Santa Fe Institute, Santa Fe, NM 87501, USA.
Proc Natl Acad Sci U S A. 2012 Aug 28;109(35):14259-64. doi: 10.1073/pnas.1203021109. Epub 2012 Aug 13.
Animals living in groups collectively produce social structure. In this context individuals make strategic decisions about when to cooperate and compete. This requires that individuals can perceive patterns in collective dynamics, but how this pattern extraction occurs is unclear. Our goal is to identify a model that extracts meaningful social patterns from a behavioral time series while remaining cognitively parsimonious by making the fewest demands on memory. Using fine-grained conflict data from macaques, we show that sparse coding, an important principle of neural compression, is an effective method for compressing collective behavior. The sparse code is shown to be efficient, predictive, and socially meaningful. In our monkey society, the sparse code of conflict is composed of related individuals, the policers, and the alpha female. Our results suggest that sparse coding is a natural technique for pattern extraction when cognitive constraints and small sample sizes limit the complexity of inferential models. Our approach highlights the need for cognitive experiments addressing how individuals perceive collective features of social organization.
群居动物共同产生社会结构。在这种情况下，个体需要对何时合作和竞争做出策略性决策。这要求个体能够感知集体动态中的模式，但目前尚不清楚这种模式提取是如何发生的。我们的目标是确定一种模型，该模型能够从行为时间序列中提取有意义的社会模式，同时通过对记忆的最低要求来保持认知上的简约。我们使用来自猕猴的精细冲突数据表明，稀疏编码是一种有效的神经压缩原理，可以有效地压缩集体行为。稀疏码被证明是高效的、可预测的和具有社会意义的。在我们的猴子社会中，冲突的稀疏码由相关个体、监管者和阿尔法雌性组成。我们的研究结果表明，当认知限制和小样本量限制推理模型的复杂性时，稀疏编码是一种用于模式提取的自然技术。我们的方法强调了需要进行认知实验，以解决个体如何感知社会组织的集体特征。
