Basal Ganglia: action selection, error prediction and reinforcement learning

The December edition of Dana Foundation's online brain journal , cerebrum , has a very informative and interesting piece on the role of basal ganglia in response selection, error prediction and reinforcement learning.

The article contains a primer on basic basal ganglia functions and pathways.

The basal ganglia are a collection of interconnected areas deep below the cerebral cortex. They receive information from the frontal cortex about behavior that is being planned for a particular situation. In turn, the basal ganglia affect activity in the frontal cortex through a series of neural projections that ultimately go back up to the same cortical areas from which they received the initial input. This circuit enables the basal ganglia to transform and amplify the pattern of neural firing in the frontal cortex that is associated with adaptive, or appropriate, behaviors, while suppressing those that are less adaptive. The neurotransmitter dopamine plays a critical role in the basal ganglia in determining, as a result of experience, which plans are adaptive and which are not.

Evidence from several lines of research supports this understanding of the role of basal ganglia and dopamine as major players in learning and selecting adaptive behaviors. In rats, the more a behavior is ingrained, the more its neural representations in the basal ganglia are strengthened and honed. Rats depleted of basal ganglia dopamine show profound deficits in acquiring new behaviors that lead to a reward. Experiments pioneered by Wolfram Schultz, M.D., Ph.D., at the University of Cambridge have shown that dopamine neurons fire in bursts when a monkey receives an unexpected juice reward. Conversely, when an expected reward is not delivered, these dopamine cells actually cease firing altogether, that is, their firing rates “dip” below what is normal. These dopamine bursts and dips are thought to drive changes in the strength of synaptic connections—the neural mechanism for learning—in the basal ganglia so that actions are reinforced (in the case of dopamine bursts) or punished (in the case of dopamine dips)

In particular it discusses the role of dopaminergic receptors in the GO and NoGO pathways that are involved in positive and negative reinforcement learning respectively.

Building on a large body of earlier theoretical work, my colleagues and I developed a series of computational models that explore the role of the basal ganglia when people select motor and cognitive actions. We have been focusing on how When the “Go” pathway is active, it facilitates an action directed by the frontal cortex, such as touching your pinkies together. But when the opposing “NoGo” pathway is more active, the action is suppressed. dopamine signals in the basal ganglia, which occur as a result of positive and negative outcomes of decisions (that is, rewards and punishments), drive learning. This learning is made possible by two main types of dopamine receptors, D1 and D2, which are associated with two separate neural pathways through the basal ganglia. When the “Go” pathway is active, it facilitates an action directed by the frontal cortex, such as touching your pinkies together. But when the opposing “NoGo” pathway is more active, the action is suppressed. These Go and NoGo pathways compete with each other when the brain selects among multiple possible actions, so that an adaptive action can be facilitated while at the same time competing actions are suppressed. This functionality can allow you to touch your pinkies together, not perform another potential action (such as scratching an itch on your neck), or to concentrate on a math problem instead of daydreaming.

But how does the Go/NoGo system know which action is most adaptive? One answer, we think (and as you might have guessed), is dopamine. During unexpected rewards, dopamine bursts drive increased activity and changes in synaptic plasticity (learning) in the Go pathway. When a given action is rewarded in a particular environmental context, the associated Go neurons learn to become more active the next time that same context is encountered. This process depends on the D1 dopamine receptor, which is highly concentrated in the Go pathway. Conversely, when desired rewards are not received, the resulting dips in dopamine support increases in synaptic plasticity in the NoGo pathway (a process that depends on dopamine D2 receptors concentrated in that pathway). Consequently, these nonrewarding actions will be more likely to be suppressed in the future.

It then goes on to consider the different types of learner: positive learners that have a more active GO system and negative learners that have a more active NoGO system.

