Assessing space and feature based attention with fMRI and multivoxel pattern anal

描述(申请人提供)：感知是认知的基石；记忆、推理和运动计划的形成都依赖于创造对周围环境的稳定表征的能力。然而，编码感觉输入的神经元天生就有噪音，所以重复呈现刺激永远不会唤起两次相同的神经活动模式。此外，环境中存在的刺激物通常比大脑同时处理的刺激物多，因此相关和不相关的项目必须竞争皮质代表。考虑到这些障碍，大脑形成连贯感知的能力是相当了不起的，理解这一壮举是如何实现的，是揭示支持意识意识的神经机制的重要的第一步。理论研究表明，感知是基于汇集其输出的少量神经元群体，因为平均减少了噪音(称为群体编码模型)。此外，注意特定的位置或特征会偏向神经活动，因此相关的刺激以牺牲无关的刺激为代价赢得表征。因此，群体编码方案是必要的，以便为知觉提供可靠的基础，并且需要选择性注意，以确保相关刺激的表征主导知觉。不幸的是，注意力和群体编码之间的关系没有得到很好的理解，部分原因是技术限制，部分原因是在群体水平上将单一单位注意调制与信息编码效率联系起来的工作很少。在这里，我们使用一个简单的计算模型来提供注意调节、种群编码和知觉之间的明确联系。为了测试该模型产生的预测，我们使用了心理物理学和新颖的多变量功能磁共振成像(FMRI)分析技术的组合，这些技术对视觉皮质功能选择区域的群体响应曲线的变化很敏感。具体地说，我们提出了一种在早期视觉皮质的非常小的区域内测量特征选择性“调谐功能”的方法。这些基于fMRI的调谐函数类似于在非人类灵长类动物中常规使用单单位记录方法获得的调谐函数，为评估人类感觉皮层的信息编码理论提供了一个强大的工具。在第一个具体目标中，我们将使用这组工具来测试不同的注意力增益模型(例如乘性增益与对比度增益)，并确定基于特征的注意是否甚至在刺激呈现之前就系统性地偏向早期视觉皮质的群体反应特征。在第二个目标中，我们批判性地评估了共同的直觉，即注意力获得应该应用于对目标刺激做出最大反应的感觉神经元。相反，我们将测试与直觉相反的预测，即有时应该将增益应用于实际上没有调整到参与功能的神经元，以最大限度地提高种群代码的效率。总而言之，这些努力将阐明观察者的行为目标如何塑造群体反应特征，从而优化视觉系统内的信息处理。由于知觉是人类信息处理的一个基本方面，我们的发现将引起关注知觉学习、决策和记忆的研究人员的兴趣。此外，这里所获得的知识将为未来注意力缺陷障碍(ADD)等注意力障碍的应用研究提供重要的基础。例如，更好地了解注意力是如何调节感觉神经元的，可能会导致更客观的诊断测试，从而可以更早、更准确地识别这些疾病。与公共健康相关无论是在教室里听老师讲课，还是在路上开车，关注环境重要部分的能力对我们的成功和生存至关重要。在目前的研究方案中，我们试图了解大脑中的神经活动模式如何支持注意力行为，以便观察者可以更有效地理解和表示传入的感觉信息。这一知识将有助于开发对常见注意力障碍--如注意力缺陷障碍--的更客观测试，以便诊断可以更精确地进行，从而可以更早开始干预。

Description (provided by applicant): Perception is the cornerstone of cognition; memory, reasoning, and the formation of motor plans all rely on the ability to create a stable representation of the surrounding environment. However, neurons that encode sensory input are inherently noisy, so repeated presentations of a stimulus never evoke the same pattern of neural activity twice. In addition, more stimuli are typically present in the environment than the brain can simultaneously process, so relevant and irrelevant items must compete for cortical representation. Given these obstacles, the brain's ability to form coherent percepts is quite remarkable and understanding how this feat is accomplished is an important first step towards revealing the neural mechanisms that support conscious awareness. Theoretical studies suggest that perception is based on small populations of neurons that pool their output, since averaging reduces noise (termed population coding models). In addition, attending to specific locations or features biases neural activity so that relevant stimuli win representation at the expense of irrelevant stimuli. Thus, population coding schemes are necessary to provide a reliable foundation for perception, and selective attention is required to ensure that representations of relevant stimuli dominate awareness. Unfortunately, the relationship between attention and population codes is not well understood, in part because of technical limitations and in part because little work has been done to link single-unit attention modulations with the efficiency of information encoding at the population level. Here, we use a simple computational model to provide an explicit link between attention modulations, population codes and perception. To test predictions generated by the model, we use a combination of psychophysics and novel multivariate functional magnetic resonance imaging (fMRI) analysis techniques that are sensitive to changes in population response profiles across feature-selective regions of visual cortex. Specifically, we present a method for measuring feature-selective `tuning-functions' within very small regions of early visual cortex. These fMRI-based tuning functions resemble the tuning functions routinely obtained using single-unit recording methods in non-human primates, providing a powerful tool for evaluating theories of information encoding in human sensory cortices. In the first Specific Aim, we will use this collection of tools to test different models of attention gain (e.g. multiplicative gain vs. contrast gain) and to determine if feature-based attention systematically biases population response profiles in early visual cortex even before a stimulus is presented. In the Second Aim, we critically evaluate the common intuition that attention gain should be applied to sensory neurons that are maximally responsive to a target stimulus. Instead, we will test the counter-intuitive prediction that gain should sometimes be applied to neurons that are actually not tuned to the attended feature in order to maximize the efficiency of population codes. Together, these efforts will shed light on how the behavioral goals of an observer can shape population response profiles so that information processing within the visual system can be optimized. Since perception is a fundamental aspect of human information processing, our findings will be of interest to investigators focusing on perceptual learning, decision making, and memory. Moreover, the knowledge gained here will provide an important foundation for future applied research into disorders of attention such as Attention Deficit Disorder (ADD). For example, developing a better understanding about how attention modulates sensory neurons may lead to more objective diagnostic tests so that these disorders may be identified earlier and with greater accuracy. PUBLIC HEALTH RELEVANCE Whether listening to a teacher in a classroom or driving a car down the road, the ability to pay attention to important parts of the environment is critical to our success and survival. In the present research proposal, we seek to understand how patterns of neural activity in the brain support attentive behavior so that an observer may more efficiently understand and represent incoming sensory information. This knowledge will aid in the development of more objective tests for common disorders of attention - such as attention deficit disorder - so that diagnosis can proceed with greater precision and so that interventions can be started earlier.