Neural dynamics of distraction under competition in visual cortex

在早期视觉皮层注意加工资源竞争相互作用的神经动力学研究中，我们观察到注意加工资源的早期易化和随后被忽略的抑制/抑制的双相神经过程，表明注意资源的转移不是一个时不变的过程。该项目的主要目的是结合计算模型（ducc模型）系统地研究观察到的“竞争下分心”双相过程的神经动力学。我们的模型整合了认知神经科学中目前最突出的两个框架（即感官增益和偏见竞争），这两个框架传统上是孤立研究的，通过包括重要的时间因素，将其整合为一个综合框架。我们将使用一个标准设计（最后一个实验除外），允许最大限度地比较结果并操纵两个过程。为此，我们打算操纵图像的背景流或前景任务的难度。最后一个实验通过多模态分析技术介绍了情绪刺激之间的直接竞争，以进一步阐明这些竞争相互作用的神经动力学。对我们来说，这样一个整合的框架是一个重要的和创新的一步，推进我们对竞争下分心的神经动力学的理解，这不仅有助于理解注意力/情绪竞争和互动，这将与目前的建议一起调查，而且可能为其他领域的研究提供一个新的框架，比如视觉搜索。

In our previous research in neural dynamics of competitive interactions for attentional processing resources in early visual cortex we observed bi-phasic neural process of early facilitation of the attended stimulus, followed by inhibition/suppression of the then to-be-ignores one, demonstrating that shifting of attentional resources is NOT a time invariant process. The major aim of the project is to systematically investigate the neural dynamics of the observed bi-phasic process of “distraction under competition” in combination with a computational model (DUC-model). Our model integrates two of the currently most prominent frameworks in the cognitive neurosciences (i.e. sensory gain and biased competition) that have traditionally been investigated in isolation, to an integrative framework by including the important factor of time. We will use one standard design (except the last experiment) allowing for maximal comparison of results and to manipulate the two processes. To this end, we intend to manipulate either the background stream of images or difficulty of the foreground task. The last experiment introduces direct competition between emotional stimuli with a multi-modal analysis technique to further elucidate neural dynamics of these competitive interactions. To us, such an integrative framework is a significant and innovative step towards advancing our understanding of neural dynamics of distraction under competition that most certainly will not only help to understand attention/emotion competition and interaction as will be investigated with the current suggestions, but also might provide a new framework for research in other fields, such as visual search.