Effects of Rewards on Perceptual Learning

描述(申请人提供)：本研究的目标是了解奖励信号对低水平视觉区域可塑性的影响。最近，人们发现，敏感度的增强不仅发生在基于被试执行任务的特征(任务相关学习)，而且发生在与任务无关的呈现特征(任务无关学习)。A关于一系列心理物理学工作的研究(Watanabe等人，2001,2002；Seitz&Watanabe，2003；Seitz等人，2004,2005 abc；Seitz和Watanabe，2005)表明，视觉学习是由包括奖励在内的内部因素触发的扩散强化信号和来自呈现的视觉特征的自下而上的刺激信号之间的相互作用的结果，无论这些特征是与任务相关还是无关。在本研究中，我们将检验这一假设，即任务无关的学习是由奖赏驱动的强化信号介导的。首先，为了检验模型的有效性，我们将解决以下问题。(1)，对任务的处理是否作为一种内部奖励？(2)，这种强化信号是否调节了低水平视觉区域的活动？(3)，对于任何可以预测试验结果的特征，是否发生了与任务无关的学习？(4)在学习形式之后，有预测错误的活动是否以与强化学习相同的方式观察到？其次，我们将使用外部液体奖励来直接测试奖励是否会导致知觉学习。如果是这样的话，我们将改变奖励发放的时机和概率，以测试知觉学习是否遵循强化学习中的规则。为了解决这些问题，我们将通过心理物理学来测量行为表现，并通过功能磁共振成像来测量人脑中的活动。就我们所知，还没有研究直接考察奖赏对低水平视觉加工和可塑性的影响，或者低水平视觉学习和强化学习之间的关系。因此，本研究具有很高的新颖性。同时，这些结果的预测是可靠的，因为它们是基于一系列心理物理研究的一致结果而做出的。我们认为R21是一个合适的赛道。拟议的研究具有临床应用的潜力，因为它促进了科学知识的发展，从而开发了改善大脑疾病和损伤的诊断和康复治疗的医疗工具。此外，研究结果可能有助于揭示学习缺陷的来源和治疗方法。

DESCRIPTION (provided by applicant): The goal of the present proposal is to understand effects of rewards signals on plasticity of low-level visual areas. Recently, it has been found that sensitivity enhancement occurs not only to features on the basis of which subjects perform tasks (task-relevant learning), but also to presented features that are irrelevant to the task (task-irrelevant learning). A on a series of psychophysical work (Watanabe et al, 2001, 2002; Seitz & Watanabe, 2003; Seitz et al, 2004, 2005abc; Seitz and Watanabe, 2005) indicates that visual learning occurs as a result of interactions between diffusive reinforcement signals triggered by internal factors including reward and bottom-up stimulus signals from presented visual features, irrespective of whether these features are task-relevant or irrelevant. In the present proposal, we will test the hypothesis that task-irrelevant learning is mediated by such reward-driven reinforcement signals. First, to test the validity of the model, we will address the following questions. (1), Does processing of a task work as an internal reward? (2), Does this reinforcement signal modulate activity in low-level visual areas? (3), Does task-irrelevant learning occur for any feature that is predictive of trial-outcome? (4), After learning forms, is activity with prediction errors observed in the same way as reinforcement learning? Second, we will use external liquid rewards to directly test whether reward leads to perceptual learning. If that is the case, we will vary the timing and probability of reward-delivery to test whether perceptual learning follows rules found in reinforcement learning. To address these questions, we will measure behavioral performance by psychophysics and activity in the human brain by means of fMRI. To our knowledge, no research has directly examined effects of reward on low-level visual processing and plasticity or relations between low-level visual learning and reinforcement learning. Thus, the present research is highly novel. At the same time, the predictions of the results are solid since they are made on the basis on consistent results of a series of psychophysical studies. We believe that R21 is an appropriate track. The proposed research has the potential for clinical applications by contributing to scientific knowledge leading to development of medical tools for improved diagnosis of, and rehabilitative therapies for, brain disorders and lesions. Moreover, the research results may help reveal sources of, and treatments for, learning deficits.