Adaptive allocation of attentional gain

 描述(申请人提供)：人类感觉系统不能同时将所有可用输入解析和重构为有意义的知觉表征。经典账户通过调用一种选择机制来解决这一处理限制，该机制优先编码环境中最突出和行为相关的刺激。这种机制通常被称为选择性注意，而经验研究传统上侧重于理解相关刺激为什么以及如何主导知觉。然而，其他视网膜外因素也会影响信息处理的效率，例如基于特定背景下的过去经验(即预期)对特定刺激(或刺激的配置)的先验概率的估计。尽管许多证据表明期望可以深刻地影响各种知觉现象，从低水平的分组到高水平的物体识别，但经验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不可关联的，但这种困惑依然存在：刺激出现在给定背景下的概率可能与行为相关性几乎没有关系。这些视网膜外因素的结合可能看起来无关紧要，因为人们可能天真地认为两者都会以类似的方式影响神经活动和行为。然而，最近的皮质信息处理理论--如预测编码--认为，稳定的知觉表征来自对世界状态(即期望)的内部概率估计与传入感觉信息的内容和质量(由任务相关性或注意力塑造)之间的动态相互作用。这里，我们采用贝叶斯框架，将知觉推理作为先前信念和可能性(即感官证据)的产物。我们将使用这个 提出和检验这样一种假设的框架，即预期作用于先验者，以调节视觉皮质的前刺激反应，并在决策过程中偏向神经代码的“读出”，而注意力通过在任务相关性的基础上塑造刺激诱发的神经反应来直接影响似然函数。我们的方法将结合心理物理学、知觉和认知过程的量化模型，以及新的EEG和fMRI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知觉表征的质量。总而言之，这项工作将提供关于不同的视网膜外偏向因素如何相互作用于形状感知的关键见解，并将更广泛地测试将感知描述为最佳统计推理问题的皮质信息处理的生成模型。反过来，这些知识应该会提高我们隔离选择性信息处理的特定方面的能力，这些方面有时会出错，从而在临床环境中实现更有针对性的诊断和干预。

 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.