在实验群体中通过选择性社会学习保存复杂的认知算法
B Thompson1, B van Opheusden2, T Sumers2
1Department of Psychology, University of California, Berkeley, Berkeley, CA, USA.
概括
选择性社会学习有助于跨世代保存复杂的认知算法. 人们倾向于支持更简单,更容易传播的解决方案,
科学领域:
- 认知科学
- 进化心理学
- 计算社会科学
背景情况:
- 人类的认知能力往往取决于几代人积累的知识.
- 由于概念转移的困难,复杂的创新算法在文化上具有挑战性.
研究的目的:
- 调查复杂的认知算法是如何保存和传承的.
- 了解社会学习在解决问题策略的文化演变中的作用.
主要方法:
- 一个大规模的实验模拟, 涉及3450名参与者,
- 参与者解决了一个困难的顺序决策问题 (数字排序) 并传递解决方案.
- 具有和没有选择性社会学习的群体之间的算法持久性比较.
主要成果:
- 在模拟中重新发现了几种已知的排序算法.
- 复杂的算法在具有选择性社会学习的群体中持续存在.
- 没有选择性学习的群体经常会丢失复杂的算法,转向更简单,更易传染的算法.
结论:
- 选择性社会学习对于保留罕见的高性能认知算法至关重要.
- 选择学习伙伴的能力有助于复杂创新的文化积累.
- 研究结果支持人类社交与高级认知功能之间的联系.
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