ICJS Social Science - August 2022
ICJS Social Science - August 2022
This month: Cultural Evolution
Welcome to ICJS Social Science - a monthly newsletter where we bring you recent developments in the diverse fields of social science. Learn more about us here.
This month, we will start by looking at research on the topic of Cultural Evolution. In biological evolution, animals differ from each other in their genetic makeup. Ultimately, the animals with an environmentally successful, or ‘fit,’ genetic makeup will have more reproductive success. So subsequent generations will have more individuals with ‘fitter’ genes. Similarly, in cultural evolution, certain cultural behaviors like rituals or languages are likely to be ‘fitter’: they lead to better outcomes. We can imitate these behaviors, and probably even innovate (in our own little ways) to make these cultural behaviors better. Ultimately, the ‘fitter’ behaviors will persist. So how does this innovation and imitation occur? What are some broad environmental forces that shape it? How do social networks influence the uptake or spread of cultural behaviors? Research hopes to answer questions like these, and here are some recent papers that help us understand cultural evolution better!
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Research Map
News from the field
Studying the agents of cultural evolution
A. Personality predicts innovation and social learning children: Implications For Cultural Evolution (Rawlings et al.) [Durham University 🇬🇧, Queen's University Belfast 🇬🇧]
📋Introduction and Motivations: While we know that innovation and imitation form the basis of cultural evolution, we don’t know much about the differences in how individuals use information around them. Knowing this would help us better understand which types of individuals tend to innovate and which tend to spread traditions.
⚒️Methods: 7-11 year old children were presented with multiple game-like tasks and were asked whether or not they wanted a demonstration before they could perform them. Their response to the question, personality traits, and performance was recorded.
➡️Results and Conclusion: ‘Conscientious’ (industrious or achievement-striving) children usually did not want demonstrations, while children with ‘agreeableness’ (the tendency to be prosocial or cooperative) usually opted for demonstrations. In the group of children receiving demonstrations, ‘openness to experience’ predicted a higher likelihood of innovating by modifying. Children who opted for no demonstrations were also more likely than those opting for demonstrations to think in more divergent or out-of-the-box ways.
✍️Related Authors:
Yue Yu (National Institute of Education 🇸🇬) The ontogeny of cumulative culture: Individual toddlers vary in faithful imitation and goal emulation
Mark Batey (Manchester Metropolitan University 🇬🇧) Creativity, Intelligence, and Personality: A Critical Review of the Scattered Literature
Sarah Ruth Beck(University of Birmingham 🇬🇧) Individual differences in children's innovative problem-solving are not predicted by divergent thinking or executive functions
B. Negative observational learning might play a limited role in the cultural evolution of technology (Nakawake & Kobayashi) [Kochi University of Technology 🇯🇵, University of Oxford 🇬🇧]
📋Introduction and Motivations: Imitation, or copying the behaviors of other better-performing individuals, is an important part of cultural evolution. But how important is avoiding the behavior of worse-performing individuals? Does it play a role in cultural evolution? The authors hypothesize that it does.
⚒️Methods: The authors asked participants to design a tool on a computer. They put participants in three situations: one with no information about others (asocial learning condition), another with information about better-performing others (positive observational learning condition), and a third with information about worse-performing others (negative observational learning condition). Then, they monitored their performance.
➡️Results and Conclusion: The positive observational learning participants created better tools than the asocial learning participants. However, the negative observational learning participants did not perform significantly better than the asocial learning participants, contrary to the authors’ hypothesis. Therefore, because of the human propensity towards imitations, negative observational learning might play a very small role in cultural evolution of technologies.
✍️Related Authors:
Emily Burdett (University of Nottingham 🇬🇧) A diverse and flexible teaching toolkit facilitates the human capacity for cumulative culture
Stefanie Keupp (German Primate Center 🇩🇪) ‘Over-imitation’: A review and appraisal of a decade of research
Alex Mesoudi (University of Exeter 🇬🇧) An experimental simulation of the "copy-successful-individuals" cultural learning strategy: Adaptive landscapes, producer-scrounger dynamics and informational access costs
C. Know your network: people infer cultural drift from network structure, and expect collaborating with more distant experts to improve innovation, but collaborating with network-neighbors to improve memory (Richardson et al.) [Yale University 🇺🇸]
📋Introduction and Motivations: We, human beings, are capable of meta-level analyses. For example, we can think about our own thinking. Are we, then, just passive beings in cultural evolution? Can our knowledge of what is around us also shape cultural evolution? More specifically, does our knowledge of our social network (in addition to the network itself) shape outcomes in cultural evolution?
