The method of exclusion (still) cannot identify specific mechanisms of cultural inheritance

The method of exclusion identifies patterns of distributions of behaviours and/or artefact forms among different groups, where these patterns are deemed unlikely to arise from purely genetic and/or ecological factors. The presence of such patterns is often used to establish whether a species is cultural or not—i.e. whether a species uses social learning or not. Researchers using or describing this method have often pointed out that the method cannot pinpoint which specific type(s) of social learning resulted in the observed patterns. However, the literature continues to contain such inferences. In a new attempt to warn against these logically unwarranted conclusions, we illustrate this error using a novel approach. We use an individual-based model, focused on wild ape cultural patterns—as these patterns are the best-known cases of animal culture and as they also contain the most frequent usage of the unwarranted inference for specific social learning mechanisms. We built a model that contained agents unable to copy specifics of behavioural or artefact forms beyond their individual reach (which we define as “copying”). We did so, as some of the previous inference claims related to social learning mechanisms revolve around copying defined in this way. The results of our model however show that non-copying social learning can already reproduce the defining—even iconic—features of observed ape cultural patterns detected by the method of exclusion. This shows, using a novel model approach, that copying processes are not necessary to produce the cultural patterns that are sometimes still used in an attempt to identify copying processes. Additionally, our model could fully control for both environmental and genetic factors (impossible in real life) and thus offers a new validity check for the method of exclusion as related to general cultural claims—a check that the method passed. Our model also led to new and additional findings, which we likewise discuss.European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No 714658; STONECULT project)

Early knapping techniques do not necessitate cultural transmission

Early stone tool production, or knapping, techniques are claimed to be the earliest evidence for cultural transmis-sion in the human lineage. Previous experimental studies have trained human participants to knap in conditions involving opportunities for cultural transmission. Subsequent knapping was then interpreted as evidence for a necessity of the provided cultural transmission opportunities for these techniques. However, a valid necessity claim requires showing that individual learning alone cannot lead to early knapping techniques. Here, we tested human participants (N = 28) in cultural isolation for the individual learning of early knapping techniques by providing them with relevant raw materials and a puzzle task as motivation. Twenty-five participants were technique naïve according to posttest questionnaires, yet they individually learned early knapping techniques, therewith producing and using core and flake tools. Early knapping techniques thus do not necessitate cultural transmission of know-how and could likewise have been individually derived among premodern hominins

What drives young children to over-imitate? Investigating the effects of age, context, action type, and transitivity

Imitation underlies many traits thought to characterize our species, which includes the transmission and acquisition of language, material culture, norms, rituals, and conventions. From early childhood, humans show an intriguing willingness to imitate behaviors, even those that have no obvious function. This phenomenon, known as “over-imitation,” is thought to explain some of the key differences between human cultures as compared with those of nonhuman animals. Here, we used a single integrative paradigm to simultaneously investigate several key factors proposed to shape children’s over-imitation: age, context, transitivity, and action type. We compared typically developing children aged 4–6 years in a task involving actions verbally framed as being instrumental, normative, or communicative in function. Within these contexts, we explored whether children were more likely to over-imitate transitive versus intransitive actions and manual versus body part actions. Results showed an interaction between age and context; as children got older, they were more likely to imitate within a normative context, whereas younger children were more likely to imitate in instrumental contexts. Younger children were more likely to imitate transitive actions (actions on objects) than intransitive actions compared with older children. Our results show that children are highly sensitive to even minimal cues to perceived context and flexibly adapt their imitation accordingly. As they get older, children’s imitation appears to become less object bound, less focused on instrumental outcomes, and more sensitive to normative cues. This shift is consistent with the proposal that over-imitation becomes increasingly social in its function as children move through childhood and beyond

A proof of concept for machine learning-based virtual knapping using neural networks

Prehistoric stone tools are an important source of evidence for the study of human behavioural and cognitive evolution. Archaeologists use insights from the experimental replication of lithics to understand phenomena such as the behaviours and cognitive capacities required to manufacture them. However, such experiments can require large amounts of time and raw materials, and achieving sufficient control of key variables can be difficult. A computer program able to accurately simulate stone tool production would make lithic experimentation faster, more accessible, reproducible, less biased, and may lead to reliable insights into the factors that structure the archaeological record. We present here a proof of concept for a machine learning-based virtual knapping framework capable of quickly and accurately predicting flake removals from 3D cores using a conditional adversarial neural network (CGAN). We programmatically generated a testing dataset of standardised 3D cores with flakes knapped from them. After training, the CGAN accurately predicted the length, volume, width, and shape of these flake removals using the intact core surface information alone. This demonstrates the feasibility of machine learning for investigating lithic production virtually. With a larger training sample and validation against archaeological data, virtual knapping could enable fast, cheap, and highly-reproducible virtual lithic experimentation