Language
Language functions such as grammar, vocabulary and literal meaning are typically lateralized to the left hemisphere, especially in right handed individuals.[3] While language production is left-lateralized in up to 90% of right-handers, it is more bilateral, or even right-lateralized, in approximately 50% of left-handers.[4]

Broca's area and Wernicke's area, two areas associated with the production of speech, are located in the left cerebral hemisphere for about 95% of right-handers, but about 70% of left-handers.[5]:69

Auditory and visual processing
The processing of visual and auditory stimuli, spatial manipulation, facial perception, and artistic ability are represented bilaterally.[4] Numerical estimation, comparison and online calculation depend on bilateral parietal regions[6][7] while exact calculation and fact retrieval are associated with left parietal regions, perhaps due to their ties to linguistic processing.[6][7]

...

Depression is linked with a hyperactive right hemisphere, with evidence of selective involvement in "processing negative emotions, pessimistic thoughts and unconstructive thinking styles", as well as vigilance, arousal and self-reflection, and a relatively hypoactive left hemisphere, "specifically involved in processing pleasurable experiences" and "relatively more involved in decision-making processes".

Chaos and Order; the right and left hemispheres: https://orthosphere.wordpress.com/2018/05/23/chaos-and-order-the-right-and-left-hemispheres/
In The Master and His Emissary, Iain McGilchrist writes that a creature like a bird needs two types of consciousness simultaneously. It needs to be able to focus on something specific, such as pecking at food, while it also needs to keep an eye out for predators which requires a more general awareness of environment.

These are quite different activities. The Left Hemisphere (LH) is adapted for a narrow focus. The Right Hemisphere (RH) for the broad. The brains of human beings have the same division of function.

The LH governs the right side of the body, the RH, the left side. With birds, the left eye (RH) looks for predators, the right eye (LH) focuses on food and specifics. Since danger can take many forms and is unpredictable, the RH has to be very open-minded.

The LH is for narrow focus, the explicit, the familiar, the literal, tools, mechanism/machines and the man-made. The broad focus of the RH is necessarily more vague and intuitive and handles the anomalous, novel, metaphorical, the living and organic. The LH is high resolution but narrow, the RH low resolution but broad.

The LH exhibits unrealistic optimism and self-belief. The RH has a tendency towards depression and is much more realistic about a person’s own abilities. LH has trouble following narratives because it has a poor sense of “wholes.” In art it favors flatness, abstract and conceptual art, black and white rather than color, simple geometric shapes and multiple perspectives all shoved together, e.g., cubism. Particularly RH paintings emphasize vistas with great depth of field and thus space and time,[1] emotion, figurative painting and scenes related to the life world. In music, LH likes simple, repetitive rhythms. The RH favors melody, harmony and complex rhythms.

...

Schizophrenia is a disease of extreme LH emphasis. Since empathy is RH and the ability to notice emotional nuance facially, vocally and bodily expressed, schizophrenics tend to be paranoid and are often convinced that the real people they know have been replaced by robotic imposters. This is at least partly because they lose the ability to intuit what other people are thinking and feeling – hence they seem robotic and suspicious.

Oswald Spengler’s The Decline of the West as well as McGilchrist characterize the West as awash in phenomena associated with an extreme LH emphasis. Spengler argues that Western civilization was originally much more RH (to use McGilchrist’s categories) and that all its most significant artistic (in the broadest sense) achievements were triumphs of RH accentuation.

The RH is where novel experiences and the anomalous are processed and where mathematical, and other, problems are solved. The RH is involved with the natural, the unfamiliar, the unique, emotions, the embodied, music, humor, understanding intonation and emotional nuance of speech, the metaphorical, nuance, and social relations. It has very little speech, but the RH is necessary for processing all the nonlinguistic aspects of speaking, including body language. Understanding what someone means by vocal inflection and facial expressions is an intuitive RH process rather than explicit.

...

RH is very much the center of lived experience; of the life world with all its depth and richness. The RH is “the master” from the title of McGilchrist’s book. The LH ought to be no more than the emissary; the valued servant of the RH. However, in the last few centuries, the LH, which has tyrannical tendencies, has tried to become the master. The LH is where the ego is predominantly located. In split brain patients where the LH and the RH are surgically divided (this is done sometimes in the case of epileptic patients) one hand will sometimes fight with the other. In one man’s case, one hand would reach out to hug his wife while the other pushed her away. One hand reached for one shirt, the other another shirt. Or a patient will be driving a car and one hand will try to turn the steering wheel in the opposite direction. In these cases, the “naughty” hand is usually the left hand (RH), while the patient tends to identify herself with the right hand governed by the LH. The two hemispheres have quite different personalities.

The connection between LH and ego can also be seen in the fact that the LH is competitive, contentious, and agonistic. It wants to win. It is the part of you that hates to lose arguments.

Using the metaphor of Chaos and Order, the RH deals with Chaos – the unknown, the unfamiliar, the implicit, the emotional, the dark, danger, mystery. The LH is connected with Order – the known, the familiar, the rule-driven, the explicit, and light of day. Learning something means to take something unfamiliar and making it familiar. Since the RH deals with the novel, it is the problem-solving part. Once understood, the results are dealt with by the LH. When learning a new piece on the piano, the RH is involved. Once mastered, the result becomes a LH affair. The muscle memory developed by repetition is processed by the LH. If errors are made, the activity returns to the RH to figure out what went wrong; the activity is repeated until the correct muscle memory is developed in which case it becomes part of the familiar LH.

Science is an attempt to find Order. It would not be necessary if people lived in an entirely orderly, explicit, known world. The lived context of science implies Chaos. Theories are reductive and simplifying and help to pick out salient features of a phenomenon. They are always partial truths, though some are more partial than others. The alternative to a certain level of reductionism or partialness would be to simply reproduce the world which of course would be both impossible and unproductive. The test for whether a theory is sufficiently non-partial is whether it is fit for purpose and whether it contributes to human flourishing.

...

Analytic philosophers pride themselves on trying to do away with vagueness. To do so, they tend to jettison context which cannot be brought into fine focus. However, in order to understand things and discern their meaning, it is necessary to have the big picture, the overview, as well as the details. There is no point in having details if the subject does not know what they are details of. Such philosophers also tend to leave themselves out of the picture even when what they are thinking about has reflexive implications. John Locke, for instance, tried to banish the RH from reality. All phenomena having to do with subjective experience he deemed unreal and once remarked about metaphors, a RH phenomenon, that they are “perfect cheats.” Analytic philosophers tend to check the logic of the words on the page and not to think about what those words might say about them. The trick is for them to recognize that they and their theories, which exist in minds, are part of reality too.

The RH test for whether someone actually believes something can be found by examining his actions. If he finds that he must regard his own actions as free, and, in order to get along with other people, must also attribute free will to them and treat them as free agents, then he effectively believes in free will – no matter his LH theoretical commitments.

...

We do not know the origin of life. We do not know how or even if consciousness can emerge from matter. We do not know the nature of 96% of the matter of the universe. Clearly all these things exist. They can provide the subject matter of theories but they continue to exist as theorizing ceases or theories change. Not knowing how something is possible is irrelevant to its actual existence. An inability to explain something is ultimately neither here nor there.

If thought begins and ends with the LH, then thinking has no content – content being provided by experience (RH), and skepticism and nihilism ensue. The LH spins its wheels self-referentially, never referring back to experience. Theory assumes such primacy that it will simply outlaw experiences and data inconsistent with it; a profoundly wrong-headed approach.

...

Gödel’s Theorem proves that not everything true can be proven to be true. This means there is an ineradicable role for faith, hope and intuition in every moderately complex human intellectual endeavor. There is no one set of consistent axioms from which all other truths can be derived.

Alan Turing’s proof of the halting problem proves that there is no effective procedure for finding effective procedures. Without a mechanical decision procedure, (LH), when it comes to … [more]

https://twitter.com/AOEUPL_PHE/status/1004807377076604928
https://archive.is/FgNHG
did i just learn something big?

Prerecorded music has ABSOLUTELY NO
SURVIVAL reward. Zero. It does not help
with procreation (well, unless you're the
one making the music, then you get
endless sex) and it does not help with
individual survival.
As such, one must seriously self test
(n=1) prerecorded music actually holds
you back.
If you're reading this and you try no
music for 2 weeks and fail, hit me up. I
have some mind blowing stuff to show
you in how you can control others with
music.

Fourth Lecture: THE ROLE OF HIGH AND OF EXTREMELY HIGH COMPLICATION

Comparisons between computing machines and the nervous systems. Estimates of size for computing machines, present and near future.

