Project A: Theoretical framework for studying causal inference in trial-based and continuous tasks

项目摘要 同样的神经活动模式可以对应于世界上的多个事件。大脑会解决这个问题 通过推断哪个因果模型最好地解释感觉输入模式，并产生关于 这个模型中的感官变量。然而，因果推理的神经基础很难研究，因为 这个内部模型只能通过行为部分实现。标准化建模提供了一种强大的方式来 绕过这个问题：如果这些计算足够接近最优，规范推断的信念 模型可以用来识别潜在的神经关联。该项目的目标是开发标准化的模型 在项目B和C中进行实验研究的运动任务，以生成逐次试验以及动态 计算中关键潜变量的逐个预测，并研究它们的神经 使用在这些项目中收集的数据进行实施。这些模型将与行为数据相匹配，以确定 大脑如何将因果推理应用于视网膜图像运动来推断动物的自我运动，以决定 物体是否在世界上移动，并推断物体的速度和深度。对于以试验为基础的 在项目B中，目标1的任务将从感觉输入的生成模型开始，并将其转化为产生因果关系 推论。初步工作将先前的工作扩展并统一为一种新的贝叶斯模型，该模型使用 视网膜运动和深度分割自运动过程中的视觉场景。心理物理测试表明这一点 静态模型与感性经验相吻合。该模型将用于预测大脑皮质的神经反应。 运动处理区域MT和MSTD，假设这些响应代表贝叶斯后验 信仰。在目标2中，由于现实世界不是静态的，团队将开发一个动态模型来描述 规范的因果推理和实时的逆向理性控制。该模型将预测哪个潜伏期 在项目C的连续自然任务中，大脑需要跟踪的变量初步工作表明 使用动态因果推理的简化模型可以保持自运动速度和运动速度的运行估计 对象是静止的还是移动的。目标2将把这个模型扩展到更复杂的感觉输入，并 支持与推理类似的时间尺度上的对象运动动力学。它还将开发一个实时的 理性控制模型，以生成关于目标导向控制的神经关联的定量假设 对于动物来说，是根据因果推理的知觉来行动的。我们将把这个模型与观察到的行为相匹配 在目标导向控制过程中，对动物的信念进行反向工程。当提议的工作完成时，静态的 模型将连接三个物理上相互关联的变量-对象运动、自我运动和深度-这可能是 在不同的神经群体中计算和表示，以预测对这些变量的信念 相互影响并在大脑中传播。动态模型将扩展因果关系的研究 推断到更现实的条件，在这种情况下，感觉数据和信念随着时间的推移而演变，以关闭 未被充分研究的知觉和行动之间的循环。

Project Summary The same pattern of neural activity can correspond to multiple events in the world. The brain resolves this ambiguity by inferring which causal model best explains a sensory input pattern, and generating beliefs about the sensory variables in this model. The neural basis of causal inference is difficult to study, however, because this internal model is only partly accessible through behavior. Normative modeling provides a powerful way to circumvent this problem: if these computations are close enough to optimal, beliefs inferred by normative models can be used to identify potential neural correlates. This project's goal is to develop normative models of the motion tasks investigated experimentally in Projects B and C, to generate trial-by-trial as well as dynamic moment-by-moment predictions of key latent variables in the computation, and to investigate their neural implementation using data collected in those projects. These models will be fit to behavioral data to determine how the brain uses causal inference applied to retinal image motion to infer the animal's self-motion, to decide whether or not the object is moving in the world, and to infer the object's velocity and depth. For the trial-based tasks in Project B, Aim 1 will start with the generative model of sensory inputs and invert it to produce causal inferences. Preliminary work has extended and unified previous efforts into a novel Bayesian model that uses retinal motion and depth to segment visual scenes during self-motion. Psychophysical tests show that this static model agrees with perceptual experience. This model will be used to predict neural responses in cortical motion-processing areas MT and MSTd by assuming that these responses represent Bayesian posterior beliefs. In Aim 2, because the real world is not static, the team will develop a dynamic model that describes normative causal inference and inverse rational control in real-time. This model will predict which latent variables the brain needs to track in the continuous, naturalistic tasks of Project C. Preliminary work shows that a simplified model using dynamic causal inference can keep a running estimate of self-motion velocity and of whether an object is stationary or moving. Aim 2 will extend this model to more complex sensory inputs and to support object motion dynamics on timescales similar to those of inference. It will also develop a real-time rational control model to generate quantitative hypotheses about the neural correlates of goal-directed control for animals acting upon the percepts from causal inference. We will fit this model to observed behavior to reverse-engineer animals' beliefs during goal-directed control. When the proposed work is complete, the static model will link three physically interconnected variables—object motion, self motion, and depth—which may be computed and represented in different neural populations, to predict how beliefs about these variables influence each other and propagate across the brain. The dynamic model will extend the study of causal inference to more realistic conditions, in which sensory data and beliefs evolve over time, to close the understudied loop between perception and action.