Doing my part for scientific literacy
Here's a familiar picture:
Endocranial volumes of Pleistocene Homo
MRG selection and the evolution of pain
I ran across a 2003 paper on the evidence of recent positive selection on the MRG gene family in humans. This gene family is specific to neurons in its activity, and specifically limited to "nociceptive sensory neurons of the dorsal root and trigeminal ganglia," in other words, neurons involved in the perception of pain.
They find evidence of positive selection in both humans and mice, and further infer that at least some of the selection in the human lineage occurred recently in evolutionary history (although they do not compare with apes to test this hypothesis).
Pairwise comparisons of evolutionary distances were carried out on these genes. They showed that human MRGX genes are closely related to one another (average pairwise Ks is 0.15; Fig. 1A). The Ks between even the most distantly related MRGX genes is much lower than the average Ks between human and mouse orthologs estimated at 0.47 (Makolowski and Boguski 1998). Hence, the human MRGX subfamily likely arose from recent amplifications that postdated human-mouse divergence. In particular, MRGX4 and 5 show very little synonymous divergence (Ks = 0.006), indicating that they are likely the result of a duplication postdating human-chimp divergence (average Ks between human and chimp orthologs is around 0.015) (Choi and Lahn 2003:2252, references therein).
On the function of the genes and the possible source of selection, they have this to say:
The perception of hazardous stimuli as being painfulÑand the subsequent avoidance of such stimuli -- are critical to the survival of animals. When a species encounters evolutionary shifts in ecological conditions (such as habitat, climate, diet, predator-prey relationship, and social interactions), or its own internal physiology, previously innocuous stimuli may now impair fitness, and conversely, formerly aversive stimuli may become harmless or even beneficial. In the face of such evolutionary changes, a species would be under selective pressure to tune its nociceptive sensitivity and selectivity, so as to continue to correctly interpret those stimuli that endanger survival as being painful, while remaining undisturbed by innocuous stimuli (Kavaliers 1988). We hypothesize that such selective pressure has operated on MRG to drive its rampant amplification and fast protein evolution. Indeed, nociceptive properties do vary remarkably between species, among individuals of the same species, and between genders (Kavaliers 1988). Humans, for example, exhibit highly variable sensitivity to pain, including drastically different responses to identical injuries or pathologies (Libman 1934; Chen et al. 1989). Exceptionally heightened or reduced nociceptive sensitivity can have severe or even fatal consequences (Indo et al. 1996; Ophoff et al. 1996; Friedberg and Jason 2001). Similarly, closely related laboratory mice can differ by orders of magnitude in their pain threshold to noxious stimuli (Mogil et al. 1999). Such diversity in nociceptive response attests to the dynamic quality in the evolution of nociception (Kavaliers 1988). It will be of interest to see what role MRG plays in between- or within-species differences of nociception (Choi and Lahn 2003:2256, references therein).
This gene accompanies several other genes related to brain function whose history of positive selection has been uncovered by the Lahn lab, including those covered by Dorus (2004), discussed in another post.
References:
Choi SS and Lahn BT. 2003. Adaptive evolution of MRG, a neuron-specific gene family implicated in nociception. Genome Res 13:2252-2259. Genome Research Online
Brain evolution::what do the genes say?
Reference: Dorus, S. et al.. 2004. "Accelerated evolution of nervous system genes in the origin of Homo sapiens." Cell 119:1027-1040.
A news story on the study is available at Medical News Today .
This paper examines "nervous system genes" in humans and other species to determine how many such genes may have undergone adaptive evolution during the course of human evolution. "Nervous system genes" were identified by amplifying cDNA (complementary DNA sequences from mRNA molecules) reflecting activity in brain tissue. The activity of these genes is by and large not known, although the authors do make a division between genes with a primarily homeostatic function and genes that may function in nervous system development.
The paper comes out of Bruce Lahn's lab at the University of Chicago. They have done a substantial amount of work on finding genes responsible for brain development in humans, and have previously published papers on two genes that may have undergone adaptive evolution during hominoid or human evolution in association with changes in brain functioning, ASPM and MCPH1. In both of those cases, the evidence for adaptive evolution came from comparisons in substitution rate between living humans and other anthropoid species.
Here, the comparisons involve 214 candidate genes in four species: rats mice, macaques, and humans. First, the differences between rats and mice are compared to the differences between humans and macaques. Although they don't put it this way, the idea is to test the null hypothesis that primates and rodents had the same rate of molecular change in these genes. They find that the primates had an apparently higher rate of molecular change, with a higher rate of protein change as opposed to synonymous change in the coding sequences of these genes.
Second, the comparison of the human lineage with the macaque lineage--rooted by the rodent outgroup--makes it clear that humans have had an especially high rate of adaptive evolution in these genes compared to other primates. This finding is supported by some comparisons with chimpanzees, which appear to show that human evolution itself during the past 7 million years has involved faster nervous system evolution than in chimpanzees, and presumably other primates.
An alternative hypothesis for a more rapid evolution of these genes in primates would be relaxed selective constraint on them. Finding that the rate of nonsynonymous changes was still low in these genes, the authors argue that this hypothesis is unlikely. To additionally test it, they compared a set of "housekeeping" genes that perform basic cellular functions that are likely to be conserved among species. I'm not convinced by the relevance of this particular comparison, since relaxed selection could certainly have occurred for nervous system genes--say, if learning causes greater phenotypic plasticity, the heritability of adaptive behaviors might decline and weaken the strength of selection on alleles correlated with adaptive behaviors. I don't believe this story, just noting that continued selection on housekeeping functions probably doesn't argue strongly for any hypothesis regarding nervous system genes.
A very interesting part is the focus on "developmentally biased" genes that are active especially during the formation of nervous system structures during development. They find that these genes have a higher rate of change in primates than other kinds of nervous system genes. Additionally, looking only at those 24 genes that had an exceptionally high number of amino acid changes during primate evolution, 7 of them affect brain size and 10 directly affect behavior in knockout mice. The authors conclude that this is evidence for a broad-based evolution of brain function and size, rather than any single critical genetic change being responsible.
On a few levels, the paper is sort of ugly. I wouldn't be so quick to assume that the rat-mouse divergence time was around 16 million years, or roughly equivalent to the human-macaque divergence. There is a lot of debate about the timing of rodent divergences, with paleontologists typically supporting lower dates for the rat-mouse divergence, and some geneticists arguing for much more ancient dates than the Miocene. And the human-macaque divergence was at least Oligocene in age, probably substantially older than 30 million years. None of this is essential to their study, but it's a little sloppy. Likewise, the use of encephalization quotient seems like an unnecessarily blunt instrument. They need a better argument for why a larger brain size should imply a substantial genetic difference rather than a simple one. But these are criticisms of form rather than of substance--just an observation that more interaction between bioinformaticians and morphologists would be fruitful and would add to studies like this one.
Basically, I think this paper is a great example of what someone can do with bioinformatics resources alone today. There is no reason why anyone else in the world couldn't have put together an equivalent set of data, although Lahn's team certainly has the advantages of familiarity with many of these genes from their work characterizing nervous system development. I expect to see many more syntheses like this one in the future.
That said, the cleverness is in matching the form to the hypothesis. Here, the use of the four species was essential because these four have large sequence sets available on GenBank. The comparison of the four was informative because the question was about the rate of change. The chimpanzee--which also has a lot of sequence available--was less useful because its similarity with humans makes it difficult to test for equality of rates. Other kinds of hypotheses are going to be tested differently, with different data and different combinations of things. Finding individual genes that caused events during the evolution of humans or other species is going to be hard or impossible. But statistical comparisons like this one will help to identify the kinds of events that are likely to happen, and that may have been important in the past.
Brain expansion in A. boisei
Elton and colleagues (2001) examined the record of brain size in early Homo with the following question in mind: we know that brain size increased in this lineage, but was that increase unusual compared to other lineages of primates at the same time? To answer this, they examined the brain sizes in fossil A. boisei and Theropithecus (the genus that includes living gelada baboons). Answering this question would determine whether the brain size of early Homo increased for reasons unique to this genus, or whether instead it was part of a broader trend that might be attributed to climatic changes or other ecological factors.
The results of the study showed that fossil Theropithecus showed no particular trends in brain size over time. But A. boisei did show a significantly positive trend toward brain growth over time. This trend exists whether the early KNM-WT 17000 specimen is included in the sample or not, which is important because this skull is both small and early, and by itself might create a trend in a sample that was otherwise static over time. Without that skull, the trend is still there, driven mainly by the late large skull from Konso KGA 10-525, and the early small juvenile skull Omo L338y-6. Although this latter skull is juvenile, they use an estimated adult size that is about 4 percent larger than the actual endocast.
The study compared these two cases with the evidence for brain size in early Homo. Looking only at Homo habilis, there is no apparent trend toward increasing brain size. This is partly because the largest specimen, KNM-ER 1470, is early and partly because of the great variation within the sample. The overall sample including H. habilis and early humans does show a significant trend over time, but this trend appears mainly to result from the presence of two distinct (and mostly discontiguous) species, one of which survives much later in time and therefore greatly influences the appearance of a trend. Considering early humans alone, there is really no trend evident before 1.5 million years ago, and only a slight increase up to the sample around a million years ago (Lee and Wolpoff 2002).
