Human Behaviour and Cultural Evolution Group, Department of Biosciences, University of Exeter, Penryn, UK.
Laboratory for Experimental Anthropology, ETHICS (EA 7446), Catholic University of Lille, Lille, France.
Nat Hum Behav. 2019 May;3(5):446-452. doi: 10.1038/s41562-019-0567-9. Epub 2019 Apr 1.
Bows and arrows, houses and kayaks are just a few examples of the highly optimized tools that humans have produced and used to colonize new environments. Because there is much evidence that humans' cognitive abilities are unparalleled, many believe that such technologies resulted from our superior causal reasoning abilities. However, others have stressed that the high dimensionality of human technologies makes them very difficult to understand causally. Instead, they argue that optimized technologies emerge through the retention of small improvements across generations without requiring understanding of how these technologies work. Here we show that a physical artefact becomes progressively optimized across generations of social learners in the absence of explicit causal understanding. Moreover, we find that the transmission of causal models across generations has no noticeable effect on the pace of cultural evolution. The reason is that participants do not spontaneously create multidimensional causal theories but, instead, mainly produce simplistic models related to a salient dimension. Finally, we show that the transmission of these inaccurate theories constrains learners' exploration and has downstream effects on their understanding. These results indicate that complex technologies need not result from enhanced causal reasoning but, instead, can emerge from the accumulation of improvements made across generations.
弓箭、房屋和皮划艇只是人类生产和使用的高度优化工具的几个例子，这些工具被用于开拓新环境。由于有大量证据表明人类的认知能力是无与伦比的，许多人认为这些技术源于我们优越的因果推理能力。然而，也有人强调，人类技术的高维度使得它们很难进行因果理解。相反，他们认为，经过优化的技术是通过在不理解这些技术如何工作的情况下，经过几代人的小改进而保留下来的。在这里，我们展示了在没有明确因果理解的情况下，物理人工制品在几代社会学习者中会逐渐得到优化。此外，我们发现，因果模型在代际之间的传递对文化进化的速度没有明显影响。原因是参与者不会自发地创建多维因果理论，而是主要产生与显著维度相关的简单模型。最后，我们表明，这些不准确的理论的传递会限制学习者的探索，并对他们的理解产生下游影响。这些结果表明，复杂的技术不一定来自增强的因果推理，而是可以通过几代人的改进积累而产生。
