论文标题
有意视觉搜索的元基础模型
A Meta-Bayesian Model of Intentional Visual Search
论文作者
论文摘要
我们提出了一个视觉搜索的计算模型，该模型结合了对分类感知和扫视计划的神经机制的贝叶斯解释。为了在模拟和人类行为之间进行有意义的比较，我们采用了一种凝视诱导的范式，要求参与者通过凝视窗口的窗口对遮挡的MNIST数字进行分类。该任务中的时间尺度分离所施加的条件独立性由对我们模型的层次结构的约束所体现。计划和决策制定是部分可观察到的马尔可夫决策过程，而本体感受和外部感受信号是通过动态模型集成的，该模型促进了视觉信息及其潜在原因的近似推断。我们的模型能够概括人类行为指标，例如分类准确性，同时保留高度的解释性，我们通过从观察到的人类行为中恢复特定于主体的参数来证明这一点。
We propose a computational model of visual search that incorporates Bayesian interpretations of the neural mechanisms that underlie categorical perception and saccade planning. To enable meaningful comparisons between simulated and human behaviours, we employ a gaze-contingent paradigm that required participants to classify occluded MNIST digits through a window that followed their gaze. The conditional independencies imposed by a separation of time scales in this task are embodied by constraints on the hierarchical structure of our model; planning and decision making are cast as a partially observable Markov Decision Process while proprioceptive and exteroceptive signals are integrated by a dynamic model that facilitates approximate inference on visual information and its latent causes. Our model is able to recapitulate human behavioural metrics such as classification accuracy while retaining a high degree of interpretability, which we demonstrate by recovering subject-specific parameters from observed human behaviour.
