Neural dynamics underlying spatiotemporal cognitive integration

项目摘要 我们用视觉解释周围世界的能力依赖于快速的自下而上的计算 从感觉输入中提取相关信息，但它还依赖于我们积累的核心知识 关于世界根据先前的经验提供自上而下的信号。这项建议的目标是研究 视觉信息在空间和时间上进行整合以自下而上和自上而下相结合的机制。 向下学习知识。为了实现这一目标，我们将行为测量、侵入性神经生理学 录音、侵入性电刺激和计算模型。我们专注于无处不在的挑战 视觉搜索，例如仅使用视觉提示搜索您的手机。行为数据将 提供对人类综合能力的关键限制，特别是通过眼球运动和 识别和目标定位的动力学。侵入性神经生理数据将提供高水平的 沿颞叶下皮质的神经活动的时空分辨率及其与前额叶的相互作用 额叶皮质，被假设为传递自顶向下信号所需的类型的关键 视觉搜索过程中的识别和注意调节。归根结底，我们提案的一个核心目标是 通过一个定量的计算模型，使我们对这些综合过程的理解形式化。这 计算模型应该能够捕获行为和生理结果，并提供可测试的 预测。在目前的奖项中，我们在阐明潜在的机制方面取得了进展 视觉系统使用的图案补全，从部分信息推断对象的身份、效果 对象识别过程中的上下文信息，以及视觉搜索的计算模型。我们有很强的 初步证据表明，最先进的纯粹自下而上的认知理论由 深度卷积网络不能解释人类的行为和生理。因此，建议的工作 旨在建立一个强大的计算、行为和生理框架，将自下而上和 自上而下的处理。此外，我们将超越相关措施，使用电刺激来 对模型进行压力测试，并在电路中的关键节点和视觉搜索之间建立因果联系 行为。了解核心知识融入感官的神经机制 加工可以说是认知科学中最大的挑战之一，它可能具有重要的 对许多以自上而下功能障碍为特征的神经和精神疾病的影响 发信号，并且仍然知之甚少。

Project Summary Our ability to visually interpret the world around us depends on rapid bottom-up computations that extract relevant information from the sensory inputs, but it also depends on our accumulated core knowledge about the world providing top-down signals based on prior experience. The goal of this proposal is to study the mechanisms by which visual information is integrated spatially and temporally to combine bottom-up and top- down knowledge. Towards this goal, we combine behavioral measurements, invasive neurophysiological recordings, invasive electrical stimulation, and computational models. We focus on the ubiquitous challenge of visual search, exemplified by searching for your phone using exclusively visual cues. The behavioral data will provide critical constraints about human integrative abilities, particularly through eye movements and the dynamics of recognition and object location. The invasive neurophysiological data will provide high spatiotemporal resolution of neural activity along the inferior temporal cortex and the interactions with the pre- frontal cortex, which are hypothesized to be critical for conveying the type of top-down signals required for recognition and attention modulation during visual search. Ultimately, a central goal of our proposal is to formalize our understanding of these integrative processes via a quantitative computational model. This computational model should be able to capture the behavioral and physiological results and provide testable predictions. During the current award, we have made progress towards elucidating the mechanisms underlying pattern completion used by the visual system to infer the identity of objects from partial information, the effects of contextual information during object recognition, and computational models of visual search. We have strong preliminary evidence that suggests that state-of-the-art purely bottom-up theories of recognition instantiated by deep convolutional networks cannot explain human behavior and physiology. Therefore, the proposed work aims to establish a strong computational, behavioral and physiological framework that merges bottom-up and top-down processing. Furthermore, we will move beyond correlative measures by using electrical stimulation to stress test the models and establish causal links between key nodes in the circuitry and visual search behavior. Understanding the neural mechanisms by which core knowledge is incorporated into sensory processing is arguably one of the greatest challenges in Cognitive Science and may have important implications for many neurological and psychiatric conditions that are characterized by dysfunctional top-down signaling and remain poorly understood.