Some issues:
The study focused on change within each fossil species. But there is no comparison to the magnitude of changes that occurred between hominid taxa. This is problematic because most of the brain evolution in early Homo likely characterized the initial origin of the lineage from an ancestral australopithecine. It is no great surprise that H. habilis does not change markedly over time, but what is surprising is the substantial jump in size from earlier australopithecines like A. afarensis or A. africanus and later Homo. The same could be observed of the change between habilines and early humans. The authors actually run a test to see if the entire early Homo sample shows a trend over time (and it does), but it is clear from the data that the major difference is the shift in size from habilines to early humans, with each of these groups showing relatively little change over time.
The trend in A. boisei depends entirely on the earliest and latest fossils. The small size of the early Omo L338y-6 specimen is unsurprising compared to the even smaller KNM-WT 17000, so the idea that the A. boisei lineage should have changed over time is possibly expected. But Omo L338y-6 is not the smallest member of the later sample (KNM-ER 407 is smaller), so it does make a difference whether KNM-WT 17000 is excluded or not. Especially considering this is a robust probable male skull, its very small endocranial volume makes a large contrast with later A. boisei, a difference extended by many other anatomical details.
What about the late end of the sample? Here, the endocranial volume of KGA 10-525 appears very large, and is at the high end of the A. boisei range. But compared to earlier hominids, the volume is not surprisingly large. For example, the endocranial volume of AL 444-2 (A. afarensis) is estimated at around 550 mL (Holloway and Yuan 2004), and the volume of STW 505 (A. africanus) is certainly larger, perhaps over 600 mL (Hawks and Wolpoff 1999; Conroy et al. 1999). Although the body size of KGA 10-525 is not known, its molars are near the top end of the A. boisei sample, exceeded only by OH 5. This might suggest that the body size of the specimen was among the largest in the sample, and at the least we can guess that the individual was larger than the average for males.
So to address whether KGA 10-525 was surprisingly large, we have to look beyond its date and ask what the expected range of brain sizes within A. boisei would have been. Including KNM-WT 17000 at the small end, and KGA 10-525 at the large end, the standard deviation of the entire A. boisei sensu lato sample in endocranial volume is 39.3 mL. With an average volume of 480 mL, this yields a CV (coefficient of variation) of 8.2 percent.
By contrast, the H. habilis sensu lato sample, including KNM-ER 1470, has a standard deviation of 79.6 mL on an average of 634 mL, yielding a CV of 12.6 percent. So the A. boisei sample is a third less variable than the H. habilis sample.
Holloway (1980) gives CV values for recent humans, from the Danish data of Pakkenberg and Voight (1964), broken down by sex. The within-sex CV's for males and females were 8.2 percent and 8.3 percent, respectively. So the variation within the extant sample of A. boisei, including KNM-WT 17000, is about the same as within one sex in living humans. This is despite the fact that the A. boisei sample spans a million years of time and appears to have been substantially greater in body size dimorphism (as indicated by cranial robusticity and tooth sizes) compared to humans.
Tobias (1971) pools data from several earlier studies of endocranial volumes in hominoids, pooling sexes together. In his summary, the smallest degree of variation is within white-handed gibbons (Hylobates lar), where the CV of endocranial volume is 7.6 percent. Other hominoids are higher: chimpanzees at 9.7 percent, siamangs at 10.7 percent, orangutans at 10.9 percent, and a male-biased sample of gorillas at 13.1 percent. Except for the small and monomorphic gibbons, all these are higher than the estimate for A. boisei.
So the problem is not that KGA 10-525 is surprisingly large. Instead, the problem is that variation in A. boisei has likely been substantially undersampled. There should be many larger and smaller crania than have yet been found in the sample.
This is a problem for testing whether there is a significant trend within the A. boisei sample. In a sample with relatively low variation, the observation of a single large specimen at the recent end of the sample may be statistically surprising--the rarity of the large size is combined with the rarity of the recent date.
In a study of fossils, we cannot really know what the underlying variability of the extinct species was. For this reason, we are left with tests that use only the observed sample variability. The best of these are randomization tests, which randomize one or more elements of the sample to determine the likelihood that the sample would have the observed characteristics based on the data at hand. But randomization tests assume that the data themselves are sufficient to represent the variation in the underlying population. If there is good reason to think that the data are not representative, then the randomization tests may mislead about the chance that the data would be ordered in the observed way at random.
What if instead of randomly ordering the data to test its significance, instead we modeled the characteristics of the underlying population. For example, we could assume that the population had been a single species with a standard deviation similar to that observed in some living or fossil species--perhaps the observed standard deviation for earlier hominids, or for recent humans. The null hypothesis would be that this population was static in mean endocranial volume. With the computer's help, we can draw random variates from a normal distribution with the assumed standard deviation, assigning them randomly to the times observed for the real fossil sample. Then, we can perform whatever statistic we prefer upon the simulated sample, repeating the process some arbitrarily large number of times. The number of times that meet or exceed the trend observed in the fossil sample provide a p value for the null hypothesis.
What would the result of such a test be for the A. boisei sample? Good question. I'll tell you when I find out.
Why is this important?
The question is really whether the brain size increase in Homo was unique among the early hominids, or whether it may have been replicated in other species. In particular, if the brain size increase also happened in A. boisei in parallel with early Homo, that would be surprising. After all, A. boisei likely had a very different paleoecology than any member of Homo, one that was almost certainly less dependent on technology, less reliant on high-energy foods such as meat, and presenting less of a necessity for group coordination of activities. If brain size increase could occur in a significant way in A. boisei, it really raises questions about the pattern of selection on brain size in hominids.
What could explain an increase in A. boisei? One hypothesis would be energetics. The brain is a great energetic drain, because nervous tissue is very costly. For this reason, there is normally fairly strong selection in favor of smaller brains--because they are more energetically efficient. This selection for smaller brains is opposed by selection for brain functions of one kind or another, because a brain that is too small risks losing some function important for survival or reproduction.
A. boisei clearly differed from earlier hominids in its dietary adaptation, and diet determines the overall energy budget available for an organism. Suppose that the robust masticatory adaptation of A. boisei allowed the species to have a more dependable source of foods during periods of scarcity--because the range of fallback foods was extended into foods unavailable to other hominids, for example. If this were the case, then A. boisei may have had significantly less resource stress during periods of resource scarcity for other hominids, and may therefore have had less trouble meeting their energetic demands. This would mean that the selection against larger brains on the basis of their energetic disadvantages might well be weaker in a robust australopithecine. With other sources of selection on brain function the same--or even possibly increased due to a small reliance on rudimentary toolmaking or other mental adaptations--the brain would increase in size.Some have used the apparent increase in brain size in A. boisei as an argument to address the importance of brain size expansion in later Homo. This is a point worth addressing, because it is a potentially misleading comparison. One way that it misleads is in the magnitude of change necessary to explain the apparent trends. In A. boisei, a straight regression through the earliest and latest observations indicates an increase in brain size of roughly 70 mL per million years. Of course, this regression like all others is most influenced by the smallest and largest values on the independent axis. Considering the probability that KGA 10-525 was actually larger than its instantaneous average, and that Omo L338y-6 was actually small, the actual amount of change in the species over time was likely much less than 70 mL per million years. A consideration of the data points excluding these extreme values yields a nonsignificant increase of only 21.5 mL per million years.
In contrast, the magnitude of the increase in endocranial volume in Middle Pleistocene humans is much larger. Over the past million years, humans have increased from an average of around 900 mL to the present average of around 1350 mL, for a rate of 450 mL per million years. This is at least fivefold and more probably twentifold higher than the rate in A. boisei, and does not consider the observation that the change was concentrated in the more recent Middle and Late Pleistocene. Moreover, this rate is indeed a difference between early and late average values rather than a regression including early and late extreme values. One might object that we should consider the rate of change relative to the current absolute size rather than the absolute change. From the perspective of selection and the function of brain tissue, this question is not easy to answer: it could go either way. But a strict consideration of relative brain increase as opposed to absolute brain increase still shows that recent humans increased at a rate probably seven to tenfold higher than in A. boisei. And the increase within the past 250,000 years--from approximately 1100 to 1350 mL--would indicate a much higher rate of change, at 1000 mL per million years.
So the observation of a slight trend toward higher brain size in A. boisei would not diminish the impressive degree of change in recent human evolution. Nor does it really lend to the idea that brain increases were widespread among fossil hominids and therefore unsurprising. In all likelihood there were other surprising changes, such as the increase from Australopithecus to Homo, and the increase from H. habilis to early humans. Each of these changes deserves a unique explanation, since the brain is not a character likely to increase in size at random or under the influence of genetic drift. And since the most recent increase in Pleistocene hominids occurred in every inhabited region of the world, it would require either gene flow between regions or several unique cases of simulaneous parallel evolution to explain.
Bottom line: is there anything to explain here in A. boisei? I don't really think so. The apparent trend is too likely to be generated by the outlying observations. Even if a trend existed in the species over time, it appears to have been pretty low in magnitude. This remains a case where the recovery of a single specimen with the right measurements and date would completely eliminate any statistically significant result.
References:
Conroy GC, Weber GW, Seidler H, Tobias PV. 1999. Endocranial capacity of early hominids. Science 283:9.
Elton S, Bishop LC, Wood B. 2001. Comparative context of Plio-Pleistocene hominin brain evolution. J Hum Evol 41:1--27.
