Affective arousal as a guide for reinforcement learning in multidimensional environments

项目摘要/摘要 到2020年，情感障碍预计将影响超过3500万美国成年人，造成超过 2000亿美元，并对受影响的人造成不可估量的个人损失。为了降低这些成本，以下领域 神经科学和心理学在旨在识别脆弱性的基础研究上投入了大量资源， 情感障碍的治疗和预防策略。这些投资转化为具体投资的速度很慢。 然而，改进的部分原因是神经和精神因素对情感的描述之间存在巨大的解释差距。 更快的进步需要从生物层面的测量到心理层面的现象之间的桥梁。 有了为期三年的NRSA奖学金，我将开始通过在以下框架内研究情感来发展这座桥梁 计算强化学习(RL)，它已经成为将神经活动与 心理现象。我建议使用创新的RL模型来连接来自两个人的行为和生物数据 神经成像研究，这两个研究的重点都是情感及其在注意力和学习中的作用。这些研究解决了一个深层次的 心理问题：在任何时候，大脑可以关注和学习什么的选择是无限的，那么如何做呢？ 大脑决定关注什么，忽略什么？例如，考虑一个常见的看似简单的任务 神经科学实验：学习文字和场景图片之间的联系。千变万化的场景 维度(位置、宜居性、美感等)，那么大脑如何决定将哪些维度与单词相关联， 而忽略哪一个呢？我认为，这样的决定是由情感唤醒引导的。具体地说，我假设这么高 唤醒将注意力引向可以呈现少量值的维度(例如，场景是否在室内 或者户外，一个只能有两个值的维度)。我把这些称为低基数维度，而不是高基数维度- 基数维度，可以采用许多值(例如，场景的特定位置)。我的假设建立在 研究表明，处于高唤醒状态的大脑选择快速、有效的学习策略；在其他条件相同的情况下，低唤醒状态的大脑 基数维度相对容易学习，而高基数维度相对较难学习 大约，如此高的唤醒应该将注意力转向前者，而不是后者。检验这一假说将 帮助我在心理学和计算神经科学的交叉点开始我的职业生涯--一种跨学科的方法 我相信，这将在将基础研究转化为临床应用方面发挥重要作用。具体地说，我将发展 弥合神经学和精神病学之间的解释鸿沟所必需的计算和神经科学技能 对情感的描述，从而有助于理解、预测和治疗情感障碍。

Project Summary/Abstract In 2020, affective disorders are expected to impact over 35 million U.S. adults, imposing an economic burden in excess of $200 billion, and exacting an immeasurable personal toll on those affected. To mitigate these costs, the fields of neuroscience and psychology have devoted considerable resources to basic research aimed at identifying vulnerabilities, treatments, and prevention strategies for affective disorders. These investments have been slow to translate into concrete improvements, however, due in part to the enormous explanatory gap between neural and mentalistic accounts of affect. Faster progress requires a bridge from measurements at the biological level to phenomena at the psychological level. With a three-year NRSA fellowship, I will begin to develop this bridge by studying affect within the framework of computational reinforcement learning (RL), which has emerged as an indispensable guide for linking neural activity to psychological phenomena. I propose to use innovative RL models to bridge behavioral and biological data from two neuroimaging studies, both of which focus on affect and its role in attention and learning. These studies address a deep psychological problem: At any moment, the options for what a brain can attend to and learn about are infinite, so how do brains decide what to focus on and what to ignore? Consider, for example a seemingly simple task commonly encountered in neuroscience experiments: learning associations between words and pictures of scenes. Scenes vary along countless dimensions (location, habitability, beauty, etc.), so how do brains decide which dimensions to associate with the words, and which to ignore? I propose that such decisions are guided by affective arousal. Specifically, I hypothesize that high arousal directs attention towards dimensions that can take on a small number of values (e.g., whether the scene is indoors or outdoors, a dimension that can take on just two values). I call these low-cardinality dimensions, in contrast to high- cardinality dimensions, which can take on many values (e.g., the specific location of the scene). My hypothesis builds on research showing that brains in states of high arousal opt for fast, efficient learning strategies; all else being equal, low- cardinality dimensions are relatively easy to learn about, and high-cardinality dimensions are relatively difficult to learn about, so high arousal should direct attention towards the former and away from the latter. Testing this hypothesis will help launch my career at the intersection of psychology and computational neuroscience — a transdisciplinary approach that, I believe, will play an essential role in translating basic research into clinical applications. Specifically, I will develop the computational and neuroscience skills necessary to bridge the explanatory gap between neural and mentalistic accounts of affect, thereby aiding in the understanding, prediction, and treatment of affective disorders.