Estimates for size for the human central nervous system. Excursus about the “mixed” character of living organisms. Analog and digital elements. Observations about the “mixed” character of all componentry, artificial as well as natural. Interpretation of the position to be taken with respect to these.

Evaluation of the discrepancy in size between artificial and natural automata. Interpretation of this discrepancy in terms of physical factors. Nature of the materials used.

The probability of the presence of other intellectual factors. The role of complication and the theoretical penetration that it requires.

Questions of reliability and errors reconsidered. Probability of individual errors and length of procedure. Typical lengths of procedure for computing machines and for living organisms--that is, for artificial and for natural automata. Upper limits on acceptable probability of error in individual operations. Compensation by checking and self-correcting features.

Differences of principle in the way in which errors are dealt with in artificial and in natural automata. The “single error” principle in artificial automata. Crudeness of our approach in this case, due to the lack of adequate theory. More sophisticated treatment of this problem in natural automata: The role of the autonomy of parts. Connections between this autonomy and evolution.

- 10^10 neurons in brain, 10^4 vacuum tubes in largest computer at time
- machines faster: 5 ms from neuron potential to neuron potential, 10^-3 ms for vacuum tubes

https://en.wikipedia.org/wiki/John_von_Neumann#Computing

The brain is neither analog nor digital, but works using a signal processing paradigm that has some properties in common with both.
 
Unlike a digital computer, the brain does not use binary logic or binary addressable memory, and it does not perform binary arithmetic. Information in the brain is represented in terms of statistical approximations and estimations rather than exact values. The brain is also non-deterministic and cannot replay instruction sequences with error-free precision. So in all these ways, the brain is definitely not "digital".
 
At the same time, the signals sent around the brain are "either-or" states that are similar to binary. A neuron fires or it does not. These all-or-nothing pulses are the basic language of the brain. So in this sense, the brain is computing using something like binary signals. Instead of 1s and 0s, or "on" and "off", the brain uses "spike" or "no spike" (referring to the firing of a neuron).

A core tenet of Minsky's philosophy is that "minds are what brains do". The society of mind theory views the human mind and any other naturally evolved cognitive systems as a vast society of individually simple processes known as agents. These processes are the fundamental thinking entities from which minds are built, and together produce the many abilities we attribute to minds. The great power in viewing a mind as a society of agents, as opposed to the consequence of some basic principle or some simple formal system, is that different agents can be based on different types of processes with different purposes, ways of representing knowledge, and methods for producing results.

This idea is perhaps best summarized by the following quote:

What magical trick makes us intelligent? The trick is that there is no trick. The power of intelligence stems from our vast diversity, not from any single, perfect principle. —Marvin Minsky, The Society of Mind, p. 308

https://en.wikipedia.org/wiki/Modularity_of_mind

The modular organization of human anatomical
brain networks: Accounting for the cost of wiring: https://www.mitpressjournals.org/doi/pdfplus/10.1162/NETN_a_00002
Brain networks are expected to be modular. However, existing techniques for estimating a network’s modules make it difficult to assess the influence of organizational principles such as wiring cost reduction on the detected modules. Here we present a modification of an existing module detection algorithm that allowed us to focus on connections that are unexpected under a cost-reduction wiring rule and to identify modules from among these connections. We applied this technique to anatomical brain networks and showed that the modules we detected differ from those detected using the standard technique. We demonstrated that these novel modules are spatially distributed, exhibit unique functional fingerprints, and overlap considerably with rich clubs, giving rise to an alternative and complementary interpretation of the functional roles of specific brain regions. Finally, we demonstrated that, using the modified module detection approach, we can detect modules in a developmental dataset that track normative patterns of maturation. Collectively, these findings support the hypothesis that brain networks are composed of modules and provide additional insight into the function of those modules.

How Deviant Recent AI Progress Lumpiness?: http://www.overcomingbias.com/2018/03/how-deviant-recent-ai-progress-lumpiness.html
I seem to disagree with most people working on artificial intelligence (AI) risk. While with them I expect rapid change once AI is powerful enough to replace most all human workers, I expect this change to be spread across the world, not concentrated in one main localized AI system. The efforts of AI risk folks to design AI systems whose values won’t drift might stop global AI value drift if there is just one main AI system. But doing so in a world of many AI systems at similar abilities levels requires strong global governance of AI systems, which is a tall order anytime soon. Their continued focus on preventing single system drift suggests that they expect a single main AI system.

The main reason that I understand to expect relatively local AI progress is if AI progress is unusually lumpy, i.e., arriving in unusually fewer larger packages rather than in the usual many smaller packages. If one AI team finds a big lump, it might jump way ahead of the other teams.

However, we have a vast literature on the lumpiness of research and innovation more generally, which clearly says that usually most of the value in innovation is found in many small innovations. We have also so far seen this in computer science (CS) and AI. Even if there have been historical examples where much value was found in particular big innovations, such as nuclear weapons or the origin of humans.

Apparently many people associated with AI risk, including the star machine learning (ML) researchers that they often idolize, find it intuitively plausible that AI and ML progress is exceptionally lumpy. Such researchers often say, “My project is ‘huge’, and will soon do it all!” A decade ago my ex-co-blogger Eliezer Yudkowsky and I argued here on this blog about our differing estimates of AI progress lumpiness. He recently offered Alpha Go Zero as evidence of AI lumpiness:

...

In this post, let me give another example (beyond two big lumps in a row) of what could change my mind. I offer a clear observable indicator, for which data should have available now: deviant citation lumpiness in recent ML research. One standard measure of research impact is citations; bigger lumpier developments gain more citations that smaller ones. And it turns out that the lumpiness of citations is remarkably constant across research fields! See this March 3 paper in Science:

I Still Don’t Get Foom: http://www.overcomingbias.com/2014/07/30855.html
All of which makes it look like I’m the one with the problem; everyone else gets it. Even so, I’m gonna try to explain my problem again, in the hope that someone can explain where I’m going wrong. Here goes.

“Intelligence” just means an ability to do mental/calculation tasks, averaged over many tasks. I’ve always found it plausible that machines will continue to do more kinds of mental tasks better, and eventually be better at pretty much all of them. But what I’ve found it hard to accept is a “local explosion.” This is where a single machine, built by a single project using only a tiny fraction of world resources, goes in a short time (e.g., weeks) from being so weak that it is usually beat by a single human with the usual tools, to so powerful that it easily takes over the entire world. Yes, smarter machines may greatly increase overall economic growth rates, and yes such growth may be uneven. But this degree of unevenness seems implausibly extreme. Let me explain.

If we count by economic value, humans now do most of the mental tasks worth doing. Evolution has given us a brain chock-full of useful well-honed modules. And the fact that most mental tasks require the use of many modules is enough to explain why some of us are smarter than others. (There’d be a common “g” factor in task performance even with independent module variation.) Our modules aren’t that different from those of other primates, but because ours are different enough to allow lots of cultural transmission of innovation, we’ve out-competed other primates handily.

We’ve had computers for over seventy years, and have slowly build up libraries of software modules for them. Like brains, computers do mental tasks by combining modules. An important mental task is software innovation: improving these modules, adding new ones, and finding new ways to combine them. Ideas for new modules are sometimes inspired by the modules we see in our brains. When an innovation team finds an improvement, they usually sell access to it, which gives them resources for new projects, and lets others take advantage of their innovation.

...

In Bostrom’s graph above the line for an initially small project and system has a much higher slope, which means that it becomes in a short time vastly better at software innovation. Better than the entire rest of the world put together. And my key question is: how could it plausibly do that? Since the rest of the world is already trying the best it can to usefully innovate, and to abstract to promote such innovation, what exactly gives one small project such a huge advantage to let it innovate so much faster?

...

In fact, most software innovation seems to be driven by hardware advances, instead of innovator creativity. Apparently, good ideas are available but must usually wait until hardware is cheap enough to support them.

Yes, sometimes architectural choices have wider impacts. But I was an artificial intelligence researcher for nine years, ending twenty years ago, and I never saw an architecture choice make a huge difference, relative to other reasonable architecture choices. For most big systems, overall architecture matters a lot less than getting lots of detail right. Researchers have long wandered the space of architectures, mostly rediscovering variations on what others found before.

Some hope that a small project could be much better at innovation because it specializes in that topic, and much better understands new theoretical insights into the basic nature of innovation or intelligence. But I don’t think those are actually topics where one can usefully specialize much, or where we’ll find much useful new theory. To be much better at learning, the project would instead have to be much better at hundreds of specific kinds of learning. Which is very hard to do in a small project.

What does Bostrom say? Alas, not much. He distinguishes several advantages of digital over human minds, but all software shares those advantages. Bostrom also distinguishes five paths: better software, brain emulation (i.e., ems), biological enhancement of humans, brain-computer interfaces, and better human organizations. He doesn’t think interfaces would work, and sees organizations and better biology as only playing supporting roles.

