Visual feature perception during dynamic spatial attention and distraction

动态空间注意力和分心期间的视觉特征感知

基本信息

  • 批准号:
    1848939
  • 负责人:
  • 金额:
    $ 45.03万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2019
  • 资助国家:
    美国
  • 起止时间:
    2019-09-01 至 2024-08-31
  • 项目状态:
    已结题

项目摘要

How does our visual system make sense of the world around us? Our brains construct incredibly rich perceptual experiences from the rawest of visual inputs: patterns of light on the eye's retina. A major challenge is that the environment presents more information than our visual system can fully process at a time, so we rely on the mechanisms of attention to prioritize the most relevant information. Attention and perception are vital cognitive processes that affect every aspect of our daily functioning. Understanding how these processes typically work - and when we are susceptible to perceptual errors and distortions - has critical repercussions for both the healthy visual system and various disorders, along with broad-reaching applications ranging from maximizing human behavior to development of artificial intelligence and technology. The project's education component will increase STEM opportunities for underrepresented racial and ethnic groups, enhance undergraduate education, and offer community outreach. This proposal outlines a three-year integrated research and education plan addressing how visual feature perception is altered during dynamic spatial attention and distraction. The proposal investigates a fundamental challenge for our visual systems: How do we successfully integrate information about 'what' an object is with 'where' it is? While the binding process is challenging enough on its own, it becomes particularly crucial during dynamic vision and cognition, where there are often multiple objects or locations of interest in the environment and spatial attention is constantly shifting. Using a paradigm recently developed by PI Golomb, we test the hypothesis that unstable spatial attention can cause errors in feature and object perception. In Aim 1, we focus on how visual feature perception might be altered during conditions of distraction, in collaboration with co-PI Leber, an expert in attentional control and distraction. In Aim 2, we expand in an even more fundamental direction, asking how different types of dynamic spatial attention might impact object integration for multi-feature objects. The experiments include a combination of perceptual feature reports, probabilistic mixture modeling, EEG alpha decoding, eye-tracking, and reward-based manipulations. This innovative and novel approach strives to advance our understanding of how we achieve stable and integrated visual perception, especially under conditions of dynamic attention and distraction.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
我们的视觉系统是如何感知周围世界的?我们的大脑通过最原始的视觉输入——眼睛视网膜上的光线模式——构建了极其丰富的感知体验。一个主要的挑战是,环境提供的信息比我们的视觉系统一次能完全处理的信息要多,所以我们依靠注意力机制来优先考虑最相关的信息。注意和感知是至关重要的认知过程,影响着我们日常运作的方方面面。了解这些过程通常是如何工作的,以及当我们容易受到感知错误和扭曲的影响时,对健康的视觉系统和各种疾病都有至关重要的影响,同时也具有广泛的应用,从最大化人类行为到人工智能和技术的发展。该项目的教育部分将增加代表性不足的种族和族裔群体的STEM机会,加强本科教育,并提供社区外展服务。该提案概述了一项为期三年的综合研究和教育计划,旨在解决视觉特征感知在动态空间注意力和分心过程中是如何改变的。该提案调查了我们视觉系统面临的一个基本挑战:我们如何成功地将关于物体是什么和它在哪里的信息整合起来?虽然绑定过程本身就足够具有挑战性,但它在动态视觉和认知过程中变得尤为重要,因为环境中通常有多个对象或感兴趣的位置,并且空间注意力不断变化。使用PI Golomb最近开发的范式,我们测试了不稳定的空间注意力可能导致特征和物体感知错误的假设。在Aim 1中,我们与co-PI Leber合作,专注于在注意力控制和注意力分散方面的专家,如何改变视觉特征感知。在Aim 2中,我们向更基本的方向扩展,询问不同类型的动态空间注意力如何影响多特征对象的对象集成。实验包括知觉特征报告、概率混合建模、脑电图解码、眼球追踪和基于奖励的操作。这种创新和新颖的方法努力推进我们对如何实现稳定和综合的视觉感知的理解,特别是在动态注意力和分心的条件下。该奖项反映了美国国家科学基金会的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。

项目成果

期刊论文数量(15)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Visual working memory items drift apart due to active, not passive, maintenance.
Revisiting mixture models of memory.
重新审视记忆的混合模型。
  • DOI:
    10.1038/s41562-020-00947-z
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    29.9
  • 作者:
    Dube,Blaire;Golomb,JulieD
  • 通讯作者:
    Golomb,JulieD
Dynamic neural reconstructions of attended object location and features using EEG
  • DOI:
    10.1152/jn.00180.2022
  • 发表时间:
    2023-07-01
  • 期刊:
  • 影响因子:
    2.5
  • 作者:
    Chen,Jiageng;Golomb,Julie D. D.
  • 通讯作者:
    Golomb,Julie D. D.
Suppression of a salient distractor protects the processing of target features
  • DOI:
    10.3758/s13423-023-02339-6
  • 发表时间:
    2023-08-01
  • 期刊:
  • 影响因子:
    3.5
  • 作者:
    Narhi-Martinez,William;Dube,Blaire;Golomb,Julie D. D.
  • 通讯作者:
    Golomb,Julie D. D.
Learned spatial suppression is not always proactive.
习得的空间抑制并不总是主动的。
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Julie Golomb其他文献

Julie Golomb的其他文献

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