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（由申请人提供）：人类感觉系统无法同时将所有可用输入解析并重建为有意义的感知表征，经典的描述通过调用仅对环境中最显着且行为相关的刺激进行编码的选择机制来解决这一处理限制。机制通常被称为选择性注意，实证研究传统上侧重于理解相关刺激为何以及如何主导知觉意识。然而，其他视网膜外因素也会影响信息处理的效率，例如对信息处理的估计。基于特定背景下的过去经验（即期望）的特定刺激（或刺激配置）的先验概率，尽管许多证据表明期望可以深刻影响从低级分组到高级对象的各种感知现象。认知、实证和理论研究几乎总是将期望和选择性注意混为一谈，尽管这些因素在逻辑上是可分离的：刺激在给定背景下出现的概率可能与行为相关性几乎没有关系或根本没有关系。这些视网膜外因素似乎无关紧要，因为两者可能会天真地期望以类似的预测方式影响神经活动和行为。然而，最近的皮质信息处理理论（例如编码）认为，稳定的感知表征是从两者之间的动态相互作用中产生的。关于世界状态（即期望）以及传入感官信息的内容和质量（由任务相关性或注意力决定）的内部概率估计在这里，我们采用贝叶斯框架来投射感知。作为先验信念和可能性的产物的推理（即感官证据）。 框架来制定和测试以下假设：预期作用于先验，以调节视觉皮层中的刺激前反应，并在决策过程中偏向神经代码的“读出”，而注意力通过塑造刺激诱发的神经直接影响似然函数我们的方法将结合心理物理学、感知和认知过程的定量模型以及新颖的脑电图和功能磁共振成像分析方法，这些方法可以确定先验和可能性如何结合来塑造特征选择性感知的质量。总的来说，这项工作将提供关于不同的视网膜外偏差因素如何相互作用以形成感知的关键见解，并将更广泛地测试皮层信息处理的生成模型，这些模型将感知表征为最佳统计推理问题。应该提高我们分离选择性信息处理的特定方面的能力，这些方面有时可能会出错，从而在临床环境中实现更有针对性的诊断和干预。