⚒️Methods: In this paper, the authors sought to understand whether our search for information depends on our commonsense intuitions about how knowledge spreads through social networks and the benefits of getting information from diverse sources. To do this, they asked participants about how useful it would be to take someone’s help to do a certain activity given their position in the network. This was split into two experiments, one where the visualization of the entire network was in front of them, and the second where only an implicit verbal description of the network was given.
➡️Results and Conclusion: The authors found that indeed adults are able to understand basic concepts of networks as common sense. This leads to a key insight: we have the ability to influence our social structures (our networks don’t just happen to us), which again influences cultural evolution. This is a give-and-take mechanism, and only one direction of this mechanism has been thoroughly studied; perhaps thinking about the other direction too will enrich our knowledge of the field.
✍️Related Authors:
Cecilia Heyes (University of Oxford 🇬🇧) Who Knows? Metacognitive Social Learning Strategies
Zoe Liberman (University of California Santa Barbara 🇺🇸) (Un)common knowledge: Children use social relationships to determine who knows what
Marco Smolla(University of Philadelphia 🇺🇸)Cultural selection shapes network structure
D. Cumulative culture spontaneously emerges in artificial navigators who are social and memory-guided (Dalmaijer) [University of Bristol 🇬🇧]
📋Introduction and Motivations: Given recent findings that a range of nonhuman animals show evidence of cultural evolution, what is the bare minimum cognitive capacity that individuals of a population should have for that population to show cultural evolution?
⚒️Methods: The author uses simulations of artificial agents—pigeons finding routes to their destination—to address the question These artificial agents had only four cognitive capacities, or rules, that they followed: goal direction (orienting towards the goal), social proximity (to be close to others), route memory (knowing the route of previous journeys), and continuity (the flight was sufficiently orderly and not completely haphazard).
➡️Results and Conclusion: Basic cultural evolution (finding and propagating the most optimal route to the goal) was seen in the agents with just goal-direction, preference for social proximity, and having a memory capacity.
✍️Related Authors:
Hannah M Lewis (University College London 🇬🇧) Transmission fidelity is the key to the build-up of cumulative culture
Lewis Dean (Wales Innovation Network 🇬🇧) Identification of the Social and Cognitive Processes Underlying Human Cumulative Culture
Brett Jesmer (Virginia Tech 🇺🇸) Is ungulate migration culturally transmitted? Evidence of social learning from translocated animals
Studying the social forces influencing cultural evolution
E. Pre-existing fairness concerns restrict the cultural evolution and generalization of inequitable norms in children (Berger et al.) [University of Bern 🇨🇭, Kalaidos University of Applied Sciences 🇨🇭, University of Oxford 🇬🇧, University of Lausanne 🇨🇭]
📋Introduction and Motivations: Norms are culturally evolved behaviors that help agents coordinate with each other in their environment. Norms can be inequitable, where one type of party clearly is benefitted more than another. Can norms in one area of decision-making generalize to other areas, leaving the less privileged party to remain underprivileged in all other aspects of their interaction with the environment or other agents?
⚒️Methods: The authors randomly divided children into two groups, and put them in pairs with one child from each group. While instructing each pair to coordinate, the authors gave the pairs some norms with which the game was to be played, in a way that the outcome was equitable in some pairs, and not in others. Then, the children were asked to play a very different coordination game, but they retained their group affiliations. The researchers observed the coordination in the second game, and whether or not it depended on the outcomes of the first.
