The Mechanisms of Perceptual Learning

描述（由申请人提供）：知觉学习（Perceptual learning， PL）被定义为由于反复的视觉经验而在视觉任务或视觉强化过程中的长期强化，被认为是视觉可塑性的表现。因此，研究视觉视觉将导致视觉可塑性的进一步阐明，这是视觉科学中最重要的目标之一。近年来，关于脑后皮层的研究有两个重要进展：一是强化信号在脑后皮层中起着重要作用；二是睡眠是脑后皮层巩固的基础。然而，脑后皮层这些方面的神经机制尚不清楚。此外，这些方面是完全分开研究的（即互不相关）。此外，尽管有(1)由重复执行任务引起的任务相关睡眠（TRPL）和(2)由暴露于视觉特征引起的任务无关睡眠（TIPL），但考虑到这两种不同类型的睡眠巩固，强化信号和睡眠巩固的作用尚未得到研究。利用fMRI信号的解码方法，我们观察到在与PL相关的各种条件下，几个大脑区域的“调谐曲线”如何变化（全局调谐曲线变化）。在本提案中，利用这种新技术的优势，我们将从相同的角度研究TRPL和TIPL中强化信号和睡眠巩固的神经机制，并旨在阐明这些机制如何相互关联。本研究的目的是阐明强化信号的神经机制。虽然已经指出，奖励驱动的强化信号在PL中起着重要作用，但奖励如何影响PL中的神经机制仍不清楚。我们将通过研究带有奖励的PL训练如何导致大脑区域的全局调谐曲线变化来解决这个问题。Aim 2的目的是阐明睡眠巩固过程中神经机制的变化。虽然已经证明睡眠是大脑皮层巩固的基础，但目前尚不清楚在不同类型的睡眠（非快速眼动和快速眼动）、不同类型的训练（任务相关和任务无关）以及有和没有奖励的情况下，神经机制在巩固过程中是如何变化的。我们将通过检查这些不同条件下的全局调优曲线变化来解决这些问题。此外，共同的神经机制是否参与奖赏和睡眠巩固将通过比较这两个目标获得的全局调谐曲线来检验。迄今为止，已经使用不同的范式和刺激来研究左脑的强化信号和睡眠巩固。系统研究强化信号和睡眠巩固的神经机制，以及它们在同一框架下在TRPL和TIPL中的相互作用，将有助于更好地理解潜在的、不同的研究人员对PL的神经机制。

DESCRIPTION (provided by applicant): Perceptual learning (PL) is defined as long-term enhancement in a visual task or the process for the enhancement as a result of repeated visual experience and is regarded as manifestation of visual plasticity. Thus, studying PL will lead to increased clarification of visual plasticity, which is one of the most important goals in the visual sciences. Recently, there have been two important developments in studies of PL. First, reinforcement signals play an important role in PL. Second, sleep is fundamental for consolidation of PL. However, the neural mechanisms for these aspects of PL are not well clarified. In addition, these aspects have been studied totally separately (i.e., without relating to each other). Furthermore, although there are (1) task- relevant PL (TRPL) resulting from repetitive performance of a task and (2) task-irrelevant PL (TIPL) resulting from exposure to a visual feature, the roles of reinforcement signals and sleep consolidation have yet to be examined taking these two different types of PL into consideration. Using a decoding method applied to fMRI signals, we have observed how the "tuning curve" in each of several brain areas changes (global tuning curve changes) under various conditions in association with PL. In this proposal, by taking advantage of using this new technique, we will investigate what the neural mechanisms are for reinforcement signal and sleep consolidation in TRPL and TIPL from the same viewpoint and aim to clarify how these mechanisms relate to each other. The goal of Aim 1 of current proposal is to clarify the neural mechanism for reinforcement signals. While it has been pointed out that reward-driven reinforcement signal plays an important role in PL, it remains unclear how the reward influences neural mechanisms in PL. We will address this question by examining how training of PL with reward leads to global tuning curve changes in brain areas. The goal of Aim 2 is to clarify neural mechanism changes during sleep consolidation. While it has been shown that sleep is fundamental for PL consolidation, it remains unclear how the neural mechanisms change during consolidation in different types of sleep (Non-REM and REM), different types of training (task-relevant and task-irrelevant PL), and with and without reward. We will address these questions by examining global tuning curve changes in these different conditions. Also, whether common neural mechanisms are involved in reward and sleep consolidation in PL will be examined by comparing global tuning curves obtained in these two aims. To date have examined reinforcement signals and sleep consolidation in PL using different paradigms and stimuli. Systematic investigation of the neural mechanisms for reinforcement signal and sleep consolidation and their interactions in each of TRPL and TIPL within the same framework will lead to significantly greater understanding of the underlying, different researchers neural mechanisms for PL.