Enquist et al. (2023) suggested that human cumulative cultural evolution was enabled by enhanced abilities to process and remember sequential information, leading to cultural and cognitive achievements unprecedented in other animals. This hypothesis mainly rests on results of sequence discrimination learning tasks, showing that unlike for humans, learning to discriminate between sequences is difficult for non-human animals, requiring them hundreds, often thousands of trials. It is unclear whether animals’ difficulty in these tasks reflect difficulties with remembering sequences per se or is mainly due to the arbitrary ordering of the sequence items, thus underestimating their sequencing abilities. To disentangle these effects, we investigate sequence discrimination learning with a physical task in which we contrast an “arbitrary” condition with one in which we use causal relations between stimuli to indicate which sequence yields a reward, aiming to facilitate encoding of sequential information. We present results from zoo-housed primates (23 squirrel and 24 capuchin monkeys, 13 chimpanzees), 77 human adults and 239 children (3-11 years) in an “AB vs BA, BB, AA” task, providing the first sequence discrimination study with chimpanzees and children, demonstrating that a lack of meaningful relations between sequence stimuli impedes learning in humans and potentially also great apes.

Sequence discrimination learning is facilitated by causal-logical framing in humans and chimpanzees

**Abstract:**

Research in cultural evolution demonstrates that humans have biases to attend to, remember, and transmit some types of content over others. These biases influence the evolution of diverse cultural phenomena across cultures, including folktales, religious myths, literature, film, and online misinformation. Given the widespread influence of these biases we can anticipate that they also influenced the training data of Large Language Models (LLMs). As the use of LLMs grows, it is important to examine whether they exhibit biases in their output. In five preregistered experiments using material from previous studies with human participants, we use a transmission chain-like methodology to test for content biases in the output of the LLM ChatGPT-3. We find that ChatGPT-3 shows biases analogous to humans for content that is gender-stereotype-consistent, social, negative, threat-related, and biologically counterintuitive, over other content. The presence of these biases in LLM output suggests that such content is widespread in its training data and could have consequential downstream effects, by magnifying pre-existing human tendencies for cognitively appealing and not necessarily informative, or valuable, content.

**Speaker's social media:**

Twitter: @JStubbersfield; Bluesky: @jstubbersfield.bsky.social

RECORDING - Large language models show human-like content biases in transmission chain experiments

**Abstract:**

In this submission, we explore the concept of "machine culture", describing culture that is mediated or generated by machines. In particular, we review existing and foreseeable instances where intelligent machines are transforming evolutionary processes of culture at the levels of variation, transmission, and selection. Empirical evidence highlights that machine culture is already occurring: models using reinforcement learning are enhancing variation when they inspire humans to develop novel behaviors, as has been demonstrated for the ancient game of Go (Shin et al. 2023); Large Language Models are facilitating the transmission of cultural knowledge and redefining the value of human creative skills (Epstein et al. 2023); and recommender systems are rewiring pathways of cultural transmission by selecting what and from whom humans learn. Compared to humans, machine intelligence is structurally different, trained differently, and subject to different computational constraints (Gershman et al. 2015, Griffiths 2020). We investigate how these idiosyncrasies of machines might reshape cultural variation, transmission, and selection. Ultimately, our contribution presents a research agenda and a conceptual framework that can be leveraged for deeper investigations of machine culture.

**Speaker's social media:**

@LevinBrinkmann

LIVE - Machine culture

**Abstract:**

Archeological findings indicate that around 400KYA-200KYA in the eastern levant, ancient humans followed a new type of habitation style, basecamp cave dwelling. This entailed multiple small groups living in the same environment for long stretches of time, foraging for resources in different parts of the environment during the day and meeting back at the cave for shelter. We suggest that this habitation pattern could support the gradual development of complex social learning skills. Initially, basecamp habitation allowed tracking different group members’ expertise in resource attainment, and the development of prestige bias. In addition, based on their knowledge of the common environment, individuals could infer where other individuals foraged that day, and gain counterfactual information about the richness of the environment. Intentional signaling using distinct local markers could increase information transmission fidelity. Using simulations of computational models, we show that material benefits gradually increase with the level of sophistication of social learning strategies. This increase was more distinct in larger environments with multiple foraging opportunities. Our work highlights the way habitation patterns and physical settings which humans occupied could support the development of complex social learning skills, and the way environmental characteristics could serve as a common framework for communication.