➡️Results and Conclusion: In the first game, children tended to follow researchers’ norms less frequently when the norms suggested inequitable outcomes. In the second game, older children did not tend to generalize the inequitable norms from the first game—rather, they tried to compensate the underprivileged ones of the first game. Younger children neither generalized, nor compensated. These findings point to a key idea. In equitable outcomes, norms stabilize, while not so in inequitable situations. There is an inherent propensity for fairness which shapes norms: how they evolve, and how they generalize.
✍️Related Authors:
Joseph Henrich (Harvard University 🇺🇸) In search of homo economicus: behavioral experiments in 15 small-scale societies
Ernst Fehr (University of Zurich 🇨🇭) The development of egalitarianism, altruism, spite and parochialism in childhood and adolescence
Cristina Bicchieri (University of Pennsylvania 🇺🇸) The Grammar of Society: the nature and dynamics of social norms
F. Social network architecture and the tempo of cumulative cultural evolution (Cantor et al.) [Max Planck Institute of Animal Behavior 🇩🇪, Universidade Federal de Santa Catarina 🇧🇷, University of Konstanz 🇩🇪, University of Zurich 🇨🇭]
📋Introduction and Motivations: While we know the micro-level factors that can impact cultural evolution, we are also increasingly able to understand that elements like population size, turnover and density can influence features of cultural evolution. What is the relative importance, then, of the social network and its architecture? Can it give us additional insights into how fast cultural evolution will happen, or what types of outcomes the evolution will get us to?
⚒️Methods: The authors modeled a range of different types of networks and types of transmission of information between agents (nodes) of the network. Then they simulated the process of cultural evolution (innovation and its spread) in all the different networks, and tracked the progress.
➡️Results and Conclusion: The authors found that structured networks, like the ones comparable to multilevel societies, can promote high level innovation. But, they are also more likely to hinder the spread of that innovation, making it likely to go extinct. Therefore, the type of transmission becomes important in determining the outcome of the evolution. Any one type of network, or any one type of transmission cannot be said to perform the best—it depends on the complex interaction of many factors.
✍️Related Authors:
Sam Yeaman (University of Calgary 🇨🇦) Social network architecture and the maintenance of deleterious cultural traits
Maxime Derex (Institute for Advanced Study in Toulouse 🇫🇷) Partial connectivity increases cultural accumulation within groups
Sinan Aral (Massachusetts Institute of Technology 🇺🇸) The Diversity-Bandwidth Trade-off
*Understanding hunter–gatherer cultural evolution needs network thinking (de Pablo et al.) [University of Alicante 🇪🇸, Avignon Université 🇫🇷, Institute for Advanced Study in Toulouse 🇫🇷, University of Cambridge 🇬🇧, Arizona State University 🇺🇸, Université de Montréal 🇨🇦, University of Texas at San Antonio 🇺🇸, University of Zurich 🇨🇭, Aarhus University 🇩🇰, Universitat de Barcelona 🇪🇸]
🔮A special mention: this article outlines how network thinking in cultural evolution can help us understand hunter gatherer societies better
G. Optimal population turnover regimes for cultural evolution depend on network size, density and behavioral transmissibility (Chimento & Aplin) [Max Planck Institute of Animal Behavior 🇩🇪]
📋Introduction and Motivations: Research has so far extensively studied the effect of population size and network structure on cultural evolution. But what influence does a parameter like population turnover (replacing individuals in a population by birth and death) have on cultural evolution? Is there an optimum turnover rate? What does that rate depend on?
⚒️Methods: To study this, the authors relied on a computer simulation where two types of behaviors could diffuse in a population: an established low-payoff one, or a new higher-payoff behavior. The authors then varied two parameters of population turnover: magnitude (percentage of population replaced) and tempo (frequency of replacement). They then varied other parameters like network size, density, the transmissibility of behaviors, and rules that agents used to make behavioral decisions and studied them.
➡️Results and Conclusion: The authors found that population turnover increased the rate of cultural evolution in populations with competing cultural variants. Also, there was a certain optimal level for population turnover variables that made the cultural evolution in the population successful (the behavior evolved was optimal), and that level depended on three variables: network structure, density, and transmissibility.
