Adaptive allocation of attentional gain

 描述（由申请人提供）：人类感觉系统不能同时解析和重建所有可用的输入为有意义的感知表示。经典的解释通过调用一种选择机制来解决这个处理限制，这种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，经验主义的工作传统上集中在理解相关刺激为什么以及如何支配感知意识。然而，其他视网膜外因素也可能影响信息处理的效率，例如基于特定背景下的过去经验（即期望）对特定刺激（或刺激配置）的先验概率的估计。尽管许多研究表明，期望可以深刻地影响从低层次分组到高层次物体识别的各种知觉现象，但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以分离的，但这种混淆仍然存在：刺激出现在给定情境中的概率可能与行为相关性关系不大或根本没有关系。将这些视网膜外因素混为一谈似乎无关紧要，因为两者可能会天真地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，稳定的感知表征来自于对世界状态的内部概率估计（即预期）与传入感官信息的内容和质量（由任务相关性或注意力塑造）之间的动态相互作用。在这里，我们采用贝叶斯框架，将感知推理作为先验信念和可能性（即感官证据）的产物。我们将使用这个 框架，制定和测试的假设，期望操作先验调制前刺激反应在视觉皮层和偏见的“读出”的神经代码在决策过程中，而注意力直接影响似然函数的基础上，塑造刺激诱发的神经反应的任务相关性。我们的方法将结合联合收割机心理物理学，感知和认知过程的定量模型，以及新的EEG和fMRI分析方法，可以确定先验和可能性如何结合联合收割机来塑造特征选择性感知表征的质量。总的来说，这项工作将提供关键的见解，不同的视网膜外偏置因素如何相互作用，以形状感知，并将更广泛地测试生成模型的皮质信息处理的特征感知作为一个问题的最佳统计推断。反过来，这些知识应该提高我们的能力，以隔离选择性信息处理的特定方面，有时会出错，从而使临床环境中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.