Neural representation of belief states during decision-making under uncertainty

不确定性决策过程中信念状态的神经表征

• 批准号: 462197630
• 负责人: Professor Dr. John-Dylan Haynes
• 金额: --
• 依托单位: Berlin Center for Advanced Neuroimaging (BCAN)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/462197630?language=en
• 关键词: Neural; representation; belief; states; during

Knowing which aspects of the environment are relevant for our goal is crucial for decision making, but also provides important context for processing sensory input. Waiting at a traffic light, a pedestrian will disregard the manufacturers and colors of the cars around him and only begin to cross the road when the light is green. When waiting for a taxi, however, a passing car’s color and model are most important for deciding when to waive the arm. But sensory information in our environment is often also noisy, and knowledge about whether a desired state (e.g., ‘the light is green’) is true or not can be uncertain. Decision-making with in realistic environments therefore requires us to process sensory input in ways that reflect the current goal and context, but also take potential uncertainty into account. In this project, we will investigate how uncertainty regarding different possible percepts and possible contexts during decision making is reflected in the human brain. Formal theories of decision making and reinforcement learning have shown that the way different states of the environment are represented has important implications for decision making (Sutton & Barto, 1998). They suggest that in the face of uncertainty, it may be optimal to integrate information about the set of possible states with the certainty that each of these states is currently true. These so-called “belief states” (Kaelbling et al., 1996) have played an important role in theoretical work on decision making, but little is known about whether corresponding representations exist in the brain. Drawing on our previous work (Schuck, 2015, 2016; Kaplan, Schuck & Doeller, 2017), we propose that probabilistic belief state representations in the brain are reflected in an integrative and distributed neural code in which state identities and the probabilities that these states are true are multiplexed, and that these representations can be found in the ventromedial prefrontal cortex. To test our hypothesis, we introduce a novel multi-step decision making task in which the events and uncertainty of previous steps provide the context for the current step. Using Bayesian classification approaches, we will examine the relative multivariate fMRI evidence in favor of different states at each step (e.g., van Bergen et al., 2015) and test whether the neurally encoded distribution over states corresponds to the predictions of a belief state model. Our project will allow us to comprehensively investigate the neural representations that provide an uncertainty-graded contextual influence on decision making in a computationally concise way.

了解环境的哪些方面与我们的目标相关对于决策至关重要，但也为处理感官输入提供了重要的背景。行人在等待红绿灯时，会无视周围汽车的制造商和颜色，只有当信号灯是绿色时，他才开始过马路。然而，在等出租车时，路过的汽车的颜色和型号对于决定何时放弃手臂是最重要的。但是我们环境中的感官信息通常也是嘈杂的，并且关于所需状态（例如，“灯是绿色的”）是真的还是假的是不确定的。因此，现实环境中的决策需要我们以反映当前目标和背景的方式处理感官输入，但也要考虑潜在的不确定性。在这个项目中，我们将研究在决策过程中，关于不同可能感知和可能背景的不确定性如何反映在人脑中。决策制定和强化学习的正式理论表明，环境的不同状态的表示方式对决策制定有重要影响（萨顿和巴托，1998）。他们认为，在面对不确定性时，最好是将关于可能状态集的信息与这些状态中的每一个当前为真的确定性相结合。这些所谓的“信念状态”（Kaelbling等人，1996）在决策的理论工作中发挥了重要作用，但对大脑中是否存在相应的表征知之甚少。借鉴我们以前的工作（Schuck，2015，2016; Kaplan，Schuck & Doeller，2017），我们提出大脑中的概率信念状态表征反映在一个整合和分布式神经代码中，其中状态身份和这些状态为真的概率是多路复用的，并且这些表征可以在腹内侧前额叶皮层中找到。为了验证我们的假设，我们引入了一种新的多步决策任务，其中前几步的事件和不确定性为当前步骤提供了背景。使用贝叶斯分类方法，我们将检查相对多变量fMRI证据，支持每一步的不同状态（例如，货车卑尔根等人，2015），并测试状态上的神经编码分布是否对应于信念状态模型的预测。我们的项目将使我们能够全面研究神经表征，以计算简洁的方式提供对决策的不确定性分级上下文影响。