Mechanisms of learning a visual discrimination

描述(由申请人提供)：我们如何看待世界会受到我们的经历的强烈影响。我们可以学会将某些视觉图像与即将到来的奖励或惩罚联系起来(例如，看到糖果店的正面)。我们还可以通过实践学习更好地区分微弱、嘈杂或模糊的图像(例如，有经验的观鸟者识别飞行中的鸟)。我们提出的研究的目标是了解这两种看似不同的视觉学习形式的共同神经机制，一种是在视觉输入和寻求奖励行为之间形成联系，另一种是提高知觉敏感度。关键思想是，视觉信息是跨大脑中不同种类的神经元群体呈现的，两种学习形式都涉及从这些群体中选择具有特定任务适当特征的神经元的过程。联想学习需要选择代表适当视觉特征的神经元来预测奖励。知觉学习需要选择具有最高敏感度的代表特定视觉特征的神经元。我们的三个具体目标将使用计算建模、人类心理物理学以及猴子的心理物理学和生理学相结合的方法来确定支配这一选择过程的神经机制。目标1将引入一种计算模型，该模型可以通过从感觉表征中选择输出来解释联想学习和知觉学习，该模型基于它们引导行为最大化奖励的能力。我们将通过几种方式测试该模型，包括与猴子学习高要求的视觉辨别任务的行为和神经数据进行比较，以及与学习类似任务的人类受试者的行为数据进行比较。该模型基于两个计算原则，将指导其他两个目标的实验。第一个原则是，学习的结果是感觉神经元和决策神经元之间功能连接的变化。AIM 2将测试两个大脑皮层区域之间相互作用的变化是否反映了这些预测的功能连接变化。第二个原则是，学习是由一个过程驱动的，这个过程识别出预测的回报和实际回报之间的差异。目标3将测试被称为尾状核的皮质下结构中的神经元是否在学习过程中编码这种奖励预测错误。这些研究将有助于统一以前不同的联想学习和知觉学习领域，并提供一个关于允许经验塑造健康视觉系统功能的机制的深远视角。 与公共健康相关：拟议的工作是基础研究，旨在为健康的神经系统如何从经验中学习以更有效地处理视觉信息提供新的见解。因此，对公众健康的直接好处是长期的，因为这些新的见解可以用来设计新的方法来诊断和治疗视觉感知障碍(即视觉失认)和学习障碍。

DESCRIPTION (provided by applicant): How we see the world can be influenced strongly by our experiences. We can learn to associate certain visual images with impending rewards or punishments (e.g., seeing the front of a candy store). We can also learn to better differentiate weak, noisy, or obscure images with practice (e.g., an experienced bird- watcher identifying a bird in flight). The goal of our proposed research is to understand neural mechanisms common to these two seemingly different forms of visual learning, one that forms associations between visual input and reward-seeking behavior, the other that improves perceptual sensitivity. The key idea is that visual information is represented across heterogeneous populations of neurons in the brain, and both forms of learning involve a process of selecting neurons from these populations that have the appropriate characteristics for a given task. Associative learning requires selecting neurons that represent the appropriate visual features that predict reward. Perceptual learning requires selecting neurons that represent a particular visual feature with the highest sensitivity. Our three specific Aims will use computational modeling, human psychophysics, and combined psychophysics and physiology in monkeys to identify neural mechanisms that govern this selection process. Aim 1 will introduce a computational model that can account for both associative and perceptual learning by selecting outputs from a sensory representation based on their ability to guide behavior that maximizes reward. We will test the model in several ways, including a comparison to behavioral and neural data from monkeys learning a demanding visual discrimination task and behavioral data from human subjects learning a similar task. The model is based on two computational principles that will guide the experiments in the other two Aims. The first principle is that learning results from changes in functional connectivity between sensory and decision neurons. Aim 2 will test whether changes in interactions between two cortical areas reflect these predicted changes in functional connectivity. The second principle is that learning is driven by a process that identifies discrepancies between predicted and actual reward. Aim 3 will test whether neurons in a subcortical structure known as the caudate encode this kind of reward prediction error during learning. These studies will help to unify previously disparate fields of associative and perceptual learning and provide a far-reaching perspective on mechanisms that allow experiences to shape the functions of a healthy visual system. PUBLIC HEALTH RELEVANCE: The proposed work is basic research, designed to provide new insights into how a healthy nervous system learns from experience to more effectively process visual information. Thus, direct benefits to public health are intended to come in the longer term, as these new insights can be used to design new ways to diagnose and treat disorders of visual perception (i.e., visual agnosias) and learning.