**Speaker's social media:**

@uhertz

RECORDING - Ancient humans’ basecamp habitation pattern and the transition from experience-based to information-based social learning

**Abstract:**

In many real-world contexts, individuals face collective action problems at multiple scales, effectively choosing between local and global cooperation. Unlike isolated public goods scenarios, which can be solved if enough players are willing to cooperate, multilevel collective action problems present an additional difficulty of coordination in working towards the same goals. Through a behavioural experiment, we explore how people navigate these multiscale cooperation dilemmas and investigate the interaction between cooperative behaviour and perceptions of social identity. We find that introducing a local cooperation option undermines global cooperation efforts. Moreover, the presence of a local option exacerbates parochial biases, partly connected to a performance-cohesion effect, whereby successful cooperation within groups is associated with social identification with that group. While increasing the relative saliency and rewards of global cooperation can recover global cooperation levels, parochial biases persist. Our results highlight how complex group structures can influence behaviour and beliefs, potentially hindering global cooperation efforts and introducing complicated social cohesion dynamics.

**Speaker's social media:**

@charliepilgrim

LIVE - Competing scales of cooperation

**Abstract:**

Mathematical and computational approaches have made important contributions to cultural evolutionary theory by highlighting the effect of factors such as population size, social network structure, and transmission fidelity on cultural evolution. However, some cultural phenomena have so far been more difficult to capture using agent-based and formal models. In particular, determining how personality, background, and cultural context shape how individuals select, transform and transmit social information has remained out of the reach of traditional modelling approaches, and has thus mainly been studied empirically. We here propose that leveraging the capacity of Large Language Models (LLMs) to “role-play” and impersonate specific profiles may be fruitful to address this gap. Indeed, these generative models have been found to exhibit quite satisfying accuracy and relatively good consistency when used to simulate different characters. We present a framework for simulating and analyzing cultural evolution in populations of role-playing LLMs, along with results from use-cases that illustrate the kind of hypotheses that can be generated using this approach. The software we developed for conducting these simulations is open-source and features an intuitive, code-free user-interface, which we hope will help to build bridges between the fields of cultural evolution and generative artificial intelligence.

**Speaker's social media:**

@Jeremy__Perez

LIVE - Extending cultural evolutionary theory to machines

**Abstract:**

Context
The cultural evolution of quantification systems is a recent phenomenon traced back to the Neolithic. Yet, research shows that basic mathematical ideas are part of human core knowledge and likely developed early in evolution. How such core knowledge gave way to complex, culturally diverse mathematical ideas is not fully understood. 
Purpose
This paper focuses on the potential mathematics implicit in material culture, including finished artefacts, the production of tools, and processing of materials, using fibre technologies as a prime example of how material culture preserves, transmits, and transforms mathematical knowledge. 
Methods
A comparative approach linking the ethnomathematics of fibre technologies to cognitive research and relevant archaeological evidence of string, basketry, and weaving, provides a way of testing hypotheses on the origins and development of mathematics in the remote past.
Key results
Results support the premise that weaving practices contain and afford operations such as geometry, measurement, ordering, grouping, and counting. Fibre technologies likely provided a context for the development of mathematical ideas over the past 100,000 years, intensifying during the Upper Palaeolithic. 
Implications
Archaeological material culture is an underused resource that can inform about the development of mathematical knowledge prior to the emergence of formal systems of quantification. 

**Speaker's social media:**



LIVE - The cultural evolution of mathematics through fibre technologies

**Abstract:**

Paleobiologists use phylogenetics to study the patterns and processes of organismal evolution. Phylogenetic methods have also been deployed for reconstructing ancestor-descendant relationships using cultural data. Within evolutionary archaeology specifically, phylogenetic analyses based on maximum parsimony and discrete traits dominate, which sets limitations for the downstream role cultural phylogenies, once derived, can play in more elaborate analytical pipelines. Moreover, the use of discrete character traits in these efforts prevails, which in turn sets a number of non-trivial challenges. Recent methodological advances in Bayesian phylogenetics, however, now allow us to infer evolutionary dynamics using continuous characters. Capitalizing on these developments, we here present an exploratory analysis of cultural macroevolution of projectile point shape evolution in the European Final Palaeolithic and earliest Mesolithic (~15,000-11,000 BP) using a Bayesian phylodynamic approach and the fossilised birth-death process model. This model-based approach leaps far beyond the application of parsimony, in that it not only produces a tree, but also divergence times, and diversification rates, while incorporating uncertainties. This allows us to compare rates to the pronounced climatic changes that occurred during our timeframe. While common in cultural evolutionary analyses of language, the extension of Bayesian phylodynamic models to archaeology represents a major methodological breakthrough.