...

Similarly, while you might imagine someday standing in awe in front of a super intelligence that embodies all the power of a new age, superintelligence just isn’t the sort of thing that one project could invent. As “intelligence” is just the name we give to being better at many mental tasks by using many good mental modules, there’s no one place to improve it. So I can’t see a plausible way one project could increase its intelligence vastly faster than could the rest of the world.

Takeoff speeds: https://sideways-view.com/2018/02/24/takeoff-speeds/
Futurists have argued for years about whether the development of AGI will look more like a breakthrough within a small group (“fast takeoff”), or a continuous acceleration distributed across the broader economy or a large firm (“slow takeoff”).

I currently think a slow takeoff is significantly more likely. This post explains some of my reasoning and why I think it matters. Mostly the post lists arguments I often hear for a fast takeoff and explains why I don’t find them compelling.

(Note: this is not a post about whether an intelligence explosion will occur. That seems very likely to me. Quantitatively I expect it to go along these lines. So e.g. while I disagree with many of the claims and assumptions in Intelligence Explosion Microeconomics, I don’t disagree with the central thesis or with most of the arguments.)

https://en.wikipedia.org/wiki/Trust_(emotion)
https://en.wikipedia.org/wiki/Envy
https://en.wikipedia.org/wiki/Guilt_(emotion)

grip strength was causally related to fitness, physical activity and other indicators of frailty, including cognitive performance scores

Stuart Richie, Bates, Plomin

SEM: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4354297/figure/fig03/

The variance explained by each path in the diagrams included here can be calculated by squaring its path weight. To take one example, reading differences at age 12 in the model shown in Figure​Figure33 explain 7% of intelligence differences at age 16 (.262). However, since our measures are of differences, they are likely to include substantial amounts of noise: Measurement error may produce spurious differences. To remove this error variance, we can take an estimate of the reliability of the measures (generally high, since our measures are normed, standardized tests), which indicates the variance expected purely by the reliability of the measure, and subtract it from the observed variance between twins in our sample. Correcting for reliability in this way, the effect size estimates are somewhat larger; to take the above example, the reliability-corrected effect size of age 12 reading differences on age 16 intelligence differences is around 13% of the “signal” variance. It should be noted that the age 12 reading differences themselves are influenced by many previous paths from both reading and intelligence, as illustrated in Figure​Figure33.

...

The present study provided compelling evidence that improvements in reading ability, themselves caused purely by the nonshared environment, may result in improvements in both verbal and nonverbal cognitive ability, and may thus be a factor increasing cognitive diversity within families (Plomin, 2011). These associations are present at least as early as age 7, and are not—to the extent we were able to test this possibility—driven by differences in reading exposure. Since reading is a potentially remediable ability, these findings have implications for reading instruction: Early remediation of reading problems might not only aid in the growth of literacy, but may also improve more general cognitive abilities that are of critical importance across the life span.

Does Reading Cause Later Intelligence? Accounting for Stability in Models of Change: http://sci-hub.tw/10.1111/cdev.12669
Results from a state–trait model suggest that reported effects of reading ability on later intelligence may be artifacts of previously uncontrolled factors, both environmental in origin and stable during this developmental period, influencing both constructs throughout development.

Twin and family studies indicate that genetic factors are estimated to play a role in at least 80% of AD cases. The inheritance of AD exhibits a dichotomous pattern. On one hand, rare mutations in APP, PSEN1, and PSEN2 virtually guarantee early-onset (<60 years) familial AD, which represents ∼5% of AD. On the other hand, common gene polymorphisms, such as the ε4 and ε2 variants of the APOE gene, can influence susceptibility for ∼50% of the common late-onset AD. These four genes account for 30%–50% of the inheritability of AD. Genome-wide association studies have recently led to the identification of 11 additional AD candidate genes.

Role of Genes and Environments for Explaining Alzheimer Disease: http://jamanetwork.com/journals/jamapsychiatry/fullarticle/209307

A journey into the brain: insight into how bacterial pathogens cross blood–brain barriers: http://sci-hub.tw/10.1038/nrmicro.2016.178
How do extracellular pathogens cross the blood-brain barrier?: https://www.ncbi.nlm.nih.gov/pubmed/11973156
Defense at the border: the blood–brain barrier versus bacterial foreigners: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3589978/

Of course, finding that the pattern already exists at the age of one month seriously weakens any idea that being poor shrinks the brain: most of the environmental effects you would consider haven’t even come into play in the first four weeks, when babies drink milk, sleep, and poop. Genetics affecting both parents and their children would make more sense, if the pattern shows up so early (and I’ll bet money that, if real,  it shows up well before one month);  but Martha Farah, and the reporter from Nature, Sara Reardon, ARE TOO FUCKING DUMB to realize this.

https://westhunt.wordpress.com/2015/03/31/scanners-live-in-vain/#comment-93791
Correlation between brain volume and IQ is about 0.4 . Shows up clearly in studies with sufficient power.

“poverty affects prenatal environment a lot.” No it does not. “poverty” in this country means having plenty to eat.

The Great IQ Depression: https://westhunt.wordpress.com/2014/03/07/the-great-iq-depression/
We hear that poverty can sap brainpower, reduce frontal lobe function, induce the fantods, etc. But exactly what do we mean by ‘poverty’? If we’re talking about an absolute, rather than relative, standard of living, most of the world today must be in poverty, as well as almost everyone who lived much before the present. Most Chinese are poorer than the official US poverty level, right? The US had fairly rapid economic growth until the last generation or so, so if you go very far back in time, almost everyone was poor, by modern standards. Even those who were considered rich at the time suffered from zero prenatal care, largely useless medicine, tabletless high schools, and slow Internet connections. They had to ride horses that had lousy acceleration and pooped all over the place.

In particular, if all this poverty-gives-you-emerods stuff is true, scholastic achievement should have collapsed in the Great Depression – and with the miracle of epigenetics, most of us should still be suffering those bad effects.

But somehow none of this seems to have gone through the formality of actually happening.

I argue that the g factor meets the fundamental criteria of a scientific construct more fully than any other conception of intelligence. I briefly discuss the evidence regarding the relationship of brain size to intelligence. A review of a large body of evidence demonstrates that there is a g factor in a wide range of species and that, in the species studied, it relates to brain size and is heritable. These findings suggest that many species have evolved a general-purpose mechanism (a general biological intelligence) for dealing with the environments in which they evolved. In spite of numerous studies with considerable statistical power, we know of very few genes that influence g and the effects are very small. Nevertheless, g appears to be highly polygenic. Given the complexity of the human brain, it is not surprising that that one of its primary faculties—intelligence—is best explained by the near infinitesimal model of quantitative genetics.

Some disease syndromes almost have to be caused by pathogens – for example, any with a fitness impact (prevalence x fitness reduction) > 2% or so, too big to be caused by mutational pressure. I don’t think that this is the case for AD: it hits so late in life that the fitness impact is minimal. However, that hardly means that it can’t be caused by a pathogen or pathogens – a big fraction of all disease syndromes are, including many that strike in old age. That possibility is always worth checking out, not least because infectious diseases are generally easier to prevent and/or treat.

There is new work that strongly suggests that pathogens are the root cause. It appears that the amyloid is an antimicrobial peptide. amyloid-beta binds to invading microbes and then surrounds and entraps them. ‘When researchers injected Salmonella into mice’s hippocampi, a brain area damaged in Alzheimer’s, A-beta quickly sprang into action. It swarmed the bugs and formed aggregates called fibrils and plaques. “Overnight you see the plaques throughout the hippocampus where the bugs were, and then in each single plaque is a single bacterium,” Tanzi says. ‘

obesity and pathogens: https://westhunt.wordpress.com/2016/05/29/alzheimers/#comment-79757
not sure about this guy, but interesting: https://westhunt.wordpress.com/2016/05/29/alzheimers/#comment-79748
http://perfecthealthdiet.com/2010/06/is-alzheimer%E2%80%99s-caused-by-a-bacterial-infection-of-the-brain/

https://westhunt.wordpress.com/2016/12/13/the-twelfth-battle-of-the-isonzo/
All too often we see large, long-lasting research efforts that never produce, never achieve their goal.

For example, the amyloid hypothesis [accumulation of amyloid-beta oligomers is the cause of Alzheimers] has been dominant for more than 20 years, and has driven development of something like 15 drugs. None of them have worked. At the same time the well-known increased risk from APOe4 has been almost entirely ignored, even though it ought to be a clue to the cause.

In general, when a research effort has been spinning its wheels for a generation or more, shouldn’t we try something different? We could at least try putting a fraction of those research dollars into alternative approaches that have not yet failed repeatedly.

