Computational mechanisms of goal-directed control
目标导向控制的计算机制
基本信息
- 批准号:8324841
- 负责人:
- 金额:$ 3.3万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2011
- 资助国家:美国
- 起止时间:2011-09-01 至 2013-08-31
- 项目状态:已结题
- 来源:
- 关键词:AnimalsArchitectureAssociation LearningAutomobile DrivingBehaviorBehavioralBiological Neural NetworksBrainCategoriesCognitiveComputer SimulationCorpus striatum structureDecision MakingDevelopmentDiseaseEatingEconomic PolicyExhibitsFunctional Magnetic Resonance ImagingFunctional disorderGoalsHabitsHippocampus (Brain)HumanImageIndividualKnowledgeLeadLearningLinkMajor Depressive DisorderMeasurementMeasuresMemoryMental disordersMethodsModelingNatureNeurotransmittersOutcomeParkinson DiseaseParticipantPatternPhysiologicalProcessPsychological reinforcementPublic HealthReactionRestaurantsRewardsRodentRoleSamplingSchizophreniaSignal TransductionSorting - Cell MovementStimulusStructureSymptomsSystemTimeUrsidae FamilyVariantWorkbaseclassical conditioningdepressive symptomsdopamine systemexperienceflexibilityhabit learninginsightnovelrelating to nervous systemresearch studyresponsetheoriestoolway finding
项目摘要
DESCRIPTION (provided by applicant): How humans and animals make decisions and decisions for rewards have been a subject of intense focus recently. These questions are compelling both for their critical relevance to real-world concerns, from daily purchases to economic policy, and their relationship to neurotransmitter systems underlying diseases from Parkinson's to schizophrenia. Much contemporary study of decisions has been spurred by the development of computational models that make specific predictions about how decisions arise. These models, based on the theories of reinforcement learning, have provided an invaluable tool for teasing apart the cognitive and physiological mechanisms of decision-making. However these models have to date only been applied to habitual decisions - those decisions that result from learning to expect a particular outcome from a particular action. Real-world decision- making also encompasses another class of decisions, which involve planning in spite of any experience with the outcome of your potential actions. When making non-habitual decisions, individuals may use information that they originally learned without any reward. For instance, we choose to sample new dishes or eat at entirely new restaurants even though we may have never before entered them. Modeling this sort of behavior has proven extremely difficult, due in part to the wide variety of information that may be brought to bear on such decisions. Recently, we have developed a reduced, constrained experimental learning task that allows us to separately measure both learned habits and non-habitual learning, simultaneously, in humans. We have modeled this second form of learning, and, using functional magnetic resonance imaging (fMRI), identified neural structures that represent the learned information. These include the hippocampus, a structure critical for normal memory, and whose dysfunction is implicated in several major mental health disorders, such as major depression and schizophrenia. The place of the hippocampus in decisions for reward is, however, unclear. This proposal builds on our previous results to identify how this information is used to make decisions, by asking participants to apply this information to making money. Specifically, we examine brain systems known to participate in decision-making, and ask what methods they use to parse through the information now available to them. We have reason to believe that these systems employ strategies to reduce the amount of information they need to work with, and that hippocampus is uniquely capable of implementing these strategies. Understanding these strategies is essential to understanding how decisions are made in the real world, and will provide valuable and novel insight into the fundamental mechanisms of hippocampal function.
描述(由申请人提供):人类和动物如何做出决定和决定的奖励一直是一个激烈的焦点最近的主题。这些问题之所以引人注目,是因为它们与现实世界的问题(从日常购买到经济政策)密切相关,而且它们与帕金森氏症到精神分裂症等疾病的神经递质系统之间存在关系。许多当代的决策研究都是由计算模型的发展所推动的,这些模型对决策是如何产生的做出了具体的预测。这些基于强化学习理论的模型为区分决策的认知和生理机制提供了宝贵的工具。 然而,这些模型迄今为止只应用于习惯性决策--那些通过学习期望特定行为产生特定结果而产生的决策。现实世界的决策还包括另一类决策,这涉及计划,尽管你对潜在行动的结果有任何经验。当做出非习惯性决定时,个体可能会使用他们最初在没有任何奖励的情况下学到的信息。例如,我们选择品尝新的菜肴或在全新的餐馆吃饭,即使我们以前可能从未进入过它们。 事实证明,对这种行为进行建模是极其困难的,部分原因是可能影响此类决策的信息种类繁多。最近,我们开发了一种简化的、受约束的实验性学习任务,使我们能够同时单独测量人类的学习习惯和非习惯性学习。我们对第二种学习形式进行了建模,并使用功能性磁共振成像(fMRI)识别了代表学习信息的神经结构。这些包括海马体,这是一种对正常记忆至关重要的结构,其功能障碍与几种主要的精神健康障碍有关,如重度抑郁症和精神分裂症。然而,海马体在奖励决定中的位置还不清楚。 这个建议建立在我们以前的结果,以确定如何使用这些信息来做出决策,通过要求参与者应用这些信息来赚钱。具体来说,我们研究了已知参与决策的大脑系统,并询问他们使用什么方法来解析现在可用的信息。我们有理由相信,这些系统采用策略来减少它们需要处理的信息量,而海马体是唯一能够执行这些策略的。理解这些策略对于理解在真实的世界中如何做出决定是至关重要的,并且将为海马功能的基本机制提供有价值的和新颖的见解。
项目成果
期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Enhanced motion perception as a psychophysical marker for autism?