Hawks J, Wolpoff MH. 1999. Endocranial capacity of early hominids. Science 283:9b.
Holloway RL. 1980. Within-species brain-body weight variability: A reexamination of the Danish data and other primate species. Am J Phys Anthropol 53:109--121.
Holloway RL, Yuan MS. 2004. Endocranial morphology of A. L. 444-2. In: Kimbel WH, Rak Y, Johanson DC, editors, The skull of Australopithecus afarensis. Oxford, UK: Oxford University Press. p 123--135.
Lee SH, Wolpoff MH. 2003. The pattern of evolution in Pleistocene human brain size. Paleobiology 29:186--196.
Pakkenberg H, Voigt J. 1964. Brain weight of the Danes: forensic material. Acta Anatomica 56:297--307.
Tobias PV. 1971. The brain in hominid evolution. Columbia: New York.
Moving eyes, moving minds
A paper by Hannah Faye Chua and colleagues of the University of Michigan asserts that there are significant differences between Chinese and American graduate students in "perceptual judgment". This basically means what parts of a scene they devote attention to. Here's the background from the paper:
A growing literature suggests that people from different cultures have differing cognitive processing styles (1, 2). Westerners, in particular North Americans, tend to be more analytic than East Asians. That is, North Americans attend to focal objects more than do East Asians, analyzing their attributes and assigning them to categories. In contrast, East Asians have been held to be more holistic than Westerners and are more likely to attend to contextual information and make judgments based on relationships and similarities.
Causal attributions for events reflect these differences in analytic vs. holistic thought. For example, Westerners tend to explain events in terms that refer primarily or entirely to salient objects (including people), whereas East Asians are more inclined to explain events in terms of contextual factors (3-5). There also are differences in performance on perceptual judgment and memory tasks (6-8). For example, Masuda and Nisbett (6) asked participants to report what they saw in underwater scenes. Americans emphasized focal objects, that is, large, brightly colored, rapidly moving objects. Japanese reported 60% more information about the background (e.g., rocks, color of water, small nonmoving objects) than did Americans. After viewing scenes containing a single animal against a realistic background, Japanese and American participants were asked to make old -new recognition judgments for animals in a new series of pictures. Sometimes the focal animal was shown against the original background; other times the focal animal was shown against a new background. Japanese and Americans were equally accurate in detecting the focal animal when it was presented in its original background. However, Americans were more accurate than East Asians when the animal was displayed against a new background. A plausible interpretation is that, compared with Americans, the Japanese encoded the scenes more holistically, binding information about the objects with the backgrounds, so that the unfamiliar new background adversely affected the retrieval of the familiar animal (Chua et al 2005:12629).
The current study measured the eye movements of Chinese and non-Chinese American graduate students upon exposure to a new scene. The result was that Chinese students spent more time looking at the background of an image, and the Americans focused slightly more quickly on the foreground object and spent less time looking at the background.
From this, much interpretation follows. Take the AP account of the work:
"Asians live in a more socially complicated world than we do," [study coauthor Richard Nesbitt] said in a telephone interview. "They have to pay more attention to others than we do. We are individualists. We can be bulls in a china shop, they can't afford it."
The findings are reported in Tuesday's issue of Proceedings of the National Academy of Sciences.
The key thing in Chinese culture is harmony, Nisbett said, while in the West the key is finding ways to get things done, paying less attention to others.
And that, he said, goes back to the ecology and economy of times thousands of years ago.
In ancient China, farmers developed a system of irrigated agriculture, Nisbett said. Rice farmers had to get along with each other to share water and make sure no one cheated.
Western attitudes, on the other hand, developed in ancient Greece where there were more people running individual farms, raising grapes and olives, and operating like individual businessmen.
So differences in perception go back at least 2,000 years, he said.
Aristotle, for example, focused on objects. A rock sank in water because it had the property of gravity, wood floated because it had the property of floating. He would not have mentioned the water. The Chinese, though, considered all actions related to the medium in which they occurred, so they understood tides and magnetism long before the West did.
Aside from short-shrifting Archimedes, this comparison masks a lot of potential variation. Americans are not Greeks. Chinese are not Japanese. American graduate students are not typical of Americans in many ways, nor are Chinese graduate students in America necessarily typical of Chinese graduate students in China, much less Chinese non-graduate students. And neither Aristotle nor Archimedes were typical of the Greeks in the first few centuries B.C. To go from an empirical difference in eye movement between 25 American graduate students and 27 Chinese graduate students to inferences about Aristotle and Sun Tsu is more than a bridge too far. It might make sense to talk about "East Asian" culture and "European" culture, but at that level the historical reality may be fairly far from the psychological present.
Personally, I think it is fairly likely that cognitive differences between cultures may be manifested as differences in eye movement. There is plenty of opportunity within a few seconds of viewing for conscious control of eye movements to kick in and inspect some areas of scenes more carefully than others. It wouldn't surprise me if the areas someone inspected varied for cultural reasons.
But let's consider what some of those cultural reasons might be. For example, have American graduate students watched cheaply-animated cartoons for a greater proportion of their lives. You know, the kind of cartoon where the only moving object is the main character, and the background is entirely static for minutes? For that matter, does television viewing in general affect attention? What about video games? Certainly in things like these Japanese and Chinese students cannot be assumed to be identical, or even to represent the same group. Nor can American males and females, or (probably) engineering graduate students and social science graduate students. All of these complexities speak to the importance of variation, but the idea of cognitive variation within cultures seems not to be an important goal of this kind of psychology.
But without a strong picture of variability, the results seem very weak to me. There are significant differences, to be sure. But the range of variation within each group is not reported. The proportion of males and females differs between the two groups. The sample pictures show drawn objects against a photographic background --- a jet against mountains and a tiger against a forest floor. I can imagine that there might be differences in the perception of these scenes between Americans and Chinese, but it seems a lot more likely that both these groups would be very similar compared to, say, hunter-gatherers.
On the other hand, I've known some graduate students that apparently lived by hunting and gathering.
References:
Chua HF, Boland JE, Nisbett RE. 2005. Cultural variation in eye movements during scene perception. Proc Nat Acad Sci USA 102:12629-12633. Abstract
Why are organisms modular?
Modularity is a property of biological organization: organisms are composed of subunits that perform different functions. At the cellular level, the cell is composed of organelles that have different functions in protein assembly, metabolism, growth, and homeostasis. This organization is reflected at the level of DNA, which consists of sequences organized into functional subunits: coding regions, introns, promoter regions, multigene complexes, and ultimately chromosomes. It characterizes the anatomy of multicellular organisms, which are divided into organs such as hearts, lungs, ganglia, and eyes, or leaves, stems, flowers, and roots.
And modularity underlies the organization of the brain. Mammalian brains are divided into different parts --- neocortex, cerebellum, thalamus, etc. These parts contribute differently to different functions, as do the subcomponents of each of the parts. The neocortex is comprised of tissues that contribute to different tasks, such as Broca's area for speech and the
Brains do a lot of things that are analogous to computers, in terms of information processing. Indeed, the things that our brains are really good at are increasingly being done by computers, from adding and subtracting to face recognition. Our brains certainly have talents that are a challenge for computers, and vice versa. For one thing, our brains are well-motivated for the most part while computers have no motivation at all. But they are broadly similar in their ability to take in and manipulate information: a computer is more similar to our brains than, say, to our muscles.
But today's computers are not modular in the same way our brains are modular. Computers have different components, that is true --- they have memory chips and disk drives and power supplies and one or more central processing units (CPUs). But the great strength of computers is that the same CPU can be programmed to perform any algorithmic task. That's the principle of the universal Turing machine: a certain kind of simple device can --- by providing different instructions --- perform any transformation on data that we could think of. Indeed, if we had the right software we could cause today's computers to act like humans; they would just be really, really, really slow.
This provides a hint about why we would want the brain to be modular, rather than to provide different instructions to a single small but fast CPU. Differrent brain tissues can perform different functions at the same time, so that we can very quickly recognize someone's face, remember where we know her from, contort our faces to an pleasant expression of recognition, and say "Hello." Specializing each of these tasks into different tissues allows us to perform them much more quickly.
But that doesn't answer the question of how mental modularity evolved in the first place. After all, at the time that face recognition became important in primate evolution, our primate ancestors weren't doing all the things that we do now. Why couldn't they have simply evolved a single system that added new capabilities as it went along? This kind of mega-system would seem to be the natural consequence of evolution: tinkering slightly with a pre-existing function ought to be much easier than adding whole new modules from scratch.
Now, I should point out that we really don't have a lot of detail about how fine-grained the modularity of the brain actually is. It might very well be that a lot of what we do actually is cobbled together into a few mega-systems. But there is abundant evidence that a lot of cognition is actually very domain-specific: different neural circuits exist to perform discrete tasks.
