave" sleep that occurs in the first half of the night, and "REM" (rapid eye movement) sleep (that occurs while you are dreaming). Experiments that have found sleep necessary for consolidation tend to support slow-wave sleep as the important part of the cycle, however REM sleep may be important for other types of memory processing.

Sleep studies cast light on the memory cycle

Two new studies provide support both for the theory that sleep is important for the consolidation of procedural memories, and the new theory of what I have termed the "memory life-cycle".

In the first study, 100 young adults (18 to 27) learned several different finger-tapping sequences. It was found that participants remembered the sequence even if they learned a second sequence 6 hours later, and performance on both sequences improved slightly after a night's sleep. However, if, on day 2, people who had learned one sequence were briefly retested on it and then trained on a new sequence, their performance on the first sequence plummeted on day 3. If the first sequence wasn't retested before learning the new sequence, they performed both sequences accurately on day 3.

In another study, 84 college students were trained to identify a series of similar-sounding words produced by a synthetic-speech machine. Participants who underwent training in the morning performed well in subsequent tests that morning, but tests later in the day showed that their word-recognition skill had declined. However, after a full night's sleep, they performed at their original levels. Participants trained in the evening performed just as well 24 hours later as people trained in the morning did. Since they went to bed shortly after training, those in the evening group didn't exhibit the temporary performance declines observed in the morning group.

On the basis of these studies, researchers identified three stages of memory processing: the first stage of memory — its stabilization — seems to take around six hours. During this period, the memory appears particularly vulnerable to being “lost”. The second stage of memory processing — consolidation — occurs during sleep. The third and final stage is the recall phase, when the memory is once again ready to be accessed and re-edited. (see my article on consolidation for more explanation of the processes of consolidation and re-consolidation)

The researchers made a useful analogy with creating a word-processing document on the computer. The first stage is when you hit “Save” and the computer files the document in your hard drive. On the computer, this takes seconds. The second stage is comparable to someone coming and tidying up your word document — reorganizing it and tightening it up.

The most surprising aspect of this research is the time it appears to take for memories to initially stabilize — seconds for the computer saving the document, but up to six hours for us!

See news reports on sleep's role in memory

See news reports on the effects of sleep deprivation

Added January 2012: a downloadable pdf with all articles and news reports pertaining to sleep, circadian rhythms, and meditation

