I was delighted to accept an invitation from Rebecca Sear to present some recent work on “Cultural Evolution and the Collective Brain” at the London School of Hygiene and Tropical Medicine Evo Demo Seminar Series. A fun evening chatting to people asking similar questions.
I gave a keynote presentation at the Lorentz Center conference on “Trusting and the Law“. Tis was my first legal conference. The audience included judges, lawyers, and legal scholars. I presented a talk on “Economic Psychology and the Science of Cultural Evolution”, where I discussed some of the “invisible cultural pillars” that uphold legal institutions. It was fascinating to discuss differences in the approach to “evidence” in science and the law.
I was invited to present my work on innovation and cultural evolution at the “Cultural Transmission and Social Norms Workshop” hosted by the School of Economics at The University of East Anglia, UK. I presented “Innovation in the Collective Brain: The Transmission and Evolution of Norms and Culture”, beginning with an introduction to cultural evolution for the audience of primarily economists. I then discussed innovation as a product of our “collective brains“.
|Muthukrishna, M. & Henrich, J. (2016). Innovation in the Collective Brain. Philosophical Transactions of the Royal Society B: Biological Sciences, 371(1690). [Telegraph] [Scientific American] [Video] [Evonomics] [LSE Business Review] [Summary Post] [Download] [Data]|
On Thursday, I was at the Council of Graduate Schools (CGS) Annual Meeting in Washington, DC to receive this year’s CGS/ProQuest Distinguished Dissertation Award in the Social Sciences. The award ceremony was held in the Regency Ballroom of the beautiful Omni Shoreham. The press release with more details can be found here: http://www.proquest.com/about/news/2016/Winners-of-2016-CGS-ProQuest-Distinguished-Dissertation-Awards.html.
It was an unexpected honor, but also validation of my research agenda and approach to science. My acceptance speech was a brief summary of my dissertation and Dual Inheritance Theory and Cultural Evolution more generally.
I spent the weekend at a productive interdisciplinary workshop on “Religion, Ritual, Conflict, and Cooperation: Archaeological and Historical Approaches” at the Center for Advanced Study in the Behavioral Sciences (CASBS) at Stanford University. CASBS is located on the top of one of the beautiful hills around Stanford.
We discussed the challenges and successes in inferring religious belief and practice from the archeological and historical record and new theoretical models and tools for exploring religious history, including the Database of Religious History (DRH).
Other attendees included:
David Carballo (Boston University)
Chris Carleton (Simon Fraser University)
Jesse Chapman (Stanford University)
Mark Csikszentmihalyi (UC Berkeley)
Megan Daniels (Stanford University)
Russell Gray (Director, Max Planck Institute for the History and the Sciences)
Conn Herriott (University of Jerusalem)
Ian Hodder (Stanford University)
Joseph Manning (Yale University)
Jessica McCutcheon (University of British Columbia)
Frances Morphy (Australian National University)
Howard Morphy (Australian National University)
Ian Morris (Stanford University)
Ara Norenzayan (University of British Columbia)
Beate Pongratz-Leisten (NYU)
Neil Price (Uppsala)
Benjamin Purzycki (University of British Columbia)
Ben Raffield (Simon Fraser University)
Katrinka Reinhart (Stanford University)
Celia Schultz (University of Michigan)
Edward Slingerland (University of British Columbia)
Charles Stanish (UCLA)
Brenton Sullivan (Colgate College)
Edward Swenson (University of Toronto)
Robban Toleno (University of British Columbia)
Robyn Walsh (University of Miami)
Joseph Watts (University of Auckland)
Last week, my paper with Joe Henrich on “Innovation in the Collective Brain” was published in Philosophical Transactions of the Royal Society B: Biological Sciences. I explain some of the key points in the video below:
To very briefly summarize, innovation is often assumed to be an individual endeavor driven by geniuses and then passed on to the masses. Consider Thomas Edison and the lightbulb or Gutenberg and the printing press. We argue that rather than a result of far-sighted geniuses, innovations are an emergent property of our species’ cultural learning abilities, applied within our societies and social networks. Our societies and social networks act as collective brains.
Innovations, large or small, do not require heroic geniuses any more than your thoughts hinge on a particular neuron.
The paper outlines how many human brains, which evolved primarily for the acquisition of culture, together beget a collective brain. Within these collective brains, the three main sources of innovation are:
- recombination, and
- incremental improvement.
We argue that rates of innovation are heavily influenced by:
- transmission fidelity, and
- cultural variance.
We discuss some of the forces that affect these factors. These factors can also shape each other. For example, we provide preliminary evidence that transmission efficiency is affected by sociality—languages with more speakers are more efficient.
We argue that collective brains can make each of their constituent cultural brains more innovative. This perspective sheds light on traits, such as IQ, that have been implicated in innovation. A collective brain perspective can help us understand otherwise puzzling findings in the IQ literature, including group differences, heritability differences, and the dramatic increase in IQ test scores over time.
Selected Media Coverage
I chaired a symposium on “Understanding Religions: Integrating experimental, ethnographic and historical approaches” at the Society for Personality and Social Psychology (SPSP) conference in San Diego, CA.
