Forty years ago, the notion that cultural change can be understood as a system of inheritance caught the popular imagination. The concept of “memes” as cultural units analogous to genes was popularized in the writings of Richard Dawkins—creating a great deal of controversy similar to what arose around E.O. Wilson’s book Sociobiology, published in the same year as Dawkin’s influential work on this subject.
Richard Dawkins really stirred things up with his best-selling book in 1976, The Selfish Gene. He did this in two ways: (1) with his story about genetic “selfishness” that gave license to those who wanted to be literal about it and say evolution is all about self-interest, greed, and otherwise selfish behavior; and (2) by introducing the concept of “memes” as cultural units of heredity that play a functional role similar to genes in biological reproduction for the transmission of cultural information.
There were two main problems with interpretations of selfish genes—one having to do with an over-emphasis on genes as the only hereditary unit worthy of consideration (often called gene-centrism as it was a narrowing of biology to a myopic treatment of all biological traits being reducible to the traits of individual or groups of genes). The other being a misinterpretation of selfishness as an anthropomorphizing of genes as literally being miniature selfish people. This gave the rugged individualists of the world free reign to claim that science was on their side when they formulated economic and political philosophies to serve themselves and their peers.
Luckily, a great deal of progress has been made on the selfish gene front. We now know that reductionisms of all kinds are inadequate for dealing with the real-world complexities of biology in the flesh. There is not one, but at least four, hereditary systems recognized by biologists today. Eva Jablonka and Marion Lamb lay this out clearly in their 2006 book, Evolution in Four Dimensions, as they walk through the research literature on genetics, epigenetics, behavioral repertoires, and symbolic culture as four distinct pathways where traits are “heritable” in appropriately defined fashion.
Similar progress has been made with the study of altruism and “prosocial” behaviors. It is now widely known that rational self-interest in economics is too narrow a view to encapsulate the richness of real human nature. Books like David Sloan Wilson’s Does Altruism Exist? And E.O. Wilson’s The Social Conquest of Earth are but a sampling among a great diversity of works showing how much the research community has advanced its understandings of social behavior in the last 40 years.
Unfortunately, the controversies around cultural memes have not been as productive. Read the cultural evolution literature today and you will find three largely distinct camps:
- Those who dismiss meme theory as wrong-headed and disproven.
- Those who embrace meme theory as richly productive and vindicated by evidence across many fields.
- Those who don’t have strong opinions one way or the other and are waiting to see how the chips fall.
I personally sit in the second camp, having used meme theory to guide my research on the spread of ideas and behaviors across social systems in both digital (social media) and physical environments. What I find interesting about the Camp 1 people—those who dismiss meme theory outright—is that their reasons seem to be based on the fallacies associated with Dawkins’ first major controversy and have little to do with the progress made in memetics research in the forty years since the term was introduced into the intellectual discourse.
A summary of the main argument against meme theory is this: There is a great deal of evidence showing that human minds do not replicate information perfectly (or even with high fidelity). Thus it is impossible to conceive of a meme that begins in one mind and somehow is replicated in the mind of another with enough informational integrity to be called a hereditary unit. In other words, the complex process of communication is reduced (that pesky reductionism again) to “thought units” with defined features that must be recreated without noise or error in two or more minds.
Students of cognitive linguistics will recognize a particular metaphor here—the Conduit Metaphor for communication that has been richly explored by scholars like Michael J. Reddy and George Lakoff. In this metaphor, a thought is treated as an “object” that passes through some kind of conduit between one mind and another. It is among the most common conceptual representations for teaching and learning (even though it is empirically incorrect). Linguistic examples include phrases like “Are you getting what I’m saying?” and “The instructor passes on knowledge to students.”
Here’s what I find interesting about this argument… it presumes that a number of advances were never made since the year 1976! Specifically, I am thinking of three areas where significant progress has been made during the last forty years: the birth of complexity science in the early 1980’s, developments in the study of human conceptualization and cognitive linguistics since the mid-70’s, and the explosion of digital media in the age of personal computers and later via the internet. Let us look at each of these in turn.
Birth of Complexity Science
Scientific reductionism has declined throughout the mid-to-late 20th Century with the rise of systems thinking across many different fields. Systems thinking arose with cybernetics, information theory, and early computing that made possible the rapid advances in fields like ecology (with ecosystem modeling of population dynamics), meteorology (with numerical weather forecasting for studying emergent patterns in the atmospere), and economics (with systems modeling of ecological throughputs like the famous Limits to Growth study by the Club of Rome).
In the early 1980’s an interdisciplinary research center called the Santa Fe Institute was founded to convene the rag-tag cadre of scholars working across fields like these around what has come to be known as complexity science. The focus of this new science is the emergent patterns and systemic behaviors for phenomena where a large number of interacting parts give rise to often paradoxical and unpredictable behaviors. It is the anti-thesis of reductionism—a research program that has given rise to sweeping advances in theoretical biology, the study of social organization, self-organizing processes, and more.
