Empirical and theoretical investigation of associative learning in groups

学习是一种主要在个体中进行研究的核心心理结构。然而，人类和其他群居动物经常与他人一起学习，产生的结果往往优于单独学习所获得的结果——这种现象被称为集体学习。该项目调查了在个体有机体层面上运作的联想学习的基本原则在多大程度上有助于集体学习。正如个体了解环境中的相关性（例如，乌云之后通常会下雨），他们也可能了解环境中的刺激和其他人的行为之间的相关性（例如，如果你看到有人带伞，通常会下雨）。研究人员将测试一个数学模型，该模型假设个人的联想学习如何有助于使用动物模型系统进行集体学习。选择特定的动物模型是因为(a)像人类一样，它们是高度社会化的，并且集体学习；(b)它们在实验室条件下提供必要的实验控制水平，以测试所提出的数学模型；(c)它们之间的进化距离足够大，可以识别集体学习的一般机制。在这些动物群体中发现的集体学习的基本规则可以用来促进集体学习的有利方面，并预测其有害后果。该项目旨在确定集体学习的基本过程。一系列的联想学习实验将测试哪一种形式的训练——个人的还是集体的——能产生更快更持久的学习效果，并且能抵御非信息性线索的干扰。对于每个实验，将基于一个简单的计算模型做出预测，该模型假设可以学习其他小组成员的行为作为信息线索。该模型的通用性将在社会性昆虫的寻巢行为和社会性哺乳动物的寻食行为上进行检验。除了它们的社会性和便利性外，选择特定物种作为动物模型是因为它们缺乏人类更高的认知复杂性。这使得揭示集体学习的基本联想机制变得更加容易，而无需控制复杂的认知非联想过程（如言语交流）的潜在干扰。尽管如此，用不同参数验证的模型的各个方面有望推广到人类学习中，并可以成为人类进一步研究的基础。因此，当人类或机器（如人工智能）作为一个群体执行任务时，这些发现将有助于确定促进或阻碍集体智能的因素。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Learning is a central psychological construct that has been studied primarily in individuals. However, humans and other social animals often learn with others, yielding outcomes that are often superior to those obtained by learning alone—a phenomenon known as collective learning. This project investigates the extent to which fundamental principles of associative learning, which operate at the level of individual organisms, contribute to collective learning. Just as individuals learn about correlations in the environment (e.g., rain typically follows dark clouds), they may also learn about correlations between stimuli in the environment and the actions of other individuals (e.g., rain typically follows if you see people carrying umbrellas). The investigators will test a mathematical model that postulates how associative learning in individuals contributes to collective learning using animal model systems. The specific animal models are selected because (a) like humans, they are highly social and learn collectively, (b) they afford the necessary level of experimental control under laboratory conditions to test the proposed mathematical model, and (c) the evolutionary distance between them is large enough to identify general mechanisms of collective learning. The basic rules of collective learning found in these animal groups can be leveraged to promote advantageous aspects of collective learning and to anticipate its detrimental consequences. This project aims to identify fundamental processes of collective learning. A series of associative-learning experiments will test which form of training—individual or collective—results in faster and more durable learning that is resilient to interference from uninformative cues. For each experiment, a prediction will be made based on a simple computational model that assumes that the behavior of other group members can be learned as an informative cue. The generality of the proposed model will be tested on nest-seeking behavior in social insects and food-seeking behavior in social mammals. Aside from their sociality and convenience, specific species were selected as animal models because they lack the higher cognitive sophistication of humans. This makes it easier to uncover the basic associative mechanisms underlying collective learning, without having to control for potential interference by cognitively complex non-associative processes, such as verbal communication. Nonetheless, aspects of the model that are validated with different parameters are expected to generalize to human learning and can be the basis of further examination in humans. These findings will, therefore, aid in determining the factors that can promote or impede collective intelligence when humans or machines (e.g., artificial intelligence) perform tasks as a group.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.