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The Evolving Human Brain

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EVOLUTION AND THE BRAIN

It has long been appreciated that there is something about the human brain that makes it unique amongst other primates and mammals in general. Dr. Greg Downey  and Dr. Daniel Lende explore how and why the human brain has evolved the way that it has in Chapter 4 of The Encultured Brain: An Introduction to Neuroanthropology. The authors are well-qualified to provide an overview on this topic as both have a wealth of publications in this area, as well as being leaders in the development of the field of Neuroanthropology.

SIZE MATTERS

Blue Whale at The American Museum of Natural History

What makes a human brain unique? Is it simply the sheer size of it? Well, no. Anyone who has visited the American Museum of Natural History in New York City can clearly see that the enormous blue whale hanging from the ceiling has a brain much larger in size than that of a human’s. Perhaps the issue is not sheer size then, but the size relative to one’s own body. Unfortunately, we once again do not have a satisfactory explanation for human’s unique cognitive capabilities. While looking at relative size does work to explain the blue whale example (a blue whale’s brain only accounts for 0.01% of its body’s mass while a human brain accounts for 2%) we see other species that are an exception to this rule. For instance, a pocket mouse has a brain that comprises 10% of their body mass, much more than that of a human and yet we don’t see the unique functionality of a human brain expressed in a mouse.

Pocket Mouse at White Sands National Monument

However, when we turn instead to the encephalization quotient (i.e. the ratio of predicted brain mass to observed brain mass) we see that humans do stand out in this respect. In fact, humans exhibit an encephalization quotient that is between five to seven times higher than what is predicted for a mammal of our size. While greater encephalization is found across primates, humans are still an outlier and it appears this has been true for quite some time. Around two million years ago the genus Homo appears and with it we see a tripling in brain size in our ancestors as compared to other apes. However, it is not just the increase in size that is notable here–brain organization is a key component in better understanding our cognitive evolution.

STRUCTURE MATTERS

So, do humans simply have brains that have a ton of new structures that other primates don’t possess? This is once again an incorrect assumption. Rather than humans and primates differing in existing regions of the brain, our current evidence suggests that the differences are actually proportional which has fascinating implications for our evolutionary understanding of cognitive function. Instead of evolving new structures, it appears that humans have modified or repurposed existing structures so that certain brain regions have expanded at a different rate than others. This evolutionary trade-off has resulted in decreased development in areas like the human olfactory bulb, while structures like the cerebellum which is involved in frontal lobe functioning has shown great expansion.

Human Olfactory Bulb


CONNECTIONS MATTER

In addition to size and structure changing across evolutionary time, connections among regions of the brain have also seen significant changes.

In particular, we have seen an increase in the total number of neurons and with this, we see that larger brains tend to develop areas that are increasingly independent or modular which requires an increase in white connective matter. Understanding the brain’s connectivity is likely a key component of understanding human consciousness. Further, many researchers are now emphasizing the failure of previous metaphors such as the brain being “hard-wired” which does not capture the way in which brains are shaped through interactions and development (i.e. “wet-wired”).

NOT A BRAIN ALONE

To better understand how it is that experiences help shape the brain, Downey and Lende draw on the concept of niche construction which emphasizes the role that organisms play in shaping their own environment and subsequent selective pressures.

The authors argue that niche construction provides a place for cultural researchers within evolutionary studies–an interdisciplinary relationship that is too rarely created. This relationship is absolutely necessary since an understanding of human “intelligence” cannot be obtained by looking simply at the size and structure of the brain. Rather, we must also consider how our social relationships allow us to transfer and amass all of the components that we regard as forms of “intelligence” (e.g., technology, skills, information). Moreover, the authors emphasize how emotions, motivation, and perception are all factors that play into our social and cultural complexity and, thus, cognitive evolution.