This theoretical framework, which integrates anatomical, physiological, and psychological data into a single coherent model, can go a long way in explaining changes in learning, memory, and decision making as a function of changes in basal ganglia dopamine. In particular, this model makes a key, previously untested, prediction that greater amounts of dopamine (via D1 receptors) support learning from positive feedback, whereas decreases in dopamine (via D2 receptors) support learning from negative feedback.

They then experimentally manipulated the dopamine levels and verified their predictions. The experiment involved a simple game in which two symbols say A and B were paired consistently (along with other symbols say 'CD') with subjects required to choose one of them. After each choosing, the subject was given feedback as to whether he had been rewarded or punished. This feedback was not consistently related to the choice : 'A' was rewarded with positive feedback 80% of times, while 'B' was punished with negative feedback 80 % of the times. Thus though an implicit learning would happen to chose A and reject B, this rule would not be explicitly learned. Now, comes the interesting part, choose A strategy is related to positive learning and Avoid B strategy with negative learning. When these symbols A and B, in test phase were paired with new symbols say E and F respectively, subjects should have implicitly still gone with choose A and Avoid B strategy with equal inclination. Yet, administering dopamine affecting drugs had dramatic effects.

We found a striking effect of the different dopamine medications on this positive versus negative learning bias, consistent with predictions from our computer model of the learning process. While on placebo, participants performed equally well at choose-A and avoid-B test choices. But when their dopamine levels were increased, they were more successful at choosing the most positive symbol A and less successful at avoiding B. Conversely, lowered dopamine levels were associated with the opposite pattern: worse choose-A performance but more-reliable avoid-B choices. Thus the dopamine medications caused participants to learn more or less from positive versus negative outcomes of their decisions

They then go on to apply these results to Parkinson's patients.In Parkinson's patients have deficits in basal ganglia dopamine levels - especially in the NoGO pathway. Medication is L-Dopa which is a dopamine precursor and acts by increasing dopamine in the basal ganglia. They hypothesized, that people with untreated Parkinson's will be negative learners (less dopamine and less the dip), while those on medication would be positive learners 9more dopamine and more the burst).

To test this idea, we presented people with Parkinson’s disease with the same choose-A/avoid-B learning task once while they were on their regular dose of dopamine medication and another time while off it.8 Consistent with what we predicted, we found that, indeed, patients who were off the medication were relatively impaired at learning to choose the most positive stimulus A, but showed intact or even enhanced learning of avoid-B. Dopamine medication reversed this bias, improving choose-A performance but impairing avoid-B. This discovery supports the idea that medication prevents dopamine dips during negative feedback and impairs learning based on negative feedback

This notion might explain why some medicated Parkinson’s patients develop pathological gambling behaviors, which could result from enhanced learning from gains together with an inability to learn from losses.

The above (gambling in those on dopamine) I have touched earlier also, in relation to psychosis and schizophrenia, where dopamine excess is suspected. In those cases, having a consistently high dopamine level may predispose towards positive behavioral learning and positive cognitive learning. The latter may be the underlying manic loop, whereby only positively rewarded cognitions become salient, leading to a rosy picture of universe. Negatively reinforced cognitions are not registered properly and not learned/ remembered.

They then go on to discuss other implications like in ADHD, wherein the total noise in dopamine neurons may be higher, leading to both lowered positive and negative learning (my conjecture, not author's) and in addiction.

Overall a very fascinating article indeed.

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Neural correlates of trust

This is the title of a new paper in PNAS by Krueger et al, that tries to find the neural correlates of conditional and unconditional trust using the sequential, reciprocal trust game. The authors premise is that conditional trust is more costly strategy compared to unconditional trust and might utilize different brain areas as well.

