SL-CN: Mapping, Measuring, and Modeling Perceptual Expertise

这个学习科学合作网络汇集了来自范德比尔特大学、卡内基-梅隆大学和加州大学圣地亚哥分校的研究人员，研究人们在识别、记忆和分类面孔和物体的能力上如何以及为什么存在差异。许多重要的现实问题，如法医学，医学成像和国土安全，都需要人类专家精确的视觉理解。与人类表现的其他方面相比，理解高级视觉认知的个体差异很少受到关注。最近的研究表明，在学习高水平视觉技能的能力方面，可能存在比通常所承认的更大的变异性，并且这种能力很难通过一般智力来预测。该项目支持一个跨学科合作研究网络，旨在开发视觉识别中个体差异的测量方法，将个体差异的行为和神经标记联系起来，开发解释个体差异的模型，并将模型与神经数据联系起来。由于许多现实世界领域的结果取决于基于视觉信息的决策，因此开发个体差异的测量，标记和模型可以对识别现实世界的视觉人才和改善视觉表现和培训产生更广泛的影响。作为该合作网络的一部分，包括女性科学家和代表性不足的少数民族在内的学生和研究员将接受来自多个学科的科学家的指导，为他们提供比单一学科更深入的理解。该项目将支持一个关于视觉识别个体差异研究的合作研究网络的活动。参与这些跨学科努力的科学家包括超高场强脑成像专家，心理测试开发的尖端方法，以及高级视觉的认知和“深度”卷积神经网络模型。该项目将研究功能性大脑活动和解剖学大脑结构如何预测视觉表现和视觉学习的质量和时间过程。该团队将开发和验证视觉能力的测试，这些测试可用于对大脑活动和行为表现进行精确预测。这些大脑测量和行为测试将与深度卷积神经网络模型相关;这些模型是迄今为止最成功的计算机视觉模型，这些分层网络的更高层提供了对物体识别至关重要的大脑区域的杰出模型。到目前为止，这些模型还没有被用来理解个体差异。协作团队将寻求能够反映人类可变性的模型，而不是寻求实现最佳性能的典型方法，当人们犯错误时会犯错误，当人们慢的时候会慢，并显示一系列视觉能力和在人类中观察到的学习。该奖项来自学习科学-协作网络（SL-CN）计划，由SBE行为和认知科学部（BCS），SBE多学科活动办公室（SMA）和CISE计算机和网络系统部（CNS）资助。

This Science of Learning Collaborative Network brings together researchers from Vanderbilt University, Carnegie-Mellon University, and University of California-San Diego to investigate how and why people differ in their ability to recognize, remember, and categorize faces and objects. Many important real-world problems, such as forensics, medical imaging, and homeland security demand precise visual understanding from human experts. Understanding individual differences in high-level visual cognition has received little attention compared to other aspects of human performance. Recent studies indicate that there likely is far greater variability than commonly acknowledged in the ability to learn high-level visual skills and that such ability is poorly predicted by general intelligence. This project supports a collaborative interdisciplinary research network that aims to develop measures of individual differences in visual recognition, relate behavioral and neural markers of individual differences, develop models that explain individual differences, and relate models with neural data. Because outcomes in many real-world domains depend on decisions based on visual information, developing measures, markers, and models of individual differences can have broader impacts on identifying real-world visual talent and improving visual performance and training. Students and fellows conducting research as part of this collaborative network, including female scientists and underrepresented minorities, will be mentored by scientists from multiple disciplines, providing them with an understanding far deeper than that achievable by a single discipline.The project will support the activities of a collaborative research network on the study of individual differences in visual recognition. The scientists involved in these interdisciplinary efforts include experts in brain imaging at ultra-high field strength, cutting-edge methods in the development of psychological tests, and cognitive and "deep" convolutional neural network models of high-level vision. The project will investigate how functional brain activity and anatomical brain structure can predict the quality and time-course of visual performance and visual learning. The team will develop and validate tests of visual ability that can be used to make precise predictions about brain activity and behavioral performance. These brain measures and behavioral tests will be related to deep convolutional neural network models; such models are the most successful computer vision models to date, and higher layers of these hierarchical networks provide outstanding models of brain areas critical to object recognition. So far these models have not been used to understand individual differences. Instead of the typical approach seeking to achieve the best performance possible, the collaborative team will seek models that can mirror human variability, making errors when people make errors, being slow when people are slow, and displaying a range of visual abilities and learning as observed in humans.The award is from the Science of Learning-Collaborative Networks (SL-CN) Program, with funding from the SBE Division of Behavioral and Cognitive Sciences (BCS), the SBE Office of Multidisciplinary Activities (SMA), and the CISE Division of Computer and Network Systems (CNS).