Mostly this applies to research efforts that at least wish they were science. ‘educational research’ is in a special class, and I hardly know what to recommend. Most of the remedial actions that occur to me violate one or more of the Geneva conventions.

APOe4 related to lymphatic system: https://en.wikipedia.org/wiki/Apolipoprotein_E

https://westhunt.wordpress.com/2012/03/06/spontaneous-generation/#comment-2236
Look,if I could find out the sort of places that I usually misplace my keys – if I did, which I don’t – I could find the keys more easily the next time I lose them. If you find out that practitioners of a given field are not very competent, it marks that field as a likely place to look for relatively easy discovery. Thus medicine is a promising field, because on the whole doctors are not terribly good investigators. For example, none of the drugs developed for Alzheimers have worked at all, which suggests that our ideas on the causation of Alzheimers are likely wrong. Which suggests that it may (repeat may) be possible to make good progress on Alzheimers, either by an entirely empirical approach, which is way underrated nowadays, or by dumping the current explanation, finding a better one, and applying it.

You could start by looking at basic notions of field X and asking yourself: How do we really know that? Is there serious statistical evidence? Does that notion even accord with basic theory? This sort of checking is entirely possible. In most of the social sciences, we don’t, there isn’t, and it doesn’t.

Hygiene and the world distribution of Alzheimer’s disease: Epidemiological evidence for a relationship between microbial environment and age-adjusted disease burden: https://academic.oup.com/emph/article/2013/1/173/1861845/Hygiene-and-the-world-distribution-of-Alzheimer-s

Amyloid-β peptide protects against microbial infection in mouse and worm models of Alzheimer’s disease: http://stm.sciencemag.org/content/8/340/340ra72

Fungus, the bogeyman: http://www.economist.com/news/science-and-technology/21676754-curious-result-hints-possibility-dementia-caused-fungal
Fungus and dementia
paper: http://www.nature.com/articles/srep15015

Porphyromonas gingivalis in Alzheimer’s disease brains: Evidence for disease causation and treatment with small-molecule inhibitors: https://advances.sciencemag.org/content/5/1/eaau3333

We apply MTAG to three large GWAS: Sniekers et al (2017) on intelligence, Okbay et al. (2016) on Educational attainment, and Hill et al. (2016) on household income. By combining these three samples our functional sample size increased from 78 308 participants to 147 194. We found 107 independent loci associated with intelligence, implicating 233 genes, using both SNP-based and gene-based GWAS. We find evidence that neurogenesis may explain some of the biological differences in intelligence as well as genes expressed in the synapse and those involved in the regulation of the nervous system.

...

Finally, using an independent sample of 6 844 individuals we were able to predict 7% of intelligence using SNP data alone.

More on Low-Trust Russia: Do Russian Who Wants To Be A Millionaire contestants avoid asking the audience because they expect audience members to deliberately mislead them?

Xenocrypt on the math of economic geography: “A party’s voters should get more or less seats based on the shape of the monotonic curve with integral one they can be arranged in” might sound like a very silly belief, but it is equivalent to the common mantra that you deserve to lose if your voters are ‘too clustered’”

Okay, look, I went way too long between writing up links posts this time, so you’re getting completely dated obsolete stuff like Actually, Neil Gorsuch Is A Champion Of The Little Guy. But aside from the Gorsuch reference this is actually pretty timeless – basically an argument for strict constructionism on the grounds that “a flexible, living, bendable law will always tend to be bent in the direction of the powerful.”

Otium: Are Adult Developmental Stages Real? Looks at Kohlberg, Kegan, etc.

I mentioned the debate over 5-HTTLPR, a gene supposedly linked to various mental health outcomes, in my review of pharmacogenomics. Now a very complete meta-analysis finds that a lot of the hype around it isn’t true. This is pretty impressive since there are dozens of papers claiming otherwise, and maybe the most striking example yet of how apparently well-replicated a finding can be and still fail to pan out.

Rootclaim describes itself as a crowd-sourced argument mapper. See for example its page on who launched the chemical attack in Syria.

Apparently if you just kill off all the cells that are growing too old, you can partly reverse organisms’ aging (paper, popular article)

The Politics Of The Gene: “Contrary to expectations, however, we find little evidence that it is more common for whites, the socioeconomically advantaged, or political conservatives to believe that genetics are important for health and social outcomes.”

Siberian Fox linked me to two studies that somewhat contradicted my minimalist interpretation of childhood trauma here: Alemany on psychosis and Turkheimer on harsh punishment.

Lyrebird is an AI project which, if fed samples of a person’s voice, can read off any text you want in the same voice. See their demo with Obama, Trump, and Hillary (I find them instantly recognizable but not at all Turing-passing). They say making this available is ethical because it raises awareness of the potential risk, which a Facebook friend compared to “selling nukes to ISIS in order to raise awareness of the risk of someone selling nukes to ISIS.”

Freddie deBoer gives lots of evidence that there is no shortage of qualified STEM workers relative to other fields and the industry is actually pretty saturated. But Wall Street Journal seems to think they have evidence for the opposite? Curious what all of the tech workers here think.

Scott Sumner: How Can There Be A Shortage Of Construction Workers? That is, is it at all plausible that (as help wanted ads would suggest) there are areas where construction companies can’t find unskilled laborers willing to work for $90,000/year? Sumner splits this question in two – first, an economics question of why an efficient market wouldn’t cause salaries to rise to a level that guarantees all jobs get filled. And second, a political question of how this could happen in a country where we’re constantly told that unskilled men are desperate because there are no job opportunities for them anymore. The answers seem to be “there’s a neat but complicated economics reason for the apparent inefficiency” and “the $90,000 number is really misleading but there may still be okay-paying construction jobs going unfilled and that’s still pretty strange”.

Study which is so delightfully contrarian I choose to reblog it before reading it all the way through: mandatory class attendance policies in college decrease grades by preventing students from making rational decisions about when and how to study.

Towards explaining non-shared-environment effects on intelligence, psychiatric disorders, and other cognitive traits - developmental noise such as post-conception mutations in individual cells or groups of cells

In a simple model, a given mutant has an equilibrium frequency μ/s, when μ is the mutation rate from good to bad alleles and s is the size of the selective disadvantage. To estimate the total impact of mutation at that locus, you multiply the frequency by the expected harm, s: which means that the fitness decrease (from effects at that locus) is just μ, the mutation rate. If we assume that these fitness effects are multiplicative, the total fitness decrease (also called ‘mutational load’) is approximately 1 – exp(-U), when U is where U=Σ2μ, the total number of new harmful mutations per diploid individual.

https://westhunt.wordpress.com/2012/10/17/more-to-go-wrong/

https://westhunt.wordpress.com/2012/07/13/sanctuary/
interesting, suggestive comment on Africa:
https://westhunt.wordpress.com/2012/07/13/sanctuary/#comment-3671
https://westhunt.wordpress.com/2012/07/14/too-darn-hot/
http://infoproc.blogspot.com/2012/07/rare-variants-and-human-genetic.html
https://westhunt.wordpress.com/2012/07/18/changes-in-attitudes/
https://westhunt.wordpress.com/2012/08/24/men-and-macaques/
I have reason to believe that few people understand genetic load very well, probably for self-referential reasons, but better explanations are possible.

One key point is that the amount of neutral variation is determined by the long-term mutational rate and population history, while the amount of deleterious variation [genetic load] is set by the selective pressures and the prevailing mutation rate over a much shorter time scale. For example, if you consider the class of mutations that reduce fitness by 1%, what matters is the past few thousand years, not the past few tens or hundreds of of thousands of years.

...

So, assuming that African populations have more neutral variation than non-African populations (which is well-established), what do we expect to see when we compare the levels of probably-damaging mutations in those two populations? If the Africans and non-Africans had experienced essentially similar mutation rates and selective pressures over the past few thousand years, we would expect to see the same levels of probably-damaging mutations. Bottlenecks that happened at the last glacial maximum or in the expansion out of Africa are irrelevant – too long ago to matter.

But we don’t. The amount of rare synonymous stuff is about 22% higher in Africans. The amount of rare nonsynonymous stuff (usually at least slightly deleterious) is 20.6% higher. The number of rare variants predicted to be more deleterious is ~21.6% higher. The amount of stuff predicted to be even more deleterious is ~27% higher. The number of harmful looking loss-of-function mutations (yet more deleterious) is 25% higher.

It looks as if the excess grows as the severity of the mutations increases. There is a scenario in which this is possible: the mutation rate in Africa has increased recently. Not yesterday, but, say, over the past few thousand years.

...

