Clarifying Misconceptions of the Zone of Latent Solutions Hypothesis: A Response to Haidle and Schlaudt: Miriam Noël Haidle and Oliver Schlaudt: Where Does Cumulative Culture Begin? A Plea for a Sociologically Informed Perspective (Biological Theory 15: 161-174, 2020).

The critical examination of current hypotheses is one of the key ways in which scientific fields develop and grow. Therefore, any critique, including Haidle and Schlaudt's article, "Where Does Cumulative Culture Begin? A Plea for a Sociologically Informed Perspective," represents a welcome addition to the literature. However, critiques must also be evaluated. In their article, Haidle and Schlaudt (Biol Theory 15:161-174, 2020. 10.1007/s13752-020-00351-w; henceforth H&S) review some approaches to culture and cumulative culture in both human and nonhuman primates. H&S discuss the "zone of latent solutions" (ZLS) hypothesis as applied to nonhuman primates and stone-toolmaking premodern hominins. Here, we will evaluate whether H&S's critique addresses its target.

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.