Human visual exploration reduces uncertainty about the sensed world

In previous papers, we introduced a normative scheme for scene construction and epistemic (visual) searches based upon active inference. This scheme provides a principled account of how people decide where to look, when categorising a visual scene based on its contents. In this paper, we use active inference to explain the visual searches of normal human subjects; enabling us to answer some key questions about visual foraging and salience attribution. First, we asked whether there is any evidence for 'epistemic foraging'; i.e. exploration that resolves uncertainty about a scene. In brief, we used Bayesian model comparison to compare Markov decision process (MDP) models of scan-paths that did - and did not - contain the epistemic, uncertainty-resolving imperatives for action selection. In the course of this model comparison, we discovered that it was necessary to include non-epistemic (heuristic) policies to explain observed behaviour (e.g., a reading-like strategy that involved scanning...既往研究中，我们提出了基于主动推理（active inference）的场景构建与认知（视觉）搜索规范框架。该框架为人们基于视觉场景内容对其进行分类时，如何决策注视位置提供了原则性阐释。本研究采用主动推理方法，对健康人类受试者的视觉搜索行为进行建模阐释，借此回答视觉觅食与显著性归因领域的若干关键问题。首先，我们探究了“认知觅食”是否存在实证依据——即旨在消解场景不确定性的探索行为。简言之，我们通过贝叶斯模型比较，对两类马尔可夫决策过程（MDP）扫描路径模型进行了对比：一类包含用于动作选择的认知性、不确定性消解行动准则，另一类则不包含该准则。在模型比较过程中，我们发现需引入非认知性（启发式）策略，方能解释观测到的行为（例如一种类阅读的扫描策略……