A new paper by Nadav Kashtan and Uri Alon of the Weizmann Institute finds a reason why modularity might commonly evolve. Here's the abstract:
Biological networks have an inherent simplicity: they are modular with a design that can be separated into units that perform almost independently. Furthermore, they show reuse of recurring patterns termed network motifs. Little is known about the evolutionary origin of these properties. Current models of biological evolution typically produce networks that are highly nonmodular and lack understandable motifs. Here, we suggest a possible explanation for the origin of modularity and network motifs in biology. We use standard evolutionary algorithms to evolve networks. A key feature in this study is evolution under an environment (evolutionary goal) that changes in a modular fashion. That is, we repeatedly switch between several goals, each made of a different combination of subgoals. We find that such "modularly varying goals" lead to the spontaneous evolution of modular network structure and network motifs. The resulting networks rapidly evolve to satisfy each of the different goals. Such switching between related goals may represent biological evolution in a changing environment that requires different combinations of a set of basic biological functions. The present study may shed light on the evolutionary forces that promote structural simplicity in biological networks and offers ways to improve the evolutionary design of engineered systems (Kashtan and Alon 2005:13773).
The study simulated the evolutionary process by allowing different kinds of circuits to compete with each other. The circuits that best solved the range of problems in the "environment" were replicated into a new generation (i.e. "selection"), with a small likelihood of random changes (i.e. "mutations") to their function. They also performed a similar simulation using neural networks instead of circuits.
These experiments resulted in many different designs that satisfied the same computational goal. So the evolution in the different simulations was contingent: random factors led different simulations to different optimal solutions.
But this experiment added a novel component: sometimes the computational goals changed slightly over time. Each of the computational goals might involve several subtasks. One possibility is that these different subtasks might remain constant over time. But an additional possibility is that they might change slightly in importance relative to each other: in other words, the circuits might be presented with slightly different problems at some times than others.
Now, if this "environment" of problems to solve changed in a consistent direction over time, then we would expect the circuits likewise to evolve to solve the newer problems more efficiently.
But instead of changing the task from the beginning to the end of the simulations, Kashtan and Alon (2005) caused the tasks to oscillate over time. Sometimes one subtask might be more important, sometimes another, but there was no long-term directional change, just a steady variability in what kinds of tasks were important.
In this fluctuating environment, the circuits evolved to be modular. Changing different requirements at different times caused different subsystems to arise to solve each of these subtasks. These modular systems were slightly less efficient than the nonmodular systems that evolved to solve fixed tasks: they required additional logic gates to do the same job. But it took them a much shorter time to make the adjustment to solving slightly different problems.
You might think that this evolution was the result of the division of the computational goal into perceivable subtasks. But interestingly, when the environment encompassed goals that could logically be divided into subtasks but did not vary over time, the circuits did not evolve toward modular solutions. Here's an example:
A human engineer would easily notice the modularity in this problem and design a network that is made of two modules, one that analyzes the left side of the retina, and the other for the right side of the retina. In contrast, the structure of the evolved networks was not modular (Fig. 5b)(Qm = 0.15 0.02). As in the case of electronic circuits, fixed-goal evolution produces a nonmodular network even though the problem itself is readily decomposable into separate subgoals (Kashtan and Alon 2005:13776).
Nor was the effect simply the result of variation over time: when tasks were made to vary randomly, instead of by the emphasis of different subtasks, no modular structure resulted in the evolving circuits.
The authors discuss their results:
Why do modularly varying goals speed up evolution (in terms of the number of generations to reach perfect solution) when compared with evolution under a fixed goal? One reason that fixed-goal evolution is often slow is that the population becomes stuck in local fitness maxima. Because the fitness landscape changes each time that the goal changes, modularly varying goals can help move the population from these local traps. Over the course of many goal changes, modularly varying goals seem to guide the population toward a region of network space that contains fitness peaks for each of the goals in close proximity. This region seems to correspond to modular networks (ibid:13777).
The other element of the study is the demonstration that these kinds of modular circuits consist of similar structures repeated to comprise different modules. Such repeated elements are here called "network motifs". This is another characteristic of some biological organization, so it is very interesting:
In addition to their modular structure, the networks evolved under modularly varying goals display significant network motifs. The same motifs are reused throughout each network in different modules. Some of these motifs are also found in biological information processing networks. For example, feed-forward loops and bifans are found in transcription networks (7). Feed-forward loops, bifans, and diamonds are found in signal transduction and synaptic neuronal networks (7). In signal transduction networks (34) and the neuronal network of C. elegans (39), multilayered feed-forward patterns similar to those in Fig. 5c, are strong network motifs. An example is multilayered protein kinase cascades, in which families of kinases in each layer activate families of kinases in the next layer (34, 40, 41).
One possible explanation for the origin of the motifs in the olved networks is that modular networks are locally denser than nonmodular networks of the same size and connectivity. This local density tends to increase the number of subgraphs (42). To test this possibility, we evolved networks to reach the same modularity measure Q as the networks evolved under modularly varying goals, but with no information-processing goal (see Supporting Text). We find that these modular networks have no significant network motifs (Fig. 9). They show relatively abundant feedback loops that are antimotifs in the networks evolved under modularly varying goals. It therefore seems that the specific network motifs found in the evolved networks are not merely caused by local density, but may be useful building blocks for information processing (ibid:13777-13778).
The authors end with a discussion of how their results may apply to biological evolution, with specific biological examples, although not drawn from the brain.
References:
Kashtan N, Alon U. 2005. Spontaneous evolution of modularity and network motifs. Proc Nat Acad Sci USA 102:13773-13778.
Complex structure of whale song
An interesting story from Howard Hughes Medical Institute (via Science Blog) about the information content of whale song. They don't know what the whales are communicating, but they can assess the number of bits transmitted:
[HHMI predoctoral fellow Ryuji] Suzuki said that information theory also enabled the researchers to determine how much information can be conveyed in a whale song. Despite the "human-like" use of hierarchical syntax to communicate, Suzuki and his colleagues found that whale songs convey less than one bit of information per second. By comparison, humans speaking English generate 10 bits of information for each word spoken. "Although whale song is nothing like human language, I wouldn't be surprised if some marine mammals have the ability to communicate in a complex way," said Suzuki. "Given that the underwater environment is very different from our world, it is not surprising that they would communicate in rather a different way from land mammals."
There seems to be much emphasis on the "hierarchical" aspect of the songs, and this is important -- a single call is made up of many smaller subunits, each of which may carry information content and the arrangement of them may itself carry information content.
Suzuki, who began the project as an electrical engineering undergraduate at the University of Massachusetts, Dartmouth, worked with Buck and Tyack to develop a computer program to break down the elements of the whale's song and assign an abstract symbol to each of those elements. Suzuki wanted to see if he could design a computer program that enabled scientists to classify the structure of the whales' songs.
He used the program to analyze structural characteristics of the humpback songs recorded in Hawaii. To measure a song's complexity, Suzuki analyzed the average amount of information conveyed per symbol. He then asked human observers who had no previous knowledge of the structure of the whale songs to classify them in terms of complexity, redundancy, and predictability. The computer-generated model and the human observers agreed that the songs are hierarchical, confirming a theory first proposed by biologists Roger Payne and Scott McVay in 1971.
...
The structure of the humpback whale song is repetitive and rigid. The whales repeat unique phrases made up of short and long segments to craft a song. There are multiple layers, or scales, of repetition, denoted as periodicities. One scale is made up of six units, while a longer one consists of 180-400 units. The combined periodicities give the song its hierarchical structure.
A hierarchical format is vastly more learnable than any nonhierarchical alternative capable of encoding an equivalent amount of information, so it should not be surprising that this structure would have arisen in another highly communicative species. It emphasizes that limits on information transfer are just as fundamental to the evolution of social intelligence as limits on optics are to visual perception.
Susan Blackmore's "The Meme Machine"
This post in progress...
Here are some thoughts on memetics as construed by Blackmore. The book has 18 short chapters along with a preface and a foreword by Richard Dawkins, so I do not comment on everything. Just some notes as I am reading.
I am mostly sympathetic with the concept of memes, but I retain some skepticism about the ways that the concept may or may not be applicable to human evolution, either cultural or biological. My basic thought is that it serves us little to consider the ways that memes may be structured to maximize their own dissemination and retention, but it is potentially very applicable to consider the ways that human minds may be adapted to retain or spread certain kinds of information. Thus, if a meme is a behavioral analog of a gene, then what I think is interesting is whatever would be the behavioral analog of developmental genetics--the biological evolution of an unfolding genetic program that creates a mind via interaction with a cultural environment. To what extent are minds genetically inherited (i.e. culture- or meme-independent) and to what extent are they culture- or meme-constructed?
This question may seem trite, in that it amounts to a restatement of the nature vs. nurture question. But there really isn't a way to address the biological evolution of human cultural capacities without grappling with it. If cultural capacities are largely bootstrapped by cultural learning in a substrate-neutral context, then the particular genetics underlying mind construction might matter relatively little in the emergence of modern human behaviors. In contrast, if most interesting human behaviors are culture-independent or universal, then we require explanations for them that explain their commonality in the face of cultural variation. One possibility is that culture is constrained, either logically or biologically. Another is that human cultural capacities are strongly genetically determined in some respects. It is very likely that most human capacities are in between, and in fact that the extent to which they are facilitated by particular genotypes may vary from person to person.
My thoughts also boil down to a question of the kind of variation that we consider to be interesting--culturally variable or culturally universal? For me, the question of memetics is whether memes are phenomena that have importance to the construction of minds from the perspective of biological evolution. I am reading the book with that question in mind. It could be the case that the behavioral choices that underlie survival and reproduction are highly influenced by the ease with which particular information structures may be transmitted and retained by individuals. In that case, the neurobiology that enables the replication of memes may be a relevant way to frame the issue of biological evolution. But it may be the case that the interesting ways in which memes are transmitted and maintained have no special relevance to survival and reproduction. Even so, memes might be very interesting in some intellectual pursuits, such as the analysis of changing hemlines or urban myths, but they would not constitute an interesting way to construct a theory of the evolution of cultural capacities. Again, I suspect the truth is somewhere in between these extremes, but if so there might remain some way other than memetics to make a productive theory of the origins and evolution of culture.
