The neural computation for perceptual filling-in

感知填充的神经计算

• 批准号: 468434407
• 负责人: Professor Dr. Mark Greenlee
• 金额: --
• 依托单位: National Science and Technology Council (NSTC)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/468434407?language=en
• 关键词: neural; computation; perceptual; filling

Filling-in is the perceptual tendency of an observer to perceive a continuous visual pattern despite the presence of an intermittent blank region. Filling-in occurs at the blindspot but also in scotomatous regions in patients with diseases of the visual pathways. Since filling-in occurs in a blank region there is no physical stimulus to elicit a response in any visual mechanisms at that retinotopic location. Observers are usually requested on a given trial to report when they experience filling-in. Neural responses to stimuli with and without filling-in can be compared to determine whether these responses differ on these two trial types. Machine learning can be applied to determine if a classifier can distinguish between these two types of events. With univariate analysis, we found no difference between BOLD activation in the filling-in and no filling-in trials. However, using a leave-one-out training procedure and a support vector machine, it was possible to classify the percepts by the activation pattern differences in early visual cortex (Lin, Greenlee & Chen, 2020). To resolve these issues, we have developed a new paradigm to assess the presence or absence of perceptual filling-in. Observers will be presented periodic patterns with intermittent blank regions, which serve as artificial scotoma. By presenting a target in the blank region after filling-in occurs we can measure the neural response to the target and determine whether this response is affected by the presence of filling-in. The target will be a stimulus that can elicit a large enough neural response to allow for a reliable measurement of neural activity. We will vary the contrast of the target to determine the contrast response function in the presence of the inducer. This allows us to determine the contrast response function to the target. Variations in the physical properties of the inducers and targets will be conducted to separate response components related to the inducer and target stimuli.In a series of three studies, we will parametrically measure the response functions to the target with functional magnetic resonance imaging (fMRI), event related potentials (ERP) and psychophysics experiments. The latter will be conducted to establish the operating range of the basic phenomena. The fMRI experiments will precisely identify the brain areas for filling-in. Such precision is required as the candidate area for filling-in, V2, is small. The ERP experiments will determine the temporal dynamics of the target response with respect to the onset of filling-in. The filling-in phenomenon will be related to other illusions involving border contrast.The results of these studies will be simulated using a computational model of early visual processing. In these models, lateral inhibition and excitation influence the neural mechanisms that respond selectively to target stimuli. In this way, we will be able to develop a new theory of perceptual filling-in using computational modelling.

填充是观察者感知连续视觉模式的感知倾向，尽管存在间歇性空白区域。填充发生在盲点处，但也发生在患有视觉通路疾病的患者的暗点区域。 由于填充发生在空白区域，因此在该视网膜定位位置处没有物理刺激来引起任何视觉机制的反应。通常要求观察员在特定试验中报告他们何时进行填充。可以比较对具有和不具有填充的刺激的神经反应，以确定这些反应在这两种试验类型上是否不同。可以应用机器学习来确定分类器是否可以区分这两种类型的事件。通过单变量分析，我们发现BOLD激活在填充试验和无填充试验中没有差异。然而，使用留一法训练程序和支持向量机，可以通过早期视觉皮层中的激活模式差异对感知进行分类（Lin，Greenlee & Chen，2020）。为了解决这些问题，我们开发了一种新的范式来评估知觉填充的存在或不存在。将向观察者呈现具有间歇性空白区域的周期性图案，其用作人工暗点。通过在填充发生后在空白区域呈现目标，我们可以测量对目标的神经反应，并确定这种反应是否受到填充的影响。目标将是一个刺激，可以引起足够大的神经反应，以允许可靠的测量神经活动。我们将改变目标的对比度，以确定诱导剂存在下的对比度响应函数。这使我们能够确定目标的对比度响应函数。通过改变诱导物和靶物的物理性质，分离诱导物和靶物刺激的反应成分，并通过功能磁共振成像（fMRI）、事件相关电位（ERP）和心理物理学实验，对靶物刺激的反应功能进行参数化测量。后者将被用来建立基本现象的操作范围。功能磁共振成像实验将精确地识别大脑区域进行填充。由于用于填充的候选区域V2很小，所以需要这样的精度。ERP实验将确定目标反应相对于填充开始的时间动态。填补现象将与其他涉及边界对比的错觉有关。这些研究的结果将使用早期视觉处理的计算模型进行模拟。在这些模型中，侧抑制和兴奋影响选择性地响应目标刺激的神经机制。通过这种方式，我们将能够开发一个新的理论，知觉填充使用计算建模。