Language and Cultural Evolution

Along with biological evolution, humans are driven by cultural evolution as well. Cultural evolution is based on the desire to pass on ideas and ways of life. For biological, or genetic, evolution DNA and genes are seen as the transmission unit. Cultural evolution is also referred to as memetic evolution because memes, "packets of information" are the units that are passed on. Richard Dawkins (1976), author of The Selfish Gene, coined the term meme as the cultural analogs to gene; they carry information about the world, just as DNA does for the one-celled organism.

The rapid evolution of the size and capacity of the human brain is believed to be associated with the evolution of language, speech, and more complex culture. Language is one of the major advantages humans have over the rest of the animal kingdom. Through time, humans have developed the ability to use words, nouns, verbs and other parts of speech to create sentences. Language has given humans the ability to vocalize their thoughts and share with their offspring and rest of their species. The emergence of language led to abstract thought and the abstract world.

The evolution of language allowed for the development of symbolic representation and more social interaction, which in turn led to a more complex world. Seth Lloyd (2006), a professor at M.I.T., goes as far to say, "With language, our ancestors were able to create their own environment - we now call it culture - and adapt to it without the need for genetic change." Additionally, language allows for humans to connect and bond at a different level than any other animal species. This both increased social complexity and forged stronger relationships amongst humans. As social relationships increased and became more complex, the brain’s size and other capacities increased.

Hawkins and the Evolutionary Advantage of Neocortex

Our large neocortex is the crux of intelligence and gives humans an innate ability to recognize patterns. Hawkins (2004) introduces his view on consciousness as "what it feels like to have a neocortex." Vernon Mountcastle (1978) proposed a fascinating hypothesis in the late 1970s, which stated that "there is a common algorithm that is performed by all the cortical regions." This algorithm has the ability to replicate itself and in a sense organize itself. The same types of layers, cell types, and connections exist in the entire cortex. Mountcastle explains that the “differences between areas of the neocortex reflect differences in their patterns of extrinsic connections” (p. 15). Therefore, there is nothing intrinsically different in the structure or function of various areas of the neocortex. Although there is a fissure separating the two cerebral hemispheres and a sulcus dividing the front and back, there are no clear delineations of certain areas constrained to certain functions. There is major overlap between the senses as well as between the senses and motor mechanisms.

Jeff Hawkins attempts to outline a framework for understanding intelligence in his book On Intelligence. He discusses many topics and uses logic to deduce how the brain may work. His definition of intelligence is most interesting. He defines intelligence as the ability to predict and remember. He creates what he calls a memory-prediction framework to understand the brain and its capacities.

Jeff Hawkins and his book On Intelligence (Picture Link)

Within this framework, there are many important aspects. To begin, Hawkins argues to steer away from our intuitive sense that intelligence is based on output behavior. Instead, he believes that intelligence is a process centered on successful prediction, experience, memory and understanding. Hawkins’ focus is on the neocortex, which is most pronounced in humans, and he focuses on four major capabilities of the cortex.

First, he describes that the cortex stores sequences of patterns, as well as, sequences of sequences. The best example would be our memory of the alphabet. It is not something recalled all together in an instant. Rather, our memory conveniently stores the information as a sequence of patterns. 

Second, the cortex recalls “auto-associative” memories. He is referring to our ability to recognize patterns when only given a part of the pattern. For example, if we hear half a melody of a song we know, we can often recognize the entire song and complete the melody. Essentially, each functional region is waiting for familiar patterns or pattern-fragments to be processed. The inputs to the brain are linking to themselves auto-associatively, filling in the present and what normally flows next (Hawkins, 2004).

