Collective dynamics play a key role in everyday decision-making. Whether social influence promotes the spread of accurate information and ultimately results in adaptive behavior or leads to false information cascades and maladaptive social contagion strongly depends on the cognitive mechanisms underlying social interactions. Here we argue that cognitive modeling, in tandem with experiments that allow collective dynamics to emerge, can mechanistically link cognitive processes at the individual and collective levels. We illustrate the strength of this cognitive computational approach with two highly successful cognitive models that have been applied to interactive group experiments: evidence-accumulation and reinforcement-learning models. We show how these approaches make it possible to simultaneously study (a) how individual cognition drives social systems, (b) how social systems drive individual cognition, and (c) the dynamic feedback processes between the two layers.

中文翻译：

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社会和集体决策的认知计算方法。


集体动力在日常决策中发挥着关键作用。社会影响是否促进准确信息的传播并最终导致适应性行为，还是导致错误信息级联和适应不良的社会传染，很大程度上取决于社会互动背后的认知机制。在这里，我们认为认知模型与允许集体动态出现的实验相结合，可以机械地将个人和集体层面的认知过程联系起来。我们用两个非常成功的认知模型来说明这种认知计算方法的优势，这两个模型已应用于交互式小组实验：证据积累和强化学习模型。我们展示了这些方法如何能够同时研究（a）个人认知如何驱动社会系统，（b）社会系统如何驱动个人认知，以及（c）两层之间的动态反馈过程。