fMRI and Behavioral Studies of Unsupervised Learning in High Level Visual Cortex

高级视觉皮层无监督学习的功能磁共振成像和行为研究

• 批准号: 8266462
• 负责人: Kalanit Grill-Spector
• 金额: $ 38.02万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-05-01 至 2014-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8266462
• 关键词: Accounting; Address; Affect; Animals; Appearance; Autistic Disorder; Behavior; Behavioral; Binding; Brain; Brain region; Child; Computer Simulation; Computing Methodologies; Data; Development; Dimensions; Discrimination; Disease; Experimental Designs; Feedback; Foundations; Frequencies; Functional Magnetic Resonance Imaging; Goals; Health; Image; Instruction; Intervention; Judgment; Knowledge; Label; Learning; Link; Measurement; Measures; Modeling; Neurons; Neurosciences; Normal Statistical Distribution; Pattern; Performance; Play; Property; Prosopagnosia; Psychophysics; Psychophysiology; Recovery; Relative (related person); Reporting; Resolution; Retinal; Role; Rotation; Shapes; Stimulus; Structure; Techniques; Testing; Time; Training; Variant; Visual; Visual Cortex; Width; Williams Syndrome; Work; brain behavior; computer studies; design; disability; experience; extrastriate visual cortex; improved; neuromechanism; novel; object recognition; object shape; prototype; receptive field; relating to nervous system; research study; response; spatiotemporal; statistics; visual neuroscience

Description (provided by applicant): Experience is thought to play a critical role in shaping the cortical representations that support object recognition by creating neural responses are selective for some dimensions of change and invariant to others. Although many previous studies have examined the effects of supervised training on object selective regions of the brain, much less is known about the degree to which statistical regularities in the retinal input can directly shape the neural substrates involved in object recognition. Unsupervised learning is important because it allows the brain to employ simple self organizing mechanisms that turn the continuous flux of visual input into the stable objects of our experience. While behavioral and computational work strongly suggests that unsupervised learning plays a key role in object recognition, most related neuroscience work examining the role of input statistics has focused on its effects in early visual areas. Here we propose experiments that combine cutting edge techniques in fMRI, psychophysics, and computational modeling to examine two hypotheses concerning unsupervised learning in object recognition. First, we propose that neural responses may become tuned to match the range and frequency of shape and object exemplars experienced during unsupervised training. That is, neural responses will increase and become more selective for items seen more frequently during unsupervised training relative to infrequently seen or untrained items. This may provide a mechanism which improves discrimination performance for stimuli seen most frequently. Second, behavioral and computational evidence suggests the intriguing hypothesis that the brain uses spatio-temporal correlations as a means for binding different images as belonging to the same object, allowing for recognition of the same object across dramatic transformations, such as changes in its appearance due to rotation. We will determine if spatio- temporal correlations in the visual input during unsupervised training increases the invariance of both brain responses and perceptual performance relative to similar items trained in an uncorrelated manner and pre- training responses (and performance). Third, we will examine if mechanisms of unsupervised learning generalize to supervised learning. In all of our experiments we will examine neural responses and performance both before and after unsupervised training, and use computational modeling to link fMRI data to the possible underlying neural mechanisms such as sharpening of neural tuning and increased firing rates. The proposed work will fill important gaps in knowledge by providing the first account of the neural mechanisms that generate effective representations for object recognition from the statistics of visual experience. PUBLIC HEALTH RELEVANCE The results of these studies will be important for understanding the role of visual experience in shaping normal visual representations. As these mechanisms do not require explicit instruction, they are especially important for unraveling the means by which pre-verbal children and animals learn to recognize objects. Understanding these mechanisms will form a much needed foundation for studying development disorders such as congenital prosopagnosia, autism and Williams Syndrome. Further, if we find significant behavioral improvements due to the statistics of the visual inputs, these training paradigms may be used as an intervention to offset developmental visual disabilities.

描述（由申请人提供）：经验被认为在塑造皮层表征方面发挥着关键作用，皮层表征通过创建神经反应来支持物体识别，该神经反应对某些变化维度是有选择性的，而对其他维度是不变的。尽管许多先前的研究已经检验了监督训练对大脑对象选择性区域的影响，但对于视网膜输入的统计规律可以在多大程度上直接塑造参与对象识别的神经基质，人们知之甚少。无监督学习很重要，因为它允许大脑采用简单的自组织机制，将连续不断的视觉输入转化为我们体验的稳定对象。虽然行为和计算工作强烈表明无监督学习在物体识别中发挥着关键作用，但大多数研究输入统计作用的相关神经科学工作都集中在其对早期视觉区域的影响。在这里，我们提出了结合功能磁共振成像、心理物理学和计算建模等尖端技术的实验，以检验有关对象识别中无监督学习的两个假设。首先，我们提出神经反应可能会被调整以匹配在无监督训练期间经历的形状和对象样本的范围和频率。也就是说，相对于不经常看到或未经训练的项目，神经反应将会增加，并且对在无监督训练期间更频繁看到的项目变得更有选择性。这可能提供一种提高对最常见刺激的辨别性能的机制。其次，行为和计算证据表明了一个有趣的假设，即大脑使用时空相关性作为将不同图像绑定为同一物体的手段，从而允许在戏剧性的转变中识别同一物体，例如由于旋转而导致的外观变化。我们将确定无监督训练期间视觉输入的时空相关性是否会增加大脑反应和感知表现相对于以不相关方式训练的类似项目和训练前反应（和表现）的不变性。第三，我们将检查无监督学习的机制是否可以推广到监督学习。在我们所有的实验中，我们将检查无监督训练前后的神经反应和表现，并使用计算模型将 fMRI 数据与可能的潜在神经机制联系起来，例如神经调谐的锐化和放电频率的增加。所提出的工作将通过提供神经机制的第一个解释来填补知识方面的重要空白，这些神经机制可以从视觉经验的统计数据中生成用于对象识别的有效表示。公共卫生相关性这些研究的结果对于理解视觉体验在塑造正常视觉表征中的作用非常重要。由于这些机制不需要明确的指导，因此它们对于揭示前语言儿童和动物学习识别物体的方式尤其重要。了解这些机制将为研究先天性面盲症、自闭症和威廉姆斯综合症等发育障碍奠定急需的基础。此外，如果我们发现由于视觉输入的统计而导致行为显着改善，这些训练范例可以用作抵消发育性视觉障碍的干预措施。