A Machine Learning Approach to Human Visual Learning

提出的研究计划包括人类视觉学习的实验和计算研究。该项目侧重于介导感知学习的信息处理机制，这是生物学、天文学和地球科学等各种STEM领域专业知识的基础。特别是，研究人员试图利用机器学习领域的见解（例如，机器学习用于概念化不同学习环境的属性的形式化，针对每种环境的强大的统计学习算法集，以及关于这些算法的优缺点的众多数学和实证发现）。这些研究着眼于在四种学习环境（监督、无监督、半监督和强化学习环境）中低水平和高水平辨别任务的学习表现。该项目还探索了基于多感官或多线索环境中相关感知信号的视觉学习，比如当一个人同时看到和触摸物体表面时。计算研究将人们的学习表现与“理想学习者”的统计最优表现进行比较，并与机器学习文献中的在线学习算法的表现进行比较。一个关键的假设是，人们可以通过转移从“标记”数据项获得的知识或通过转移从其他感官模式获得的知识，从视觉上学习“未标记”数据项（即，没有被讲师标记为特定类别兴趣的例子的项目）。这项工作对STEM培训环境的设计具有重要意义。

The proposed research program consists of experimental and computational studies of human visual learning. The project focuses on the information processing mechanisms mediating the perceptual learning that underlies expertise in a variety of STEM fields, such as biology, astronomy, and geoscience. In particular, the investigators attempt to take advantage of insights from the field of Machine Learning (e.g., its formalisms for conceptualizing the properties of different learning environments, its powerful sets of statistical learning algorithms for each environment, and its numerous mathematical and empirical findings about the advantages and disadvantages of these algorithms). The studies look at learning performance on lower-level and higher-level discrimination tasks in four types of learning environments: supervised, unsupervised, semi-supervised, and reinforcement learning environments. The project also explores visual learning based on correlated perceptual signals in multisensory or multi-cue environments, such as when a person both sees and touches surfaces. The computational studies compare people's learning performances with the statistically optimal performances of "ideal learners", and also with the performances of on-line learning algorithms from the Machine Learning literature. A key hypothesis is that people can visually learn with "unlabeled" data items (i.e., items that are not labeled by an instructor as examples of a particular category of interest) by transferring knowledge gained with "labeled" data items or by transferring knowledge gained from other sensory modalities. The work has important implications for the design of STEM training environments.