References: 
  1. Drummond, S.P.A., Brown, G.G., Stricker, J.L., Buxton, R.B., Wong, E.C. & Gillin, J.C. 1999. Sleep deprivation-induced reduction in cortical functional response to serial subtraction. NeuroReport, 10 (18), 3745-3748.
  2. Drummond, S.P.A., Brown, G.G., Gillin, J.C., Stricker, J.L., Wong, E.C. & Buxton, R.B. 2000. Altered brain response to verbal learning following sleep deprivation. Nature, 403 (6770),655-7.
  3. Drummond, S.P.A., Gillin, J.C. & Brown, G.G. 2001. Increased cerebral response during a divided attention task following sleep deprivation. Journal of Sleep Research, 10 (2), 85-92.
  4. Fenn, K.M., Nusbaum, H.C. & Margoliash, D. 2003. Consolidation during sleep of perceptual learning of spoken language. Nature, 425, 614-616.
  5. Frank, M.G., Issa, N.P. & Stryker, M.P. 2001. Sleep Enhances Plasticity in the Developing Visual Cortex. Neuron, 30, 275-287.
  6. Graves, L.A., Heller, E.A., Pack, A.I. & Abel, T. 2003. Sleep Deprivation Selectively Impairs Memory Consolidation for Contextual Fear Conditioning. Learning & Memory, 10, 168-176.
  7. Harrison, Y. & Horne, J.A. 2000. Sleep loss and temporal memory. The Quarterly Journal of Experimental Psychology, 53A (1), 271-279. Research report
  8. Laureys, S., Peigneux, P., Perrin, F. & Maquet, P. 2002. Sleep and Motor Skill Learning. Neuron, 35, 5-7.
  9. Laureys, S., Peigneux, P., Phillips, C., Fuchs,S., Degueldre, C., Aerts, J., Del Fiore,G., Petiau, C., Luxen, A., Van der Linden, M., Cleeremans, A., Smith, C. & Maquet, P. (2001). Experience-dependent changes in cerebral functional connectivity during human rapid eye movement sleep [Letter to Neuroscience]. Neuroscience, 105 (3), 521-525.
  10. Mednick, S.C., Nakayama, K., Cantero, J.L., Atienza, M., Levin, A.A., Pathak, N. & Stickgold, R. 2002. The restorative effect of naps on perceptual deterioration. Nature Neuroscience, 5, 677-681.
  11. Ohayon,M.M.& Vecchierini,M.F. 2002. Daytime sleepiness and cognitive impairment in the elderly population. Archives of Internal Medicine, 162, 201-8.
  12. Siegel, J.M. 2001. The REM Sleep-Memory Consolidation Hypothesis. Science, 294 (5544), 1058-1063.
  13. Sirota, A., Csicsvari, J., Buhl, D. & Buzsáki, G. 2003. Communication between neocortex and hippocampus during sleep in rodents. Proc. Natl. Acad. Sci. USA, 100 (4), 2065-2069.
  14. Spiegel, K., Leproult, R. & Van Cauter, E. 1999. Impact of sleep debt on metabolic and endocrine function, The Lancet, 354 (9188), 1435-1439.
  15. Stickgold, R., Hobson, J.A., Fosse, R., Fosse, M. 2001. Sleep, Learning, and Dreams: Off-line Memory Reprocessing. Science, 294 (5544), 1052-1057.
  16. Stickgold, R., James, L. & Hobson, J.A. 2000. Visual discrimination learning requires sleep after training. Nature Neuroscience, 3, 1237-1238.
  17. Walker, M.P., Brakefield, T., Hobson, J.A. & Stickgold, R. 2003. Dissociable stages of human memory consolidation and reconsolidation. Nature, 425, 616-620.

Correlation between emotional intelligence and IQ

A study shows that IQ and conscientiousness significantly predict emotional intelligence, and identifies shared brain areas that underlie this interdependence.

By using brain scans from 152 Vietnam veterans with a variety of combat-related brain injuries, researchers claim to have mapped the neural basis of general intelligence and emotional intelligence.

There was significant overlap between general intelligence and emotional intelligence, both in behavioral measures and brain activity. Higher scores on general intelligence tests and personality reliably predicted higher performance on measures of emotional intelligence, and many of the same brain regions (in the frontal and parietal cortices) were found to be important to both.

More specifically, impairments in emotional intelligence were associated with selective damage to a network containing the extrastriate body area (involved in perceiving the form of other human bodies), the left posterior superior temporal sulcus (helps interpret body movement in terms of intentions), left temporo-parietal junction (helps work out other person’s mental state), and left orbitofrontal cortex (supports emotional empathy). A number of associated major white matter tracts were also part of the network.

Two of the components of general intelligence were strong contributors to emotional intelligence: verbal comprehension/crystallized intelligence, and processing speed. Verbal impairment was unsurprisingly associated with selective damage to the language network, which showed some overlap with the network underlying emotional intelligence. Similarly, damage to the fronto-parietal network linked to deficits in processing speed also overlapped in places with the emotional intelligence network.

Only one of the ‘big five’ personality traits contributed to the prediction of emotional intelligence — conscientiousness. Impairments in conscientiousness were associated with damage to brain regions widely implicated in social information processing, of which two areas (left orbitofrontal cortex and left temporo-parietal junction) were also involved in impaired emotional intelligence, suggesting where these two attributes might be connected (ability to predict and understand another’s emotions).

It’s interesting (and consistent with the growing emphasis on connectivity rather than the more simplistic focus on specific regions) that emotional intelligence was so affected by damage to white matter tracts. The central role of the orbitofrontal cortex is also intriguing – there’s been growing evidence in recent years of the importance of this region in emotional and social processing, and it’s worth noting that it’s in the right place to integrate sensory and bodily sensation information and pass that onto decision-making systems.