Third, patterns are stored in an invariant form. This idea of storing ideas in an invariant form can be traced back to ancient philosophers such as Plato and Aristotle. However, Hawkins steers away from most philosophical implications and focuses on our ability to store a belief or an idea of an object regardless of its current context. For example, an object such as a table is stored in our brains in an invariant form. Although there are many individual instances of a table, i.e. coffee tables, kitchen tables, restaurant tables, we know that an object with four legs and a top is most often a table regardless of context and individual differences. We have the ability to identify a novel table without ever seeing one that looked exactly like the one being viewed. This system allows knowledge of past events to be applied to new situations that are similar but not identical to the past (Hawkins, 2004).

Fourth, Hawkins explains that the cortex stores patterns in a hierarchy. The neocortex, if stretched flat, is about the size of a large table napkin and 2mm and consists of six layers that are roughly the thickness of a playing card (Hawkins). These six layers are separated mostly by cell type and neuronal connections. Among these layers there is a branching hierarchy. In a certain sense, raw sensory data is being sent “up” the hierarchy, as more and more abstract and generalized versions of information are sent “down” the hierarchical layers and compared with known patterns. While reading words on a page, the higher levels of the cortical hierarchy are sending more signals down to the primary visual cortex than your eyes are receiving from the page (Hawkins, 2004).

This hierarchical structure gives us our ability to store patterns of patterns. He often uses music as a metaphor and describes how a song is understood. A song is made up of melodies, which are made up of a sequence of notes that are certain intervals away from one another. The nested structure of the world can be seen in our model of the world. Another great example is a piece of writing such as a book. From the simplest element lines make up letters, letters make up words, words make up sentences, sentences make up paragraphs, paragraphs make up chapters, and multiple chapters combine to form a book. Hawkins (2004) describes in great detail how this hierarchical structure seen in the world is actually similar to how the brain processes and stores information.

Our brains use stored memories to constantly make predictions about everything we see, feel and hear. Prediction is so pervasive that the way we “perceive” the world does not come solely from our senses. We perceive a combination of what we sense and of our brain’s memory-derived prediction. In this predictive process, neurons involved in sensing become active in advance of them actually receiving sensory input. These predictions are not always perfect, but the probabilistic predictions are often reliable. This concept is understood at some level to be intuition (Hawkins, 2004).

A large area of our neocortex expanded dramatically only a couple millions years ago. The neocortex is built using a common repeated element so evolution led to the rapid copying of an existing structure. As the cortex got larger over evolutionary time it was able to remember more and more about the world, form memories and the ability to make more predictions. Through time, the complexity of the memories and predictions increased (Hawkins, 2004).

By adding a memory system to the sensory paths of the primitive brain, an animal gains an ability to predict the future. Memory and prediction provide an animal a way to use its existing “old brain” behavior more intelligently. These capabilities of the neocortex allow an animal to use its existing hardware more effectively and compliments Ornstein’s and Allman’s view of the retrofitted brain. This ability creates a better adapted species. At a future time when the animal encounters the same or a similar situation the memory recognizes the input as similar and recalls what happened in the past. Recalled memory is compared with the sensory input streams and it “fills in” the current input and predicts what will be sensed next. In a certain sense, this allows an animal to see into the future (Hawkins, 2004).

As we interact with the environment, whether consciously or unconsciously, we are constantly predicting what is going to happen next. For example, as you read this sentence you are predicting what the next word will be in the sentence. While a stream of sensory information is coming into our brains we have a significant amount of more information flowing back down the hierarchical memory system. This feedback is sending predictions of what to expect next (Hawkins, 2004).

Multiple regions of the neocortex are simultaneously trying to predict what their next experience will be. Visual areas make predictions about edges, shapes, objects, locations and motions. Auditory areas make predictions about tones, directions to sources and patterns of sound. Somatosensory areas make predictions about touch, texture, contour and temperature (Hawkins, 2004). When the sensory input does arrive, it is compared with what was expected. When your predictions are met, you’ll continue without consciously knowing that your predictions were verified. For example, our auditory areas predict that background noise will continue, in continuation, moment after moment, and as long as the noise does not change our expectations are not violated. However, when a background noise ceases, this violates our prediction and attracts our attention.

