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.

尽管存在间歇性空白区域，但填充是观察者感知连续视觉模式的感知趋势。填充发生在盲点，也发生在视觉途径疾病患者的苏托症区域。 由于填充发生在空白区域，因此在该视网膜位置的任何视觉机制中都没有物理刺激。通常，观察者在给定试验中要求进行填写时报告。可以比较有或没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有所不同。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，我们发现填充中的大胆激活与没有填充试验之间没有差异。但是，使用一项外出的训练程序和支持向量机，可以通过早期视觉皮层的激活模式差异对感知进行分类（Lin，Greenlee＆Chen，2020年）。为了解决这些问题，我们开发了一种新的范式来评估感知填充的存在或不存在。观察者将以间歇性空白区域的周期性模式呈现，这些模式用作人工scotoma。通过填充后，通过在空白区域显示目标，我们可以测量对目标的神经反应，并确定该响应是否受填充的存在影响。目标将是一种刺激，可以引起足够大的神经反应，以允许对神经活动的可靠测量。我们将改变目标的对比度，以确定诱导剂存在的对比响应函数。这使我们能够确定目标对比响应函数。将进行诱导者和靶标的物理特性的变化，以分离与诱导者和靶刺激相关的响应组件。在一系列三项研究中，我们将使用功能磁共振成像（FMRI），事件相关电位（ERP）和Psychophysics实验来测量对目标的响应函数。后者将进行建立基本现象的工作范围。 fMRI实验将精确识别用于填充的大脑区域。需要这样的精度，因为填充v2的候选区域很小。 ERP实验将确定目标响应相对于填充的开始的时间动力学。填充现象将与涉及边界对比的其他幻觉有关。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，横向抑制和激发会影响对目标刺激有选择反应的神经机制。这样，我们将能够使用计算建模来开发一种新的感知填充理论。