✍️Related Authors:
Peng He (University of Cambridge 🇬🇧) The role of habitat configuration in shaping animal population processes: a framework to generate quantitative predictions
Robert Boyd, Peter Richerson (Arizona State University, University of California Davis 🇺🇸) The evolution of reciprocity in sizable groups
Michael Muthukrishna (London School of Economics 🇬🇧) Sociality influences cultural complexity
H. Cumulative cultural evolution, population structure and the origin of combinatoriality in human language (Kirby & Tamariz) [University of Edinburgh 🇬🇧, Heriot-Watt University 🇬🇧]
📋Introduction and Motivations: Language is one of the biggest examples of collective human intelligence. Combinatoriality, or the way in which meaningless syllables are constructed into words with meanings, is a feature that is present in most languages. What can explain its presence in different populations and its transmission throughout centuries? The concepts of cultural evolution can perhaps give the answer.
⚒️Methods: The authors constructed a model to test their hypothesis that individuals’ biases of learning in certain ways leads to combinatoriality in language during cultural transmission, and that population structure explains differences across languages. Agents in the model, biased towards making language simple and expressive, spoke with each other in pairs, and learnt language from certain others in a changing population (i.e., with births and deaths). The authors studied the evolution of that language.
➡️Results and Conclusion: The results of the simulation do suggest that combinatoriality emerges as a response to keeping language simple and expressive during transmission across generations (cultural transmission). Also, they found that evolution is faster when agents learn from other learners than when they learn from old individuals, which suggests that population structure affects the development of combinatoriality in language.
✍️Related Authors:
Michael Tomasello (Duke University 🇺🇸) The cultural origins of human cognition
Hannah Cornish, Kenny Smith (University of Edinburgh 🇬🇧) Compression and communication in the cultural evolution of linguistic structure
Thomas L. Griffiths (Princeton University 🇺🇸) Language evolution by iterated learning with Bayesian agents
Limitations to studying Cultural Evolution
I. The uses and abuses of tree thinking in cultural evolution (Evans et al.) [Max Planck Institute for the Science of Human History 🇩🇪, Australian National University 🇦🇺, University of Otago 🇳🇿, Washington University in St Louis 🇺🇸, University of Auckland 🇳🇿, University of Toronto 🇨🇦]
📋Introduction: Phylogenetic trees, also called evolution trees, offer a good template to study not just biological evolution but also cultural evolution. Mapping out the current evidence of cultural artifacts in terms of phylogenetic trees can illuminate the past, and help us infer causal events.
📋Goals: However, not all cultural data can be fit into phylogenetic trees—and even if the data can be fit into trees, it is not guaranteed to be the best or most accurate approach to studying it. In this paper, the authors attempt to help researchers determine whether or not to use phylogenetic trees in studying cultural evolution, and if yes, how best to apply them.
📋Details: Specifically, the paper gives examples from the literature and advice on important factors to consider, and mistakes to avoid when deciding the units to represent the underlying cultural data with, the details on how to construct the trees, and when to assume tree-like transmission of other cultural features.
✍️Related Authors:
Dan Sperber (Central European University 🇦🇹, 🇭🇺) Why Modeling Cultural Evolution Is Still Such a Challenge
Michele Gelfand (Stanford University 🇺🇸) Grand challenges for the study of cultural evolution
Anne Kandler (Max Planck Institute for Evolutionary Anthropology 🇩🇪) Generative inference for cultural evolution
*Leveling the playing field in studying cumulative cultural evolution: Conceptual and methodological advances in nonhuman animal research (Rawlings et al.) [Durham University 🇬🇧, University of Texas at Austin 🇺🇸, Georgia State University 🇺🇸]
🔮A special mention: This article summarizes some hurdles to understanding and moving forward in research about cultural evolution of nonhuman animals.
*Evolutionary Foundations for Organizational Culture (Brahm & Poblete) [London Business School 🇬🇧, Pontificia Universidad Católica de Chile 🇨🇱]
🔮A special mention: The authors of this article create a model, based on cultural evolution theory, that integrates the different measures currently used to qualify organization culture.
Research Community
This month’s edition featured research from 13 countries, and 5 US states
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