Conditional trust assumes that one's partner is self-interested and estimates the expected value of one's strategy with respect to the benefits of cooperating, the risk of defection, and the future value of past decisions; it causes less balanced goodwill and results in greater variance in cooperative decisions and, therefore, is cognitively more costly to maintain. In contrast, unconditional trust assumes that one's partner is trustworthy and updates the value of one's partner with respect to their characteristics and past performance; balanced goodwill occurs more quickly, allowing the partners to attain high levels of synchronicity in their decisions and, therefore, is cognitively less costly to maintain. In this work, an examination of functional brain activity supports the hypothesis that the preferential activation of different neuronal systems implements these two trust strategies.

The results of their experiments supported their initial hypothesis and they found that while Para Cingulate cortex (PcC) activation was necessary for menatlizing and initial building of trust; later unconditional and conditional trust strategies deployed different brain areas viz Septal Area (SA) and Ventral Tegmental Area (VTA) respectively.

Unconditional trust assumes that one's partner is trustworthy. During the building stage, first movers in the nondefector group showed higher activation in the PcC compared with first movers in the defector group. Through mentalizing, partners of this group verified their prior trustworthy assumption, updated the value of one's partner's strategy with respect to their past performance, and maintained a balanced goodwill toward each other, allowing them to avoid defections. By developing "better" mental models in this early stage, partners in the nondefector group accumulated sufficient mutual goodwill to become socially attached to each other and adopted an unconditional trust strategy.

During the maintenance stage, the nondefector group showed a higher activation in the SA compared with the defector group. Across groups, pairs who showed the highest trust-reciprocate history in their decisions also showed the highest activation in this region. Furthermore, analyses of pre- and postscan behavioral ratings confirmed that only nondefector pairs felt significantly closer to each other and ranked themselves as being more of a partner to the other person after the experiment. Through early mentalizing, partners in the nondefector group must have balanced goodwill more quickly, allowing them to become synchronized in their decision patterns. Brain-to-brain correlations only increased in the SA region for the nondefector group across stages, and only partners in the nondefector group became synchronized in their SA BOLD amplitudes as first movers in adjacent trials of trust games. Synchronization in the SA led to social attachment associated with a significant decrease in activation in the PcC during the maintenance stage. By adopting this cognitively less costly strategy, decision times became significantly faster for the nondefector group across stages of the experiment.

Conditional trust assumes that one's partner is self-interested. During the building stage, first movers in the defector group showed less activation in the PcC compared with the nondefector group. Through less mentalizing in the building stage, partners in this group produced higher errors in the inferences of second movers' goodwill toward them, resulting in less balanced goodwill and, therefore, in less overall trust compared with the nondefector group. More importantly, they started to trust more in the low-payoff games and less in the high-payoff games. This decision pattern implies that defectors were adapting a conditional trust strategy by evaluating the expected value of one's strategy with respect to the risks and benefits of cooperation.

During the maintenance stage, the defector group showed higher activations in the VTA compared with the nondefector group, a region linked to the dopaminergic mesolimbic reward system providing a general reinforcement mechanism to encode expected and realized reward . Across groups, pairs who shared the lowest trust-reciprocate history in their decisions also showed the highest activation in this region. By adopting a cognitively more costly strategy, partners in the defector group showed a significant increase in activation in the PcC over the experiment. Through more mentalizing in this late stage, first movers in the defector group tried to develop more accurate models about the likelihood of their partner's choices so that they could make a more advantageous decision about when to trust. The conditional trust strategy paid off less over time as total earnings decreased for the defector group (but increased for the nondefector group) across stages.

Thus, it seems that SA, based on oxytocin and vasopressin and social bonding is a more cost-effective strategy than the VTA based on dompainerigic system based on reward monitoring.

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cortex maturation: found the references

In my earlier post on cortex maturation, I was unable to find the references for the claims that in Autism cortex matures earlier during toddler phase and that even in adulthood, it may be thicker.

In a recent PNAS commentary, reagarding the delay rather than deviance theory of ADHD, I came across the appropriate references to back the above observations, as well as the accelerated pruning in child-onset schizophrenia. Passing that along.

