A Meta-Bayesian Model of Intentional Visual Search
This work addresses the challenge of understanding and simulating human visual search processes, which is incremental as it builds on existing Bayesian and computational frameworks.
The authors tackled the problem of modeling human visual search behavior by developing a computational model that integrates Bayesian interpretations of neural mechanisms for perception and saccade planning. The model successfully recapitulated human classification accuracy and allowed recovery of subject-specific parameters from observed behavior.
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.