Perceptual decision making over the lifespan

项目摘要 视觉感知决策是一个无处不在的过程，它将我们嘈杂的视觉体验转化为 将环境转化为相关的感性决策。产品数据管理很重要，因为它直接影响我们 与我们周围的环境互动，形成我们的行为。从先前的研究中，我们知道许多认知能力 随着我们年龄的增长，过程经历了一个发展变化，通常以倒U形模式为特征： 从儿童期到成年期早期功能增强，随后衰老下降。然而，没有 直接证明了产品数据管理的全生命周期轨迹。我们发现，我们对产品数据管理的理解存在三个差距： 1)对儿童的丙二醛没有得到很好的研究，可能是因为方法学的问题，2)关于寿命的结论 产品数据管理轨迹仅通过不同研究之间的间接比较得出，并且3)我们没有 清楚地了解噪声如何与儿童和老年人的PDM能力相互作用；这一点值得注意 在高刺激噪声条件下，产品数据管理常常成为行为的关键限制因素。总目标 这项建议的目的是通过仔细检查与年龄相关的pdm影响以及 使用不同的方法绘制出产品数据管理在整个生命周期中的典型发展。在目标1中，我们建议 使用两个任务(即随机网点运动任务和分类任务)检查整个生命周期内的产品数据管理。 我们将使用经典的反应时间(RT)方法进行建模(即DDM：漂移-扩散模型)，允许 US将RT分解为其组成部分(即证据积累、非决策时间和决策 时间)来说明不同的产品数据管理组件的年龄相关的影响。目标1是第一步 了解产品数据管理的年龄相关效应，是制定产品数据管理典型发展计划的重要一步 产品数据管理。在目标2中，我们将探索使用一种补充方法，持续时间阈值，其特征如下 以足以让个人感知刺激的最短时间，作为一种新的方法 使用与目标1相同的任务来估计产品数据管理的时间限制。这是一种具有明显优势的方法 需要复杂的建模，更适合儿童和老年人，因为它消除了非 决策部分(即，马达反应)。在目标3中，我们将同时使用经典(RT/DDM)和新颖(持续时间 阈值)方法通过将不同级别的噪声包括到 目标1中提到的任务。我们的感官体验天生就是嘈杂的，有证据表明儿童和 老年人更容易受到刺激性噪音的影响。我们的假设是，噪音的存在会 加剧了产品数据管理中与年龄相关的缺陷。这将是我们对pdm理解的一个重大进步。 作为有效的PDM的寿命轨迹在嘈杂的感觉条件下尤其重要(例如，骑在 在低能见度条件下骑自行车)。总而言之，拟议的研究将提供关于 产品数据管理的典型发展和衰落，进而为研究非典型性奠定了基础 疾病管理的发展和老化(例如，自闭症谱系障碍、阿尔茨海默病)。

Project Summary Visual perceptual decision-making (PDM) is a ubiquitous process that translates our noisy visual experience of the environment into associated perceptual decisions. PDM is important as it directly influences how we interact with our surroundings and form our behavior. From prior studies, we know that many cognitive processes undergo a developmental change as we age, usually characterized by an inverted U-shape pattern: increasing function from childhood to early adulthood and a later decline in senescence. However, there is no direct evidence about the full lifespan trajectory of PDM. We identified three gaps in our understanding of PDM: 1) PDM in children is not well studied, likely because of methodological issues, 2) conclusions about lifespan PDM trajectory are only derived by indirect comparisons between separate studies, and 3) we do not have a clear understanding about how noise interacts with PDM ability in children and older adults; this is notable as PDM often becomes a key limiting factor in behavior under conditions of high stimulus noise. The overall goal of this proposal is to address these gaps in the literature by scrutinizing age-related effects of PDM as well as using different methods to map out the typical development of PDM across the lifespan. In Aim 1, we propose to examine PDM across the lifespan using two tasks (i.e., a random dot motion task and a categorization task). We will use the classical reaction times (RT) method with modeling (i.e., DDM: drift-diffusion model), allowing us to decompose RT into its components (i.e., evidence accumulation, non-decision time, and the decision time) to illuminate age-related effects of the different components of PDM. Aim 1 is the first step to understanding age-related effects of PDM and an important step to mapping out the typical development of PDM. In Aim 2, we will explore the use of a complementary method, duration threshold which is characterized by the shortest amount of time that is sufficient for individuals to perceive a stimulus, as a novel approach for estimating temporal limits of PDM using the same tasks as Aim 1. This is a method with a clear benefit of not necessitating complex modeling and it is more suitable for children and older adults as it eliminates non- decision components (i.e., motor response). In Aim 3 we will use both classical (RT/DDM) and novel (duration threshold) methods to examine how stimulus noise impacts PDM by including different levels of noise to the tasks mentioned in Aim 1. Our sensory experience is inherently noisy and there is evidence that children and older adults are more susceptible to stimulus noise. Our hypothesis is that the presence of noise will exacerbate age-related deficits in PDM. This would be a significant advance in our understanding of PDM lifespan trajectory as effective PDM can be particularly important under noisy sensory conditions (e.g., riding a bike under low visibility conditions). In summary, the proposed research will provide novel insights about the typical development and decline of PDM and, by extension, establish a baseline for studying atypical development and aging in PDM (e.g., Autism Spectrum Disorder, Alzheimer’s Disease).