CRCNS: Decision Making in Changing Environments

CRCNS：不断变化的环境中的决策

• 批准号: 9920777
• 负责人: JOSHUA I GOLD
• 金额: $ 37.63万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9920777
• 关键词: Area; Arousal; Attention deficit hyperactivity disorder; Basic Science; Behavior; Behavioral; Brain; Brain region; Caliber; Cognition; Complex; Computer Models; Data; Data Set; Decision Making; Diagnosis; Disease; Educational process of instructing; Ensure; Environment; Eye Movements; Goals; Human; Instruction; Measures; Modeling; Monkeys; Motor output; Nature; Neurons; Neurosciences; Parietal; Physiological; Population; Prefrontal Cortex; Process; Property; Pupil; Reflex action; Research; Research Design; Rewards; Schizophrenia; Sensory; Source; Stimulus; System; Testing; Time; Work; autism spectrum disorder; base; behavior prediction; cingulate cortex; computer studies; environmental change; expectation; experimental study; falls; flexibility; frontal eye fields; heuristics; human subject; information processing; insight; lateral intraparietal area; neural circuit; neuromechanism; neurophysiology; neurotransmission; novel; oculomotor; rate of change; relating to nervous system; response; sensory input

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).

感知决策、基于奖励的决策和其他决策都是深思熟虑的过程，取决于以下能力： 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 有效说明新投入的相关性和可靠性的变化。例如环境 在决策过程中可能会发生变化。这种变化可能使以前的输入过时，因此需要 调整积累过程。最近的工作已经开始审查这些政策下的决策。 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，在我们的国家， 了解潜在的神经机制：迄今为止，还没有研究确定了这种表达。 这种自适应决策变量可以灵活地积累信息来驱动行为。我们的目标是填补这个 差距使用高度互动的理论和实验方法来了解如何以及在哪里， 这样的决策变量被编码。具体地说，我们将测试这样一个假设： 在静态条件下对证据进行近乎完美的编码集成具有高度灵活性， 在动态条件下近似理想观测器模型的关键特征的自适应过程。我们 提出三个具体目标如下。目标1是开发神经回路的计算模型， 动态环境中的近似规范性证据积累。目标2：确定 自适应证据积累用于人类主体执行动态决策任务。目标3是 自适应证据积累的表征和前额叶活动 执行动态决策任务的猴子。总之，这些集成的计算，行为， 神经生理学方法将为高级脑功能的许多方面提供新的见解， 复杂的行为，依赖于处理信息的方式，不依赖于反射， 即时感觉输入或运动输出。我们还有一个强大的数据共享战略， 确保这套独特的数据集可用于研究和教学目的。 1 --- 相关性（参见说明）： 建议的工作是基础研究，旨在确定灵活的决策机制。在 从长远来看，这项工作将有助于为新的诊断和治疗疾病提供信息，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/多动症 （ADHD）。

项目成果

Human inference reflects a normative balance of complexity and accuracy.

• DOI: 10.1038/s41562-022-01357-z
• 发表时间: 2022-08
• 期刊: NATURE HUMAN BEHAVIOUR
• 影响因子: 29.9
• 作者: Tavoni, Gaia;Doi, Takahiro;Pizzica, Chris;Balasubramanian, Vijay;Gold, Joshua, I
• 通讯作者: Gold, Joshua, I

Stochastic dynamics of social patch foraging decisions

社会斑块觅食决策的随机动力学

• DOI: 10.1103/physrevresearch.4.033128
• 发表时间: 2022
• 期刊: Physical Review Research
• 影响因子: 4.2
• 作者: Bidari, Subekshya;El Hady, Ahmed;Davidson, Jacob D.;Kilpatrick, Zachary P.
• 通讯作者: Kilpatrick, Zachary P.

Analyzing dynamic decision-making models using Chapman-Kolmogorov equations

使用 Chapman-Kolmogorov 方程分析动态决策模型

• DOI: 10.1007/s10827-019-00733-5
• 发表时间: 2019
• 期刊: Journal of Computational Neuroscience
• 影响因子: 1.2
• 作者: Barendregt, Nicholas W.;Josić, Krešimir;Kilpatrick, Zachary P.
• 通讯作者: Kilpatrick, Zachary P.

Hive geometry shapes the recruitment rate of honeybee colonies

蜂巢几何形状决定蜂群的招募率

• DOI: 10.1007/s00285-021-01644-9
• 发表时间: 2021
• 期刊: Journal of Mathematical Biology
• 影响因子: 1.9
• 作者: Bidari, Subekshya;Kilpatrick, Zachary P
• 通讯作者: Kilpatrick, Zachary P

Normative decision rules in changing environments.

• DOI: 10.7554/elife.79824
• 发表时间: 2022-10-25
• 期刊: ELIFE
• 影响因子: 7.7
• 作者: Barendregt, Nicholas W.;Gold, Joshua, I;Josic, Kresimir;Kilpatrick, Zachary P.
• 通讯作者: Kilpatrick, Zachary P.