All of this is to say that emotional intelligence depends on social information processing and general intelligence. Traditionally, general intelligence has been thought to be distinct from social and emotional intelligence. But humans are fundamentally social animals, and – contra the message of the Enlightenment, that we have taken so much to heart – it has become increasingly clear that emotions and reason are inextricably entwined. It is not, therefore, all that surprising that general and emotional intelligence might be interdependent. It is more surprising that conscientiousness might be rooted in your degree of social empathy.

It’s also worth noting that ‘emotional intelligence’ is not simply a trendy concept – a pop quiz question regarding whether you ‘have a high EQ’ (or not), but that it can, if impaired, produce very real problems in everyday life.

Emotional intelligence was measured by the Mayer, Salovey, Caruso Emotional Intelligence Test (MSCEIT), general IQ by the Wechsler Adult Intelligence Scale, and personality by the Neuroticism-Extroversion-Openness Inventory.

One of the researchers talks about this study on this YouTube video and on this podcast.

Reference: 

The importance of cognitive control for intelligence

Brain imaging points to the importance of cognitive control, mediated by the connectivity of one particular brain region, for fluid intelligence.

What underlies differences in fluid intelligence? How are smart brains different from those that are merely ‘average’?

Brain imaging studies have pointed to several aspects. One is brain size. Although the history of simplistic comparisons of brain size has been turbulent (you cannot, for example, directly compare brain size without taking into account the size of the body it’s part of), nevertheless, overall brain size does count for something — 6.7% of individual variation in intelligence, it’s estimated. So, something, but not a huge amount.

Activity levels in the prefrontal cortex, research also suggests, account for another 5% of variation in individual intelligence. (Do keep in mind that these figures are not saying that, for example, prefrontal activity explains 5% of intelligence. We are talking about differences between individuals.)

A new study points to a third important factor — one that, indeed, accounts for more than either of these other factors. The strength of the connections from the left prefrontal cortex to other areas is estimated to account for 10% of individual differences in intelligence.

These findings suggest a new perspective on what intelligence is. They suggest that part of intelligence rests on the functioning of the prefrontal cortex and its ability to communicate with the rest of the brain — what researchers are calling ‘global connectivity’. This may reflect cognitive control and, in particular, goal maintenance. The left prefrontal cortex is thought to be involved in (among other things) remembering your goals and any instructions you need for accomplishing those goals.

The study involved 93 adults (average age 23; range 18-40), whose brains were monitored while they were doing nothing and when they were engaged in the cognitively challenging N-back working memory task.

Brain activity patterns revealed three regions within the frontoparietal network that were significantly involved in this task: the left lateral prefrontal cortex, right premotor cortex, and right medial posterior parietal cortex. All three of these regions also showed signs of being global hubs — that is, they were highly connected to other regions across the brain.

Of these, however, only the left lateral prefrontal cortex showed a significant association between its connectivity and individual’s fluid intelligence. This was confirmed by a second independent measure — working memory capacity — which was also correlated with this region’s connectivity, and only this region.

In other words, those with greater connectivity in the left LPFC had greater cognitive control, which is reflected in higher working memory capacity and higher fluid intelligence. There was no correlation between connectivity and crystallized intelligence.

Interestingly, although other global hubs (such as the anterior prefrontal cortex and anterior cingulate cortex) also have strong relationships with intelligence and high levels of global connectivity, they did not show correlations between their levels of connectivity and fluid intelligence. That is, although the activity within these regions may be important for intelligence, their connections to other brain regions are not.

So what’s so important about the connections the LPFC has with the rest of the brain? It appears that, although it connects widely to sensory and motor areas, it is primarily the connections within the frontoparietal control network that are most important — as well as the deactivation of connections with the default network (the network active during rest).

This is not to say that the LPFC is the ‘seat of intelligence’! Research has made it clear that a number of brain regions support intelligence, as do other areas of connectivity. The finding is important because it shows that the left LPFC supports cognitive control and intelligence through a mechanism involving global connectivity and some other as-yet-unknown property. One possibility is that this region is a ‘flexible’ hub — able to shift its connectivity with a number of different brain regions as the task demands.

