Computational mechanisms of goal-directed control

描述(申请人提供)：人类和动物如何做出决定和奖励决定最近一直是一个密切关注的主题。这些问题令人信服，既是因为它们与现实世界的关注密切相关，从日常购物到经济政策，也是因为它们与从帕金森氏症到精神分裂症等潜在疾病的神经递质系统的关系。许多当代决策研究都是由计算模型的发展推动的，这些模型对决策是如何产生的做出具体预测。这些基于强化学习理论的模型为梳理决策的认知和生理机制提供了宝贵的工具。然而，到目前为止，这些模型只适用于习惯性的决定--这些决定是通过学习预期特定行动的特定结果而产生的。现实世界的决策还包括另一类决策，这类决策涉及计划，尽管你对潜在行动的结果有任何经验。在做出非习惯性决定时，个人可能会使用他们最初在没有任何报酬的情况下学到的信息。例如，我们选择品尝新菜肴或在全新的餐厅就餐，尽管我们以前可能从未进入过这些餐厅。事实证明，对这类行为进行建模极其困难，部分原因是可能会对此类决策产生影响的信息种类繁多。最近，我们开发了一种简化的、受限的实验学习任务，允许我们同时在人类中分别测量习得的习惯和非习惯性的学习。我们已经对第二种形式的学习进行了建模，并使用功能磁共振成像(FMRI)识别了代表学习信息的神经结构。其中包括海马体，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疾病有关，如严重的抑郁症和精神分裂症。然而，海马体在奖励决策中的位置尚不清楚。这项建议建立在我们之前的结果的基础上，通过要求参与者将这些信息应用于赚钱，来确定这些信息是如何用于决策的。具体地说，我们检查已知的参与决策的大脑系统，并询问它们使用什么方法来解析现在可用的信息。我们有理由相信，这些系统采用了减少它们需要处理的信息量的策略，而海马体是唯一能够实施这些策略的。了解这些策略对于了解现实世界中的决策是如何做出的是至关重要的，并将为海马体功能的基本机制提供有价值的新见解。 与公共健康相关：我的目的是阐明海马体与纹状体和皮质决策结构相互作用的机制，以影响目标导向的规划行为。这项工作与公共卫生有关，因为海马体功能和结构缺陷与许多严重的精神健康障碍密切相关。特别是，这些障碍中的几个--例如精神分裂症和严重抑郁症--表现出核心症状，这些症状反映了旨在支持目标导向决策的各种联想学习机制的功能障碍。因此，对这些机制的理解将为此类干扰的性质和程度提供关键的见解，并为行为和生理疗法的日益复杂和有针对性的开发提供信息。

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. PUBLIC HEALTH RELEVANCE: I aim to elucidate the mechanisms by which hippocampus interacts with striatal and cortical decision structures to effect goal-directed planning behavior. This work is relevant to public health as functional and structural hippocampal deficits are strongly associated with numerous severe mental health disorders. In particular, several of these disorders - for example, schizophrenia and major depression - exhibit core symptoms which reflect dysfunction of exactly the sorts of associative learning mechanisms proposed to underlie goal-directed decisions. An understanding of these mechanisms will thus provide crucial insights into the nature and extent of such disruptions and inform increasingly sophisticated and targeted development of behavioral and physiological therapies.