A few notes before starting. Here I use several terms in a way that is meant to avoid confusing myself. For this reason, they may seem idiosyncratic, but hey, it's my weblog. I use cultural evolution to refer to changes in culture over time. This has no necessary implications for the utility of cultural categories or their value to individuals, but those may be factors that affect the pattern of cultural evolution, certainly. My main interest is in the biological evolution of those mental functions that allow cultural behavior in humans and other animals. For this, I refer to the evolution of cultural capacities. With respect to the study of "capacities," it goes without saying that no two individuals are likely to be identical in performance, and in practice their manifestations of surface behaviors are likely to be very different. From a biological perspective--especially as applied to survival and reproduction--differences in performance have to be considered in a statistical framework. That is to say, do two individuals have the same likelihood of exhibiting particular behaviors in particular contexts? Could they both be expected to learn the same information given the same inputs? These are questions that focus on capacities rather than performance, and they illustrate that selection on mental capacities may occur as a function of statistical relations between minds and behavior, even if there may be no sense in which the structure of minds can be said to determine behavior.
Foreword
Dawkins originated the meme concept in his 1976 book The Selfish Gene, and as such he is certainly the best qualified to provide an introduction and contextualization of this book.
The lateral brain-tail connection
This is too weird:
Thus when dogs were attracted to something, including a benign, approachable cat, their tails wagged right, and when they were fearful, their tails went left, Dr. Vallortigara said. It suggests that the muscles in the right side of the tail reflect positive emotions while the muscles in the left side express negative ones.
That's from a NY Times article by Sandra Blakeslee. The whole article's about this dog tail-wagging emotional asymmetry.
And then there is all this:
Honeybees learn better when using their right antenna, she said. Male chameleons show more aggression, reflected as changes in body color, when they look at another chameleon with their left eye. A toad is more likely to jump away when a predator is introduced to its left visual field (right brain/fear). The same toad prefers to flick its tongue to the right side when lashing out at a cricket (left brain/ nourishment).
Chicks prefer to use their left eye to search for food and right eye to watch for predators overhead, Dr. Rogers said. But when chicks are raised in the dark, they do not develop normal brain asymmetry. In trying to eat and watch for hawks overhead, such nonlateralized chicks become confused and vulnerable to attack.
Now that's one messed-up experiment. Chicks raised in the dark, suddenly put out in the open where hawks are circling overhead.
Hmmm:
And left-handed chimps are more fearful of novel stimuli than right-handers. Their dominant right brains may make them more cautious.
The article ends with a bunch of adaptive-sounding explanations for asymmetry and lateralization of "approach and withdrawal" traits, but nothing very convincing. Personally, I would guess the mechanism is essentially like gene duplication: you get two copies of something, and one of them may mutate to take on new functions. Lateralization should be favored as a pathway above functionally redundant brain structures.
But then, there seems to be incredible plasticity to much of brain development, including lateralization in humans. Maybe lateralization in humans has high plasticity because enlarged human brain sizes are comparatively recent -- there hasn't been a lot of time for the evolution of functional lateralization in the new volume of the neocortex. As it becomes clearer what is new and what is old in the human brain, there will be the chance to test hypotheses about the origins of lateralized functions.
Setting the neural path of development
A really big problem in studying the evolution of the brain is that we have very little idea how the organ develops. So this paper by Bystron and colleagues in Nature Neuroscience is pretty interesting:
We describe a distinctive, widespread population of neurons situated beneath the pial surface of the human embryonic forebrain even before complete closure of the neural tube. These 'predecessor' cells include the first neurons seen in the primordium of the cerebral cortex, before the onset of local neurogenesis. Morphological analysis, combined with the study of centrosome location, regional transcription factors and patterns of mitosis and neurogenesis, indicates that predecessor cells invade the cortical primordium by tangential migration from the subpallium. These neurons, described here for the first time, precede all other known cell types of the developing cortex.
The question is whether these early-migrating neurons, which make it into the developing cortical regions before any local neurons originate, might be essential to laying down pathways that later develop.
There's some clever work detecting gene expression in these neurons to determine if they belong to one or another already-known neural population (they don't). And they're not like any early neurons so-far observed in any other species:
No equivalent of predecessor cells has been described in any other species. In rats there is evidence that the earliest neurons migrating tangentially to the cortex arise from the VZ of the lateral ganglionic eminence at embryonic day 12.5â13, when neurogenesis has already started in the dorsal telencephalon. In contrast, human predecessor cells invade the cortical primordium from the basal telencephalon at CS12, 1 week before the appearance of the lateral eminence. A re-examination of early neurogenesis in rodents and other species is urgently needed to determine whether predecessor cells are unique to the human brain.
So they represent an early step in a "developmental cascade" in the cortex, and they are possibly primate- or even human-specific.
References:
Bystron I, Rakic P, Molnár Z, Blakemore C. 2006. The first neurons of the human cerebral cortex. Nature Neurosci 9:880-886. DOI link
Baby monkey see...
This PLoS Biology paper by Pier Ferrari et al. is highly interesting:
Here we report the behavioral responses of infant rhesus macaques (Macaca mulatta) to the following human facial and hand gestures: lip smacking, tongue protrusion, mouth opening, hand opening, and opening and closing of eyes (control condition). In the third day of life, infant macaques imitate lip smacking and tongue protrusion. On the first day of life, the model's mouth openings elicited a similar matched behavior (lip smacking) in the infants. These imitative responses are present at an early stage of development, but they are apparently confined to a narrow temporal window. Because lip smacking is a core gesture in face-to-face interactions in macaques, neonatal imitation may serve to tune infants' affiliative responses to the social world. Our findings provide a quantitative description of neonatal imitation in a nonhuman primate species and suggest that these imitative capacities, contrary to what was previously thought, are not unique to the ape and human lineage. We suggest that their evolutionary origins may be traced to affiliative gestures with communicative functions.
That's the abstract. The second paragraph of the paper is essential background if you don't know much about babies:
To date, studies of early signs of this matching capacity have been largely limited to human infants. Almost 30 years ago, Meltzoff and Moore [3] reported that 2- to 3-wk-old infants responded with corresponding matching behaviors to specific human facial gestures, such as mouth opening (MO), tongue protrusion (TP), lip protrusion, and hand opening (HO). Other studies confirmed this early investigation, although there is still considerable debate about which gestures are actually imitated [4-9]. To avoid the possible interferences of early learning experiences with innate imitation processes, Meltzoff and Moore conducted further investigations immediately after birth and demonstrated that newborns also can imitate adult facial gestures [4,5]. They argued that the specificity of the imitative response indicates a capacity to accurately match the body parts involved. Because newborns cannot see their own face but can only perceive it through proprioception, the matching of their own acts to those observed should require a supramodal representation of the observed gesture, called active intermodal matching [3-5,10].
They note later in the paper that some human babies just don't imitate in this way at all. It's pretty striking when you see it happen, so the variation between infants ought to be explained somehow. The most consistent imitation is sticking out the tongue (this may be why we all try to do it to babies!). And there is a window in humans -- starting at around 2 weeks, the imitations last only up to around 3 months.
Ferrari and colleagues explain the windows for imitation in terms of social learning and the development of independence. They note that human infants start to exhibit new social abilities at around 2-3 months such as smiling and cooing. Chimpanzees have a slightly earlier window for imitation than humans, and also start to show independence earlier. The imitation that they observe in the macaque infants ends by 7 days of age -- and they write that the infants start to show motor independence away from the mother for short periods of time by this age:
Already at 1 wk, infant macaques may leave their mother for short periods of time. Infant exploration, involving motherâinfant separation, increases over time. In our experiments, we noticed that holding a 2-wk-old or older infant and capturing its attention with the stimulus became more and more difficult with increasing age. In humans and chimpanzees, neonates stay in body contact with their mother for much longer, and the mother is the only one responsible for maintaining the infant. Thus, neonatal imitation in rhesus macaques occurs with a timing that, considering the species-specific patterns of development of motor and cognitive skills, is comparable with those reported for humans and chimpanzees.
There is a lot in the discussion about "imitation" and what it means, and how it varies among species, such as between apes and monkeys. It seems to me that "imitation" is a term that is starting to cause more confusion than it resolves. Lately, the term has been limited to cases of learning where an individual is replicating the behaviors of another individual -- not only the end result, but also all the steps that lead to that end result. But the infant "imitation" quite clearly doesn't require the kind of conceptual learning that instances of "imitation" among older juveniles and adults seems to take.
Here they focus on a possible neural basis for the infant imitation, suggesting that "mirror neurons" may be responsible. "Mirror neurons" are activated both when doing an action and when seeing another individual do the same action. To the extent that these mirror neurons are "pre-wired" to connect visual and proprioceptive inputs, they might spur an individual to "imitate" the actions he or she sees.
