scholarly journals High-fidelity copying is not necessarily the key to cumulative cultural evolution: a study in monkeys and children

2019 ◽  
Vol 286 (1904) ◽  
pp. 20190729 ◽  
Author(s):  
Carmen Saldana ◽  
Joël Fagot ◽  
Simon Kirby ◽  
Kenny Smith ◽  
Nicolas Claidière

The unique cumulative nature of human culture has often been explained by high-fidelity copying mechanisms found only in human social learning. However, transmission chain experiments in human and non-human primates suggest that cumulative cultural evolution (CCE) might not necessarily depend on high-fidelity copying after all. In this study, we test whether defining properties of CCE can emerge in a non-copying task. We performed transmission chain experiments in Guinea baboons and human children where individuals observed and produced visual patterns composed of four squares on touchscreen devices. In order to be rewarded, participants had to avoid touching squares that were touched by a previous participant. In other words, they were rewarded for innovation rather than copying. Results nevertheless exhibited fundamental properties of CCE: an increase over generations in task performance and the emergence of systematic structure. However, these properties arose from different mechanisms across species: children, unlike baboons, converged in behaviour over generations by copying specific patterns in a different location, thus introducing alternative copying mechanisms into the non-copying task. In children, prior biases towards specific shapes led to convergence in behaviour across chains, while baboon chains showed signs of lineage specificity. We conclude that CCE can result from mechanisms with varying degrees of fidelity in transmission and thus that high-fidelity copying is not necessarily the key to CCE.

2020 ◽  
Vol 287 (1928) ◽  
pp. 20200090
Author(s):  
Marcel Montrey ◽  
Thomas R. Shultz

A defining feature of human culture is that knowledge and technology continually improve over time. Such cumulative cultural evolution (CCE) probably depends far more heavily on how reliably information is preserved than on how efficiently it is refined. Therefore, one possible reason that CCE appears diminished or absent in other species is that it requires accurate but specialized forms of social learning at which humans are uniquely adept. Here, we develop a Bayesian model to contrast the evolution of high-fidelity social learning, which supports CCE, against low-fidelity social learning, which does not. We find that high-fidelity transmission evolves when (1) social and (2) individual learning are inexpensive, (3) traits are complex, (4) individual learning is abundant, (5) adaptive problems are difficult and (6) behaviour is flexible. Low-fidelity transmission differs in many respects. It not only evolves when (2) individual learning is costly and (4) infrequent but also proves more robust when (3) traits are simple and (5) adaptive problems are easy. If conditions favouring the evolution of high-fidelity transmission are stricter (3 and 5) or harder to meet (2 and 4), this could explain why social learning is common, but CCE is rare.


2021 ◽  
pp. 095679762110322
Author(s):  
Marcel Montrey ◽  
Thomas R. Shultz

Surprisingly little is known about how social groups influence social learning. Although several studies have shown that people prefer to copy in-group members, these studies have failed to resolve whether group membership genuinely affects who is copied or whether group membership merely correlates with other known factors, such as similarity and familiarity. Using the minimal-group paradigm, we disentangled these effects in an online social-learning game. In a sample of 540 adults, we found a robust in-group-copying bias that (a) was bolstered by a preference for observing in-group members; (b) overrode perceived reliability, warmth, and competence; (c) grew stronger when social information was scarce; and (d) even caused cultural divergence between intermixed groups. These results suggest that people genuinely employ a copy-the-in-group social-learning strategy, which could help explain how inefficient behaviors spread through social learning and how humans maintain the cultural diversity needed for cumulative cultural evolution.


2016 ◽  
Vol 113 (44) ◽  
pp. E6724-E6725 ◽  
Author(s):  
Joseph Henrich ◽  
Robert Boyd ◽  
Maxime Derex ◽  
Michelle A. Kline ◽  
Alex Mesoudi ◽  
...  

2020 ◽  
Author(s):  
Federico Pianzola ◽  
Alberto Acerbi ◽  
Simone Rebora

We analyse stories in Harry Potter fan fiction published on Archive of Our Own (AO3), using concepts from cultural evolution. In particular, we focus on cumulative cultural evolution, that is, the idea that cultural systems improve with time, drawing on previous innovations. In this study we examine two features of cumulative culture: accumulation and improvement. First, we show that stories in Harry Potter’s fan fiction accumulate cultural traits—unique tags, in our analysis—through time, both globally and at the level of single stories. Second, more recent stories are also liked more by readers than earlier stories. Our research illustrates the potential of the combination of cultural evolution theory and digital literary studies, and it paves the way for the study of the effects of online digital media on cultural cumulation.


2021 ◽  
Author(s):  
Alberto Acerbi

Cultural evolution researchers use transmission chain experiments to investigate which content is more likely to survive when transmitted from one individual to another. These experiments resemble oral storytelling, where individuals need to understand, memorise, and reproduce the content. However, prominent contemporary forms of cultural transmission—think an online sharing— only involve the willingness to transmit the content. Here I present two fully preregistered online experiments that explicitly investigated the differences between these two modalities of transmission. The first experiment (N=1080) examined whether negative content, information eliciting disgust, and threat-related information were better transmitted than their neutral counterpart in a traditional transmission chain set-up. The second experiment (N=1200), used the same material, but participants were asked whether they would share or not the content in two conditions: in a large anonymous social network, or with their friends, in their favourite social network. Negative content was both better transmitted in transmission chain experiments and shared more than its neutral counterpart. Threat-related information was successful in transmission chain experiments but not when sharing, and, finally, information eliciting disgust was not advantaged in either. Overall, the results present a composite picture, suggesting that the interactions between the specific content and the medium of transmission are important and, possibly, that content biases are stronger when memorisation and reproduction are involved in the transmission—like in oral transmission—than when they are not—like in online sharing.


Author(s):  
Alberto Acerbi

Chapter 8 considers what cultural evolutionists call cumulative cultural evolution, that is, the idea that culture increases in complexity. For a cultural domain being defined as cumulative, it needs to show accumulation (more traits), improvement (traits are more efficient), and ratcheting (new traits build on previous innovations). The author proposes that this is not a necessary outcome, and that different domains show different signs of cumulation. It is suggested that the fidelity and the hyper-availability provided by digital media allow for more cumulation in domains where it was limited before. Not surprisingly, they also allow for the retention of vast amounts of useless information—junk culture. A central challenge for the coming years is thus finding efficient mechanisms of online cultural selection. Algorithmic selection is finally discussed, along with how mainstream criticisms, such as the fact that algorithms are biased or opaque to users, are not decisive arguments against their efficacy and utility.


2014 ◽  
Vol 347 ◽  
pp. 74-83 ◽  
Author(s):  
Laureano Castro ◽  
Miguel A. Toro

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