Neural reconstructions of visual information across dynamic shifts of attention and working memory
Neural reconstructions of visual information across dynamic shifts of attention and working memory
批准号:
9328233
负责人:
Emma Wu Dowd
金额:
$5.67万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-09-01 至 2020-08-31
关键词:
AdoptedAffectAttentionAttention deficit hyperactivity disorderAutistic DisorderBasic ScienceBehaviorBehavioralBehavioral ModelBindingBrainClinicalClinical TreatmentCognitionCognitiveColorComplementComputer SimulationCuesDataDimensionsDiseaseEnvironmentEyeEye MovementsFunctional Magnetic Resonance ImagingFutureGoalsHealthHumanIncomeKnowledgeLinkLocationMaintenanceMeasuresMediatingMemoryMindModelingParkinson DiseasePerceptionPerceptual distortionsPlant RootsPopulationProcessPsychophysicsPublic HealthReportingResearchResearch PersonnelSchizophreniaSensoryShort-Term MemoryTechnical ExpertiseTechniquesTrainingTranslatingUpdateVisionVisualVisual attentionVisual impairmentVisual system structureWorkattentional controlbehavioral responsebrain researchcareercognitive capacitycohesionflexibilityimprovedinnovationinsightinternal controlmental representationneural modelneural patterningneuroimagingneuromechanismneurotransmissionnovelnovel strategiesreconstructionrelating to nervous systemresponsesensory inputtheoriestooltranslational impactvisual cognitionvisual informationvisual processing
中文摘要
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英文摘要
Project Summary
At any given moment, the human visual system is overwhelmed by a wealth of sensory inputs,
necessitating attentional mechanisms that selectively filter and process information for effective
behavior. Attention, however, is dynamic—we are constantly distracted by competing inputs and
goals, such that our attention (and our eyes) are always moving across the visual environment. The
overarching goal of this research is to better understand the visual system, by investigating how
humans update and integrate visual information across dynamic and unstable shifts of attention.
Using a combination of behavioral, neuroimaging (fMRI), and computational modeling techniques,
Aim 1 examines how external shifts of attention affect perceptual and neural representations of visual
information that is visible to the eyes, while Aim 2 examines how internal shifts of attention modulate
information maintained in visual working memory. Together, these aims demonstrate how dynamic
changes of attentional focus can impact—and distort—our perception of the world, and provide
insight into how humans flexibly prioritize information that is more or less relevant for current
behavioral goals. The current proposal adopts a novel approach of measuring specific visual
information in the mind and in the brain by combining computational models of behavior with
computational models of neural activity—which may be ultimately integrated into a cohesive theory
for the perceptual and neural mechanisms of visual stability. This research will have an immediate
impact on the understanding of typical visual functioning in healthy human populations. While the
proposed work is rooted in basic science, these advances would have a longer-term translational
impact on public health, by informing the knowledge, assessment, and treatment of clinical disorders
that are characterized by deficits in visual processing (e.g., schizophrenia, autism, ADHD). This
research thus complements several training goals that will help the applicant acquire new technical
skills and theoretical knowledge to prepare for a future career as an independent investigator.
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