Data from: 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 from left to right). Despite this use of heuristic policies, model comparison showed that there is substantial evidence for epistemic foraging in the visual exploration of even simple scenes. Second, we compared MDP models that did - and did not - allow for changes in prior expectations over successive blocks of the visual search paradigm. We found that implicit prior beliefs about the speed and accuracy of visual searches changed systematically with experience. Finally, we characterised intersubject variability in terms of subject-specific prior beliefs. Specifically, we used canonical correlation analysis to see if there were any mixtures of prior expectations that could predict between-subject differences in performance; thereby establishing a quantitative link between different behavioural phenotypes and Bayesian belief updating. We demonstrated that better scene categorisation performance is consistently associated with lower reliance on heuristics; i.e., a greater use of a generative model of the scene to direct its exploration.

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进行分类时如何决定注视位置提供了具有原则性的解释。本文中，我们采用主动推理来解释健康人类受试者的视觉搜索行为，借此解答视觉觅食与显著性归因相关的若干关键问题。首先，我们探究是否存在“认知觅食”的相关证据——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，对比了两类扫描路径的马尔可夫决策过程（Markov decision process, MDP）模型：一类包含用于动作选择的认知性、不确定性消解目标，另一类则不包含该目标。在模型比较过程中，我们发现有必要加入非认知性（启发式）策略以解释观测到的行为，例如类似阅读的从左至右扫描策略。尽管使用了启发式策略，贝叶斯模型比较结果显示，即便在简单场景的视觉探索中，也存在大量支持认知觅食的证据。其次，我们对比了两类马尔可夫决策过程模型：一类允许视觉搜索范式的连续试次中先验期望发生变化，另一类则不允许。结果发现，关于视觉搜索速度与准确性的内隐先验信念会随着实验经验发生系统性改变。最后，我们基于受试者特异性先验信念对受试者间差异进行了表征。具体而言，我们采用典型相关分析（canonical correlation analysis）来检验是否存在能够预测受试者间行为表现差异的先验期望组合，从而建立不同行为表型与贝叶斯信念更新之间的定量关联。我们证实，更优异的场景分类表现始终与更低的启发式策略依赖度相关，即更多地使用场景生成模型来指导探索行为。