An important question is whether the delay of brain maturation is a specific characteristic of ADHD or is shared by other child psychiatric disorders. So far, none of the other major psychiatric disorders have been associated with a maturational delay of brain structure. However, to my knowledge, longitudinal structural studies have been conducted only in patients with ADHD, childhood-onset schizophrenia (COS), and autism, finding maturational deviance rather than delay. Adolescents with COS are characterized by a striking nonlinear, progressive acceleration of the normal gray matter and volume decrease in cortical regions that levels off in adulthood (22). In autism, there is an early left hemispheric overgrowth of gray and white matter at young toddler age with conflicting findings of either arrested growth or remaining brain enlargement in adolescence and adulthood (23). The findings of delayed structural brain maturation seem, thus far, to be specific to ADHD and may be an important neuroanatomic trait. However, further exploration of the developmental trajectories in other child psychiatric disorders is needed to establish the importance of a delay of brain maturation as a specific neuroanatomic marker for ADHD.
(emphasis mine, references below)
22. Greenstein D, Lerch J, Shaw P, Clasen L, Giedd J, Gochman P, Rapoport J, Gogtay N (2006) J Child Psychol Psychiatry 47:1003–1012.
23. Bashat DB, Kronfeld-Duenias V, Zachor DA, Ekstein PM, Hendler T, Tarrasch R, Even A, Levy Y, Sira LB (2007) NeuroImage 37:40–47.

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IQ matters...or does it?

This is just an FYI post regarding two great articles on IQ.

The first addressees the white-black IQ gap and shows that the gap is due to environmental factors and not genetic. This is a well written article by Malcom Gladwell and is strongly recommended to be read in its entirety. The arguments are manifold:

1. Flynn effects show that IQ scores have increased over time, and hence IQ is malleable and prone to environmental influences.
2. Intelligence is also a cultural construct and what may be intelligent behavior in one culture may be deemed stupid in another.
3. Intelligence can be raised by providing the right socio-cultural environment and cognitive grooming and scaffolding. High heritability may partially be due to the fact that high SES groups are considered in such studies. In poor families IQ heritability drops to 10 to 20 % and environmental factors play a much higher role.
4. IQ tests are renormed (to take care of the Flynn effect and the definition of IQ as relative to mean IQ of population) and sometimes data that supports claims like Asians have higher IQ than white which have higher than blacks are comparing apples to oranges.
5. IQ gap is narrowing and the average scores of blacks increasing at a faster rate than whites, which is further proof that there is not a racial gap that is due to genetics.

The second article is by Flynn himself and covers some of the same ground. The main essay is followed by several commentaries and it makes for a stimulating exchange.

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Perfectionism: devleopmental influences?

A recent Mind Hacks post, and a comment by John Bunch there, set me thinking, regarding whether perfectionism could have a developmental genesis. Perfectionism , like other personality traits, would likely be having both genetic and environmental factors contributing ti its development. So, before proceeding further, I would like to list the factors of perfectionism as identified in a recent study by Frost et al, they are:

1. Excessive concern over making mistakes
2. The doubting of quality of one's actions
3. High personal standards
4. Perception of high parental expectations and criticisms
5. A preference for order and organization

To me they present in a nice order on the five developmental factors related to trust (whether mistakes will be tolerated or not), Autonomy vs shame and doubt(doubting quality of one's actions), Initiative vs guilt (setting high standards to avoid guilt), industry vs inferiority(judging oneself by perceived parental standards) and finally Identity vs role confusion (having order and organization in life to relive the role confusion). Thus, all the perfectionist traits are a result of some deficient achieving of a developmental milestone - especially in relation to goal pursuing.