Another great example of this memory-prediction framework is of our mind predicting and filling in the small blind spot in each eye where the optic nerve exits each retina. Even with one eye open, our visual system “fills in” missing information to make one coherent vision. The visual cortex is drawing on memories of similar patterns and makes a continuous stream of predictions that fill in for any missing input. We perceive clear lines and boundaries separating objects when we look at the world, but the raw data entering our eyes is often noisy and ambiguous. Our eyes saccade about three times every second. A saccade is when eyes fixate on one point and then suddenly jump to another point. We are not aware of these movements, and do not consciously control them. Each time our eyes fixate on a new point, the pattern entering your brain from the eyes changes completely from the last fixation. Yet, we are only aware of a continuous view of the world.

Minsky’s Society of Mind

Marvin Minsky (2007), co-founder of MIT’s AI laboratory, argues that “our minds did not evolve to serve as instruments for observing themselves, but for solving such practical problems as nutrition, defense, and reproduction” (p. 109). He describes the mind as being composed of many partially autonomous “agents.” These agents self-organize to create a “society” of smaller minds. This is basically another way of viewing the brain as a complex adaptive system. Minsky views the functions of the brain as being performed by “thousands of different, specialized sub-systems.” He continues to explain that we can interpret “mental states” and “partial mental state” as subsets of the states of the parts of the mind. For example, certain divisions specialize in sensory processing, language, long-range planning, etc. Each “agent” is made up of multiple subspecialists that embody smaller elements of an individual’s knowledge, skills and methods. In an earlier work, Minsky (1980) explains that “no single one of these little agents knows very much by itself, but each recognized certain configurations of a few associates and responds by altering its state” (p. 119). This type of interrelationship among the components is characteristic of complex systems. He sees the construction of the mind as the synthesis of organization systems that can support a large enough diversity of different schemes, yet enable them to work together to exploit one another’s abilities. These agencies self-organize into larger conglomerates with the ability to perform more complex functions, and then these conglomerates combine to form higher and higher levels of self-organization and the emergence of the “abilities we attribute to minds.”

Later in his argument, Minsky (2007) claims that consciousness is “used mainly for the myth that human minds are ‘self-aware’ in the sense of perceiving what happens inside themselves” (p. 327). He believes that human consciousness can never truly represent what is happening at the present moment, but only a little of the recent past. He postulates that each “agency” has a limited capacity to represent what happened recently and the fact that it takes time for agencies to communicate with one another. Consciousness in a unique way follows the observer effect in physics; an attempt to examine temporary memories distorts the very records it is trying to inspect.

Minsky’s “society of mind” challenges the commonly accepted “single-self” concept, or the idea that there is a unitary being “inside us that does all the feeling or thinking for us (Wadhawan, 2010). Proponents claim that the “single-self” concept may be helpful and useful, but it not grounded in science. Minksy (2007) explains that unifying our idea of the mind can hide “how much we’re controlled by all sorts of conflicting unconscious goals” (p. 15). When trying to answer questions about ourselves, Minsky claims, “We are switching among a huge network of models which tries to represent some particular aspects of your mind” (p. 16). Even though we feel as if our brain represents a unified self, there are many different systems working within particular models we have created.

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Minsky's Full Text is available freely online - The Society of Mind

Dennett’s Model of Consciousness

Daniel Dennett, one of the most highly regarded philosophers and cognitive scientists, continues along a similar line of reasoning as Minsky. They both give due respect to internal subjective experience such as feelings and emotions, but these are only evidence of how things appear to them to be, rather than direct evidence of the way things actually are. Dennett’s view of consciousness is proposed as the multiple draft model of consciousness. First off, mental processes are spread over the dimensions of both space and time. He then uses the analogy of the preparation and publication of a book. The original text undergoes a number of draftings and is sent to editors before it is finalized. There are multiple drafts but only one may get chosen in a certain situation. This process is similar to how consciousness is represented in the brain (Dennett, 1994).