In other words, what may count is how many different connectivity patterns the left LPFC has in its repertoire, and how good it is at switching to them.

An association between negative connections with the default network and fluid intelligence also adds to evidence for the importance of inhibiting task-irrelevant processing.

All this emphasizes the role of cognitive control in intelligence, and perhaps goes some way to explaining why self-regulation in children is so predictive of later success, apart from the obvious.

Reference: 

Sleep preserves your feelings about traumatic events

New research suggests that sleeping within a few hours of a disturbing event keeps your emotional response to the event strong.

Previous research has shown that negative objects and events are preferentially consolidated in sleep — if you experience them in the evening, you are more likely to remember them than more neutral objects or events, but if you experience them in the morning, they are not more likely to be remembered than other memories (see collected sleep reports). However, more recent studies have failed to find this. A new study also fails to find such preferential consolidation, but does find that our emotional reaction to traumatic or disturbing events can be greatly reduced if we stay awake afterward.

Being unable to sleep after such events is of course a common response — these findings indicate there’s good reason for it, and we should go along with it rather than fighting it.

The study involved 106 young adults rating pictures on a sad-happy scale and their own responses on an excited-calm scale. Twelve hours later, they were given a recognition test: noting pictures they had seen earlier from a mix of new and old pictures. They also rated all the pictures on the two scales. There were four groups: 41 participants saw the first set late in the day and the second set 12 hours later on the following day (‘sleep group’); 41 saw the first set early and the second set 12 hours later on the same day; 12 participants saw both sets in the evening, with only 45 minutes between the sets; 12 participants saw both sets in the morning (these last two groups were to rule out circadian effects). 25 of the sleep group had their brain activity monitored while they slept.

The sleep group performed significantly better on the recognition test than the same-day group. Negative pictures were remembered better than neutral ones. However, unlike earlier studies, the sleep group didn’t preferentially remember negative pictures more than the same-day group.

But, interestingly, the sleep group was more likely to maintain the strength of initial negative responses. The same-day group showed a weaker response to negative scenes on the second showing.

It’s been theorized that late-night REM sleep is critical for emotional memory consolidation. However, this study found no significant relationship between the amount of time spent in REM sleep and recognition memory, nor was there any relationship between other sleep stages and memory. There was one significant result: those who had more REM sleep in the third quarter of the night showed the least reduction of emotional response to the negative pictures.

There were no significant circadian effects, but it’s worth noting that even the 45 minute gap between the sets was sufficient to weaken the negative effect of negative scenes.

While there was a trend toward a gender effect, it didn’t reach statistical significance, and there were no significant interactions between gender and group or emotional value.

The findings suggest that the effects of sleep on memory and emotion may be independent.

The findings also contradict previous studies showing preferential consolidation of emotional memories during sleep, but are consistent with two other recent studies that have also failed to find this. At this stage, all we can say is that there may be certain conditions in which this occurs (or doesn’t occur), but more research is needed to determine what these conditions are. Bear in mind that there is no doubt that sleep helps consolidate memories; we are talking here only about emphasizing negative memories at the expense of emotionally-neutral ones.

Reference: 

[2672] Baran, B., Pace-Schott E. F., Ericson C., & Spencer R. M. C. (2012).  Processing of Emotional Reactivity and Emotional Memory over Sleep. The Journal of Neuroscience. 32(3), 1035 - 1042.

Working memory capacity not 4 but 2+2

A monkey study finds that our very limited working memory capacity of around 4 items reflects two capacities of two items. The finding has practical implications for information presentation.

In the study, two rhesus monkeys were given a standard human test of working memory capacity: an array of colored squares, varying from two to five squares, was shown for 800 msec on a screen. After a delay, varying from 800 to 1000 msec, a second array was presented. This array was identical to the first except for a change in color of one item. The monkey was rewarded if its eyes went directly to this changed square (an infra-red eye-tracking system was used to determine this). During all this, activity from single neurons in the lateral prefrontal cortex and the lateral intraparietal area — areas critical for short-term memory and implicated in human capacity limitations — was recorded.