Here I come to my second theme- the comment by John Bunch, tries to draw fascinating parallels between the need to avoid mistakes in Perfectionists and the avoidance of risks in Passive Aggressives - and relates both of them to Carol Dwecks work with parsing and installing in children a fixed, entity like belief of personality and intelligence versus a growth mindset that has room for improvements and change. It is worthwhile here to recount Carol Dwecks experiments in which her team found that those children who had fixed, entity like view of intelligence gave up earlier on solving difficult tasks , avoided hard tests, weer more concerned with their image and projecting a good face than in learning - and one can easily see that these are seed for the later perfectionist traits of fear of mistakes, perceived high expectations and criticisms of parents etc. Similarly in Passive Aggressives this translates into risk avoidance - different mechanism chosen, due to underlying genetic temperaments, but to the same environmental stimuli of the fixed intelligence or talent myth installation.

By the way, the five factors of Perfectionsim can also be construed in terms of the big five factors -

1. Neuroticism (cognitive)- worry over mistakes.
2. Conscientiousness (motivational)- high and unrealistic standards
3. Extraversion (behavioural) - doubt over actions
4. Agreeableness (social)- perceived criticism and expectations
5. Openness (exploratory) - organization and order

I would love to hear more comments on this developmental theory of perfectionism. A quick search on Google revealed a promising dissertation that links perfectionism to helpless explanatory style which fits with Dwecks theory.

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Rasing Successful kids

Carol Dweck, whose research I have covered extensively earlier, writes in this month's Scientific American Mind , regarding how to raise a successful child. She touches upon the entity vs incremental theories of intelligence, which she frames as fixed and innate abilities vis-a-vis a growth mindset. As per this theory having successful and intelligent children depends on not praising the children for their smarts or intelligence or talent , but on their efforts and hard work. Also, to inculcate in them a sense of brain's malleability and to view challenges as resulting in growth as a result of facing difficulties and seeing the challenges as opportunities for brain development and learning. this view purportedly leads to more motivation and effort while facing life challenges or solving educational problems. Ironically, the article is titled The Secret to Raising Smart Kids, while in my opinion , to not reinforce the 'smart' stereotype, it should have been labeled The Secret to Raising Successful Kids. this would have also captured the recent Strenberg's emphasis on successful intelligence.

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Let the Mouse Party begin : Encehpalon #37 is up!

Encephalon #37 is up at A Blog Around The Clock. While I liked, amongst others , the post pitting amygadala (subjective) with insula (objective) in beauty perception, what I was mesmerized with was the Mouse Party post.

Mouse Party is a web resource developed by university of Utah, that lets you see with very cool animations the effects of common drugs of abuse like LSD, Ecstasy etc. Lately I have been blogging a bit about ecstasy , LSD (Leary's model of consciousness etc), so along with the mouse theme, this immediately caught my eye.

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The eight-fold structure of evolutionary biology/ cultural evolution

Regulars readers of this blog will know that I am sold on the eight-fold developmental theory that assumes that there are eight stages of development/evolution of any feature and I have explored this extensively. Five of these lower stages are at a different level and the upper 3 at a different level explain the development of the same phenomenon. for a quick summary and links to my eight-fold fascination please see this the first paragraph of this post. So it is no surprise that I was fascinated when I discovered that evolutionary biology is conceptualized as eight subjects or methods of inquiry and they also follow a 5 +3 pattern with 5 lower levels referring to within species evolution and the last 3 referring to between species evolution. This structure of evolutionary biology I discovered via a fascinating artcile that tries to find parallels between cultural evolution and biological evolution. The article is by Mesoudi et al (2006) and I will be heavily quoting from that paper.

First a very beautiful figure that shows the structure of evolutionary biology and draws parallels to cultural evolution. Explanation of figure follows.

