CAREER: The Role of Self-Directed Learning in Facilitating Concept Acquisition: Advancing Research and Training in the Cognitive Science of Learning

人类，特别是儿童，经常被比作海绵，在学习新概念和技能时吸收环境中的模式和知识。 然而，与被动的海绵不同，人类学习者能够通过与世界互动来改变他们的经验。 例如，当学习考试时，学习者可以决定如何在材料中分配学习努力。 任何对人类学习的完整描述都必须不仅解释我们从经验中学到了什么，而且还要解释我们的行为和选择暴露这些信息的能力。 许多认知心理学中使用的学习实验室研究对这个问题保持沉默，因为它们限制了参与者对信息流的控制（强调一种“被动”学习，学习者无法控制体验到的信息）。 这往往会在我们的理论中造成一个盲点，即人们如何收集信息，何时决定停止，以及这些正在进行的决定如何影响学习。这项提案中的研究旨在通过扩展人类学习理论来解释自我导向学习行为（即，学习者可以通过他们正在进行的决策来控制他们所体验的流信息的情况）。 拟议的研究解决了两个具体的问题，相关的认知科学的学习。 首先，人们如何决定学习时需要了解哪些有用的信息？第二，自我导向学习对新概念的获得有什么影响？ 在回答这些问题时，测试了人类学习的计算理论，该理论试图解释人们如何做出决策以收集更多信息。 该理论的一个新颖之处在于，它可以解释自主学习和被动学习之间的差异。 自我导向学习是认知心理学的一个核心问题，对教育政策有潜在的影响。 此外，在这项研究中确定的原则可以转化为辅助学习技术，可以缩短所需的时间，通过定制培训，以个人学习者的能力，在一个领域发展专业知识。除了这一研究目标，这一建议的总体目标是整合计算认知科学的研究和培训活动。一项创新的培训计划将为下一代心理科学家做好准备，因为这个领域越来越多地围绕计算方法来研究心灵。

NSF Summary StatementHumans, particularly children, are often likened to sponges, soaking up patterns and regularities in their environment as they learn new concepts and skills. However, unlike a passive sponge, human learners are able to alter their experience in important ways by interacting with the world. For example, when studying for a test, learners can decide how to allocate study effort across materials. Any complete account of human learning must explain not only what is learned from the information we experience, but also the capacity for our actions and choices to expose that information. Many laboratory studies of learning used in cognitive psychology are silent on this issue because they limit participants' control over the flow information (emphasizing instead a type of "passive" learning where the learner does not have control over which information is experienced). This tends to create a blind spot in our theories for how people gather information, when they decide to stop, and for how these ongoing decisions impact learning.The research in this proposal seeks to address this gap by extending theories of human learning to account for self-directed learning behaviors (i.e., situations where learners can control of the flow information they experience by way of their ongoing decisions). The proposed research addresses two specific questions relevant to the cognitive science of learning. First, how do people decide what information will be useful to know when learning? Second, what impact does self-directed learning have on the acquisition of novel concepts? In answer these questions, a computational theory of human learning is tested which attempts to explain how people make decisions to gather more information. A novel aspect of the theory is that it can account for the differences in learning resulting from either self-directed or passive learning. Understanding self-directed learning is a core issue in cognitive psychology which has potential implications for education policy. In addition, the principles identified in this research may translate into assistive learning technologies which may shorten the time required to develop expertise in a domain by tailoring training to the capabilities of individual learners. Alongside this research objective, an overarching goal of this proposal is to integrate research and training activities in computational cognitive science. An innovative training plan will prepare the next generation of psychological scientists for a field that is increasingly organized around computational approaches to the study of the mind.