Dennett, similar to Minsky, stresses that it is only an illusion that a person is conscious of what is perceived as “now.” Processes in in the brain are happening simultaneously and are at the millisecond level. Because it is working at a finite speed, it is impossible to order events in the brain below the millisecond time scale (Wadhawan). There is a choice made by the brain from among the recent events and processes occurring that make up the subjective “now.” Within this argument, Dennett rejects the idea of qualia, so called “feelings that are associated with a sensation independent of sensory input.”

From Dennett’s view, consciousness arises from the processes associated with information exchange in the brain. Conflicting pieces of sensory information, memories and emotional cues are competing with each other at all times in the brain. Every instant, a new set of factors can dominate your awareness. Dennett also believes that a necessary prerequisite for consciousness to emerge is the acquisition of a human language. Dennett (2006) claims that without language, “there is no organized subject (yet) to be the enjoyer or sufferer, no owner of the experience as contrasted with a mere cerebral locus of effects.” As our brain organizes information exchange processes, consciousness arises.

Edelman’s Brain-Based Theory of Consciousness

Gerald Edelman, American biologist and 1972 Nobel Prize winner, offers a biological theory of consciousness founded on Darwin’s Theory of Natural Selection. His most recent book, Second Nature: Brain Science and Human Knowledge, outlines the key tenets of his theory of consciousness developed throughout his career. In Edelman’s theory of Neural Darwinism, he describes three components of his neuronal group selection. First, he explains that “the development of neuronal circuits in the brain leads to enormous microscopic anatomical variation that is a result of a process of continual selection” (Edelman, 2006, p. 27). He refers to this as developmental selection and explains “the high degree of functional plasticity and the extraordinary density of their [neurons] interconnections enables neuronal groups to self-organize into many complex and adaptable modules” (Edelman, 2006). This idea is consistent with Hawkins, Dennett and Minsky, as well as, systems theory in general.

Edelman then describes experiential selection which he defines as the continuous process of synaptic selection that occurs within the diverse repertoires of neuronal groups throughout the development of the brain. He explains that “experiential selection generates dynamic systems that can map complex spatio-temporal events from the sensory organs, body systems and other neuronal groups in the brain onto other selected neuronal groups” (Edelman, 2007). Edelman views this dynamic selective process as working analogously to the processes of selection that act on populations of individuals which leads to the name of his theory Neural Darwinism. This is based off of Darwin’s fundamental idea of population thinking in which variation in a population provides the basis for selection and survival.

Edelman’s third tenet, perhaps the most important in understanding higher capacities of the brain, is the concept of reentrant signaling between neuronal groups. Edelman (2007) demonstrates that there is a “recursive dynamic interchange of signals that occurs in parallel between brain maps, and which continuously interrelates these maps to each other in time and space.” This reentrant circuitry appears to be unique to animal brains and he describes, “there is no other object in the known universe so completely distinguished by reentrant circuitry as the human brain” (Edelman, 2001, p. 44). Reentry is seen within the neuroanatomy of the brain as a dense meshwork of reciprocal connectivity among different cortical areas as well as between the cortex and the thalamus (Edelman, 2006). Hawkins (2006) explored these pathways and explains that in the circuitry between the neocortex and the thalamus, “the connections going backward (toward the input) exceed the connections going forward by almost a factor of ten” (p.25). This reentrant circuitry allows humans to link numerous sensory signals together, make perceptual categorization and then connect them in various combinations to memory (Edelman, 2006).

This reentrant system in influenced by value systems and by selected synaptic changes by previous experiences. Edelman (2006) explains that “from very early developmental times, signals from the body to the brain and from the brain to itself lay the grounds for the emergence of a self” (p.37). Similar to Hawkins’ idea of auto-associative memories, conscious experience relies on references to its own memories. Additionally, conscious experience enhances communication with other individuals and is deeply rooted with language. Edelman proposes that at some point in high primate evolution, “a new set of reciprocal pathways was developed” which made “reentrant connections between conceptual maps of the brain and those areas capable of symbolic or semantic reference” (p. 38). According to Neural Darwinism, this reentry in the enormously complex dynamic core was the “key integrative event that led to the emergence of conscious experience” (p. 39). The feedback and messages the brain sends to itself which is often ignored in studies of intelligence may be the decisive factor to understand human experience.