Of course, that doesn't really address just how such a system might evolve, or what it's adaptive value might be. Some have suggested that infant imitation mainly functions to "make them cute" to adults, or to demonstrate some kind of incipient ability for social cognition to make it more likely that the mother will provide timely care (i.e., by showing that the child is a good investment because it is neurologically OK).
But I really don't see these explanations accounting for a developmental window. To me, a window implies opening and closing constraints on neural development. To that end, it is very interesting that the end of the window appears relevant to social development.
The real question is whether monkey mothers inspire imitation and look for it in their infants. Knowing this would help test whether the imitation itself is an adaptation or whether it is a side-effect of some developmental process (presumably related to the development of social cognition and signalling). Since there is so much variation among human infants in this quality, I am pretty tempted to think it is not a significant point where behavior rubs against survival. It may better be seen as a portal on aspects of development that manifest later. For instance, it may be a frequent consequence of the wiring of mirror neurons that they have this effect on infants, but the adaptive consequences of the wiring are manifested later as juveniles learn to associate the facial expressions of others with their likely behaviors.
From that perspective, the most significant aspect of the paper is its demonstration that the window in apes, and then humans, has greatly increased in length and is delayed relative to birth. And yet, despite these relatively great changes in timing, it is still after birth. In other words, it appears to show a necessary early environmental role for the development of social cognition (if it's that), which has extended in the ape and human lineages.
It has stuck a long chunk of learning into a developmental process as a trade-off against earlier independence. Of course, social independence and motor independence are different things...
UPDATE (9/7/2006): Brainethics has pictures.
References:
Ferrari PF, Visalberghi E, Paukner A, Fogassi L, Ruggiero A, et al. (2006) Neonatal Imitation in Rhesus Macaques. PLoS Biol 4(9): e302. Free full text
Ninety percent of your brain is (not) useless
I posted about the "Sherlock Holmes theory of mind" last month, which I often mention to my classes. The idea is that the mind has a limited (and small) capacity, so that filling it with useless information takes away space that could be better devoted to useful knowledge. The opposite number to the "Sherlock Holmes theory", naturally, is the "Einstein theory of mind" -- namely, the idea that the ordinary human uses only 10 percent of his or her brain.
At the outset, I should point out that the Einstein story is a total urban myth, which lately has been used to great effect by psychics, self-actualization seminar leaders, and various other charlatans. There is no record that Einstein ever wrote or said anything about the useful percentage of his or anyone else's brain.
Neuroscientist and skeptic Barry Beyerstein (1999) traced the myth ultimately to the "New Thought" movement, which "blossomed following the U.S. Civil War among the prosperity-obsessed yet anxiety-ridden middle classes" (Beyerstein 1999:6, paraphrasing Meyer 1965). Popularizers of the idea ranged from devotees of numerology to self-help guru Dale Carnegie, who himself cited William James for the idea. Beyerstein himself investigated whether there is any truth to the Einstein story (there isn't), and with some assistance was able to track down the idea in the text of William James' public lectures.
But despite the fact that the myth itself is bunk, various neurologists have from time to time advanced evidence to support this myth. I'm writing a bit about this, because I've been thinking about the context of the LB1 brain and the hypothesis that it might have had "advanced" capacities. It is obvious that natural selection could reduce the size of the human brain by half or more with functional loss -- this would simply be a reversal of the Pleistocene evolution of brain size. The basic question is whether natural selection could reduce the brain size of a hominid population to half or less, without reducing some cognitive capabilities. Is it possible to build a leaner, meaner brain?
Should we have a strong opinion about this? So much about the brain is unknown, that the hypothesis may simply be untestable. How could we demonstrate that a population with hobbit-sized brains could not have been just as cognitively adept as some modern human group? It is a daunting question to try to answer. I'm not going to try to answer it here.
What I do want to do is give an account of some of the examples from the neuroscience literature that people have used to support the idea that brains could evolve to be smaller without functional compromise. Many distinct conditions lead to small brain tissue volume, including hydrocephalus, microcephaly, and deliberate hemispherectomy. A number of studies have claimed that people with profound reductions in brain volume -- down to as little as 150ml -- nevertheless have entirely normal cognitive function. This would be a real-world manifestation of the 10 percent myth: a person using literally 10 percent of the average brain volume to live an ordinary life.
But the research in this area is essentially anecdote leavened by CT and psychometric results that might -- or might not -- show what they are proposed to demonstrate. I'm going to focus here on two examples, the work of John Lorber on profound reduction in brain volume associated with hydrocephalus, and hemispherectomy. There are several others that deserve some treatment, including clinical microcephaly presenting with normal intelligence, although in many such cases we are merely looking at unusually small brain volumes and not reductions to half or more of the average size. The examples I'm examining here are some of the most extreme claims of cognitive performance with minimal brain size.
Developing that TV sensibility
The Times had this article the other day discussing whether TV is good for preschool-age kids. It's not all that interesting, but this bit near the end caught my attention:
Developmental psychologists say the Vanderbilt research offers an intriguing clue to a phenomenon called the "video deficit." Toddlers who have no trouble understanding a task demonstrated in real life often stumble when the same task is shown onscreen. They need repeated viewings to figure it out. This deficit got its name in a 2005 article by Daniel R. Anderson and Tiffany A. Pempek, psychologists at the University of Massachusetts, who reviewed literature on young children and television.
...
But psychologists still want to get to the bottom of what might explain the difference. Is it the two-dimensionality of the screen? Do young children have some innate difficulty in remembering information transmitted as symbols?
The paper linked in an article is a broad review of early childhood TV viewing, and isn't all that helpful, although it gives about 2 pages of review on the topic. Interestingly, the "video deficit" includes aspects of language learning:
A third line of research is concerned with language learning. Children 2 years and older can clearly learn vocabulary from television (Naigles & Kako,1993; Rice, Huston, Truglio, & Wright, 1990;Rice & Woodsmall, 1988). Nevertheless, when comparisons are made between video and equivalent live conditions in children younger than 2 1/2years, the results suggest a video deficit. Grela, Lin,
and Krcmar (2003) tried to teach object labels either live, in an equivalent video, or in a version of Teletubbies that used the labels. They found better learning in the live as compared to video conditions. Learning from video by children near their 2nd birthday was substantially better than that by younger children.
Infants are able to perceive many phonetic contrasts that are not found in their native language; this ability is lost by about 12 months of age if infants are not exposed to other languages. Kuhl, Tsao, and Liu (2003) exposed American infants to contrasts found in Mandarin. One group of infants was exposed to live speakers of Mandarin for about 5 hours during 12 sessions between 9 and 10 months of age. Other groups were exposed to equivalent audiovisual or audio-only DVDs. The infants exposed to live speakers did not experience the loss of ability to perceive Mandarin contrasts. Infants exposed to the DVD stimuli, however, showed the same loss as infants exposed to no Mandarin at all. Again, this research indicates a profound video (and audio) deficit (Anderson and Pempek 2005:513).
This paper by Georgene Troseth and colleagues delves into the problem:
Young Children's Use of Video as a Source of Socially Relevant Information
Although prior research clearly shows that toddlers have difficulty learning from video, the basis for their difficulty is unknown. In the 2 current experiments, the effect of social feedback on 2-year-olds' use of information from video was assessed. Children who were told "face to face" where to find a hidden toy typically found it, but children who were given the same information by a person on video did not. Children who engaged in a 5-min contingent interaction with a person (including social cues and personal references) through closed-circuit video before the hiding task used information provided to find the toy. These findings have important implications for educational television and use of video stimuli in laboratory-based research with young children.
These researchers frame the issue in terms of the strategies children use to identify socially relevant information:
By the time they reach their second birthday, toddlers have figured out that a prime source of information is other people. They are attuned to socially relevant information: information that is presented by a social partner and accompanied by appropriate cues indicating a shared focus on an aspect of the environment. For instance, numerous studies demonstrate that 2-year-olds are skilled users of a range of social cues for word-learning purposes, including eye gaze, gesture, discourse novelty, and emotional outbursts (see Baldwin & Tomasello, 1998, for a review). Young children's skill at obtaining information from social others may rest in part on their attention to features common to animate agents -- such as the potential for contingent movement and the presence of faces (e.g., Johnson, Booth, & O'Hearn, 2001; Johnson, Slaughter, & Carey, 1998; Shimizu & Johnson, 2004). It is possible that the presence of such features is a precondition for toddlers to use others as potential sources of information. This may be one factor underlying the video deficit in toddlers: in the absence of contingent interaction, they usually fail to regard people on TV as viable information sources. In the current study, when contingent interaction was lacking, children failed to use identical verbal information to solve a problem.
The connection to the ways that children learn to use and respond to explicitly social cues and situations is important. Social interactions are two-way, and that modality is a fundamental part of human reality. Two-way interactions really can't be modeled well by television. But in contrast, three-way interactions are modeled very well by TV. Any program that shows two individuals interacting with each other is fundamentally a three-way interaction, since it implicates the viewer as a third party.
Understanding three-way interactions may be well above the cognitive skills of toddlers. Seeing the relationship between two other individuals is a three-way interaction. Three-way interactions are more difficult for an additional reason -- there are vastly more of them in any social group. With two-way interactions, the total number accessible to any individual is simply the number of individuals in the group, minus the focal individual herself. In other words, it scales linearly with group size. But with three-way interactions, the total possible number of interactions in a group is combinatorial.
