Mirza M Berk, Adams Rick A, Mathys Christoph, Friston Karl J
Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, United Kingdom.
Institute of Cognitive Neuroscience, University College London, London, United Kingdom.
PLoS One. 2018 Jan 5;13(1):e0190429. doi: 10.1371/journal.pone.0190429. eCollection 2018.
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.
在之前的论文中，我们基于主动推理引入了一种场景构建和认知（视觉）搜索的规范方案。该方案为人们在根据视觉场景内容进行分类时如何决定注视位置提供了一个有原则的解释。在本文中，我们使用主动推理来解释正常人类受试者的视觉搜索；这使我们能够回答一些关于视觉觅食和显著性归因的关键问题。首先，我们询问是否有“认知觅食”的证据；即解决关于场景不确定性的探索。简而言之，我们使用贝叶斯模型比较来比较扫描路径的马尔可夫决策过程（MDP）模型，这些模型包含和不包含用于动作选择的认知、不确定性解决指令。在这个模型比较过程中，我们发现有必要纳入非认知（启发式）策略来解释观察到的行为（例如，一种类似阅读的策略，涉及从左到右扫描）。尽管使用了启发式策略，但模型比较表明，即使在简单场景的视觉探索中，也有大量证据支持认知觅食。其次，我们比较了允许和不允许在视觉搜索范式的连续块中先验期望发生变化的MDP模型。我们发现，关于视觉搜索速度和准确性的隐含先验信念会随着经验而系统地改变。最后，我们根据受试者特定的先验信念来描述个体间的变异性。具体来说，我们使用典型相关分析来查看是否存在任何先验期望的组合可以预测受试者之间的表现差异；从而在不同的行为表型和贝叶斯信念更新之间建立定量联系。我们证明，更好的场景分类表现始终与对启发式策略的较低依赖相关；即更多地使用场景生成模型来指导其探索。
