Doctoral Dissertation Research in DRMS: Boundedly optimal sampling for decisions under uncertainty

DRMS 博士论文研究:不确定性下决策的有限最优抽样

基本信息

  • 批准号:
    0850414
  • 负责人:
  • 金额:
    $ 2.34万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2009
  • 资助国家:
    美国
  • 起止时间:
    2009-04-15 至 2010-03-31
  • 项目状态:
    已结题

项目摘要

To model an individual's choices under uncertainty, theorists typically assume the choices made maximize the individual's utility. While frequently a good description of observed behavior, there are instances where people instead choose alternatives in proportion to their associated probabilities of reward. This probability matching behavior is sub-optimal. Probability matching behavior and optimal behavior would both result depending on the time available to make decisions (where more time produces more optimal decisions) if individuals base their choices on a sampling algorithm. In this Doctoral Dissertation Improvement grant, the PI will test whether such an algorithm is responsible for observed choices and, furthermore, whether people are optimally suboptimal (i.e., optimal in their decision regarding when to be more, or less, optimal.To test these hypotheses, experimental subjects will be assessed in terms of how flexible they are at making tradeoffs between speed and accuracy in motor decisions under uncertainty and how generic decision processes are across decision domains. Subjects are then tested for whether their decisions under cognitive stress deteriorate to probability-matching, as predicted by the proposed algorithm. Finally, subjects will be tested using fMRI to determine whether one brain structure represents expected utility arising from different sources of uncertainty. This research holds promise for reconciling models of humans as ideal agents with established failures and limitations of human decision-making.
为了模拟不确定性下的个人选择,理论家通常假设所做的选择使个人效用最大化。虽然这通常是对观察到的行为的一个很好的描述,但在某些情况下,人们选择的选项与他们获得奖励的相关概率成比例。这种概率匹配行为是次优的。如果个体基于抽样算法做出选择,则概率匹配行为和最优行为的结果都取决于可用于决策的时间(时间越长,决策越优)。在这个博士论文改进拨款中,PI将测试这样的算法是否对观察到的选择负责,此外,人们是否最优次优(即,在决定何时更优或更少最优时是最优的)。为了验证这些假设,实验对象将被评估在不确定的情况下,他们在运动决策的速度和准确性之间做出权衡的灵活性,以及决策过程在决策领域中的通用性。然后测试受试者在认知压力下的决策是否会恶化为概率匹配,正如所提出的算法所预测的那样。最后,受试者将使用功能磁共振成像(fMRI)进行测试,以确定一种大脑结构是否代表来自不同不确定性来源的预期效用。这项研究有望调和人类作为理想代理的模型与人类决策的既定失败和局限性。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

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Nancy Kanwisher其他文献

Repetition blindness and illusory conjunctions: errors in binding visual types with visual tokens.
Privileged representational axes in biological and artificial neural networks
生物和人工神经网络中的特权表示轴
  • DOI:
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Meenakshi Khosla;Alex Williams;Josh McDermott;Nancy Kanwisher
  • 通讯作者:
    Nancy Kanwisher
MIT Open Access Articles An integrative computational architecture for object-driven cortex
麻省理工学院开放获取文章对象驱动皮层的综合计算架构
  • DOI:
  • 发表时间:
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Ilker Yildirim;Jiajun Wu;Nancy Kanwisher;Joshua B. Tenenbaum
  • 通讯作者:
    Joshua B. Tenenbaum
Dissociating language and thought in large language models
在大型语言模型中分离语言与思维
  • DOI:
    10.1016/j.tics.2024.01.011
  • 发表时间:
    2024-06-01
  • 期刊:
  • 影响因子:
    17.200
  • 作者:
    Kyle Mahowald;Anna A. Ivanova;Idan A. Blank;Nancy Kanwisher;Joshua B. Tenenbaum;Evelina Fedorenko
  • 通讯作者:
    Evelina Fedorenko
Animal models of the human brain: Successes, limitations, and alternatives
人类大脑的动物模型:成功、局限性与替代方案
  • DOI:
    10.1016/j.conb.2024.102969
  • 发表时间:
    2025-02-01
  • 期刊:
  • 影响因子:
    5.200
  • 作者:
    Nancy Kanwisher
  • 通讯作者:
    Nancy Kanwisher

Nancy Kanwisher的其他文献

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{{ truncateString('Nancy Kanwisher', 18)}}的其他基金

Collaborative Research: NCS-FR: Beyond the ventral stream: Reverse engineering the neurocomputational basis of physical scene understanding in the primate brain
合作研究:NCS-FR:超越腹侧流:逆向工程灵长类大脑中物理场景理解的神经计算基础
  • 批准号:
    2124136
  • 财政年份:
    2021
  • 资助金额:
    $ 2.34万
  • 项目类别:
    Standard Grant
Attention and Performance Meeting to be held July 1-7, 2002 in Erice, Sicily
注意力和绩效会议将于 2002 年 7 月 1 日至 7 日在西西里岛埃里切举行
  • 批准号:
    0201898
  • 财政年份:
    2002
  • 资助金额:
    $ 2.34万
  • 项目类别:
    Standard Grant

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