Now it is interesting that language learning has been grafted into human development at a time when these kinds of social learning are still being worked out. Plausibly, it reflects the fact that language helps people to organize those more numerous and more complex three-way interactions.
References:
Anderson DR, Pempek TA. 2005. Television and very young children. American Behavioral Scientist 48:505-522. Abstract
Troseth GL, Saylor MM, Archer AH. 2006. Young children's use of video as a source of socially relevant information. Child Development 77:786. DOI link
Fodor on Buller's Adapting Minds
Jerry Fodor reviews David J. Buller's book, Adapting Minds: Evolutionary Psychology and the Persistent Quest for Human Nature in last week's Times Literary Supplement. This is one high-octane review, and from the start, I have to say, if TLS typically has reviews like this, I'm going to subscribe.
In short, Fodor likes the second part of the book, which skewers empirical arguments from Evolutionary Psychology. But he is critical of Buller's own adaptationism. On this subject, Fodor gives much food for thought. Consider:
The project of Evolutionary Psychology is to exhibit propensities for acting out of beliefs and desires as adaptations. Well, it can't be done. Not, anyhow, so long as adaptive propensities are, by stipulation, ones that increase the likelihood of having children (or that would have done so, Back Then). That sort of story may work when the traits in question are morphological; there are those who think it does and there are those who think it doesn't. But, to repeat one last time, it can't work when the propensities are intentional in the philosopher's sense of that term. The intentional content of the mental propensity that one's behavior manifests (Òwhat you had in mindÓ in behaving as you did) can't be reconstructed from the effects of the behaviour; that's true of proximal and ultimate propensities indifferently. Suppose there's a question about whether you like marriage because it's nice having a spouse to help with the children, or whether you like marriage because you want to maximize your opportunities for breeding. That question just can't be decided by determining which motive would have led to reproductive success in the ancestral environment. It just can't be; that's not the way that belief/desire explanations work. (Or have I mentioned that?)
There is a lot leading up to that quote, and Fodor treats a likely response to his argument after.
Fodor is at his best in critical mode, and this is no exception. All the same, he's not an evolutionary biologist, and so doesn't anticipate all the answers one might provide. For people thinking about the evolution of the mind, though, if you can't provide an answer that Fodor will accept on these questions, keep thinking.
Especially this part:
The real issue is the biological plausibility of pluralism about motives; it's whether biology entails that, in some sense or other, there is only one goal that we ever pursue. One can imagine selection pressures so intense that no trait survives unless it conduces to reproductive success: but is there any reason at all to suppose that those were the conditions under which we evolved? To the contrary, as far as anybody knows, it looks like we've been singing for fun and dancing for fun and painting for fun and gossiping for fun and copulating for fun right from the start; there isn't, to my knowledge, the slightest shred of evidence to the contrary. It's not, in short, part of "the scientific world-view" that only mental traits that favoured reproductive success would have survived in the ancestral environment. The scientific world-view does not entail that writing The Tempest was a reproductive strategy; that's the sort of silliness that gives it a bad name. First blush, there seem to be all sorts of things that we like, and like to do, for no reason in particular, not for any reason that we have, or that our genes have; or that the Easter Bunny has, either. Perhaps we're just that kind of creature.
That's the problem with adaptationism sometimes. The logic is impeccable; the evidence, not so much.
"Altruism" in the brain
One of those impressively short brief communications in Nature Neuroscience by Dharol Tankersley et al. claims to have spotted a brain correlate of altruism:
Although the neural mechanisms underlying altruism remain unknown, empathy and its component abilities, such as the perception of the actions and intentions of others, have been proposed as key contributors. Tasks requiring the perception of agency activate the posterior superior temporal cortex (pSTC), particularly in the right hemisphere. Here, we demonstrate that differential activation of the human pSTC during action perception versus action performance predicts self-reported altruism.
I'm more interested in the "agency" part than the "altruism" part -- there is so much ambiguity about how "altruism" should really be defined. In this case, the mental task involved playing a computer game for charity.
But there is a literature associating the perception of agency with the posterior superior temporal cortex:
Perceptual models suggest that an early-developing and rudimentary capacity to perceive another agent's action as self-generated and goal-oriented may form the basis of empathic perception and, in turn, altruism. Neuroimaging studies indicate that brain regions in the pSTC contribute to the perception of agency. Both low-level perceptual tasks, such as target detection and prediction of complex movements, and more complex tasks, such as consideration of other agents' beliefs or (inter)actions in the environment evoke activation in the pSTC. For example, right pSTC activation increases when people watch geometrical shapes performing seemingly purposeful acts, but not when the shapes move at random. The pSTC may support rudimentary computations about the meaning of perceived actions, which might in turn subserve more complex social capacities, including empathy and theory of mind. Thus, the functional integrity of the pSTC may be a prerequisite for prosocial traits such as empathy and altruism.
The cited paper by Castelli et al. (2000) seems helpful:
We report a functional neuroimaging study with positron emission tomography (PET) in which six healthy adult volunteers were scanned while watching silent computer-presented animations. The characters in the animations were simple geometrical shapes whose movement patterns selectively evoked mental state attribution or simple action description. Results showed increased activation in association with mental state attribution in four main regions: medial prefrontal cortex, temporoparietal junction (superior temporal sulcus), basal temporal regions (fusiform gyrus and temporal poles adjacent to the amygdala), and extrastriate cortex (occipital gyrus). Previous imaging studies have implicated these regions in self-monitoring, in the perception of biological motion, and in the attribution of mental states using verbal stimuli or visual depictions of the human form. We suggest that these regions form a network for processing information about intentions, and speculate that the ability to make inferences about other people's mental states evolved from the ability to make inferences about other creatures' actions.
That study is the one with the moving geometric shapes. It includes a short section of the discussion on the superior temporal region, including this passage:
Puce et al. (1998) found increased superior temporal sulcus activation when viewing faces in which eye gaze repeatedly changed direction, and faces in which the mouth opened and closed. Similarly Calvert et al. (1997) observed increased activation in a region of the superior temporal gyrus during silent lip-reading of numbers versus still lips, and Grezes et al. (1999) reported activation of the superior/middle temporal region during viewing of meaningful hand gestures with tools and objects compared to stationary hands. Taken together these studies implicate the superior temporal sulcus and adjacent cortex in the perception of a variety of human body movements. This region is anterior and superior to the visual motion area MT/V5 (Puce et al., 1998), indicating that these activations are not attributable to movement per se. It is notable, too, that all our animations (including Random) displayed self-propelled movement as might be expected of animate agents. Our triangles, when described as moving purposefully and intentionally, activated the key brain regions that have been activated by viewing biological motion. Human-like face or body characteristics thus do not appear to be necessary to trigger the attribution of mental states. Future investigations are needed to clarify what particular properties of biological motion are functionally associated with temporoparietal activation, and whether distinct regions respond preferentially to specific visual attributes of biological stimuli (Castelli et al. 2000:321).
True enough, and isn't that interesting. Our interpretation of intent and agency filters through a perception system that doesn't care if the actor is human or not.
References:
Castelli F, Happé F, Frith U, Frith C. 2000. Movement and mind: a functional imaging study of perception and interpretation of complex intentional movement patterns. NeuroImage 12:314-325. doi:10.1006/nimg.2000.0612
Tankersley D, Stowe CJ, Huettel SA. 2007. Altruism is associated with an increased neural response to agency. Nature Neurosci (online early) doi:10.1038/nn1833
What astrocytes do
This LiveScience article reviews some recent research.
A new study finds that a cell once believed to serve neurons instead may perform the crucial function of regulating blood flow in the brain.
The discovery challenges a basic assumption in neuroscience and could have implications for interpreting brain scans and understanding what occurs during brain trauma and Alzheimer's disease.
Neuroscientists have long known astrocytes help to support neurons. Wikipedia is terser than the neuroscience text on my shelf, but it will serve:
Astrocytes are sub-type of the glial cells in the brain. They are also known as astrocytic glial cells. Star-shaped, their many arms span all around neurons. They outnumber the neurons ten to one. Astrocytes are classically identified histologically by their expression of glial fibrillary acidic protein (GFAP). Previously in medical science, the neuronal network was considered the only important one, and astrocytes were looked upon as gap fillers. But recently they have been reconsidered and are now thought to play a number of active roles in the brain.
The current research has found that astrocytes can regulate blood flow themselves with no neural involvement:
Recent experiments, however, revealed that astrocytes form connections with blood vessels and control the flow of nutrients, including oxygen, to neurons. When brain activity increases, neurons trigger astrocytes to release calcium, which in turn affects other chemical messengers that can cause blood vessels to either dilate or contract.
From start to finish, the process takes about 1 second.
"That's amazing; no other organs can increase their blood flow so fast," Nedergaard said.
While neurons and astrocytes usually work closely with one another, the new finding raises the possibility that there may be times when astrocytes increase blood flow on their own without any prompting from neurons.
The article discusses the possibility that Alzheimer's may be initiated by astrocyte malfunction rather than neuron death.
Reading it, I wonder whether it is neuroscience or genomics that poses the greatest unsolved problems right now. I guess studying the evolution of the brain tends to stack both sets right on top of each other.
A brain for music
From Brainethics, I was pointed to this article in the Boston Globe about evolutionary explanations for music.
