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

项目摘要 相同的神经活动模式可以对应世界上的多个事件。大脑解决了这个问题 通过推断哪种因果模型最好地解释了感觉输入模式，并产生关于 这个模型中的感官变量。因果推理的神经基础很难研究，因为 这种内在模式只能部分地通过行为来实现。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，那么由规范推断的信念 模型可用于识别潜在的神经相关物。该项目的目标是开发规范模型， 在项目B和C中实验研究的运动任务，以产生一次又一次的试验以及动态的 计算中关键潜在变量的时刻预测，并研究其神经网络 利用这些项目中收集的数据进行实施。这些模型将适合行为数据，以确定 大脑如何利用因果推理应用于视网膜图像运动来推断动物的自我运动， 物体是否在世界上移动，并推断物体的速度和深度。对于基于试验的 项目B中的任务，目标1将从感官输入的生成模型开始，并将其转化为因果关系。 推论初步工作已经扩展和统一了以前的努力，成为一个新的贝叶斯模型， 视网膜运动和深度，以分割自我运动期间的视觉场景。心理物理测试表明， 静态模型符合感性经验。该模型将用于预测皮层神经元的反应， 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，由于真实的世界不是静态的，团队将开发一个动态模型， 规范因果推理和实时逆理性控制。这个模型将预测哪些潜在的 在C项目中，大脑需要跟踪连续的、自然的任务。初步工作表明， 使用动态因果推理的简化模型可以保持对自运动速度和 无论物体是静止的还是运动的。Aim 2将把这个模型扩展到更复杂的感觉输入， 在类似于推理的时间尺度上支持对象运动动力学。它还将开发一个实时 理性控制模型，以产生关于目标导向控制的神经相关物的定量假设 动物根据因果推理的感知而行动。我们将使该模型适合于观察到的行为， 在目标导向的控制过程中逆向工程动物的信念。当建议的工作完成时，静态 模型将连接三个物理上相互关联的变量-物体运动，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，以预测关于这些变量的信念如何 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 推理到更现实的条件，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研究不足。

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.