Eye tracking and computational approaches to understand the roles of maturation and experience in infant looking
Eye tracking and computational approaches to understand the roles of maturation and experience in infant looking
批准号:
10602809
负责人:
Brianna Keenan Hunter
金额:
$7.16万
依托单位国家:
美国
项目类别:
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-08-01 至 2026-07-31
关键词:
AdultAgeAttentionBehaviorBehavioralBrainBrain regionCaliforniaCategoriesCerebrumChildCitiesClinicalClutteringsComplexComputer ModelsDevelopmentEnvironmentEventExposure toEyeEye MovementsFaceFamiliarityFutureGoalsHealth BenefitHomeHumanImageIndividualIndividual DifferencesInfantInfant DevelopmentInstructionInterventionKnowledgeLearningLifeLinkMentorshipMethodologyMethodsModelingNeural Network SimulationParentsPatternPersonsPopulationProcessPropertyProxyPublic HealthResearchRoleScanningShapesStimulusStreamSupervisionTechniquesTestingTrainingUniversitiesVariantVisionVisualVisual SystemVisual attentionVisual impairmentWorkattentional controlcareerconvolutional neural networkcortical visual impairmentexperienceexperimental studygazeinfancyinformation gatheringinnovationinsightmovement analysisprenatalsample fixationskillstheoriesvision developmentvisual processvisual processingvisual tracking
中文摘要
项目摘要/摘要
婴儿用眼睛从他们的环境中收集信息,并了解独特的场景和
它们周围的物体。随着婴儿积累这些经验,他们的大脑皮层同时成熟到
支持更可控的注意力和视觉过程。然而,尚不清楚在多大程度上
凝视控制反映的是大脑皮质的成熟,而不是个体的经历。因此,拟议的项目寻求
评估关于成熟和经验各自作用的三个相互竞争的假设。我们将使用
既有传统的眼睛跟踪分析,也有先进的计算建模。具体地说,在目标1中,我们
将记录在观看熟悉程度不同的场景和对象时的婴儿看起来的行为,并评估
刺激属性(例如,显著、有意义)与熟悉和陌生图像的观看模式相关
在生命的第一年。在目标2中,我们将把这些观察行为与卷积神经网络相关联
灵感来自成熟大脑的视觉系统。这一创新方法将评估两者之间的关系
婴儿在熟悉度不同的环境中的看起来行为和高级视觉加工的使用。
最终,这一提议的结果将通过表征视觉注意的特征,为视觉注意的发展理论提供信息。
成熟因素对经验因素的贡献。从这个项目的结果中获得的信息
可能有助于未来对临床人群进行非典型视觉扫描和干预
注意控制。为了成功地实现这些目标,申请者必须获得理论上的和
在婴儿和成人注意力研究方面的方法专长。这种综合的视角将使她
使用复杂的方法和分析来描述婴儿的注意力和视觉行为
方法,这最终将产生广泛的后续研究问题,使用类似的
技巧。这些目标将在丽莎博士的监督和指导下圆满完成。
加州大学戴维斯分校的奥克斯和史蒂文·勒克是婴儿和成人注意力问题的专家
分别为种群数量。拟议的培训目标侧重于扩大注意力的内容知识和
获得卷积神经网络和代表性相似性分析方面的方法论专长。
完成这些培训目标将使申请者为成功的独立研究做好更好的准备
推动注意力领域向前发展的事业。
英文摘要
Project Summary/Abstract
Infants use their eyes to gather information from their environment and learn about the unique scenes and
objects that surround them. As infants accumulate these experiences, their cortex is concurrently maturing to
support more controlled attention and visual processes. However, it is unclear how much of the development of
gaze control reflects cortical maturation versus individual experiences. Therefore, the proposed project seeks
to evaluate three competing hypotheses regarding respective roles of maturation and experience. We will use
both traditional eye tracking analyses as well as advanced computational modeling. Specifically, in Aim 1, we
will record infant looking behaviors while viewing scenes and objects that range in familiarity and assess how
stimulus properties (e.g., salience, meaningfulness) relate to looking patterns for familiar and unfamiliar images
across the first year of life. In Aim 2, we will relate these looking behaviors to a convolutional neural network
inspired by the mature brain’s visual system. This innovative approach will evaluate the relationship between
infants’ looking behaviors and use of higher-level visual processing across contexts that vary in familiarity.
Ultimately, results from this proposal will inform developmental theories of visual attention by characterizing the
contributions of maturational versus experiential factors. The information gained from the results of this project
may facilitate future assessments and interventions of clinical populations with atypical visual scanning and
attention control. To successfully accomplish these aims, it is necessary that the applicant gains theoretical and
methodological expertise in both infant and adult attention research. This integrative perspective will allow her
to characterize infant attention and visual behaviors using sophisticated methodological and analytical
approaches, which will ultimately generate a wide range of subsequent research questions using similar
techniques. These aims will be most successfully completed under the supervision and mentorship of Drs. Lisa
Oakes and Steven Luck at the University of California, Davis, who are experts on attention in infant and adult
populations, respectively. The proposed training goals focus on expanding content knowledge of attention and
gaining methodological expertise in convolutional neural networks and representational similarity analysis.
Accomplishing these training goals will better prepare the applicant for a successful independent research
career that pushes the field of attention development forward.
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