Abstract
This paper shows that it is possible to estimate the subjective precision (inverse variance) of Bayesian beliefs during oculomotor pursuit. Subjects viewed a sinusoidal target, with or without random fluctuations in its motion. Eye trajectories and magnetoencephalographic (MEG) data were recorded concurrently. The target was periodically occluded, such that its reappearance caused a visual evoked response field (ERF). Dynamic causal modelling (DCM) was used to fit models of eye trajectories and the ERFs. The DCM for pursuit was based on predictive coding and active inference, and predicts subjects' eye movements based on their (subjective) Bayesian beliefs about target (and eye) motion. The precisions of these hierarchical beliefs can be inferred from behavioural (pursuit) data. The DCM for MEG data used an established biophysical model of neuronal activity that includes parameters for the gain of superficial pyramidal cells, which is thought to encode precision at the neuronal level. Previous studies (using DCM of pursuit data) suggest that noisy target motion increases subjective precision at the sensory level: i.e., subjects attend more to the target's sensory attributes. We compared (noisy motion-induced) changes in the synaptic gain based on the modelling of MEG data to changes in subjective precision estimated using the pursuit data. We demonstrate that imprecise target motion increases the gain of superficial pyramidal cells in V1 (across subjects). Furthermore, increases in sensory precision – inferred by our behavioural DCM – correlate with the increase in gain in V1, across subjects. This is a step towards a fully integrated model of brain computations, cortical responses and behaviour that may provide a useful clinical tool in conditions like schizophrenia.
Highlights
In recent work (Adams et al, 2015), we used a generative model of oculomotor pursuit based on predictive coding and active inference – a Bayes-optimal formulation of action and perception – to predict the eye movements of subjects viewing targets whose velocities vary in precision
This study offers a construct validation of a generative model of pursuit that was designed to estimate subjective precisions from eye movements
We have shown – using dynamic causal modelling (DCM) to invert a generative model of oculomotor behaviour – that when tracking a sinusoidally moving target whose motion is noisy, subjects increase their sensory precision; i.e., they attend more to the sensory attributes of the target
Summary
In recent work (Adams et al, 2015), we used a generative model of oculomotor pursuit based on predictive coding and active inference – a Bayes-optimal formulation of action and perception – to predict the eye movements of subjects viewing targets whose velocities vary in precision (inverse variance). This dynamic causal modelling (DCM) of behaviour provides estimates of (subjective) precision that subjects adopt in their hierarchical models of sensory input. We establish this sort of construct validity using behavioural and biophysical DCMs of pursuit and MEG data, respectively
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