MY THOUGHTS

This last section of the chapter was by far my favorite as I feel the authors made a convincing argument for the role of culture and social relationships in our understanding of human evolution. Additionally, I think that they do a great job of not allowing those who are skeptical or critical of previous evolutionary research to “throw the baby out with the bathwater.” I think their point is best summed up in the following quote:

Powerful, but overly simple, models of evolution that assume evolutionary traits will necessarily result in human universals need to give way, not to erase evolutionary explanations, but to provide richer accounts that incorporate data emerging from genetics, paleoanthropology, comparative neuroscience, and anthropology, including research on human diversity (p. 124).

EVOLUTION OF THE CEREBELLAR CORTEX: THE SELECTIVE EXPANSION OF PREFRONTAL-PROJECTING CEREBELLAR LOBULES

The lead author for this paper is Dr. Joshua Balsters whose research interests are in the area of social and emotional decision making. While not covered in the article, Dr. Balsters states that his specific interest is in Autism Spectrum Conditions (ASC) which he studies using a combination of fMRI, EEG, and computational modeling.

STUDY OVERVIEW

Capuchin Monkey

At the broadest level, the researchers are interested in whether the process of brain evolution is mosaic (i.e. evolutionary pressures act on individual neural structures) or concerted (i.e. evolution acts on interconnected parts of the brain that comprise whole functional systems). To test this, the researchers examine the cortico-cerebellar system in three different primate species: humans, chimpanzees, and capuchin monkeys.

METHODS

Chimpanzee

The study consisted of obtaining high-resolution MRI scans from 10 primates from each of the previously mentioned species (5 females and 5 males). All of the included primates had either reached sexual maturity or were close. The researchers were able to isolate the cerebellum and examine the lobules related to the primary motor cortex and the prefrontal cortex.

RESULTS

The data demonstrate that the lobules related to motor and prefrontal cortex occupy a greater proportion of the human cerebellum (83.87%) as compared to chimpanzees (67.1%) and capuchin monkeys (56.82%). Moreover, the results show that where there were increases in the prefrontal cortex, there were proportional decreases in the motor cortex. Since the volume of areas of the prefrontal cortex increased relative to cerebellar lobules connected to the motor cortex, these data suggest that these associated functional systems evolved together.

Cerebellum in Humans

DISCUSSION

This study provides support for the idea that brain systems evolve in a concerted fashion. The results from this study are important as they suggest a potential route to find clues regarding the evolutionary pressures that may have contributed to various expansions in the brain. Additionally, this research demonstrates how comparative MRI can be utilized to examine differences across primates.

MY THOUGHTS

I was able to somewhat follow the methodology of this study; however, I found myself both intrigued and somewhat intimidated by what I couldn’t grasp. This makes me wonder about some of the practical issues with interdisciplinary collaboration. I loved Downey and Lende’s description of how cultural researchers could and should be involved in evolutionary research, but there will likely be some limitations to this collaboration. In many ways, Balsters et al. (2009) is speaking a different language with words and acronyms that will have no meaning to someone who is not well-versed in the cognitive literature. Even simply grasping the hypothesis or overall finding for the study would likely be quite difficult for someone outside the field to grasp. Here is our challenge: if we were to reduce the complexity of the article, perhaps more researchers could understand the results; contrastly, researchers most likely to utilize this study will need a detailed report of the methodology and results in order to replicate or expand on this study. How do we find this balance? 

DISCUSSION QUESTIONS

  • How would our understanding of human brain evolution be different if we didn’t consider it in terms of niche construction?
  • What are some arguments against the idea that humans have “unusual cognitive abilities?”
  • In light of new ideas regarding “dual-inheritance,” what are some reasons why anthropologists might be uniquely qualified to examine human cognitive evolution?
  • How might we define “culture” in evolutionary terms?
  • With the full acknowledgement that there is very likely more than one explanation for human brain encephalization, what is your favorite theory for why humans evolved such large and complex brains?
  • How can we encourage interdisciplinary research when each field has their own “language”?
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Monkey See, Monkey Do

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Cognition, learning, and evolution in human and non-human primates

Primate Social Cognition, Human Evolution, and Niche Construction
Evolution of a student

The old image of a human evolving from an ape by gradually getting more upright is a common way to portray the concept of evolution, even though the imagery portrays a slightly incorrect concept: humans did not evolve “from apes,” modern day humans and modern day non-human primates evolved from a common ancestor. While this distinction may seem semantic, it’s important to note because the study of modern non-human primates is not quite exactly the same as peering back into our own evolutionary history. It can, however, still offer incredible insights into the overall evolution of our species, especially when it comes to cognition and learning, and offers clues as to how our species’ brain evolved the way it did. That is, studying cognition across the Primate order can provide a framework for understanding cognitive functioning and evolution.

