Neacsu Victorita, Convertino Laura, Friston Karl J
Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom.
School of Life and Medical Sciences, Institute of Cognitive Neuroscience, University College London, London, United Kingdom.
Front Neurosci. 2022 Feb 8;16:802396. doi: 10.3389/fnins.2022.802396. eCollection 2022.
Humans are highly proficient in learning about the environments in which they operate. They form flexible spatial representations of their surroundings that can be leveraged with ease during spatial foraging and navigation. To capture these abilities, we present a deep Active Inference model of goal-directed behavior, and the accompanying belief updating. Active Inference rests upon optimizing Bayesian beliefs to maximize model evidence or marginal likelihood. Bayesian beliefs are probability distributions over the causes of observable outcomes. These causes include an agent's actions, which enables one to treat planning as inference. We use simulations of a geocaching task to elucidate the belief updating-that underwrites spatial foraging-and the associated behavioral and neurophysiological responses. In a geocaching task, the aim is to find hidden objects in the environment using spatial coordinates. Here, synthetic agents learn about the environment via inference and learning (e.g., learning about the likelihoods of outcomes given latent states) to reach a target location, and then forage locally to discover the hidden object that offers clues for the next location.
人类在了解其所处环境方面具有很高的能力。他们会形成周围环境的灵活空间表征，在空间觅食和导航过程中能够轻松利用这些表征。为了捕捉这些能力，我们提出了一种目标导向行为的深度主动推理模型以及伴随的信念更新。主动推理基于优化贝叶斯信念以最大化模型证据或边际似然性。贝叶斯信念是可观察结果的原因上的概率分布。这些原因包括主体的行动，这使得人们能够将规划视为推理。我们使用地理寻宝任务的模拟来阐明支持空间觅食的信念更新以及相关的行为和神经生理反应。在地理寻宝任务中，目标是利用空间坐标在环境中找到隐藏物体。在这里，合成主体通过推理和学习（例如，学习给定潜在状态下结果的可能性）来了解环境，以到达目标位置，然后在局部区域觅食以发现隐藏物体，该物体为下一个位置提供线索。
