A state space model for spatial updating of remembered visual targets during eye movements

Abstract

In the oculomotor system, spatial updating is the ability to aim a saccade toward a remembered visual target position despite intervening eye movements. Although this has been the subject of extensive experimental investigation, there is still no unifying theoretical framework to explain the neural mechanism for this phenomenon, and how it influences visual signals in the brain. Here, we propose a unified state-space model (SSM) to account for the dynamics of spatial updating during two types of eye movement; saccades and smooth pursuit. Our proposed model is a non-linear SSM and implemented through a recurrent radial-basis-function neural network in a dual Extended Kalman filter (EKF) structure. The model parameters and internal states (remembered target position) are estimated sequentially using the EKF method. The proposed model replicates two fundamental experimental observations: continuous gaze-centered updating of visual memory-related activity during smooth pursuit, and predictive remapping of visual memory activity before and during saccades. Moreover, our model makes the new prediction that, when uncertainty of input signals is incorporated in the model, neural population activity and receptive fields expand just before and during saccades. These results suggest that visual remapping and motor updating are part of a common visuomotor mechanism, and that subjective perceptual constancy arises in part from training the visual system on motor tasks.