**Speaker's social media:**

Twitter: @david_matzig

RECORDING - A macroevolutionary analysis of European late upper palaeolithic stone tool shape using a Bayesian phylodynamic framework

**Abstract:**

Social learning is a key driver of the success of the human species (Henrich, 2015), enabling the preservation of technologies that individuals could not possibly have acquired on their own (Tennie, Call, & Tomasello, 2009). Presently, we are witnessing the rise of intelligent machines – “AI systems” – that are capable of making discoveries beyond the human horizon (Wang et al., 2023). This raises the question of whether social learning enables humans to also adopt and preserve inconceivable discoveries made by intelligent machines. In our behavioral experiment, participants’ success in a strategic task hinged on adopting a solution that humans struggle to discover due to a characteristic bias to avoid large losses. We contrast populations of humans transmitting solutions over multiple generations on their own with populations that had the opportunity to learn from AI agents in their first generation. Our findings show that an algorithmic model succeeds in discovering the adaptive strategy where humans fail on their own. Most importantly, humans are able to adopt this machine-introduced strategy and preserve it over multiple generations. Taken together, our results showcase the potential of intelligent machines to transform human cultural evolution, and an opportunity for designing them towards benefitting human progress.

**Speaker's social media:**

Twitter: @ThomasF_Mueller
Bluesky: @thomasfmueller.bsky.social

RECORDING - Humans can learn and culturally preserve inconceivable strategies discovered by intelligent machines

**Abstract:**

Humans adapt, not just through organic evolutionary processes but also through innovation, technology, communication, social organization, environmental exploitation and alteration, developing complex skills passed on within and between social groups. Evolutionary theorists recognize our species has a distinctive capacity to learn across generations and share that knowledge in dynamic and cumulative ways. However, much of the theorizing in evolutionary approaches are too reductive for most socio-cultural anthropologists. We argue that approaches to culture in cultural evolutionary theory can be augmented by envisioning culture as a dynamic assemblage of entangled processes at multiple levels, not necessarily reducible to constituent parts. Using such a framework, more anthropologists would be able to engage constructively with cultural evolutionary theory, and evolutionary theorists would have access to a wider range of anthropological resources. We offer Concepts in dynamic assemblages as an approach. We argue that focussing on Concepts and the dynamic assemblages within which they interface can augment exchange between cultural evolutionary theory and anthropology. Our goal is to foster dialogue and center the nuance that anthropologists require in evolutionary approaches and encourage recognition by evolutionary scholars that the cultural resources they are modelling can be more complex and yet tractable in an anthropological sense. 

**Speaker's social media:**

Twitter: @Anthrofuentes

LIVE - More than “Information in the head”: Culture, cultural evolution, and concepts in dynamic assemblages

**Abstract:**

Arguments claiming that extensions of evolutionary theory, and cultural evolution in particular, are grounded not on metaphors or analogies but on a generalisation of evolutionary principles (e.g. Aldritch et al 2008) have been criticized as lacking proper ontological bases (Reydon & Scholz, 2015) or constituting a “modest unification” that provides guidance, but not explanation (Reydon, 2021). Baraghith (2023) deals with this by proposing shared ontological grounds for generalised evolutionary theory (GET) which, while based on cultural evolution, are argued to have independent evidence in other non-biological ‘evolutionary systems’ (e.g. ‘neural darwinism’), validating GET as strong unification. 
This work considers what constitutes ‘one evolutionary system’, and asks – can culture be considered one such system? 
Guided by proposals for the structure of evolutionary explanations (Bock, 2006; Reydon, 2023), this contribution suggests evolutionary systems might be characterized/demarcated through the particular processes involved in evolutionary explanations within each system – transmission, generation of variation, population structuration, etc. Given the particularities of different cultural systems, it is argued that culture constitutes not one single evolutionary system, but many. This provides a rebuttal to claims that cultural evolution is “reductionist” – but also suggests it depends on input from other approaches to the social and language sciences. 

**Speaker's social media:**

https://www.linkedin.com/in/thiago-zahn-0747b813/

RECORDING - Is cultural evolution one example of an evolutionary system beyond biology?

**Abstract:**

Ontogenetic niche construction has been suggested as a powerful driver influencing learning capabilities and survival across a wide range of species, including humans. A key component of the human ontogenetic niche is play objects. They are ubiquitous ethnographically and display remarkable similarities cross-culturally; they also appear archaeologically in periods often characterized by increased overall rates of innovation. This study explores the role of object play as a cultural variation-generating mechanism that fuels adaption during periods of climatic stress. In a natural experimental paradigm, children’s material culture in the Inuit and Norse societies that inhabited Greenland during the so-called Little Ice Age (~1350-1800 CE) have been compared, demonstrating marked differences in corpus volume, diversity, and change across time. Notably, the Norse settlers disappeared at this time, while Inuit communities thrived. Previous hypotheses for the demise of the Norse in Greenland have been proposed, often deterministically stressing climate or land degradation. As both societies experienced largely identical climatic stress, this case study affords the opportunity to explore what cultural factors – including ontogenetic niche construction and object play – may additionally have promoted survival or collapse. 

**Speaker's social media:**

@MathildeVester5

RECORDING - A natural experiment of history: Ontogenetic niche construction, explorative object play, and adaptation during the Little Ice Age in Greenland

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