Active inference and the anatomy of oculomotion

Abstract

Given that eye movement control can be framed as an inferential process, how are the requisite forces generated to produce anticipated or desired fixation? Starting from a generative model based on simple Newtonian equations of motion, we derive a variational solution to this problem and illustrate the plausibility of its implementation in the oculomotor brainstem. We show, through simulation, that the Bayesian filtering equations that implement ‘planning as inference’ can generate both saccadic and smooth pursuit eye movements. Crucially, the associated message passing maps well onto the known connectivity and neuroanatomy of the brainstem – and the changes in these messages over time are strikingly similar to single unit recordings of neurons in the corresponding nuclei. Furthermore, we show that simulated lesions to axonal pathways reproduce eye movement patterns of neurological patients with damage to these tracts.

Highlights

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  Eye movement control is cast as an inferential process that induces Newtonian rotational forces.

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  A plausible computational anatomy of Bayesian filtering in the brainstem is proposed.

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  Smooth pursuit and saccadic eye movements are simulated.

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  Lesions to the model cause similar patterns of eye movements to those found in neurological patients.