Adaptive allocation of attentional gain

注意力增益的自适应分配

• 批准号: 9187018
• 负责人: John T Serences
• 金额: $ 38.75万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-12-01 至 2020-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9187018
• 关键词: Address; Adopted; Afferent Neurons; Attention; Attention Deficit Disorder; Automobile Driving; Awareness; Bayesian Modeling; Behavior; Behavioral; Belief; Clinical; Code; Cognition Disorders; Confusion; Data; Decision Making; Development; Diagnosis; Diagnostic; Diffusion; Disease; Electroencephalography; Environment; Fluid Shifts; Frequencies; Functional Magnetic Resonance Imaging; Funding; Generic Drugs; Grouping; Human; Income; Intervention; Intuition; Knowledge; Likelihood Functions; Link; Mediating; Memory; Mental Depression; Methods; Modeling; Parietal; Perception; Perceptual Disorders; Performance; Placebo Effect; Population; Probability; Psychophysics; Research; Research Proposals; Response to stimulus physiology; Retinal; Schizophrenia; Sensory; Shapes; Signal Detection Analysis; Source; Stimulus; Techniques; Testing; Theoretical Studies; Update; Variant; Visual Cortex; Visual system structure; Work; base; behavior influence; cognitive process; expectation; experience; improved; information processing; insight; novel; object recognition; public health relevance; relating to nervous system; response; selective attention; sensory input; sensory stimulus; sensory system; teacher; theories

 DESCRIPTION (provided by applicant): Human sensory systems cannot simultaneously parse and reconstruct all available inputs into meaningful perceptual representations. Classic accounts address this processing limit by invoking a selection mechanism that preferentially encodes only the most salient and behaviorally relevant stimuli in the environment. This mechanism is typically referred to as selective attention, and empirical work has traditionally focused on understanding why and how relevant stimuli dominate perceptual awareness. However, other extra-retinal factors can also impact the efficiency of information processing, such as estimates of the prior probability of a particular stimulus (or configuration of stimuli) based on past experience in a particular context (i.e. expectation). Despite many demonstrations that expectation can profoundly influence a variety of perceptual phenomena ranging from low-level grouping to high-level object recognition, empirical and theoretical studies almost always conflate expectation and selective attention. This confusion persists even though these factors are logically dissociable: the probability that a stimulus will appear in a given context may have little or nothing to do with behavioral relevance. The conflation of these extra-retinal factors may seem inconsequential, as both might naively be expected to influence neural activity and behavior in a similar way. However, recent theories of cortical information processing - such as predictive coding - hold that stable perceptual representations emerge from the dynamic interplay between internal probability estimates about the state of the world (i.e. expectations) and the content and quality of incoming sensory information (which is shaped by task-relevance, or attention). Here, we adopt a Bayesian framework that casts perceptual inference as the product of prior beliefs and likelihoods (i.e. sensory evidence). We will use this framework to formulate and test the hypothesis that expectation operates on priors to modulate pre-stimulus responses in visual cortex and to bias the `read-out' of neural codes during decision-making, whereas attention directly impacts likelihood functions by shaping stimulus-evoked neural responses on the basis of task relevance. Our approach will combine psychophysics, quantitative models of perceptual and cognitive processes, and novel EEG and fMRI analysis methods that can determine how priors and likelihoods combine to shape the quality of feature-selective perceptual representations. Collectively, this work will provide key insights into how different extra-retinal biasing factors interact to shape perception, and will more broadly test generative models of cortical information processing that characterize perception as a problem of optimal statistical inference. In turn, this knowledge should improve our ability to isolate specific aspects of selective information processing that can sometimes go awry, thereby enabling more targeted diagnoses and interventions in clinical settings.

 描述（由适用提供）：人类感觉系统不能简单地解析和重建所有可用的输入，以有意义的感知表示。经典帐户通过调用选择机制，该机制优先编码环境中最显着，最相关的刺激，以解决此处理限制。这种机制通常被称为选择性关注，而经验工作传统上一直集中在理解相关刺激以及如何占主导感知意识的原因上。但是，其他视网膜外因素也可能影响信息处理的效率，例如根据特定情况下（即期望）在特定情况下经验的特定刺激（或刺激配置）的先验概率（或刺激的配置）。尽管有许多证明，期望可以深刻影响从低级分组到高级对象识别，经验和理论研究的各种感知现象，几乎总是会使期望和选择性关注混为一谈。即使这些因素在逻辑上是可解散的，这种混乱仍然存在：在给定上下文中出现刺激的可能性可能与行为相关性几乎没有关系。这些视网膜外因子的融合似乎无关紧要，因为自然而然地希望以类似的方式影响神经元活动和行为。但是，最近的皮质信息处理理论（例如预测性编码）认为，稳定的感知表示来自有关世界状况（即期望）（即期望）的动态相互作用，以及传入的感官信息的内容和质量（由任务 - 利益或关注者塑造）。在这里，我们采用贝叶斯框架，将知觉推断作为先前的信念和可能性的产物（即感官证据）。我们将使用这个 框架制定和检验的假设是，对先验的预期操作可以调节视觉皮质中的刺激前反应，并在决策过程中偏向神经阿群的“读出”，而关注直接通过根据任务相关的基础来塑造受刺激的神经支持的刺激功能。我们的方法将结合心理物理学，感知过程和认知过程的定量模型，以及新型的EEG和FMRI分析方法，这些方法可以确定可以确定先验和可能性如何组合以塑造特征选择性感知表示的质量。总的来说，这项工作将提供关键的见解，以了解不同的视网膜外偏置因素如何相互作用以塑造感知，并将更广泛地测试将感知视为最佳统计推断问题的皮质信息处理的通用模型。反过来，这些知识应提高我们隔离有时会出现的选择性信息处理特定方面的能力，从而在临床环境中提供更多针对性的诊断和干预措施。