Empirical and theoretical investigation of associative learning in groups

学习是一个核心的心理结构，主要是在个人中进行研究。然而，人类和其他社会性动物经常与其他人一起学习，产生的结果往往比单独学习的结果要上级，这种现象被称为集体学习。本项目研究在个体生物体水平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一样（例如，雨通常跟随着乌云），他们还可以了解环境中的刺激与其他个体的动作之间的相关性（例如，如果你看到人们拿着雨伞，雨通常会随之而来）。研究人员将使用动物模型系统测试一个数学模型，该模型假设个体的联想学习如何有助于集体学习。选择特定的动物模型是因为（a）像人类一样，它们具有高度的社会性和集体学习能力，（B）它们在实验室条件下提供了必要的实验控制水平，以测试所提出的数学模型，以及（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被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种形式的训练--个人或集体--能产生更快、更持久的学习效果，并且能抵抗来自无信息线索的干扰。对于每个实验，预测将基于一个简单的计算模型，假设其他群体成员的行为可以作为信息线索学习。所提出的模型的通用性将测试在社会性昆虫和社会性哺乳动物的觅食行为的巢寻求行为。除了它们的社会性和便利性，特定的物种被选为动物模型，因为它们缺乏人类更高的认知复杂性。这使得更容易揭示集体学习背后的基本联想机制，而不必控制复杂的非联想过程（如言语交流）的潜在干扰。尽管如此，使用不同参数验证的模型的各个方面预计将推广到人类学习，并可以成为人类进一步检查的基础。因此，这些发现将有助于确定当人类或机器（例如，该奖项反映了NSF的法定使命，并通过使用基金会的智力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.