增强的运动感知作为自闭症的心理物理标志?
- DOI:10.1523/jneurosci.2945-13.2013
- 发表时间:2013
- 期刊:
- 影响因子:0
- 作者:Wallisch,Pascal;Bornstein,AaronM
- 通讯作者:Bornstein,AaronM
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Aaron Michael Bornstein其他文献
Aaron Michael Bornstein的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Aaron Michael Bornstein', 18)}}的其他基金
Improving multi-step planning in aging by overcoming deficits in memory encoding
通过克服记忆编码的缺陷来改善衰老的多步骤计划
- 批准号:
10631480 - 财政年份:2021
- 资助金额:
$ 3.3万 - 项目类别:
Improving multi-step planning in aging by overcoming deficits in memory encoding
通过克服记忆编码的缺陷来改善衰老的多步骤计划
- 批准号:
10222051 - 财政年份:2021
- 资助金额:
$ 3.3万 - 项目类别:
相似海外基金
CAREER: Efficient Algorithms for Modern Computer Architecture
职业:现代计算机架构的高效算法
- 批准号:
2339310 - 财政年份:2024
- 资助金额:
$ 3.3万 - 项目类别:
Continuing Grant
CAREER: Creating Tough, Sustainable Materials Using Fracture Size-Effects and Architecture
职业:利用断裂尺寸效应和架构创造坚韧、可持续的材料
- 批准号:
2339197 - 财政年份:2024
- 资助金额:
$ 3.3万 - 项目类别:
Standard Grant
Travel: Student Travel Support for the 51st International Symposium on Computer Architecture (ISCA)
旅行:第 51 届计算机体系结构国际研讨会 (ISCA) 的学生旅行支持
- 批准号:
2409279 - 财政年份:2024
- 资助金额:
$ 3.3万 - 项目类别:
Standard Grant
Understanding Architecture Hierarchy of Polymer Networks to Control Mechanical Responses
了解聚合物网络的架构层次结构以控制机械响应
- 批准号:
2419386 - 财政年份:2024
- 资助金额:
$ 3.3万 - 项目类别:
Standard Grant
I-Corps: Highly Scalable Differential Power Processing Architecture
I-Corps:高度可扩展的差分电源处理架构
- 批准号:
2348571 - 财政年份:2024
- 资助金额:
$ 3.3万 - 项目类别:
Standard Grant
Collaborative Research: Merging Human Creativity with Computational Intelligence for the Design of Next Generation Responsive Architecture
协作研究:将人类创造力与计算智能相结合,设计下一代响应式架构
- 批准号:
2329759 - 财政年份:2024
- 资助金额:
$ 3.3万 - 项目类别:
Standard Grant
Hardware-aware Network Architecture Search under ML Training workloads
ML 训练工作负载下的硬件感知网络架构搜索
- 批准号:
2904511 - 财政年份:2024
- 资助金额:
$ 3.3万 - 项目类别:
Studentship
The architecture and evolution of host control in a microbial symbiosis
微生物共生中宿主控制的结构和进化
- 批准号:
BB/X014657/1 - 财政年份:2024
- 资助金额:
$ 3.3万 - 项目类别:
Research Grant
RACCTURK: Rock-cut Architecture and Christian Communities in Turkey, from Antiquity to 1923
RACCTURK:土耳其的岩石建筑和基督教社区,从古代到 1923 年
- 批准号:
EP/Y028120/1 - 财政年份:2024
- 资助金额:
$ 3.3万 - 项目类别:
Fellowship
NSF Convergence Accelerator Track M: Bio-Inspired Surface Design for High Performance Mechanical Tracking Solar Collection Skins in Architecture
NSF Convergence Accelerator Track M:建筑中高性能机械跟踪太阳能收集表皮的仿生表面设计
- 批准号:
2344424 - 财政年份:2024
- 资助金额:
$ 3.3万 - 项目类别:
Standard Grant