One of the key commonalities all primates share is a dependence on close social relationships for support with security, food resources, and child rearing (MacKinnon and Fuentes 2012). Living in stable social groups allowed early primates to be able to deal with threats more efficiently. This lead to changes in the environment, such as, among other things, predators deciding to go after other pray. As threats lessened as a result of the adaptation of social groups, primates were then able to spend more time and energy in building social relationships, exploring territory, and experimenting with different foraging strategies (MacKinnon and Fuentes 2012). All of this lead to primates both requiring and having the opportunity to increase cognitive functioning. In this way, primates shaped their environment and were in turn shaped by the changing environment. This concept is called niche construction—primates created a niche for themselves in their environment that shaped both the environment and their evolution. This concept illuminates some of the intricacies involved in understanding evolution: the model of organisms merely adapting to their environments for the purpose of survival doesn’t quite capture the complexity involved.

Human niche construction and evolution, specifically, depended upon an increasingly sophisticated way of interacting with the environment. With the use of more tools, better survivability rates for infants, and increasingly complex methods of communication, early humans were able to efficiently increase their territory and cooperate within and among groups. The success of these adaptations meant more resources, and the conditions were fertile for the evolution of human cognition.

This chapter gives a good, easy to understand overview of the evolution of primate cognition, and makes a good case for the purpose of studying primate cognition in neuroanthropology. Of course, as an overview it ends up lacking in some specificity of the concepts covered, but the following articles address some of the more important areas more in-depth.

Understanding Primate Brain Evolution

The increasingly social nature of primates, as well as the increasing complexity of interactions with the environment, lead to an increase in the types of interactions and concepts that needed to be exchanged. To put it another way, the complexity of interactions increased. This is the basic idea behind the social brain hypothesis, which says that brain size, specifically the neocortex, is correlated with not just group size but the complexity of relationships within a social group (Dunbar and Shultz 2007). Some examples of complex social interactions necessary for survival in large groups that primates exhibit that require higher cognitive functioning include tactical deception, social play, and the use of subtle social strategies (Dunbar and Shultz 2007). The increase in neocortex size does not come without some tradeoffs, however: diet, infant care, and development have all shifted to account for the change in brain size necessitated by and necessary for increasingly complex social interactions.

This article is a thorough examination of the variables at stake in understanding the evolution of primate cognition. However, the statistical analyses and language used make it unapproachable for a casual reader. The previous piece covers the subject material in a more approachable way, though certainly doesn’t go into the depth of what’s involved in the social brain hypothesis.

Play, Social Learning, and Teaching

Complex social interactions like the ones required for primate survival, and that lead to the evolved human brain, needed to have been passed down from generation to generation in order to be evolutionary. One primary way learning of this kind takes place is through social play. Play is, in terms of survival, both costly and risky, which means that it likely has significant adaptive value (Konner 2010). As it turns out, the smartest animals are the ones that play the most, and it’s likely these two things co-evolved (Konner 2010). Interestingly, while the size of the neocortex is associated with intelligence and social complexity, the capacity for play appears to be housed in the limbic system, an older and more primitive part of the brain; however, animals with larger brains do play more and the animals with the largest brains play the most (Konner 2010), perhaps reflecting the increased complexity of the learning that needs to occur. For more information on the regions of the brain, see Kalat (2012).