When Dawkins introduced the concept of memes in 1976 there were few who thought in terms of emergent complexity. A language has gradually developed around concepts like self-organized criticality, emergence, pattern formation, and diffusion-limited aggregation to model, simulate, and visualize the interactions within a complex system that give rise to emergent outcomes. Without such a language, it is difficult to conceive of memes as dynamic, emergent patterns of social information arising from many interacting parts.
Developments in Human Conceptualization
The mid-1970’s were a time of great progress for many fields. Around the time that meme theory was capturing the public imagination there were several researchers giving name to recognizable patterns of human thought and behavior that emerge over and over again.
In sociology and linguistics, it was frame semantics that explored the conceptual structure of social settings and thought processes. Social psychologists and anthropologists talked about script theory as a way to make sense of routine behaviors that people “act out” in common social interactions. Computer scientists and information theorists grappled with image schemas as a way to represent modular logical structures in algorithms as they created software for machine learning.
What all of these approaches shared in common was an emphasis on distinct conceptual structures that can be discerned and analyzed for their inherent logic, roles and relationships, and heuristic uses by people as they navigated the complexities of real-world social environments. They led to the development of research methodologies that are now routinely used to study political discourse, conduct ethnographic research, engage in branding and marketing exercises, and more.
An example of a “recurring thought structure” in politics is the concept of tax relief—which uses the metaphor that a tax is a burden to introduce an inferential logic about fairness, suffering, and relief. This concept is used over and over again in politics. It has been translated into slogans, political speeches, editorial commentaries, and dinner table debates more times than can be counted.
Applied to meme theory, this body of tools and techniques demonstrates that researchers across many fields have found value in the perspective that culture can be studied as information patterns that arise in a variety of social settings routinely and with modular elements that are readily discernible in each new instance. The claim that information patterns do not replicate is contradicted by the evidence for image-schematic structures (like the metaphor for taxes above with its distinctive inferential logic and recognizable use cases).
Explosion of Digital Media
Add to these developments the explosion of digital media since the advent of personal computers in the 1980’s and ascension of the Internet for public use in the 90’s up to the present. There are now so many technological tools for digital reproduction of content (where replication is done with such high fidelity that it cannot be questioned) that the theory of memes is vindicated on technical grounds alone.
Consider the digital storage of 1’s and 0’s to generate an image for our profile picture on Facebook (which is created in an identical manner for each user who views it). Or the spread of “internet memes” where distinct lineages of descent-with-modification have been studied for the spreading patterns of ideas as they hybridize, mutate, and quite literally evolve leaving a data trail that can be analyzed with unprecedented methodological rigor. An example is this study of information diffusion on Facebook.
Digital media represents a phase transition in cultural research—sometimes called the Big Data Explosion or the “dataclysm” by social scientists who analyze patterns in the massive datasets now used to study emotional sentiments on Twitter, track themes with keyword searches of text on Lexus-Nexus, or deconstruct narrative tropes in the media.
The theory of memes is highly valued by researchers who take an epidemiological approach to the spread of information. Some ideas are more “contagious” than others for psychological reasons that are becoming known with greater clarity and insight with each passing year. For example, this study looking at campaign donations as a kind of social contagion. Network scientists are mapping out the spread of ideas and behaviors in real time with tracking algorithms that monitor the World Wide Web. Discourse analysts are characterizing the composition of themes and frame semantic structures that shape how various publics think and feel about important topics.
Weaving It All Together
Combine these three major domains of progress—complexity science enters the scene, human conceptualization is now studied with great rigor, and so much of human culture has gone digital—and it is clear that meme theory has been highly generative and productive in the study of human culture.
It is time to update our debates about cultural transmission to include developments like these. The old debates that reduce all of biological evolution to genes have fallen into disuse. We can now do the same for their analogues in the study of culture. I am not suggesting that memes are THE way to make sense of social learning and cultural evolution (as there are other very important frameworks like gene-culture coevolution and dual-inheritance theory that provide additional bridges between culture and biology). But we can now recover the baby from the thrown-out bathwater and see how a dynamic systems point-of-view melded with advances in other social sciences is highly productive and generative for shaping the research practices of the future.
Looking at meme theory forty years later, we can see that much more is now known and there are things we collectively have figured out how to do that would seem like magic to the 1976 mind of a social scientist.
I hope this article stimulates a healthy dialogue and debate so we can move toward the goal of consilience across fields and get away from the narrowing binaries of “true/false” and “right/wrong” in future conversations. It is not that meme theory is right or wrong, but rather that it has been (and will continue to be) highly valuable for cultural research across the social and biological sciences.