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

项目概要 相同的神经活动模式可以对应世界上的多个事件。大脑解决这个问题 通过推断哪个因果模型最能解释感官输入模式并生成关于的信念来消除歧义 该模型中的感觉变量。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地访问。规范建模提供了一种强大的方法 规避这个问题：如果这些计算足够接近最优，则由规范推断的信念 模型可用于识别潜在的神经关联。该项目的目标是开发规范模型 项目 B 和 C 中通过实验研究了运动任务，以生成逐项试验以及动态任务 计算中关键潜在变量的即时预测，并研究它们的神经网络 使用这些项目中收集的数据实施。这些模型将适合行为数据以确定 大脑如何使用应用于视网膜图像运动的因果推理来推断动物的自我运动，从而做出决定 物体是否在世界中移动，并推断物体的速度和深度。对于基于试用的 项目 B 中的任务，目标 1 将从感官输入的生成模型开始，并将其反转以产生因果关系 推论。初步工作将之前的工作扩展并统一为一种新颖的贝叶斯模型，该模型使用 视网膜运动和深度以在自我运动期间分割视觉场景。心理物理测试表明，这 静态模型符合感知经验。该模型将用于预测皮质的神经反应 运动处理区域 MT 和 MSTd 假设这些响应代表贝叶斯后验 信念。在目标 2 中，由于现实世界不是静态的，因此团队将开发一个动态模型来描述 实时规范因果推理和逆理性控制。该模型将预测哪些潜在的 大脑在项目 C 的连续、自然任务中需要跟踪的变量。初步工作表明 使用动态因果推理的简化模型可以持续估计自运动速度和 物体是静止的还是运动的。目标 2 将该模型扩展到更复杂的感官输入并 支持类似于推理的时间尺度上的物体运动动力学。它还将开发实时 理性控制模型，用于生成有关目标导向控制的神经相关性的定量假设 对于动物根据因果推理的感知采取行动。我们将把这个模型拟合到观察到的行为 在目标导向控制过程中对动物的信念进行逆向工程。当建议的工作完成时，静态 模型将链接三个物理上相互关联的变量——物体运动、自运动和深度——这可能是 在不同的神经群体中进行计算和表示，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 推断更现实的条件，其中感官数据和信念随着时间的推移而演变，以关闭 感知和行动之间的循环已被充分研究。

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.