CAREER: Model-Based fMRI of Human Object Recognition

物体识别是一项基本的认知任务，每天都会毫不费力地执行无数次，比如在评估交谈伙伴的面部表情时，在人群中寻找朋友的脸，或者阅读这篇摘要的话。所有这些任务都依赖于视觉系统识别特定对象的能力，尽管它们的外观因照明、位置、视点或其他对象的同时存在而发生显著变化。重要的是，视觉系统不是硬连线的，但可以接受训练，以执行特定任务，例如在卫星图像中探测恐怖分子营地或在X射线胶片中探测肿瘤。尽管我们看到视觉识别看起来很容易，但人们普遍认为视觉识别是一个非常困难的计算问题。从生物系统的角度来看，视觉识别涉及几个层面的理解，从计算层面，到细胞和生物物理机制层面，神经回路层面，直到行为层面。视觉识别的计算方法正变得越来越重要，它将来自不同实验和描述水平的数据(如电生理学、脑成像和行为)整合到一个连贯的、定量的框架中，然后可以用来为进一步的实验提供严格的假设。在获得国家科学基金会颁发的职业奖后，马克西米利安·里森胡伯博士将继续他在大脑皮层物体识别计算模型方面的工作。他正在应用这个模型来研究视觉体验和特定任务的训练如何塑造大脑对外部世界的表征及其物体识别能力，以及视觉系统如何即使在干扰刺激的存在下也能成功识别物体。特别是，该模型被用来提供详细的假设，关于特定物体识别任务的训练(从辨别新刺激到视觉杂波中的分类和物体识别)如何改变视觉系统不同水平的处理，以及这些变化如何与行为表现的改善相关。这导致了一系列假设，这些假设将在一系列行为和大脑成像实验中与人类志愿者进行测试，使用的刺激和任务与模拟中相同。重要的是，模拟和实验紧密结合在一起，因此简单任务的实验结果可以用来改进模型，然后可以用来为更复杂的任务提供更具体的假设。这项研究的结果将与人工智能中更好地模拟人类观看方式的机器视觉系统的设计相关，对于开发最佳利用大脑处理视觉信息的能力的人机界面，以及涉及从行李检查到卫星图像分析等对象识别任务的人类培训的应用程序。了解典型大脑中参与物体识别的神经电路对于了解并最终治疗自闭症、精神分裂症和阅读障碍等神经疾病中的物体识别缺陷也很重要。职业奖的一个关键因素是，Riesenuber博士计划通过开发综合认知神经科学的基于模型的课程，将构成研究工作基础的同一计算模型用作教育工具。

Object recognition is a fundamental cognitive task that is performed effortlessly countless times every day, such as when gauging a conversation partner's facial expression, looking for a friend's face in a crowd, or reading the words of this abstract. All these tasks depend on the visual system's ability to recognize specific objects, despite significant variations in their appearance due to changes in lighting, position, viewpoint, or the simultaneous presence of other objects. Importantly, the visual system is not hard-wired but can be trained for specific tasks, e.g., detecting terrorist camps in satellite images or tumors in X-ray films. Despite the apparent ease with which we see, visual recognition is widely acknowledged to be a very difficult computational problem. From a biological systems perspective, visual recognition involves several levels of understanding, from the computational level, to the levels of cellular and biophysical mechanisms and the level of neuronal circuits, up to the level of behavior. Computational approaches to visual recognition are becoming increasingly important to integrate data from different experiments and levels of description (such as electrophysiology, brain imaging, and behavior) into one coherent, quantitative framework that can then be used to provide rigorous hypotheses for further experiments. With a CAREER award from the National Science Foundation, Dr. Maximilian Riesenhuber is continuing his work on a computational model of object recognition in cortex. He is applying this model to study how visual experience and training on specific tasks shape the brain's representation of the external world and its object recognition capabilities, and how the visual system can successfully recognize objects, even in the presence of interfering stimuli. In particular, the model is being used to provide detailed hypotheses on how training on specific object recognition tasks (ranging from the discrimination of novel stimuli to categorization and object recognition in visual clutter) can modify processing at different levels of the visual system, and how these changes are related to improvements in behavioral performance. This leads to a set of hypotheses that are to be tested with human volunteers in a series of behavioral and brain imaging experiment, using the same stimuli and tasks as in the simulations. Importantly, simulations and experiments are tightly integrated so that experimental results from simpler tasks can be used to refine the model, which can then be used to provide more specific hypotheses for more complex tasks.The results of this research will be relevant for the design of machine vision systems in artificial intelligence that better mimic how humans see, for the development of human-machine interfaces that optimally leverage the brain's ability to process visual information, and for applications involving human training on object recognition tasks ranging from baggage screening to satellite image analysis. Understanding the neural circuitry involved in object recognition in the typical brain is also important for understanding and ultimately treating object recognition deficits in neural disorders such as autism, schizophrenia, and dyslexia. A key element of the CAREER award is Dr. Riesenhuber's plan to use the same computational model that forms the basis of the research effort as an educational tool by developing a model-based curriculum in integrative cognitive neuroscience.