Grounding computational models of vision with infant brain data

儿童如何学习识别视觉物体？他们很快就学会了很快地识别猫、杯子或家人的脸，通常不需要任何明显的努力。人类的视觉是我们身体中最复杂的系统之一。我们几乎一半的大脑都用来控制视觉系统。神经科学家对理解视觉知觉有着浓厚的兴趣，视知觉是了解世界的重要感觉窗口，也是人类智能的一个关键领域。计算机科学家还对理解人类视觉感兴趣，以获得可能有助于开发更好的计算机视觉系统的见解。人工神经网络(ANN)--人工智能的一种形式--也可以被训练成识别视觉对象。因此，虽然理解人类视觉可能会对计算机视觉产生影响，但反过来也是可能的，因此神经网络被提出作为理解人类视觉的模型。然而，人工神经网络模型还不能完全匹配或解释人类的视觉。例如，ANN模型的训练方式与人类从婴儿期及以后学习的方式根本不同。训练计算机识别视觉对象(如猫)的一种方法是向它提供数百万张已标记的猫的图像。然而，孩子们只需几次短暂的接触就可以学会识别猫。因此，为了更好地模拟人类的视觉，我们需要知道人类婴儿的视觉是如何发展的。这个项目的研究目标将是研究婴儿的大脑如何代表视觉对象。作为该项目的一部分开发的数据和工具将与其他科学家共享，以促进该领域的进一步研究。该项目还将涉及本科生和研究生，以及与K-12学生、STEM教师和当地家庭的联系。该项目旨在联合为人类视觉的人工模型提供信息，增强计算机视觉，并促进我们对婴儿大脑的理解。研究人员将使用安全和非侵入性的脑电(脑电)技术来测量婴儿(12-15个月大)的大脑活动。这项研究的第一个目标将是比较婴儿和神经代表视觉对象的方式。脑电数据将根据不同人工神经网络(ANN)模型的预测，使用多变量模式分析(MVPA)和代表性相似性分析(RSA)进行分析。第二个目标将是探索婴儿如何在部分隐藏的物体的背景下表现视觉物体。一个相关的目标是发现口语单词和语言表达如何影响物体识别。婴儿在听到一致的语音提示或不一致的提示后，会看到物体的全部或部分遮挡的图像。同义词被预测以增强对部分图像的视觉处理，这表明重复或自上而下的处理。这将检验自上而下因素塑造婴儿大脑如何呈现视觉对象的假设。这项研究的发现可能最终有助于在视觉感知领域设计更像人类的人工智能。该项目的研究还将在计算和发展神经科学与机器学习和计算机视觉之间架起桥梁。对于发育研究者来说，所提出的方法(MVPA)为婴儿脑电数据的分析提供了一种新的有前途的工具，其结果将为理解婴儿的视觉加工提供一个新的角度。对于计算神经科学的研究人员来说，该项目的结果提供了一个令人兴奋的机会，使一个广泛的目标--开发像人脑学习一样学习的神经网络--与正在学习的婴儿大脑的实际数据保持一致。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

How do children learn to recognize visual objects? They soon learn to recognize a cat or a cup or the faces of their family very quickly, often with no apparent effort. Human vision is one of the most complex systems in our body. Almost half of our brains are devoted to the visual system. Neuroscientists have a deep interest in understanding visual perception as an important sensory window to the world and as a key domain of human intelligence. Computer scientists are also interested in understanding human vision to gain insights that may help develop better computer vision systems. Artificial neural networks (ANNs) - a form of artificial intelligence - can be trained to recognize visual objects as well. Thus, while understanding human vision may have implications for computer vision, the converse is also possible and hence ANNs have been proposed as a model to understand human vision. However, ANN models do not yet fully match or explain human vision. For example, ANN models are trained in fundamentally different ways from how humans learn from infancy and beyond. One way to train a computer to recognize visual objects, such as cats, is to give it millions of images of cats that have been labeled. However, children can learn to recognize cats after only a few short encounters. Thus, to better model human vision, we need to know how vision develops in human infants. The goal of the research in this project will be to study how the infant brain represents visual objects. Data and tools developed as part of the project will be shared with other scientists to stimulate further research in this field. The project will also involve undergraduate and graduate students as well as outreach to K-12 students, STEM teachers, and local families.This project aims to jointly inform artificial models of human vision, enhance computer vision and advance our understanding of the infant brain. Researchers will use the safe and non-invasive technique of EEG (electro-encephalography) to measure brain activity in infants (12-15 months old). The first aim of the research will be to compare how infants and ANNs represent visual objects. The EEG data will be analyzed using multivariate pattern analysis (MVPA) and representational similarity analysis (RSA) in relation to predictions from different artificial neural network (ANN) models. A second aim will be to explore how infants represent visual objects in the context of partly hidden objects. A related aim is to discover how spoken words, and linguistic representation, can influence object recognition. Infants will be presented with whole or partially occluded images of objects, after hearing a congruent spoken cue or an incongruent cue. Congruent words are predicted to enhance visual processing for partial images, an indication of recurrent or top-down processing. This will test the hypothesis that top-down factors shape how the infant brain represents visual objects. Findings from this research may ultimately contribute to the design of more human-like artificial intelligence in the domain of visual perception. The studies in this project will also build bridges between computational and developmental neuroscience and machine learning and computer vision. For developmental researchers, the proposed methods (MVPA) provide a new and promising tool for the analysis of infant EEG data and results will offer a fresh angle for understanding infants’ visual processing. For researchers in computational neuroscience, results from the project offer an exciting opportunity to align a wide-spread goal, developing neural networks that learn like the human brain learns, to actual data from the learning infant brain.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.