What is the most likely cause of such variations in the mutation rate? Right now, I’d say differences in average paternal age. We know that modest differences (~5 years) in average paternal age can easily generate ~20% differences in the mutation rate. Such between-population differences in mutation rates seem quite plausible, particularly since the Neolithic.
https://westhunt.wordpress.com/2016/04/10/bugs-versus-drift/
more recent: https://westhunt.wordpress.com/2017/06/06/happy-families-are-all-alike-every-unhappy-family-is-unhappy-in-its-own-way/#comment-92491
Probably not, but the question is complex: depends on the shape of the deleterious mutational spectrum [which we don’t know], ancient and recent demography, paternal age, and the extent of truncation selection in the population.

https://www.gwern.net/docs/dnb/2010-zeidan.pdf
http://www.pnas.org/content/104/43/17152.short
https://puredhamma.net/wp-content/uploads/Psychological-effects-of-meditation-Sedlmeir-2012.pdf

We observed variation in allelic differentiation between populations at tissue-specific expression quantitative trait loci (eQTL), with greatest effects found for genes expressed in a region of the brain that has been linked to schizophrenia and bipolar disorder. Consistent with this, genome-wide association study regions also showed high levels of population differentiation for these diseases suggesting that loci linked to neurological function evolve rapidly. Clear differences for genetic structure in populations were observed for closely related complex human phenotypes. We show that the evolutionary forces acting at pleiotropic loci are often neutral by comparing directional effects of traits under selection. Our results illustrate the value of within species comparisons to understanding complex trait evolution.

tiny sample size
https://news.ycombinator.com/item?id=14067732
https://www.reddit.com/r/Nootropics/comments/646807/modafinilinduced_changes_in_functional/

Results indicated that the association of education with improved cognitive test scores is not mediated by g, but consists of direct effects on specific cognitive skills. These results suggest a decoupling of educational gains from increases in general intellectual capacity.

look at Model C for the coefficients

How much does education improve intelligence? A meta-analysis: https://psyarxiv.com/kymhp
Intelligence test scores and educational duration are positively correlated. This correlation can be interpreted in two ways: students with greater propensity for intelligence go on to complete more education, or a longer education increases intelligence. We meta-analysed three categories of quasi-experimental studies of educational effects on intelligence: those estimating education-intelligence associations after controlling for earlier intelligence, those using compulsory schooling policy changes as instrumental variables, and those using regression-discontinuity designs on school-entry age cutoffs. Across 142 effect sizes from 42 datasets involving over 600,000 participants, we found consistent evidence for beneficial effects of education on cognitive abilities, of approximately 1 to 5 IQ points for an additional year of education. Moderator analyses indicated that the effects persisted across the lifespan, and were present on all broad categories of cognitive ability studied. Education appears to be the most consistent, robust, and durable method yet to be identified for raising intelligence.

three study designs: control for prior IQ, exogenous policy change, and school age cutoff regression discontinuity

https://westhunt.wordpress.com/2017/11/07/skoptsys/#comment-97601
It’s surprising that there isn’t much of a fadeout (p11) – half of the effect size is still there by age 70 (?!). That wasn’t what I expected. Maybe they’re being pulled upwards by smaller outlier studies – most of the bigger ones tend towards the lower end.

https://twitter.com/gwern/status/928308706370052098
https://archive.is/v98bd
These gains are hollow, as they acknowledge in the discussion. Examples:

A cross-sectional cohort study performed on 36,564 subjects (5,087 Toxoplasma free and 741 Toxoplasma infected) showed that infected and noninfected subjects differ in their sexual behavior, fantasies, and preferences when age, health, and the size of the place where they spent childhood were controlled (F(24, 3719) = 2.800, p < .0001). In agreement with our a priori hypothesis, infected subjects are more often aroused by their own fear, danger, and sexual submission although they practice more conventional sexual activities than Toxoplasma-free subjects. We suggest that the later changes can be related to a decrease in the personality trait of novelty seeking in infected subjects, which is potentially a side effect of increased concentration of dopamine in their brain.

In each brain independently, 84% of unique transcripts on the microarrays (29,412, referred to as genes for this manuscript) were found to be expressed in at least one structure (91.4% overlap in expressed gene sets between brains), consistent with the percentage of genes expressed in mouse brain by ISH (80%; ref. 1) and fetal human brain by microarrays (76%; ref. 11).

A number of recent studies suggest that some polymorphisms in the androgen receptor gene AR, the dopamine receptor gene DRD4, and the 5-HTTLPR VNTR of the serotonin transporter gene are associated with risk acceptance versus prudence and a short-term versus long-term time orientation, which are important aspects of LHS. We integrated studies from diverse nations reporting the prevalence of these three polymorphisms for many countries. We collected national indices for each of the three polymorphisms and found that they define a strong, single factor, yielding a single LHS-related, national genetic index. As expected, this index is strongly associated with reported national measures of LHS and time orientation, even after controlling for socioeconomic variables. The genetic effect seems especially strong across societies with high socioeconomic inequality.

https://twitter.com/DoctorOcelot/status/836672661736550403

Having an accurate evaluation of a syndrome as a generally bad thing isn’t equivalent to attacking those with that syndrome. Being a leper is a bad thing, not just another wonderful flavor of humanity [insert hot tub joke] , but that doesn’t mean that we have to spend our spare time playing practical jokes on lepers, tempting though that is.. Leper hockey. We can cure leprosy, and we are right to do so. Preventing deafness through rubella vaccination was the right thing too – deafness sucks. And so on. As we get better at treating and preventing, humans are going to get more uniform – and that’s a good thing. Back to normalcy!

focus: https://westhunt.wordpress.com/2017/02/22/neurodiversity/#comment-88691
interesting discussion of mutational load: https://westhunt.wordpress.com/2017/02/22/neurodiversity/#comment-88793

https://westhunt.wordpress.com/2013/04/30/blurry/
I was thinking again about the consequences of having more small-effect deleterious mutations than average. I don’t think that they would push hard in a particular direction in phenotype space – I don’t believe they would make you look weird, but by definition they would be bad for you, reduce fitness. I remembered a passage in a book by Steve Stirling, in which our heroine felt as if her brain ‘was moving like a mechanism of jewels and steel precisely formed.’ It strikes me that a person with an extra dollop of this kind of genetic load wouldn’t feel like that. And of course that heroine did have low genetic load, being the product of millennia of selective breeding, not to mention an extra boost from the Invisible Crown.

https://westhunt.wordpress.com/2013/04/30/blurry/#comment-12769
Well, what does the distribution of fitness burden by frequency look like for deleterious mutations of a given fitness penalty?
--
It’s proportional to the mutation rate for that class. There is reason to believe that there are more ways to moderately or slightly screw up a protein than to really ruin it, which indicates that mild mutations make up most load in protein-coding sequences. More of the genome is made up of conserved regulatory sequences, but mutations there probably have even milder effects, since few mutations in non-coding sequences cause a serious Mendelian disease.

https://westhunt.wordpress.com/2013/04/30/blurry/#comment-12803
I have wondered if there was some sort of evolutionary tradeoff between muscles and brains over the past hundred thousand years through dystrophin’s dual role. There is some evidence of recent positive selection among proteins that interact with dystrophin, such as DTNBP1 and DTNA.

Any novel environment where higher intelligence can accrue more caloric energy than brute strength alone (see: the invention of the bow) should relax the selection pressure for muscularity. The Neanderthals didn’t fare so well with the brute strength strategy.
--
Sure: that’s what you might call an inevitable tradeoff, a consequence of the laws of physics. Just as big guys need more food. But because of the way our biochemistry is wired, there can be tradeoffs that exist but are not inevitable consequences of the laws of physics – particularly likely when a gene has two fairly different functions, as they often do.

The origins of human abilities for mathematics are debated: Some theories suggest that they are founded upon evolutionarily ancient brain circuits for number and space and others that they are grounded in language competence. To evaluate what brain systems underlie higher mathematics, we scanned professional mathematicians and mathematically naive subjects of equal academic standing as they evaluated the truth of advanced mathematical and nonmathematical statements. In professional mathematicians only, mathematical statements, whether in algebra, analysis, topology or geometry, activated a reproducible set of bilateral frontal, Intraparietal, and ventrolateral temporal regions. Crucially, these activations spared areas related to language and to general-knowledge semantics. Rather, mathematical judgments were related to an amplification of brain activity at sites that are activated by numbers and formulas in nonmathematicians, with a corresponding reduction in nearby face responses. The evidence suggests that high-level mathematical expertise and basic number sense share common roots in a nonlinguistic brain circuit.

