Integration of perception and conception in the human brain

人脑中感知和概念的整合

• 批准号: RGPIN-2020-05747
• 负责人: Barense, Morgan
• 金额: $ 5.68万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=717133
• 关键词: Integration; perception; conception; human; brain

In any given day, we interact with hundreds of objects. The ability to do so is central to our survival. When we look at an object a hairdryer, for example we are able to effortlessly recognize it and use it in a way that matches its function. We don't, for example, mistake it for the handheld power drill, despite the fact that they have a similar visual appearance and engage similar hand movements. And conversely, despite the fact that tape and glue neither look alike nor engage similar hand movements, we have no trouble recognizing that they would be both be suitable objects for the task of sticking two things together. Thus, in any given set of objects, many visual features will be highly similar and will not always have a direct correspondence with an object's identify. How, then, are we able to understand what we see? In a series of inter-related projects, we will address this question. We will focus on a core hypothesis: that the ability to understand and interact with objects depends on a particular kind of neural code. Specifically, we will argue that because neither perceptual nor conceptual features are perfectly diagnostic for an object's identity when considered on their own, the brain must integrate perceptual and conceptual features to make a highly specific object representation. We will show that these integrated representations are essential to behaving in a flexible manner when faced with many features that are distracting to the task at hand. Project 1 will use cutting-edge neuroimaging techniques to map information patterns in the brain. We predict to observe an integrated information code, such that when participants think about the visual attributes of objects, brain activity will also reflect the conceptual properties of those objects (and vice versa). We predict to observe this in the perirhinal cortex, a part of the brain that is connected both to regions important for perception, as well as to regions important for conceptual processing. Thus, it is well-placed to integrate these streams of information. Project 2 will investigate how behaviour is affected when this integrated information code is compromised. Specifically, we will work with patients who have perirhinal cortex damage due to an acute injury (e.g., an infection or tumor), and also those who have subtle perirhinal damage because they are in the early stages of Alzheimer's disease. Project 3 will investigate how different brain regions communicate with each other to understand how neural coding dynamically adapts based on task demands. Project 4 will investigate how object knowledge is formed in the first place focusing on how information becomes integrated from multiple modalities to create a coherent representation if an object. For each project we will create large stimulus sets that precisely characterize the objects' perceptual and conceptual features. These stimulus sets may be helpful to other researchers, and so we will freely share them.

在任何一天，我们都与数百个物体互动。这样做的能力对我们的生存至关重要。当我们看到一个物体，例如吹风机，我们能够毫不费力地识别它，并以符合其功能的方式使用它。例如，我们不会把它误认为是手持电钻，尽管它们有着相似的视觉外观和相似的手部动作。反过来说，尽管胶带和胶水看起来既不相似，也没有类似的手部动作，但我们不难认识到，它们都是将两个东西粘在一起的合适对象。因此，在任何给定的对象集合中，许多视觉特征将是高度相似的，并且将不总是与对象的标识直接对应。那么，我们如何能够理解我们所看到的呢？ 在一系列相互关联的项目中，我们将解决这个问题。我们将集中讨论一个核心假设：理解和与物体互动的能力取决于一种特定的神经代码。具体来说，我们将认为，因为无论是知觉还是概念的功能是完美的诊断对象的身份时，考虑自己的，大脑必须整合知觉和概念的功能，使一个高度具体的对象表示。我们将证明，当面对许多分散注意力的功能时，这些整合的表征对于以灵活的方式行事至关重要。 项目1将使用尖端的神经成像技术来绘制大脑中的信息模式。我们预测会观察到一个整合的信息代码，这样当参与者思考物体的视觉属性时，大脑活动也会反映出这些物体的概念属性（反之亦然）。我们预测在嗅周皮层中会观察到这一点，这是大脑的一部分，既与感知的重要区域相连，也与概念处理的重要区域相连。因此，它完全有能力整合这些信息流。项目2将调查当这种综合信息代码被破坏时，行为会受到什么影响。具体来说，我们将与因急性损伤（例如，感染或肿瘤），以及那些有轻微的鼻周损伤的人，因为他们处于阿尔茨海默病的早期阶段。项目3将研究不同的大脑区域如何相互通信，以了解神经编码如何根据任务需求动态适应。项目4将首先研究对象知识是如何形成的，重点是如何从多个模态中整合信息，以创建对象的连贯表示。对于每个项目，我们将创建大的刺激集，精确地描述对象的感知和概念特征。这些刺激集可能对其他研究人员有帮助，因此我们将免费分享它们。