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

人类，尤其是儿童，经常被比作海绵，在学习新概念和技能的过程中吸收环境中的模式和规律。然而，与被动的海绵不同，人类学习者能够通过与世界的互动，以重要的方式改变他们的经验。例如，在为考试而学习时，学习者可以决定如何在不同的材料之间分配学习精力。任何关于人类学习的完整描述都必须不仅解释我们从经验信息中学到了什么，还必须解释我们的行为和选择暴露这些信息的能力。许多用于认知心理学的实验室学习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（相反强调一种“被动”学习，学习者无法控制所经历的信息）。这往往会在我们的理论中造成盲点，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。本提案中的研究旨在通过扩展人类学习理论来解决这一差距，以解释自主学习行为（即学习者可以通过持续决策控制他们所经历的信息流的情况）。提出的研究解决了与学习认知科学相关的两个具体问题。首先，人们在学习时如何决定哪些信息是有用的？第二，自主学习对新概念的习得有什么影响？为了回答这些问题，一种人类学习的计算理论被测试，它试图解释人们如何做出决定来收集更多的信息。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导致的学习差异。理解自主学习是认知心理学的一个核心问题，它对教育政策具有潜在的影响。此外，本研究中确定的原则可以转化为辅助学习技术，通过根据个别学习者的能力量身定制培训，可以缩短在某个领域培养专业知识所需的时间。除了这个研究目标，本提案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这个领域的下一代心理科学家做好准备，这个领域越来越多地围绕着思维研究的计算方法来组织。

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.