Neural habituation: A unified account of visual identification dynamics across tasks

神经习惯化：跨任务视觉识别动态的统一解释

• 批准号: 1431147
• 负责人: David Huber
• 金额: $ 37.39万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-15 至 2018-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1431147&HistoricalAwards=false
• 关键词: Neural; habituation; unified; account; visual

People often fail to see things that appear right before their eyes. This can occur in many different situations and for many different types of information, ranging from failing to identify where or when something appeared to not noticing that it appeared it all. This project aims to explain failures of detection across a wide range of situations using a unified theoretical framework. Understanding the mechanisms of visual detection failure and its role in normal visual processing is important for many societal problems that involve continuously monitoring visual information, including forensic applications. For example, accurate screening of baggage at security checkpoints or monitoring security video requires an understanding of when human detection is likely to fail. Also, designing successful artificial vision systems requires an understanding of why some types of detection failure are actually necessary for the processing of continuous visual information. The brain's mechanism for separating what is different from what is the same in the constant stream of visual information may come at a cost in some situations. This is relevant to designing real-time computer vision systems that can process ongoing visual information accurately. The theory under investigation in this project is that visual detection failures are often due to neural habituation, which occurs when a neural representation within a network becomes temporarily over-activated after prolonged visual exposure. During the habituation period, the neural representation is temporarily less accessible, which can lead to a failure to consciously notice or detect that particular piece of information if it appears again during the habituation period. The hypothesis tested is that the brain uses transient neural habituation to track information that is constant versus changing. For example, by becoming habituated to recently viewed faces, the brain avoids confusing the attributes of those faces with a currently viewed face, helping to tell people apart. The cost is that people often fail to detect recently seen attributes if they re-appear during the habituation period. To probe visual detection and detection failures, scientists often use rapid serial visual presentation tasks, in which multiple visual images are presented in quick succession. This project aims to explain detection failure across a wide range of such tasks with a unified theoretical model of neural habituation. This effort will increase current understanding of how processes occurring at the level of neural networks can explain processes occurring at the level of visual detection and awareness.

人们常常看不见眼前的东西。这可能发生在许多不同的情况和许多不同类型的信息中，从无法识别某些东西出现的地点或时间到没有注意到它出现了。该项目旨在使用统一的理论框架解释各种情况下的检测失败。了解视觉检测失败的机制及其在正常视觉处理中的作用对于涉及持续监测视觉信息的许多社会问题（包括法医应用）非常重要。例如，要在安检处对行李进行准确筛查或监控安全视频，就需要了解什么时候人工检测可能会失败。此外，设计成功的人工视觉系统需要理解为什么某些类型的检测失败对于连续视觉信息的处理实际上是必要的。在某些情况下，大脑在不断的视觉信息流中区分不同与相同的机制可能会付出代价。这与设计能够准确处理正在进行的视觉信息的实时计算机视觉系统有关。这个项目正在研究的理论是，视觉检测失败通常是由于神经习惯化，当网络中的神经表征在长时间的视觉暴露后暂时过度激活时，就会发生这种情况。在习惯期，神经表征暂时不太容易接近，这可能导致无法有意识地注意到或检测到在习惯期再次出现的特定信息。经过测试的假设是，大脑使用短暂的神经习惯来跟踪恒定与变化的信息。例如，通过习惯最近看过的脸，大脑可以避免将这些脸的属性与最近看过的脸混淆，从而有助于区分不同的人。这样做的代价是，如果最近看到的属性在习惯化期间再次出现，人们往往无法发现它们。为了研究视觉检测和检测失败，科学家们经常使用快速连续视觉呈现任务，在该任务中，多个视觉图像快速连续呈现。该项目旨在用神经习惯化的统一理论模型来解释在广泛的此类任务中的检测失败。这一努力将增加当前对发生在神经网络层面的过程如何解释发生在视觉检测和意识层面的过程的理解。