Neural Substrates of Deterministic Decision Making

确定性决策的神经基础

• 批准号: 9020277
• 负责人: Julie A Fiez
• 金额: $ 18.41万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-04-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9020277
• 关键词: Accounting; Address; Affect; Basal Ganglia; Behavior; Behavioral; Brain; Code; Computer Simulation; Decision Making; Development; Disease; Dissociation; Environment; Episodic memory; Event; Exhibits; Foundations; Functional Magnetic Resonance Imaging; Future; Health; Ice Cream; Image; Impaired cognition; Individual; Investigation; Judgment; Knowledge; Lead; Learning; Learning Disorders; Link; Literature; Medial; Mediating; Memory; Memory Disorders; Methods; Modeling; Nature; Outcome; Parkinson Disease; Patients; Performance; Pharmaceutical Preparations; Play; Probability; Process; Protocols documentation; Psychological reinforcement; Punishment; Rain; Recording of previous events; Research; Response to stimulus physiology; Rewards; Role; Sampling; Signal Transduction; Source; System; Temporal Lobe; Testing; Time; Update; Variant; Work; base; classical conditioning; cognitive performance; design; experience; innovation; insight; memory encoding; memory retrieval; mild cognitive impairment; neuroimaging; neuropsychological; normal aging; patient population; relating to nervous system; research study; response; scaffold; skills

 DESCRIPTION (provided by applicant): Our environment is composed of pervasive deterministic and quasi-deterministic (very high- probability) relationships. A basal ganglia (BG) reinforcement learning system plays a key role in optimizing probabilistic decisions based upon an accrued history of experience, but it is unknown if this system contributes to deterministic decision-making. A medial temporal lobe (MTL) associative memory system could play a dominant role, since experiencing a deterministic choice outcome even once can provide sufficient information to optimize future decisions. We will use functional neuroimaging (fMRI) and neuropsychological methods to investigate how the BG and MTL systems work together to support deterministic decision- making. There is a crucial need to understand this question. Normal aging slows down memory- retrieval processes, leading to slower, inefficient, or impaired decision-making skills for even simple judgments. In patient populations with memory disorders, severely affected individuals can become unable to utilize information from deterministic outcomes, leading to a reduced ability to learn from basic everyday experiences. To address our research questions, we will use an innovative approach that will (a) incorporate factors drawn from both the decision and memory literatures, (b) allow us to investigate alternative computational models of deterministic decision-making, and (c) unmask effects of medication state on the cognitive performance of patients with Parkinson's disease. Experiment 1 will test how two factors differentially linked to reinforcement learning and associative memory are encoded and subsequently used to guide deterministic decision-making. The main objectives are to determine what information is gained from an initial decision event, and how this information influences a subsequent choice (Aim 1a) and value learning (Aim 1b). We expect activity in the BG and the MTL during an initial choice will predict subsequent choice-making ability across changes in value and associative context, respectively. This would indicate that these two systems make separable contributions to deterministic decision-making, a finding with significant theoretical impact. We will use computational agents to evaluate whether value learning from repeated choice experiences is best described as "model-based" or "model-free," a question that has critical implications for how individuals learn from sampling their environment. Experiment 2 will involve a neuropsychological investigation of deterministic decision-making behavior in Parkinson's Disease (PD) and Mild Cognitive Impairment (MCI) patients. Deficits arising from problems with BG-mediated reinforcement learning in PD and MTL-mediated associative memory in MCI should reflect an inability to update value or encode associative information, respectively. This leads us to predict that subjects with PD will show reduced effects of value updating, but intact effects of experience (e.g., context) on choice accuracy. The converse is expected for MCI subjects. Such findings would point toward preserved learning and memory mechanisms that could be exploited to reduce the functional impact of these disorders.

 描述（由申请人提供）：我们的环境是由普遍的确定性和准确定性（非常高的概率）的关系。基底神经节（BG）强化学习系统在基于累积的经验历史优化概率决策方面发挥着关键作用，但尚不清楚该系统是否有助于确定性决策。内侧颞叶（MTL）联想记忆系统可能发挥主导作用，因为即使经历一次确定性选择结果也可以提供足够的信息来优化未来的决策。我们将使用功能性神经影像学（fMRI）和神经心理学方法来研究BG和MTL系统如何协同工作以支持确定性决策。理解这个问题是非常必要的。正常的衰老会减缓记忆提取过程，导致即使是简单的判断也会变得缓慢、低效或受损。在患有记忆障碍的患者群体中，严重受影响的个体可能无法利用来自确定性结果的信息，导致从基本日常经验中学习的能力降低。为了解决我们的研究问题，我们将使用一种创新的方法，将（a）纳入决策和记忆文献中的因素，（B）使我们能够研究确定性决策的替代计算模型，（c）揭示药物状态对帕金森病患者认知表现的影响。实验1将测试与强化学习和联想记忆有差异的两个因素是如何编码的，并随后用于指导确定性决策。主要目标是确定从初始决策事件中获得了哪些信息，以及这些信息如何影响后续选择（目标1a）和价值学习（目标1b）。我们期望在BG和MTL的活动，在最初的选择将预测随后的选择决策能力的变化，在价值和关联的情况下，分别。这表明这两个系统对确定性决策的贡献是可以分开的，这一发现具有重大的理论影响。我们将使用计算代理，以评估是否从重复选择经验的价值学习是最好的描述为“基于模型”或“无模型”，一个问题，具有重要意义的个人如何学习采样他们的环境。实验2将涉及帕金森病（PD）和轻度认知障碍（MCI）患者的确定性决策行为的神经心理学调查。由BG介导的PD强化学习和MTL介导的MCI联想记忆问题引起的缺陷应该分别反映无法更新值或编码联想信息。这使我们预测，PD受试者将显示价值更新的影响减少，但经验的影响不变（例如，选择的准确性。MCI受试者预期情况匡威。这些发现将指向保存的学习和记忆机制，可以用来减少这些疾病的功能影响。

项目成果

Manipulating memory efficacy affects the behavioral and neural profiles of deterministic learning and decision-making.

• DOI: 10.1016/j.neuropsychologia.2018.04.023
• 发表时间: 2018-06
• 期刊: Neuropsychologia
• 影响因子: 2.6
• 作者: Tremel JJ;Ortiz DM;Fiez JA
• 通讯作者: Fiez JA

Neural signatures of experience-based improvements in deterministic decision-making.

确定性决策的基于经验改进的神经签名。

• DOI: 10.1016/j.bbr.2016.08.023
• 发表时间: 2016-12-15
• 期刊: BEHAVIOURAL BRAIN RESEARCH
• 影响因子: 2.7
• 作者: Tremel, Joshua J.;Laurent, Patryk A.;Wolk, David A.;Wheeler, Mark E.;Fiez, Julie A.
• 通讯作者: Fiez, Julie A.