  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.