Environmental and Genetic Interaction

Research has found that environmental factors can actually change the physical structure of the brain (Lenroot and Giedd, 2008). The mental state of an animal can be responsible for the release or suppression of a chemical, which can influence gene expression. Environmental influences, the presence of certain chemicals and the release of hormones and neurotransmitters can modify gene expression. Gene expression can be modified without changing the underlying DNA. There are certain areas that have been identified as “switch” DNA which previously was thought to be "junk" DNA. However, the more we research the area the more we realize how important this "non-coding" DNA is. The switches do not directly lead to the synthesis of proteins. However, switches and homeobox genes, which control switches, tell the protein-synthesizing DNA when to turn on and turn off and how "intense" the synthesis should be.

Genes affect people’s behavior and experience, but their experiences and behavior also affect gene expression (Gottlieb, 2003; Rutter 2006). This bidirectional relationship between heredity and environment is known as the epigenetic framework (Gottlieb). Development takes place through ongoing, bidirectional exchanges between heredity and all levels of the environment.

The Center on the Developing Child at Harvard University have been exploring how early experiences can alter gene expression and affect long-term development. They explain that “the approximately 23,000 genes that children inherit from their parents” form what they call a “structural genome” (Shonkoff et al., 2010, p.1). This structural genome is compared to the hardware of a computer because it determines the boundaries of what’s possible, but does not work without an operating system to tell it what to do. In the genome, the operating system is called the “epigenome” and it determines which functions the genetic “hardware” does and does not perform (Shonkoff et al., 2010). Through time, positive experiences, such as rich learning opportunities, or negative influences, such as “environmental toxins” or stressful life circumstances, leave a chemical “signature” on the genes. These signatures can either temporarily or permanently affect how easily the genes are switched on or off. These experience-driven, chemical changes can play particularly key roles in brain and behavioral development (Shonkoff et al., 2010).

Laura Berk, a leading psychologist on child development, explains that providing a baby with a healthy environment and diet increases brain growth, leading to new connections between nerve cells which influence gene expression. This allows for new “gene-environment exchanges” such as advanced exploration of objects and interaction with caregivers (Berk, 2010). This helps to further enhance brain growth and gene expression. Supportive environments and rich learning experiences generate positive epigenetic signatures that “activate” genetic potential. The stimulation that occurs in the brain through active use of learning and memory circuits can establish a foundation for more effective learning capacities in the future because it is rooted to these epigenetic changes (Shonkoff et al., 2009).

In contrast, harmful environments can dampen gene expression. At times, National Scientific Council explains, “the effect can be so profound that later experiences can do little to change certain characteristics that were initially flexible” (Shonkoff et al., 2010). Epigenetic changes can be caused by repetitive, highly stressful experiences that can damage the systems that manage one’s response to adversity later in life (Szyf, 2009). For some, this epigenome provides a molecular level explanation for why and how early experiences, whether positive or negative, can have an impact that last for life.

Effective interventions can literally alter how children’s genes work and thereby have long-lasting effects on their mental and physical health, learning and behavior. In this respect, “the epigenome is the crucial link between the external environments that shape our experiences and the genes that guide our development” (Shonkoff, 2010, p.2). Gaining a greater understanding of the environmental factors that influence the development of our brains and behavior will allow us to more effectively create policy to provide a healthy environment for developing children.

Since environmental factors can change how the components of the brain exchange information and interact, it must be accounted for in a systems analysis. This external influence of the environment makes it difficult to define clear boundaries for the system; this property is characteristic of complex, open systems.

