Eye Movements and the Dynamics of Adaptive Learning

项目摘要/摘要 这个项目考察了主观信念分布的构建和更新机制。人类 决策者必须经常更新他们的信念，以回应新的观察结果。信仰的程度 应该更新高度依赖于环境：在稳定但嘈杂的环境中，新的观测应该 需要给予有限的重视，而动荡的环境需要更快的更新。这项工作的重点是 决定决策者(及其大脑)如何表现主观信念的精确度(或广度) 分布，这一点还没有得到明确的证明。通过使用眼睛跟踪和神经成像 方法，我们可以在空间预测任务中获得连续的、隐含的和多方面的预测集 这一样本更密集地来自个人潜在的信念分布。这将使我们能够调查 是否以及如何表现信仰的整个分布，包括其中心趋势以及 他们的宽度。此外，关于感觉不确定性如何与中枢唤醒和更高- 对认知过程进行排序以支持适应性学习。结合眼睛丰富的行为数据流 功能磁共振成像追踪将为理解这些相互作用及其神经机制提供新的工具。 该项目的第一个目标是探索在免费观看中不确定性和唤醒程度的影响。 内隐空间预测任务。眼球跟踪将使我们能够监控个体预测能力的演变 在逐一试验的水平上分配，并调查奖惩对学习效率和学习成绩的影响 扫视动态。此外，瞳孔测量学将使我们能够探索信念更新的机制 固定空间预测任务。我们将评估奖惩是否会提高学习率 通过共同的唤醒/激励机制。这将开始帮助我们理清是否存在变化 唤醒水平是学习速率动态适应机制的一部分。对于第二个目标，功能 磁共振成像将使我们能够检查不同类型的不确定性(噪音和 波动性)，同时保持不确定性的量不变。我们将首先调查大胆的 额区(即前额叶皮质、前扣带回皮质和前脑岛)的反应是 不同地依赖于波动而不是噪声主导的条件的预测误差。我们 下一步将测试这些因素如何改变额叶大脑区域和视觉之间的功能连接 皮质。最终，这项研究将帮助我们理解信念分布是如何表示和更新的， 以及大脑如何在不断变化的环境中动态适应这一过程。

PROJECT SUMMARY/ABSTRACT This project examines the mechanisms of construction and updating of subjective belief distributions. Human decision-makers must often update their beliefs in response to new observations. The degree to which beliefs should be updated is highly dependent on context: In stable but noisy environments, new observations should be given limited weight, while volatile environments require more rapid updating. This work focuses on determining how decision makers (and their brains) represent the precision (or width) of subjective belief distributions, which has not yet been explicitly demonstrated. By using eye tracking and neuroimaging approaches, we can obtain a continuous, implicit and multifaceted set of predictions in a spatial prediction task that samples more densely from individuals' underlying distributions of beliefs. This will allow us to investigate whether and how entire distributions of beliefs are represented, including both their central tendency as well as their width. Additionally, little is known about how sensory uncertainty interacts with central arousal and higher- order cognitive processes to support adaptive learning. Combining the rich behavioral data stream of eye tracking with fMRI will provide new tools for understanding these interactions and their neural mechanisms. The first aim of the project is to explore the influence of amount of uncertainty and arousal in a free-viewing implicit spatial prediction task. Eye tracking will allow us to monitor the evolution of individuals' predictive distributions on a trial-by-trial level, and investigate the effects of reward and punishment on learning rate and saccade dynamics. Additionally, pupillometry will allow us to probe the mechanisms of belief updating in a fixational spatial prediction task. We will assess whether reward and punishment enhance learning rate through common arousal/incentive mechanisms. This will begin to help us disentangle whether changes in arousal level are part of the mechanism of dynamic learning rate adaptation. For the second aim, functional magnetic resonance imaging will allow us to examine the effects of differing types of uncertainty (noise and volatility), while holding the amount of uncertainty constant. We will first investigate whether the BOLD response in frontal regions (i.e. anterior prefrontal cortex, anterior cingulate cortex, and anterior insula) is differentially dependent on prediction error for the condition dominated by volatility as opposed to noise. We will next test how these factors change the functional connectivity between frontal brain regions and visual cortices. Ultimately, this research will help us understand how belief distributions are represented and updated, and how the brain dynamically adapts this process in a changing environment.