Joe Henrich began by introducing the broader research agenda, describing the two puzzles of (1) the rise of societal complexity and large-scale cooperation and (2) the emergence and spread of particular religious elements, such as big, powerful, moralizing gods and ritual behavior.
Coren Apicella presented recent evidence of high levels of rule bending in the Hadza, a a minimally religious foraging population.
I then introduced the Database of Religious History and presented some preliminary analyses, showing the relationship between ritual and cooperative behavior. I also updated the audience on data collection and some of the directions we’re going in (such as measuring cultural distance–more soon!).
Finally, Ted Slingerland gave an overview of what the humanities can offer the psychology of religion, with an entertaining presentation of how a lack of deep understanding of history and culture can lead to misinterpretations (such as claims that Chinese don’t have religious beliefs, nor mind-body dualism).
Other highlights of the conference included a debate between Leda Cosmides and Joe Henrich (moderated by Jon Haidt) on “Big Questions in Evolutionary Science and What They Mean for Social-Personality Psychology” and a debate between Jon Haidt and Kurt Gray on “Purity and Harm in the American Culture War: A Debate on the Structure of Morality“.
The chapter provides a brief overview of the science of cultural evolution, including its psychological foundations and implications. We discuss how humans evolved a second-line of inheritance, crossing the threshold into a world of cumulative culture. We begin by asking how culture can evolve, dispelling the mythical requirement of discrete genes and exact replication.
Evolutionary adaptation has three basic requirements: (1) individuals vary, (2) this variability is heritable (information transmission occurs), and (3) some variants are more likely to survive and spread than others. Genes have these characteristics so they evolve and adaptive. Culture also meets all three requirements, but in different ways. Like bacterial genes, cultural information spreads horizontally and need not be limited to parental transmission to offspring.
We discuss the evolution of our capacity for culture, asking how and when capacities for culture will evolve (when is cultural learning genetically adaptive).
The answer: culture is adaptive when asocial learning is hard and environments fluctuate a lot, but not too much.
We also outline the evolution of some of our social learning biases (a large part of the third requirement of an evolutionary system):
- Who we learn from (e.g. skilled, successful, and prestigious models; conformist transmission)
- What moderates these choices (e.g. self-similarity, age, sex, ethnicity; Credibility Enhancing Displays, CREDs).
- Some examples in the real world, such as the social spread of suicides (Werther effect) and literally learning better from same-sex and same-race instructors.
- Content biases on what to learn: e.g. animals and plants, dangers, fire, reputation, social norms, and social groupings.
Cultural evolution shapes the beliefs and behaviors of groups so that they come adapted to the local environment (including culture) over time, shaping preferences and psychology.
Turning to the population-level, we explain why sociality influences cultural complexity (larger, more interconnected populations have more terms and technologies), how cultural evolution can lead to maladaptive behavior, and how intergroup competition can help eliminate these maladaptive behaviors, briefly discussing the viability of cultural-group selection.
Finally, we discuss how genes can adapt to culture: culture-gene coevolution and how this process may have led to the rapid expansion of the human brain.
Tom Morgan, Joe Henrich and I recently published a paper on the “The When and Who of Social Learning and Conformist Transmission” in Evolution and Human Behavior.
Conformist transmission is a type of frequency dependent social learning
strategy in which individuals are disproportionately inclined to copy the most common trait in their sample of the population (e.g. individuals have a 90% probability of copying a trait that 60% of people possess). The bias is particularly important, because it tends to homogenize behavior within groups increasing between group differences relative to within group differences.
Our three key findings across two experiments were:
- Substantial amounts of conformist transmission. We found substantial reliance on conformist biased social learning, with only 3% and 9% (or 15%) showing no bias in Experiments 1 and 2, respectively.
- Increased social learning and stronger conformist bias as the number of options increased. Both the amount of social learning and the strength of conformist biases increased as the number of options increased (i.e. 60% of people wearing black shirts is more persuasive in a world of black, red, blue, yellow, and white shirt colors than in a world of only black shirts and white shirts). These results mean that all prior experiments have underestimated reliance on social learning and the strength of conformist transmission, since all use only 2 options.
- IQ predicts both social learning and the strength of the conformist bias. IQ predicts less social learning, but has a U-shaped relationship to the strength of the conformist bias. These results suggest that higher IQ individuals are strategically using social learning (using it less, but with a stronger conformist bias when they choose to use other information).
For a list and discussion of all key findings, see the Discussion section of the paper.
Selected Media Coverage
I was invited to present the Database of Religious History at the Department of Statistics Seminar Series. Nancy Heckman, Head of the Statistics Department, watched our award winning video on the database and was interested in possible connections with researchers in statistics. I presented some of the technical design aspects of the database as well as our statistical approach to analyzing the data.
Afterwards, I had lunch with several members of the department, including Nancy Heckman, Ruben Zamar, Cindy Greenwood, and Davor Cubranic, as well as with Andrew Trites, Director of the Marine Mammal Research Unit and North Pacific Universities Marine Mammal Research Consortium and Fisheries Centre Co-Director. I hope that collaborations with the Department of Statistics will allow us to find new ways to share and analyze our rapidly growing data.