Conclusion

It remains difficult to define many of these concepts such as mind, intelligence and consciousness. Our intuition and fundamental beliefs about what exactly these phenomena are often lead us to misinterpreting what they truly are. Regardless of the exact definition, it is important to realize that an evolutionary and systems analysis is applicable and intelligence and complex behavior can be viewed as an emergent phenomenon. A systems analysis draws upon nearly all disciplines which attempt to answer these questions.

Future research cannot ignore fundamental truths of the brain. The importance of feedback, the cross-association of the senses as well as the deeply rooted overlap between the “motor cortex” and “sensory cortex” must be addressed when developing a framework to understand behavior and intelligence. We can no longer view each sense of the brain as independent. Rather, we must realize that our senses are deeply intertwined with our thoughts, actions and movements within a memory-prediction system.

Hawkins has offered an explanation of what the brain, specifically the neocortex, which is responsible for complex behavior, does at the computational level. He explains that the brain is a predictive modeling system. It makes predictions about what we are going to hear, see and feel. It detects anomalies and makes you aware of something that you didn’t expect. Finally, based off of these predictions, it takes actions which generates our behavior. When viewing the brain as a predictive-modeling system it is clear that this system allows human to adapt to practically any environment. By having a framework in place, we can better identify and understand the specific operating principles of the brain.

It is of the utmost importance to further examine the architecture of the brain to see how structure and function interact. Additionally, the development of the brain must be put in the proper context of epigenetics and analyzed within its environment. A deeper explanation of the evolutionary advantage of neurons and the nervous system will lead to a greater understanding of human behavior. It is the process underlying intelligent behavior that needs to be explored, not just the output.

The brain is an organ that has evolved through time. It can be viewed as a complex, dynamic, adaptive system made up of multiple interconnected elements that have the capacity to change and learn from experience. There are billions of neurons that self-organize and create a network, which gives rise to our sense of identity as a living being. The brain has sensors that receive certain stimuli (i.e. physical, emotional, spatio-temporal patterns), an output or response, as well as, a heavy reliance on feedback. Therefore, a systems analysis is applicable; this approach helps illuminate the intricacies and nuances of the brain as a system. The system is dynamic meaning that it changes through time; the dynamic aspects of the brain can be observed in the formation of new neural networks and connections through time. The system is adaptive meaning that it changes in response to the environment; this allows for an evolutionary analysis. Complexity theory discusses the emergence of complex properties from the interaction of simpler components; systems have the potential to spontaneously generate new collective behaviors and structures. A multi-disciplinary view is necessary to comprehend the amazing brain that makes us human. How these phenomena arise does not need to be a mystery. With the proper framework we can further understand the brain, mind, intelligence and consciousness.

Many of the theories presented which discuss the brain’s capacity for consciousness and intelligence at their most basic level can be viewed using a systems analysis. Any complex system, especially the brain, is much greater than a sum of its parts. The intricate web of interrelationships of simpler components can shed light on underlying processes of the system.

The study of the true functioning of the brain is contingent on so many issues that seem small, but can really change your view of modeling human behavior. In research, we have certain choices. One approach is to compare and contrast certain models and then highlight the differences. However, to unlock the real functioning of the neocortex, the root of our complex behavior, we cannot be looking for differences among the systems, when there are so many similarities throughout. In a similar sense, we can decide to examine the brain and human behavior on the individual or group level. Psychology primarily examines the brain on the individual level, while anthropology, sociology and other related fields examine human behavior on the group level. As we continue to gain knowledge about human behavior on these different levels of analysis, we have to work towards aligning these levels into one cohesive model.

Similar to any complex adaptive system, a few simple fundamental rules or laws cannot fully explain how the human brain functions. Intelligence and consciousness emerge in a system that is powerful enough to have a self-referential, self-modeling capability. We are a part of nature, embedded in the environment, and as conscious beings we have the unique ability to think about and represent ourselves. Our ability to thrive in the future is contingent on how accurately our self-reflective cortical models reflect the true nature of the world.