Empirical and theoretical investigation of associative learning in groups

团体联想学习的实证和理论研究

• 批准号: 2118012
• 负责人: Takao Sasaki
• 金额: $ 74.06万
• 依托单位: University of Georgia Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118012&HistoricalAwards=false
• 关键词: Empirical; theoretical; investigation; associative; learning

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.

学习是一种主要研究个人的中心心理结构。但是，人类和其他社会动物经常与他人一起学习，产生通常比单独学习获得的结果，这种结果被称为集体学习。该项目研究了在各个组织层面上运作的协会学习的基本原理在多大程度上有助于集体学习。正如个人了解环境中的相关性（例如，雨水通常遵循乌云）一样，他们也可能会学习环境中刺激与其他人的行为之间的相关性（例如，如果您看到人们携带雨伞，通常会降雨）。研究人员将测试一个数学模型，该模型假设个人中的联想学习如何使用动物模型系统为集体学习做出贡献。之所以选择特定的动物模型，是因为（a）像人类一样，它们是高度社交的，并且集体学习，（b）在实验室条件下，它们提供了必要的实验控制水平，以测试提出的数学模型，（c）它们之间的进化距离足以确定集体学习的一般机制。可以利用这些动物群体中发现的集体学习的基本规则，以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。一系列的关联学习实验将测试哪种形式的培训形式（个体或集体）在更快，更耐用的学习方面进行了培训，这对来自非信息性提示的干扰有弹性。对于每个实验，将基于一个简单的计算模型进行预测，该模型假定可以将其他组成员的行为学习为有益的提示。拟议模型的普遍性将在社交昆虫的寻求巢穴行为和社交哺乳动物中寻求食物的行为上进行测试。除了它们的社会性和便利性外，特定物种被选为动物模型，因为它们缺乏人类的认知社会化更高。这使得揭示集体学习基础的基本关联机制变得更加容易，而不必控制认知上复杂的非缔合过程（例如言语交流）来潜在的干扰。尽管如此，被不同参数验证的模型的各个方面预计将推广到人类学习，并且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可以促进或阻碍人类或机器（例如人工智能）执行任务的因素。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来支持的。