The Mechanisms of Perceptual Learning

描述（申请人提供）：知觉学习（Perceptual learning，PL）是指视觉任务中的长期增强或重复视觉体验导致的增强过程，被视为视觉可塑性的表现。因此，研究PL将导致视觉可塑性的进一步澄清，这是视觉科学中最重要的目标之一。近年来，PL的研究有两个重要的进展。首先，强化信号在PL中起着重要作用。其次，睡眠是巩固PL的基础。然而，这些方面的PL的神经机制还没有很好地阐明。此外，这些方面已经完全分开研究（即，彼此无关）。此外，虽然存在（1）由任务的重复执行导致的任务相关PL（TRPL）和（2）由暴露于视觉特征导致的任务无关PL（TIPL），但是还需要考虑这两种不同类型的PL来检查强化信号和睡眠巩固的作用。使用解码方法应用于功能磁共振成像信号，我们已经观察到的“调谐曲线”在每个几个脑区的变化（全球调谐曲线的变化）在各种条件下与PL。在这个提议中，通过利用这种新技术，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。当前提案的目标1的目标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。我们将通过研究奖励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。目标2的目的是阐明睡眠巩固过程中的神经机制变化。虽然已经表明睡眠是PL巩固的基础，但仍然不清楚在不同类型的睡眠（非REM和REM），不同类型的训练（任务相关和任务无关PL）以及有和没有奖励的巩固过程中神经机制如何变化。我们将通过研究这些不同条件下的全局调谐曲线变化来解决这些问题。此外，是否共同的神经机制参与奖励和睡眠巩固在PL将通过比较全球调谐曲线在这两个目标。到目前为止，已经研究了强化信号和睡眠巩固PL使用不同的范式和刺激。在同一个框架内的TRPL和TIPL的强化信号和睡眠巩固及其相互作用的神经机制的系统调查将导致显着更深入的了解潜在的，不同的研究人员的PL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.