The left hand side of Figure 1 illustrates the overall structure of evolutionary biology, as described by Futuyma (1998, pp. 12-14) in what is, perhaps, the most widely used undergraduate textbook in the field. The study of biological macroevolution deals with change at or above the species level, while biological microevolution concerns changes within populations of a single species. The former comprises systematics, paleobiology and biogeography, while the latter involves population genetics (theoretical, experimental and field-based), evolutionary ecology and molecular genetics. In Sections 2 and 3 we examine each of the sub-disciplines of evolutionary biology in turn, first outlining their general methods then briefly describing examples of recent studies to illustrate how those methods are applied and the kind of results they yield. This is followed in each case by a discussion of existing analogous or equivalent methods within the social sciences regarding human culture, again describing recent key studies. These cultural disciplines, and the way in which they map onto the structure of evolutionary biology, are illustrated on the right hand side of Figure 1. While there may be no obvious precedent for two distinct fields to exhibit the same internal structure, the similarity of underlying processes leads us to expect a correspondence.

Now let me come to the central theme of the paper that cultural evolution has parallels in evolutionary biology and the sub disciplines and methodologies from one can inform the other.

Parallels or analogies between biological and cultural evolution have been noted by a number of eminent figures from diverse fields of study.The implication of this growing body of theory is that culture exhibits key Darwinian evolutionary properties. If this is accepted, it follows that the same tools, methods and approaches that are used to study biological evolution may productively be applied to the study of human culture, and furthermore that the structure of a science of cultural evolution should broadly resemble the structure of evolutionary biology. In the present paper we attempt to make this comparison explicit, by examining the different approaches and methods used by evolutionary biologists and assessing whether there is an existing corresponding approach or method in the study of cultural evolution. Where such an existing correspondence is not found, we explore whether there is the potential to develop one. We also explore potential differences between biological and cultural evolution.

They also elaborate on benefits of the evolutionary eight-fold approach.

Second, and particularly relevant to this article, the theory of evolution encompasses and integrates a multitude of diverse sub-disciplines within biology, from behavioural ecology to paleobiology to genetics, with each sub-discipline stimulating and contributing to several others (see Mayr, 1982 for further details of this 'evolutionary synthesis'). The social sciences, in contrast, have no such general synthesising framework, and the greater part of disciplines such as cultural anthropology, archaeology, psychology, economics, sociology and history remain relatively insular and isolated, both from each other and from the biological and physical sciences. Adopting an evolutionary framework can potentially serve to highlight how these disciplines are, in fact, studying complementary aspects of the same problems, and emphasise how multiple and multidisciplinary approaches to these problems are not only possible but necessary for their full exposition. At present, many of the individual studies considered below are the result of independent developments at the fringes of separate fields of study. Placing these disparate studies side-by-side within a broader evolutionary framework, as is done here, will hopefully contribute towards creating a coherent unified movement and bring evolutionary analyses of cultural phenomena into the mainstream. They then go on and explore each of the subdivision in detail and draw parallels to cultural evolution and show how methods of evolutionary biology when applied to culture have helped solve many problems there.

They also analyze psychology as equivalent to experimental population genetics. Reproducing the relevant sections below:

One parallel with this work lies in laboratory based psychological experiments simulating cultural transmission. Where population genetic experiments simulate biological evolution by studying the transmission of genetic information from generation to generation through the reproduction of individuals, psychological experiments can potentially simulate cultural evolution by studying the transmission of cultural information (e.g. texts or behavioural rules) from one individual to another through social learning.

One method for simulating cultural evolution was developed by Gerard, Kluckhohn and Rapoport (1956) and Jacobs and Campbell (1961). A norm or bias is established in a group of participants, usually by using confederates, and one by one these participants are replaced with new, untrained participants. The degree to which the norm or bias remains in the group after all of the original group members have been replaced represents a measure of its tansmission to the new members.

For example, Baum et al. (2004) studied the transmission of traditions using a task in which participants received financial rewards for solving anagrams. Groups of individuals could choose to solve an anagram printed on either red or blue card: the red anagrams gave a small immediate payment, while the blue anagrams gave a larger payoff but were followed by a ‘time-out’ during which no anagrams could be solved. By manipulating the length of this time-out, the experimenters were able to determine which of the two anagrams gave the highest overall payoff (i.e. where the blue time-out was short, blue was optimal, and where the blue time-out was long, red was optimal). Every 12 minutes one member of the group was replaced with a new participant. It was found that traditions of the optimal choice emerged under each experimental condition, with existing group members instructing new members in this optimal tradition by transmitting information about payoffs and timeouts, or through
coercion.