Usually, learning immediately after training is so unstable that it can be disrupted by subsequent new learning until after passive stabilization occurs hours later. However, overlearning so rapidly and strongly stabilizes the learning state that it not only becomes resilient against, but also disrupts, subsequent new learning. Such hyperstabilization is associated with an abrupt shift from glutamate-dominant excitatory to GABA-dominant inhibitory processing in early visual areas. Hyperstabilization contrasts with passive and slower stabilization, which is associated with a mere reduction of excitatory dominance to baseline levels. Using hyperstabilization may lead to efficient learning paradigms.

Our initial meta-analysis supported the association of the DRD4 C-521T polymorphism, but not the VNTR polymorphism, with approach-related traits. This conclusion was qualified by evidence of significant publication bias and the failure to detect association in a replication sample comprising individuals at the extremes of the trait distribution. The association of the C-521T polymorphism observed in our initial meta-analysis was robust to the inclusion of these new data, but our revised meta-analysis indicated that the association was present for measures of novelty seeking and impulsivity but not for measures of extraversion.

Meta-analysis of the heterogeneity in association of DRD4 7-repeat allele and AD/HD: stronger association with AD/HD combined type: https://www.ncbi.nlm.nih.gov/pubmed/20468072

Molecular Psychiatry - High prevalence of rare dopamine receptor D4 alleles in children diagnosed with attention-deficit hyperactivity disorder: http://www.nature.com/mp/journal/v8/n5/full/4001350a.html

The D4 dopamine receptor (DRD4) locus may be a model system for understanding the relationship between genetic variation and human cultural diversity. It has been the subject of intense interest in psychiatry, because bearers of one variant are at increased risk for attention deficit hyperactivity disorder (ADHD) (1). A survey of world frequencies of DRD4 alleles has shown striking differences among populations (2), with population differences greater than those of most neutral markers. In this issue of PNAS Ding et al. (3) provide a detailed molecular portrait of world diversity at the DRD4 locus. They show that the allele associated with ADHD has increased a lot in frequency within the last few thousands to tens of thousands of years, although it has probably been present in our ancestors for hundreds of thousands or even millions of years.

...

Because the prominent phenotypic effects of 7R are in males, we need to ask what is the niche in human societies for males who are energetic, impulsive (i.e., unpredictable), and noncompliant? Whereas tests of hypotheses ought to be careful and conservative, generation of hypotheses ought to be speculative and free-ranging. There is a tradition of caution approaching self-censorship in discussions of human biological diversity, but we will break that tradition in what follows.

https://twitter.com/whyvert/status/827182543594086400
http://ipsr.berkeley.edu/uploads/department_events/1455839539-b80d181fb169f70b4/Dopamine-system%20genes%20and%20cultural%20acquisition.pdf

Deep learning has revolutionized the world of artificial intelligence. But how much does it improve performance? How have computers gotten better at different tasks over time, since the rise of deep learning?

In games, what the data seems to show is that exponential growth in data and computation power yields exponential improvements in raw performance. In other words, you get out what you put in. Deep learning matters, but only because it provides a way to turn Moore’s Law into corresponding performance improvements, for a wide class of problems. It’s not even clear it’s a discontinuous advance in performance over non-deep-learning systems.

In image recognition, deep learning clearly is a discontinuous advance over other algorithms. But the returns to scale and the improvements over time seem to be flattening out as we approach or surpass human accuracy.

In speech recognition, deep learning is again a discontinuous advance. We are still far away from human accuracy, and in this regime, accuracy seems to be improving linearly over time.

In machine translation, neural nets seem to have made progress over conventional techniques, but it’s not yet clear if that’s a real phenomenon, or what the trends are.

In natural language processing, trends are positive, but deep learning doesn’t generally seem to do better than trendline.

...

The learned agent performs much better than the hard-coded agent, but moves more jerkily and “randomly” and doesn’t know the law of reflection. Similarly, the reports of AlphaGo producing “unusual” Go moves are consistent with an agent that can do pattern-recognition over a broader space than humans can, but which doesn’t find the “laws” or “regularities” that humans do.

Perhaps, contrary to the stereotype that contrasts “mechanical” with “outside-the-box” thinking, reinforcement learners can “think outside the box” but can’t find the box?

http://slatestarcodex.com/2017/08/02/where-the-falling-einstein-meets-the-rising-mouse/

r ≈ .3

The results indicate that mental rotation skills are significantly correlated with wayfinding performance on an orienteering task. The findings also replicate sex differences in spatial ability as found in laboratory-scale studies. However, the findings complicate the discussion of mental rotation skills and sex because women often performed as well as men despite having lower mean test scores. This suggests that mental rotation ability may not be as necessary for some women's wayfinding as it is for men's navigation.

Sex Differences in Furniture Assembly Performance: An Experimental Study: http://onlinelibrary.wiley.com/doi/10.1002/acp.3182/abstract
fucking lol

Sex hormones predict the sensory strength and vividness of mental imagery: https://www.ncbi.nlm.nih.gov/pubmed/25703930
- not in the direction I would expect (women have more vivid mental imagery)
- visual working memory is different

Sex hormones and mental rotation: An intensive longitudinal investigation: http://www.sciencedirect.com.sci-hub.tw/science/article/pii/S0018506X12003066
For males and females, estradiol and testosterone were significantly linearly and quadratically related to interindividual variation in performance at the beginning of the study (progesterone was linearly related to performance for females). The association between testosterone and performance differed across sexes: for males, it had an inverse U-shape, for females it was U-shaped. Towards the end of the study, none of the hormones were significantly related to performance anymore. Thus, the relationship between hormones and mental rotation performance disappeared with repeated testing.

very confusing study. seems sketchy.

Is There a Relationship Between the Performance in a Chronometric Mental-Rotations Test and Salivary Testosterone and Estradiol Levels in Children Aged 9–14 Years?: http://sci-hub.tw/10.1002/dev.21333
Results showed a significant gender difference in reaction time and rotational speed in favor of boys, and a significant age, but no gender difference in testosterone and estradiol levels. We found no significant relationships between hormonal levels and any measure of mental-rotation performance.

Having a Male Co-Twin Masculinizes Mental Rotation Performance in Females: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4438761/
There were 351 females from same-sex pairs, 223 males from same-sex pairs, 120 females from opposite-sex pairs, and 110 males from opposite-sex pairs.

hmm:
Sex Differences in Mental Rotation Ability Are a Consequence of Procedure and Artificiality of Stimuli: https://link.springer.com/article/10.1007/s40806-017-0120-x
Our results suggest that the sex difference found on this test is not due to a male advantage in spatial ability, but is an artifact of the stimuli.

The more g-loaded, the more heritable, evolvable, and phenotypically variable: Homology with humans in chimpanzee cognitive abilities: http://www.sciencedirect.com/science/article/pii/S0160289615000495

Relaxed genetic control of cortical organization in human brains compared with chimpanzees: http://www.pnas.org/content/112/48/14799.abstract
We show that the morphology of the human cerebral cortex is substantially less genetically heritable than in chimpanzees and therefore is more responsive to molding by environmental influences. This anatomical property of increased plasticity, which is likely related to the human pattern of development, may underlie our species’ capacity for cultural evolution.

The heritability of chimpanzee and human brain asymmetry: http://rspb.royalsocietypublishing.org/content/283/1845/20161319
humans more lateralized and have lower heritability for degree of asymmetry

A Review of Cognitive Abilities in Dogs, 1911 Through 2016: More Individual Differences, Please!: http://www.lse.ac.uk/CPNSS/people/Staff/rosalind-arden/arden-psychological-science-2016.pdf

A possible structural correlate of learning performance on a colour discrimination task in the brain of the bumblebee: http://rspb.royalsocietypublishing.org/content/royprsb/284/1864/20171323.full.pdf
Here, we explored how the density of microglomeruli (synaptic complexes) within specific regions of the bumblebee (Bombus terrestris) brain relates to both visual learning and inter-individual differences in learning and memory performance on a visual discrimination task. Using whole-brain immunolabelling, we measured the density of microglomeruli in the collar region (visual association areas) of the mushroom bodies of the bumblebee brain. We found that bumblebees which made fewer errors during training in a visual discrimination task had higher microglomerular density.

- a few for conscientiousness and neuroticism, several for extraversion
- specific SNPs linking conscientiousness and educational attainment, extraversion and low neuroticism
- neuroticism SNP located in region that is "related to innate immunity and the nervous system and [...] a potential hub for cancer and developmental neuropsychiatric disorders" (!!, X germ hypothesis!)
- neuroticism negatively correlated w/ other 4 personality factors (general positive personality factor?)
- ADHD strongly correlated w/ extraversion
- openness correlated w/ bipolar disorder and schizophrenia (and also depression)
- neuroticism correlated w/ depression
- conscientiousness protective against just about every disorder

Two competing philosophical paradigms characterize approaches to the evolution of the human mind. One postulates continuity between animal and human behavioral capacities. The other assumes that humans and animals are separated by major qualitative behavioral and mental gaps. This paper presents a continuity model that suggests that expanded human mental capacities primarily reflect the increased information processing capacities of the enlarged human brain including the enlarged neocortex, cerebellum, and basal ganglia. These increased information processing capacities enhance human abilities to combine and recombine highly differentiated actions, perceptions, and concepts in order to construct larger, more complex, and highly variable behavioral units in a variety of behavioral domains including language, social intelligence, tool-making, and motor sequences.