While the process is not fully understood, social learning, unlike basic learning processes, likely takes place due imitative learning, assisted by the mirror-neuron system (MNS; Konner 2010). The MNS activates not only when one observes an action, but also right before an action is taken, which suggests that there is a link to the ability to perceive the intentions of others (Konner 2010).

This chapter comes from a book on childhood and development, so this chapter on social play doesn’t quite go into the specific depth that we might be interested in as neuroanthropologists, especially the neurobiology of social learning. While the mirror-neuron system is interesting and an exciting step toward understanding, its treatment is rather shallow and other systems aren’t included in the explanation.

Primate Cognition

While the above articles explain in varying degrees of accessibility the arguments for the evolutionary path of human cognition, they don’t go into much detail about the cognitive capabilities of our primate ancestors. Understanding the extent of primate cognition could help to understand the capabilities that primates had, prehistorically, that could contribute to and be shaped by their social complexity. According to Beran et al. (2016), controlled attention, episodic and prospective memory, metacognition, and delay of gratification have all been observed in chimpanzees. Non-human primates don’t match the cognitive abilities of humans in these areas, but their presence sheds light on the potential cognitive capabilities of our primate ancestors. Of course, it needs to be kept in mind that their study was done in a controlled laboratory setting with a modern chimpanzee, so the results would be different than a wild primate ancestor.

The scope of research included in this paper is impressive. Each component of cognition is tested well, with good results. While it is a psychology-oriented paper, more discussion of the implications for the understanding of primate evolution would have been welcome. Additionally, there isn’t any discussion of how these cognitive capabilities would be expressed in natural settings.

Questions for consideration:

What is niche construction, and how does it relate to our understanding of evolution? Can you think of any other examples of it?

What is the social brain hypothesis, and how does it relate to the evolution of the brain? What lines of evidence do we have that support this hypothesis?

How do modern advancements in technology alter the way we think about “play” as it relates to social learning?

What do you think about the understanding of gender in play relationships described in Konner’s article?

Resources:

Beran, Michael J., Charles R. Menzel, Audrey E. Parrish, et al.
2016   Primate Cognition: Attention, Episodic Memory, Prospective Memory, Self-Control, and Metacognition as Examples of Cognitive Control in Nonhuman Primates. Wiley Interdisciplinary Reviews: Cognitive Science 7(5): 294–316.

Dunbar, R.I.M, and S. Shultz
2007   Understanding Primate Brain Evolution. Philosophical Transactions of the Royal Society B: Biological Sciences 362(1480): 649–658.

Kalat, James W.
2012   Biological Psychology. Cengage Learning.

Konner, Melvin
2010   The Evolution of Childhood: Relationships, Emotion, Mind. Harvard University Press.

Lende, Daniel H., and Greg Downey, eds.
2012   The Encultured Brain: An Introduction to Neuroanthropology. Cambridge, Mass: The MIT Press.

 

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Marambe, Vermunt, Boshuizen in their “A cross-cultural comparison of student learning patterns in higher educaiton” remind us that there are not simple models of the Asian learner, especially because of the way that education systems are set up and the impact of colonialism. They show that an ANOVA analysis of cognitive batteries, in this case the ISL, ICB, and ARPM can show differences in the use of cognitive faculties. In particular, while there was a significance between Dutch learners compared to Sri Lankan and Indonesian learners, there were almost as many differences between Sri Lankan and Indonesian students.

More than just establishing the significance of their results, Marambe, Vermunt, and Boshuizen place the results in a cultural context, noting how similarities correspond to the place of the student in all of these societies, while cognitive differences match socio-cultural differences as well as the relationship between educators and educated.

Because of how well this cognitive test could correspond to cultural differences, I was tempted to include it in the proposal. Ultimately, it seemed like an interesting test, but one that would add too much workload to the researchers, especially if they were going to process heat maps and the simpler memory scale. Although the Weschler memory scale is not as comprehensive, it does have clear elements related to visual processing, which can be compared to the attention data.

Marambe, K., Vermunt, J. j., & Boshuizen, H. (2012). A cross-cultural comparison of student learning patterns in higher education. Higher Education, 64(3), 299-316.