As with humans, the more squares in the array, the worse the performance (from 85% correct for two squares to 66.5% for 5). Their working memory capacity was calculated at 3.88 objects — i.e. the same as that of humans.

That in itself is interesting, speaking as it does to the question of how human intelligence differs from other animals. But the real point of the exercise was to watch what is happening at the single neuron level. And here a surprise occurred.

That total capacity of around 4 items was composed of two independent, smaller capacities in the right and left halves of the visual space. What matters is how many objects are in the hemifield an eye is covering. Each hemifield can only handle two objects. Thus, if the left side of the visual space contains three items, and the right side only one, information about the three items from the left side will be degraded. If the left side contains four items and the right side two, those two on the right side will be fine, but information from the four items on the left will be degraded.

Notice that the effect of more items than two in a hemifield is to decrease the total information from all the items in the hemifield — not to simply lose the additional items.

The behavioral evidence correlated with brain activity, with object information in LPFC neurons decreasing with increasing number of items in the same hemifield, but not the opposite hemifield, and the same for the intraparietal neurons (the latter are active during the delay; the former during the presentation).

The findings resolve a long-standing debate: does working memory function like slots, which we fill one by one with items until all are full, or as a pool that fills with information about each object, with some information being lost as the number of items increases? And now we know why there is evidence for both views, because both contain truth. Each hemisphere might be considered a slot, but each slot is a pool.

Another long-standing question is whether the capacity limit is a failure of perception or  memory. These findings indicate that the problem is one of perception. The neural recordings showed information about the objects being lost even as the monkeys were viewing them, not later as they were remembering what they had seen.

All of this is important theoretically, but there are also immediate practical applications. The work suggests that information should be presented in such a way that it’s spread across the visual space — for example, dashboard displays should spread the displays evenly on both sides of the visual field; medical monitors that currently have one column of information should balance it in right and left columns; security personnel should see displays scrolled vertically rather than horizontally; working memory training should present information in a way that trains each hemisphere separately. The researchers are forming collaborations to develop these ideas.

Reference: 

[2335] Buschman, T. J., Siegel M., Roy J. E., & Miller E. K. (2011).  Neural substrates of cognitive capacity limitations. Proceedings of the National Academy of Sciences.

Many genes are behind human intelligence

A large-scale genome-wide analysis has confirmed that half the differences in intelligence between people of similar background can be attributed to genetic differences — but it’s an accumulation of hundreds of tiny differences.

There has been a lot of argument over the years concerning the role of genes in intelligence. The debate reflects the emotions involved more than the science. A lot of research has gone on, and it is indubitable that genes play a significant role. Most of the research however has come from studies involving twins and adopted children, so it is indirect evidence of genetic influence.

A new technique has now enabled researchers to directly examine 549,692 single nucleotide polymorphisms (SNPs — places where people have single-letter variations in their DNA) in each of 3511 unrelated people (aged 18-90, but mostly older adults). This analysis had produced an estimate of the size of the genetic contribution to individual differences in intelligence: 40% of the variation in crystallized intelligence and 51% of the variation in fluid intelligence. (See http://www.memory-key.com/memory/individual/wm-intelligence for a discussion of the difference)

The analysis also reveals that there is no ‘smoking gun’. Rather than looking for a handful of genes that govern intelligence, it seems that hundreds if not thousands of genes are involved, each in their own small way. That’s the trouble: each gene makes such a small contribution that no gene can be fingered as critical.

Discussions that involve genetics are always easily misunderstood. It needs to be emphasized that we are talking here about the differences between people. We are not saying that half of your IQ is down to your genes; we are saying that half the difference between you and another person (unrelated but with a similar background and education — study participants came from Scotland, England and Norway — that is, relatively homogenous populations) is due to your genes.

If the comparison was between, for example, a middle-class English person and someone from a poor Indian village, far less of any IQ difference would be due to genes. That is because the effects of environment would be so much greater.

These findings are consistent with the previous research using twins. The most important part of these findings is the confirmation it provides of something that earlier studies have hinted at: no single gene makes a significant contribution to variation in intelligence.

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