Researchers at the Montreal Neurological Institute, for example, have scanned musicians' brains and found that the "chills" that they feel when they hear stirring passages of music result from activity in the same parts of the brain stimulated by food and sex.
As evidence mounts that we're somehow hard-wired to be musical, some thinkers are turning their attention to the next logical question: How did that come to be? And as the McGill University neuroscientist Daniel Levitin writes in his just-published book, "This is Your Brain on Music," "To ask a question about a basic, omnipresent human ability is to implicitly ask questions about evolution."
It's a good article, that draws clear contrasts between some of the main hypotheses, and has quotes from folks like Geoffrey Miller and Steven Mithen. Personally, my favorite part of the article is Pinker's contribution:
To Steven Pinker, though, none of this adds up to a convincing case for music's evolutionary purpose. Pinker is not shy about seeing the traces of evolution in modern man-in "How the Mind Works" he devoted a chapter to arguing that emotions were adaptations-but he stands by his "auditory cheesecake" description.
"They're completely bogus explanations, because they assume what they set out to prove: that hearing plinking sounds brings the group together, or that music relieves tension," he says. "But they don't explain why. They assume as big a mystery as they solve." Music may well be innate, he argues, but that could just as easily mean it evolved as a useless byproduct of language, which he sees as an actual adaptation.
Levitin's book, by the way, has a very well-produced website (warning: Flash). I'll be talking about music and evolution later in my Biology of Mind course, so I may pick up this book and do a review.
Meanwhile, Brainethics has some good links for recent papers trying to fuse music and evolution.
And yes, I have had "Crazy" in my head all week.
Broca's area and temporal organization
I was looking in Neuron to find this paper by Koechlin and Jubault about Broca's area. Here's the abstract:
The prefrontal cortex subserves executive control, i.e., the organization of action or thought in relation to internal goals. This brain region hosts a system of executive processes extending from premotor to the most anterior prefrontal regions that governs the temporal organization of behavior. Little is known, however, about the prefrontal executive system involved in the hierarchical organization of behavior. Here, we show using magnetic resonance imaging in humans that the posterior portion of the prefrontal cortex, including Broca's area and its homolog in the right hemisphere, contains a system of executive processes that control start and end states and the nesting of functional segments that combine in hierarchically organized action plans. Our results indicate that Broca's area and its right homolog process hierarchically structured behaviors regardless of their temporal organization, suggesting a fundamental segregation between prefrontal executive systems involved in the hierarchical and temporal organization of goal-directed behaviors (Koechlin and Jubault 2006:963).
Some definition of exactly what is meant by hierarchical behaviors vs. temporal control and temporal organization is in the first couple of paragraphs:
As revealed by previous studies (Braver et al., 2003, Fuster, 2001 and Koechlin et al., 2003), the temporal dimension of executive control is processed in the lateral prefrontal cortex by a top-down control system of executive processes extending from premotor to the most anterior prefrontal regions. In this system, more anterior regions integrate temporally more dispersed information for selecting appropriate behaviors at each time. This prefrontal system, however, is not involved in the precise timing of motor acts underlying the execution of motor sequences, a distinct function associated with medial regions of the premotor cortex including the supplementary motor area complex (Kennerley et al., 2004 and Tanji, 2001).
It seems plausible to me that these systems are involved in learning the steps of temporally organized processes that later become more-or-less automatic, as sequences are slowly accommodated by executive (i.e., conscious) control and then are established by repeated use into rapid motor sequences. So how does "hierarchical organization" differ from this sequential organization?
Another basic dimension of executive control is the hierarchical organization of behavior. In this dimension, appropriate actions are selected as subordinate elements that compose ongoing structured action plans rather than from occurrences of temporally distant events. In other words, action selection may result from processing the hierarchical structure of action plans evoked by external events rather than processing crosstemporal contingencies between events. Little is known about the prefrontal executive system subserving action selection based on hierarchical structures of behavioral plans.
In other words, not just what to do at which time, but what to do in relation to other actions, all organized in time. At one level, these hierarchical plans may include discrete or automatic motor sequences in combination with executive direction on the basis of external events, and in ways responsive to different, possible distant, parts of the action sequence.
These kinds of hierarchical behaviors are necessary for language, since meaning depends on grammatical relations, which are themselves hierarchical rules for encoding relational information in a temporal sequence. So, the authors hypothesized that Broca's area, necessary for grammar, might also be necessary for these kinds of temporal plans.
And they discovered that they were right:
[A]nterior BCA regions show phasic activation at boundaries of superordinate chunks only, providing evidence that these regions are specifically involved in selecting or inhibiting superordinate action chunks. Compared to anterior BCA regions, posterior BCA regions additionally exhibited phasic activations at boundaries of simple chunks and in the transitions between simple chunks forming superordinate actions. Thus, posterior BCA regions are involved in selecting and inhibiting simple action chunks in response to external signals or as successive components of ongoing superordinate actions. Posterior BCA regions also showed phasic activation at boundaries of superordinate chunks. As explained above, such activations are unlikely to result from externally guided, bottom-up selection/inhibition of simple chunks in response to start and stop cues. Instead, such activations provide evidence that top-down control is exerted from anterior to posterior BCA regions and conveys trigger signals for starting and stopping successive selection of component simple chunks at the boundaries of superordinate chunks. Premotor regions showed the same activation profile as posterior BCA regions except that they showed additional phasic activations in transitions between motor responses composing simple action chunks (Figure 4). Reasoning as above, we conclude that premotor regions are involved in selecting motor acts in response to stimuli or as successive components of ongoing simple action chunks. Top-down control is exerted from posterior BCA to premotor regions for starting and stopping successive selection of component motor acts at the boundaries of simple chunks.
The whole prefrontal cortex seems to be involved in a series of finer and finer temporal planning and processing from anterior to posterior. Broca's area, and the posterior part of it referenced here, are relatively far back in this series of areas, and are involved with starting and stopping sequences that are carried out by the next area back, the premotor cortex. Toward the front, more general temporal planning occurs, along with the activation of entire hierarchical sequences in response to external events.
This front-to-back organization appears to hold within Broca's area itself, with respect to language processing:
Clearly, these accounts of the role of Broca's area in language appear compatible with the system of hierarchical control we propose. Language studies also reveal that in Broca's area posterior regions (i.e., BA 44/BA6) are preferentially engaged in language tasks based on phonological processing, whereas anterior regions (i.e., BA 45/BA 44) and anterior-ventral regions (i.e., BA 47/BA 45) are more specifically involved in tasks based on syntactic and semantic processing, respectively (review in Bookheimer [2002]; e.g., Gough et al., 2005). Given that syntactic and semantic processing involve hierarchically higher linguistic representations (i.e., words and multiword utterances) than those involved in phonological processing (phonemes/syllables within words), such functional segregation in the language domain appears consistent with our findings indicating an anterior-posterior organization of Broca's area in hierarchical control. Thus, our results support the view that Broca's area implements an executive function specialized for processing hierarchical structures in multiple domains of human cognition (Thompson-Schill et al., 2005). We speculate that the modular executive system of hierarchical control we describe possibly captures key functional components that may explain the critical contribution of Broca's area to human language.
It was interesting to me that I was just thinking and writing a bit about this topic yesterday. A longstanding issue has been whether grammar was itself an adaptation that followed (or coincided with) the evolution of language, or whether basic grammatical abilities might have evolved earlier for some other purpose, later exapted for use in language. The latter position has been at times advocated by Stephen J. Gould and Noam Chomsky.
This study would seem to make clear that grammatical aspects of language are lodged in our left Broca's area precisely because they require the kinds of temporal hierarchical organization that this region of cortex already carried out in earlier primates. This seems a bit distant from some suggestions about the prelinguistic function of grammar -- for instance, that it may have functioned as some kind of "language of thought". But at the same time it raises the possibility that other nonlinguistic but temporally hierarchical actions in early hominids may have biased them toward greater abilities in this area -- for example, tool production and associated action sequences.
Anyway, these are not new thoughts. I think the important thing is the emergence through these studies of a kind of simplicity in the organization of the prefrontal cortex, at least with respect to planning and carrying out time-related activities. It may be an interesting developmental analogue to other areas of the cortex, such as the sensory and motor cortices (each consisting of a homuncular map of the body) and the visual cortex (which includes a map of the visual field).
References:
Koechlin E, Jubault T. 2006. Broca's area and the hierarchical organization of human behavior. Neuron 50:963-974.DOI link
Top-down versus bottom-up attention
This article in last week's Science seems interesting:
Top-Down Versus Bottom-Up Control of Attention in the Prefrontal and Posterior Parietal Cortices
Timothy J. Buschman and Earl K. Miller
Attention can be focused volitionally by "top-down" signals derived from task demands and automatically by "bottom-up" signals from salient stimuli. The frontal and parietal cortices are involved, but their neural activity has not been directly compared. Therefore, we recorded from them simultaneously in monkeys. Prefrontal neurons reflected the target location first during top-down attention, whereas parietal neurons signaled it earlier during bottom-up attention. Synchrony between frontal and parietal areas was stronger in lower frequencies during top-down attention and in higher frequencies during bottom-up attention. This result indicates that top-down and bottom-up signals arise from the frontal and sensory cortex, respectively, and different modes of attention may emphasize synchrony at different frequencies.
At the end, they speculate that this difference in frequency has to do with the greater transmissibility of low frequency signals across different areas.