CRCNS: Decision Making in Changing Environments

感性的、基于奖励的决定和其他决定都是深思熟虑的过程，取决于 随着时间的推移积累不确定的信息。在动态环境中，此过程必须适应 有效地考虑到新投入的相关性和可靠性方面的变化。例如，环境 变化可能会在决策过程中发生。这样的更改可能会使以前的输入过时，因此需要 调整积累过程。最近的工作已经开始审查在这些情况下的决策 各种动态条件，导致对计算、行为和 适应性证据积累的生理特性。然而，我们仍然存在一个严重的差距 对潜在神经机制的理解：到目前为止还没有研究确定这一点的表现 一种灵活积累信息以驱动行为的适应性决策变量。我们的目标是填补这一空白 GAP使用高度交互的理论和实验方法来了解 大脑对这样的决策变量进行编码。具体地说，我们将测试大脑环路的假设 编码在静态条件下近乎完美的证据集成高度灵活，实现了更多 在动态条件下逼近理想观察者模型的关键特征的自适应过程。我们 提出三个具体目标，具体如下。目标1是开发神经电路的计算模型，该模型可以 动态环境中的近似规范证据积累。目标2是确定以下原则 执行动态决策任务的人类受试者使用的自适应证据积累。目标3是 Identify-representations-of-adaptive-evidence-accumulation-in-parietaJ-and-prefrontaJ-ne1.jra1-active 11-of 执行动态决策任务的猴子。这些集成的计算、行为和 神经生理学方法将为更高级的大脑功能和 依赖于以一种不受条件反射约束的方式处理信息的复杂行为 即时感觉输入或运动输出。我们还拥有强大的数据共享战略，这将有助于 确保这一独特的数据集将用于研究和教学目的。 1 相关性(请参阅说明)： 拟议的工作是一项基础研究，旨在确定灵活决策的机制。在 从长远来看，这项工作将有助于为疾病的新诊断和治疗提供信息，这些疾病包括 认知和决策，包括精神分裂症、自闭症和注意力缺陷/多动障碍 (ADHD)。

Perceptual, reward-based, and other decisions are deliberative processes that depend on the ability to accumulate uncertain information over time. In dynamic environments, this process must be adaptive to account effectively for changes in the relevance and reliability of new inputs. For example, environmental changes can occur mid-decision. Such changes can render previous inputs obsolete and thus require adjustment of the accumulation process. Recent work has begun to examine decision-making under these kinds of dynamic conditions, resulting in a growing understanding of computational, behavioral, and physiological properties of adaptive evidence accumulation. However, a critical gap remains in our understanding of the underlying neural mechanisms: no study to date has identified representations of this kind of adaptive decision variable that flexibly accumulate information to drive behavior. Our goal is to fill this gap using highly interacting theoretical and experimental approaches to understand how and where in the brain such decision variables are encoded. Specifically, we will test the hypothesis that brain circuits that encode near-perfect integration of evidence under static conditions are highly flexible and implement more adaptive processes that approximate key features of ideal-observer models under dynamic conditions. We propose three Specific Aims, as follows. Aim 1 is to develop computational models of neural circuits that can approximate normative evidence accumulation in dynamic environments. Aim 2 is to determine principles of adaptive evidence accumulation used by human subjects performing dynamic decision tasks. Aim 3 is to identify-representations-of-adaptive-evidence-accumulation-in-parietaJ-and-prefrontaJ- ne1.Jra1-acti11ity-of monkeys performing dynamic decision tasks. Together, these integrated computational, behavioral, and neurophysiological approaches will provide novel insights into the many aspects of higher brain function and complex behaviors that depend on processing information in a manner that is not tied reflexively to immediate sensory inputs or motor outputs. We also have a strong data-sharing strategy that will help ensure that this unique data set will be made available for research and teaching purposes. 1 --- RELEVANCE (See instructions): The proposed work is basic research designed to identify mechanisms of flexible decision-making. In the long run, this work will help inform new diagnoses and treatments of disorders that include deficits in cognition and decision-making, including schizophrenia, autism, and attention-deficit/hyperactivity disorder (ADHD).