There are plenty of other challenges that humans of that era (~100,000 years ago) never met: for example they never colonized the high Arctic, the Americas, or Australia/New Guinea. Even though Neanderthals and Africans had brains that were as large as or larger than those of modern humans, even though humans in Africa were reasonably modern-looking, modern behavioral capacities did not yet exist. They didn't yet have the spark. Come to think of it, most people today still don't. We'll have more to say on that in a moment.

...

The Neanderthals had big brains (averaging about 1500 cubic centimeters, noticeably larger than those of modern people) and a technology like that of their anatomically modern contemporaries in Africa, but were quite different in a number of ways: different physically, but also socially and ecologically. Neanderthals were cold-adapted, with relatively short arms and legs in order to reduce heat loss - something like Arctic peoples today, only much more so. Considering that the climate the Neanderthals experienced was considerably milder than the high Arctic (more like Wisconsin), their pronounced cold adaptation suggest that they may have relied more on physical than cultural changes. Of course they spent at least six times as many generations in the cold as any modern human population has, and that may have had something to do with it as well.

...

Like other early humans, Neanderthals were relatively uncreative; their tools changed very slowly and they show no signs of art, symbolism, or trade. Their brains were large and had grown larger over time, in parallel with humans in Africa, but we really have no idea what they did with them. Since brains are metabolically expensive, natural selection wouldn't have favored an increase in brain size unless it increased fitness, but we don't know what function that those big brains served. Usually people explain that those big brains are not as impressive as they seem, since the brain-to-body weight ratio is what’s really important, and Neanderthals were heavier than modern humans of the same height.

You may wonder why we normalize brain size by body weight. We wonder as well.

Among less intelligent creatures, such as amphibians and reptiles, most of the brain is busy dealing with a flood of sensory data. You’d expect that brain size would have to increase with body size in some way in order to keep up. If you assume that the key is how much surface the animal has, in order to monitor what’s causing that nagging itch and control all the muscles needed for movement, brain size should scale as the 2/3rds power of weight. If an animal has a brain that’s bigger than predicted by that 2/3rds power scaling law, then maybe it’s smarter than average. That argument works reasonable well for a wide range of species, but it can’t make sense for animals with big brains. In particular it can’t make sense for primates, since in that case we know that most of the brain is used for purposes other than muscle control and immediate reaction to sensation. Look at this way - if dividing brain volume by weight is a valid approach, Nero Wolfe must be really, really stupid.

We think that Neanderthal brains really were large, definitely larger than those of people today. This doesn’t necessarily mean that they were smarter, at least not as a culture. The archaeological record certainly indicates that they were not, since their material culture was definitely simpler than that of their successors. In fact, they may have been relatively unintelligent, even with their big brains. Although brain size certainly is correlated with intelligence in modern humans, it is not the only factor that affects intelligence. By the way, you may have read somewhere (The Mismeasure of Man) that brain volume has no relationship to intelligence, but that’s just a lie.

One paradoxical possibility is that Neanderthals lacked complex language and so had to be smart as individuals in order to learn their culture and technology, while that same lack severely limited their societal achievements. Complex language of the type we see in modern humans makes learning a lot easier: without it, learning to create even Mousterian tools may have been difficult. In that case, individuals would have to repeatedly re-invent the wheel (so to speak) while there would have been little societal progress.

It could also be that Neanderthal brains were less powerful than you’d expect because there just weren’t enough Neanderthals. That may sound obscure, but bear with us. The problem is that evolution is less efficient in small populations, in the same way that any statistical survey – polls, for example -becomes less accurate with fewer samples.

...

Our favorite hypothesis is that Neanderthals and other archaic humans had a fundamentally different kind of learning than moderns. One of the enduring puzzles is the near-stasis of tool kits in early humans - as we have said before, the Acheulean hand-axe tradition last for almost a million years and extended from the Cape of Good Hope to Germany, while the Mousterian lasted for a quarter of a million years. Somehow these early humans were capable of transmitting a simple material culture for hundreds of thousands of years with little change. More information was transmitted to the next generation than in chimpanzees, but not as much as in modern humans. At the same time, that information was transmitted with surprisingly high accuracy. This must be the case, since random errors in transmission would have caused changes in those tool traditions, resulting in noticeable variation over space and time – which we do not see.

It looks to us as if toolmaking in those populations was, to some extent, innate: genetically determined. Just as song birds are born with a rough genetic template that constrains what songs are learned, early humans may have been born with genetically determined behavioral tendencies that resulted in certain kinds of tools. Genetic transmission of that information has the characteristics required to explain this pattern of simple, near-static technology, since only a limited amount of information can be acquired through natural selection, while the information that is acquired is transmitted with very high accuracy.

...

Starting 70,000 or 80,000 years ago, we begin to see some signs of increased cultural complexity in Africa. There is evidence of long-distance transport of tool materials (obsidian) in Ethiopia, which could be the first signs of trade. A set of pierced snail shells (~75,000 years old) in Blombos Cave in South Africa seem, judging from wear, to be the remains of a necklace, although there is no evidence that tools were used to pierce the shells. In that same site, researchers found pieces of ochre with a crosshatched pattern inscribed. We have found manufactured ostrich-egg beads in Kenya that are about 50,000 years old, the first clear examples of artificial decorative or symbolic (that is to say, useless) objects. We see a new kind of small stone points that must have been used on darts that were considerably smaller than previous spears. Although it would seem likely that such darts would have been propelled by atlatls, no atlatls have yet been found that date anywhere near that far back. There are reports of 90,000 year-old bone fish spears from central Africa which, if correct, would be evidence of a significant advance in tool complexity. However, since no other similar tools found in Africa are older than 30,000 years, those fish spears are roughly as anomalous as a Neanderthal-era thumb drive, and we have our doubts about that date. On the whole, the African archeological data of this period furnishes examples of new technology and simple symbolic objects, but the evidence is patchy, and it seems that some innovations appeared and then faded away for reasons that we don’t understand.

A note on behavioral modernity: the consensus seems to be that any clear evidence of a population making symbolic or decorative objects establishes their behavioral modernity, defined as cultural creativity and reliance on abstract thought. For some reason, anthropologists treat behavioral modernity as a qualitative character: an ancient population either had it or not, just as women are pregnant or not, never a ‘little bit pregnant’. It’s treated as a Boolean variable. Like so many basic notions in anthropology, this makes no sense. The components of ‘behavioral modernity’ had to be evolved traits with heritable variation, subject to natural selection – how else would they have come into existence at all? Surely ancient individuals and populations varied in their capacity for abstract thought and cultural innovation – behavioral modernity must be more like height than pregnancy.

...

The fact the ability to learn complex new ideas and transmit them to the next generation is universal in modern humans suggests that natural selection favored that kind of receptivity. On the other hand, the rarity of individual creativity suggests that the trait itself was not favored by selection in the past, but is instead a rare side effect.

We think that the archaeological record in Africa before the expansion of modern humans shows a gradual but slow increase in such abilities, which is the usual pattern for a trait favored by selection. On the other hand, the rate of change in the European Upper Paleolithic seems faster, almost discontinuous – but there is a well-understood biological pattern that may explain that as well.

The most dramatic evidence of some kind of significant change is the fact that anatomically modern humans expanded out of Africa about 50,000 years ago.

http://www.unz.com/article/iq-or-the-mathverbal-split/
Commenter Gwen on the blog Infoproc hints at a possible neurological basis for this phenomenon, stating that “one bit of speculation I have: the neuroimaging studies seem to consistently point towards efficiency of global connectivity rather than efficiency or other traits of individual regions; you could interpret this as a general factor across a wide battery of tasks because they are all hindered to a greater or lesser degree by simply difficulties in coordination while performing the task; so perhaps what causes Spearman is global connectivity becoming around as efficient as possible and no longer a bottleneck for most tasks, and instead individual brain regions start dominating additional performance improvements. So up to a certain level of global communication efficiency, there is a general intelligence factor but then specific abilities like spatial vs verbal come apart and cease to have common bottlenecks and brain tilts manifest themselves much more clearly.” [10] This certainly seem plausible enough. Let’s hope that those far smarter than ourselves will slowly get to the bottom of these matters over the coming decades.