Key similarities exist between this study and the experimental simulations of natural selection described above. In Kennington et al.’s (2003) study with Drosophila, where the experimentally determined conditions of low humidity favoured small body size, smaller individuals out-reproduced larger individuals. Hence genetic information determining ‘small body size’ was more likely to be transmitted to the next generation through biological reproduction, and the average body size of the population became gradually smaller. In Baum et al.’s (2004) study, where the experimentally determined conditions favoured red anagrams (when the blue time-out was relatively long), choosing red anagrams gave a larger payoff to the participants. Hence the behavioural rule ‘choose red’ was more likely to be transmitted to the new participants through cultural transmission, and the overall frequency of choosing red
gradually increased.

Baum et al.’s (2004) method could easily be adapted to study the cultural evolution of attitudes or beliefs. Groups of participants could be asked to discuss a contentious issue, then every generation the participant with the most extreme opinion in a certain direction removed and replaced with a random participant. After a number of generations the group should hold more extreme views (in the opposite direction to those of the removed participants) than average members of the larger population.

Finally they discuss psychology in relation to evolutionary ecology and neursocience / memetics in relation to molecular biology.

While genetic information is represented in sequences of DNA molecules, cultural information is represented primarily in the brain. Viewing culture as comprised of discrete units of information, or memes, can potentially make a complex system theoretically and empirically tractable, in the same way as the gene concept advanced biologists’ understanding of biological evolution. Although memes can be characterised as vague entities with flexible and fuzzy boundaries, so can the modern concept of the gene. It should be remembered that there was at least 50 years of productive investigation into biological microevolution before the molecular basis of genetic inheritance was determined, and even now it is only partly understood.
A deeper understanding of the neural and molecular basis of culturally acquired information must rely on technological advances in, for example, neuroimaging techniques. However, we should also reserve the possibility that the same cultural information is specified by different neural substrates in different brains, severely limiting such methods for studying cultural transmission. In this case there may be no cultural equivalent to molecular biology, although models and methods examining cultural transmission at the behavioural and cognitive levels can still provide important insights.

To me all this seems very interesting and I end with their conclusion:

The evidence discussed in this paper suggests that much potential exists for a comprehensive science of cultural evolution with broadly the same structure as the science of biological evolution, as outlined in Figure 1. This potential is already being realised for the study of cultural macroevolution and the mathematical modelling of cultural microevolution, with methods developed within evolutionary biology, such as phylogenetic analyses and population genetic models, being applied to cultural data. A number of opportunities exist for psychologists, sociologists and experimental economists to adopt the experimental methods and tools developed in population genetics to simulate cultural microevolution, and detect cultural evolution ‘in the wild’. Finally, the study of the neural basis of cultural transmission is seemingly dependent on advances in new technologies that should reveal how culturally acquired information is represented in the brain.

In short, we submit that the argument that culture exhibits a number of key Darwinian
properties is well-supported, and advocate taking advantage of this in order to use evolutionary biology as a model for integrating a multitude of separate approaches within the social sciences, and, where appropriate, borrowing some of the methods developed by evolutionary biologists to solve similar problems. Putting disparate studies from presently unconnected disciplines together into a broad evolutionary context adds value to each of the individual studies, because it illustrates that the degree of progress in this area is far more impressive than hitherto conceived. We suggest that these studies can now be said to be aligned within a unified ‘movement’, and that if this Darwinian evolutionary movement could be better co-ordinated, a more persuasive and important direction could be put on much work in the social sciences.

Hat tip: Natural Rationality

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