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My main prediction here then is that based on HBD, I don’t expect China or East Asia to rival the Anglosphere in the life sciences and medicine or other verbally loaded scientific fields. Perhaps China can mirror Japan in developing pockets of strengths in various areas of the life sciences. Given its significantly larger population, this might indeed translate into non-trivial high-end output in the fields of biology and biomedicine. The core strengths of East Asian countries though, as science in the region matures, will lie primarily in quantitative areas such as physics or chemistry, and this is where I predict the region will shine in the coming years. China’s recent forays into quantum cryptography provide one such example. [40]

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In fact, as anyone who’s been paying attention has noticed, modern day tech is essentially a California and East Asian affair, with the former focused on software and the latter more so on hardware. American companies dominate in the realm of internet infrastructure and platforms, while East Asia is predominant in consumer electronics hardware, although as noted, China does have its own versions of general purpose tech giants in companies like Baidu, Alibaba, and Tencent. By contrast, Europe today has relatively few well known tech companies apart from some successful apps such as Spotify or Skype and entities such as Nokia or Ericsson. [24] It used to have more established technology companies back in the day, but the onslaught of competition from the US and East Asia put a huge dent in Europe’s technology industry.

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Although many will point to institutional factors such as China or the United States enjoying large, unfragmented markets to explain the decline of European tech, I actually want to offer a more HBD oriented explanation not only for why Europe seems to lag in technology and engineering relative to America and East Asia, but also for why tech in the United States is skewed towards software, while tech in East Asia is skewed towards hardware. I believe that the various phenomenon described above can all be explained by one common underlying mechanism, namely the math/verbal split. Simply put, if you’re really good at math, you gravitate towards hardware. If your skills are more verbally inclined, you gravitate towards software. In general, your chances of working in engineering and technology are greatly bolstered by being spatially and quantitatively adept.

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If my assertions here are correct, I predict that over the coming decades, we’ll increasingly see different groups of people specialize in areas where they’re most proficient at. This means that East Asians and East Asian societies will be characterized by a skew towards quantitative STEM fields such as physics, chemistry, and engineering and towards hardware and high-tech manufacturing, while Western societies will be characterized by a skew towards the biological sciences and medicine, social sciences, humanities, and software and services. [41] Likewise, India also appears to be a country whose strengths lie more in software and services as opposed to hardware and manufacturing. My fundamental thesis is that all of this is ultimately a reflection of underlying HBD, in particular the math/verbal split. I believe this is the crucial insight lacking in the analyses others offer.

http://www.unz.com/article/iq-or-the-mathverbal-split/#comment-2230751

Sailer In TakiMag: What Does the Deep History of China and India Tell Us About Their Futures?: http://takimag.com/article/a_pair_of_giants_steve_sailer/print#axzz5BHqRM5nD
In an age of postmodern postnationalism that worships diversity, China is old-fashioned. It’s homogeneous, nationalist, and modernist. China seems to have utilitarian 1950s values.

For example, Chinese higher education isn’t yet competitive on the world stage, but China appears to be doing a decent job of educating the masses in the basics. High Chinese scores on the international PISA test for 15-year-olds shouldn’t be taken at face value, but it’s likely that China is approaching first-world norms in providing equality of opportunity through adequate schooling.

Due to censorship and language barriers, Chinese individuals aren’t well represented in English-language cyberspace. Yet in real life, the Chinese build things, such as bridges that don’t fall down, and they make stuff, employing tens of millions of proletarians in their factories.

The Chinese seem, on average, to be good with their hands, which is something that often makes American intellectuals vaguely uncomfortable. But at least the Chinese proles are over there merely manufacturing things cheaply, so American thinkers don’t resent them as much as they do American tradesmen.

Much of the class hatred in America stems from the suspicions of the intelligentsia that plumbers and mechanics are using their voodoo cognitive ability of staring at 3-D physical objects and somehow understanding why they are broken to overcharge them for repairs. Thus it’s only fair, America’s white-collar managers assume, that they export factory jobs to lower-paid China so that they can afford to throw manufactured junk away when it breaks and buy new junk rather than have to subject themselves to the humiliation of admitting to educationally inferior American repairmen that they don’t understand what is wrong with their own gizmos.

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This Chinese lack of diversity is out of style, and yet it seems to make it easier for the Chinese to get things done.

In contrast, India appears more congenial to current-year thinkers. India seems postmodern and postnationalist, although it might be more accurately called premodern and prenationalist.

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Another feature that makes our commentariat comfortable with India is that Indians don’t seem to be all that mechanically facile, perhaps especially not the priestly Brahmin caste, with whom Western intellectuals primarily interact.

And the Indians tend to be more verbally agile than the Chinese and more adept at the kind of high-level abstract thinking required by modern computer science, law, and soft major academia. Thousands of years of Brahmin speculations didn’t do much for India’s prosperity, but somehow have prepared Indians to make fortunes in 21st-century America.

http://www.sciencedirect.com/science/article/pii/S0160289616300757
- Study used two moderately large American community samples.
- Verbal and not nonverbal ability drives relationship between ability and ideology.
- Ideology and ability appear more related when ability assessed professionally.
- Self-administered or nonverbal ability measures will underestimate this relationship.

https://www.unz.com/gnxp/the-universal-law-of-interpersonal-dynamics/
Every once in a while I realize something with my conscious mind that I’ve understood implicitly for a long time. Such a thing happened to me yesterday, while reading a post on Stalin, by Amritas. It is this:

S = P + E

Social Status equals Political Capital plus Economic Capital

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Here’s an example of its explanatory power: If we assume that a major human drive is to maximize S, we can predict that people with high P will attempt to minimize the value of E (since S-maximization is a zero-sum game). And so we see. Throughout history there has been an attempt to ennoble P while stigmatizing E. Conversely, throughout history, people with high E use it to acquire P. Thus, in today’s society we see that socially adept people, who have inborn P skills, tend to favor socialism or big government – where their skills are most valuable, while economically productive people are often frustrated by the fact that their concrete contribution to society is deplored.

Now, you might ask yourself why the reverse isn’t true, why people with high P don’t use it to acquire E, while people with high E don’t attempt to stigmatize P? Well, I think that is true. But, while the equation is mathematically symmetrical, the nature of P-talent and E-talent is not. P-talent can be used to acquire E from the E-adept, but the E-adept are no match for the P-adept in the attempt to stigmatize P. Furthermore, P is endogenous to the system, while E is exogenous. In other words, the P-adept have the ability to manipulate the system itself to make P-talent more valuable in acquiring E, while the E-adept have no ability to manipulate the external environment to make E-talent more valuable in acquiring P.

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1. All institutions will tend to be dominated by the P-adept
2. All institutions that have no in-built exogenous criteria for measuring its members’ status will inevitably be dominated by the P-adept
3. Universities will inevitably be dominated by the P-adept
4. Within a university, humanities and social sciences will be more dominated by the P-adept than … [more]

Fluid intelligence is a crucial cognitive ability that predicts key life outcomes across the lifespan. Strong empirical links exist between fluid intelligence and processing speed on the one hand, and white matter integrity and processing speed on the other. We propose a watershed model that integrates these three explanatory levels in a principled manner in a single statistical model, with processing speed and white matter figuring as intermediate endophenotypes. We fit this model in a large (N=555) adult lifespan cohort from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) using multiple measures of processing speed, white matter health and fluid intelligence. The model fit the data well, outperforming competing models and providing evidence for a many-to-one mapping between white matter integrity, processing speed and fluid intelligence. The model can be naturally extended to integrate other cognitive domains, endophenotypes and genotypes.

The primary outcome measure was the effect on cognitive function (CF) after the consumption of meals varying in GI. Eleven eligible studies were identified. The age range of the participants varied from 6 to 82 y old. Overall, the findings were inconsistent, with some studies showing benefits toward either the high-GI or the low-GI meal, others not finding any differences between the 2 meals, and other studies showing a positive or negative effect on performance on only some cognitive domain or domains after consumption of 1 of the 2 meals. A number of methodologic and confounding factors were identified that could explain these inconsistencies.

mixed evidence for ego-depletion:
We did not find a uniform influence of blood glucose on decision making. Instead, we found that low levels of blood glucose increase the willingness to pay and willingness to work when a situation is food related, but decrease willingness to pay and work in all other situations. Low levels of blood glucose increase the future discount rate for food; that is, decision makers become more impatient, and to a lesser extent increase the future discount rate for money. Low levels of blood glucose also increase the tendency to make more intuitive rather than deliberate decisions. However, this effect was only observed in situations unrelated to food.
http://daniellakens.blogspot.nl/2017/07/impossibly-hungry-judges.html