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Dynamic causal modelling of eye movements during pursuit: Confirming precision-encoding in V1 using MEG.

Adams RA, Bauer M, Pinotsis D, Friston KJ - Neuroimage (2016)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: The Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3BG, UK. Electronic address: rick.adams@ucl.ac.uk.

No MeSH data available.


Related in: MedlinePlus

Comparison of empirical and predicted position errors and parameter estimates in this and a previous dataset.The graphs on the first row show empirically observed position error (target position — eye position) in arbitrary units (the traces have been normalised with respect to displacement) for both Smooth (red line) and Noisy (blue line) conditions. Note that being behind the target entails being above the black line in the first half of the cycle and below it in the second. It is clear that the pattern of eye movements in each condition is very similar in both experiments; the major difference is an increase in lag in the Smooth condition in the second experiment, especially in the first quarter cycle (please see the main text for discussion of this phenomenon). The graphs on the second row show the position errors predicted by the generative model in Fig. 1, using the posterior expectations of the parameters in the lower two rows: in both experiments, the models fit the data well. The previous experiment (left panels) used two different speeds and hence the plots on the left have been normalised with respect to time, but those on the right – using only one speed – have not.The graphs on the third and fourth rows depict the parameters used to generate the predicted position errors on the second row. The graphs on the third row display the posterior expectations of the model parameters (averaged over conditions), plotted as the changes from prior expectations listed in Table 1. The graphs on the fourth row display the changes in parameters due to the noise of target motion. The changes in kinetic parameters (θ1, …, θ6) are absolute, but the changes in precision parameters (teal) and prior parameters are log scaled. The pink bars correspond to 90% Bayesian confidence intervals. The posterior expectations in each dataset are remarkably similar: please see the text for a discussion of their minor differences.
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f0040: Comparison of empirical and predicted position errors and parameter estimates in this and a previous dataset.The graphs on the first row show empirically observed position error (target position — eye position) in arbitrary units (the traces have been normalised with respect to displacement) for both Smooth (red line) and Noisy (blue line) conditions. Note that being behind the target entails being above the black line in the first half of the cycle and below it in the second. It is clear that the pattern of eye movements in each condition is very similar in both experiments; the major difference is an increase in lag in the Smooth condition in the second experiment, especially in the first quarter cycle (please see the main text for discussion of this phenomenon). The graphs on the second row show the position errors predicted by the generative model in Fig. 1, using the posterior expectations of the parameters in the lower two rows: in both experiments, the models fit the data well. The previous experiment (left panels) used two different speeds and hence the plots on the left have been normalised with respect to time, but those on the right – using only one speed – have not.The graphs on the third and fourth rows depict the parameters used to generate the predicted position errors on the second row. The graphs on the third row display the posterior expectations of the model parameters (averaged over conditions), plotted as the changes from prior expectations listed in Table 1. The graphs on the fourth row display the changes in parameters due to the noise of target motion. The changes in kinetic parameters (θ1, …, θ6) are absolute, but the changes in precision parameters (teal) and prior parameters are log scaled. The pink bars correspond to 90% Bayesian confidence intervals. The posterior expectations in each dataset are remarkably similar: please see the text for a discussion of their minor differences.

Mentions: The empirical eye trajectories in Smooth and Noisy conditions are plotted on the left of Fig. 5 and the eye velocities (excluding saccades) are shown on the right. When the target was occluded, smooth pursuit eye movement (SPEM) velocity in both conditions decreased to a steady ‘residual predictive velocity’ of either − 3°/s (decelerating target) or 6°/s (accelerating target). The corresponding position errors between eye and target in each condition are shown on the top right of Fig. 6, together with the predictions of the pursuit model below (second row, right column). For comparison, the results of an earlier behavioural experiment (Adams et al., 2015) conducted with the same stimuli are displayed on the top left of Fig. 6, with the model predictions following DCM inversion below it. The model predictions of these independent data are reasonably accurate in both cases and consistent with each other (see the Discussion for comments on the few inconsistencies).


Dynamic causal modelling of eye movements during pursuit: Confirming precision-encoding in V1 using MEG.

Adams RA, Bauer M, Pinotsis D, Friston KJ - Neuroimage (2016)

Comparison of empirical and predicted position errors and parameter estimates in this and a previous dataset.The graphs on the first row show empirically observed position error (target position — eye position) in arbitrary units (the traces have been normalised with respect to displacement) for both Smooth (red line) and Noisy (blue line) conditions. Note that being behind the target entails being above the black line in the first half of the cycle and below it in the second. It is clear that the pattern of eye movements in each condition is very similar in both experiments; the major difference is an increase in lag in the Smooth condition in the second experiment, especially in the first quarter cycle (please see the main text for discussion of this phenomenon). The graphs on the second row show the position errors predicted by the generative model in Fig. 1, using the posterior expectations of the parameters in the lower two rows: in both experiments, the models fit the data well. The previous experiment (left panels) used two different speeds and hence the plots on the left have been normalised with respect to time, but those on the right – using only one speed – have not.The graphs on the third and fourth rows depict the parameters used to generate the predicted position errors on the second row. The graphs on the third row display the posterior expectations of the model parameters (averaged over conditions), plotted as the changes from prior expectations listed in Table 1. The graphs on the fourth row display the changes in parameters due to the noise of target motion. The changes in kinetic parameters (θ1, …, θ6) are absolute, but the changes in precision parameters (teal) and prior parameters are log scaled. The pink bars correspond to 90% Bayesian confidence intervals. The posterior expectations in each dataset are remarkably similar: please see the text for a discussion of their minor differences.
© Copyright Policy - CC BY
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4862965&req=5

f0040: Comparison of empirical and predicted position errors and parameter estimates in this and a previous dataset.The graphs on the first row show empirically observed position error (target position — eye position) in arbitrary units (the traces have been normalised with respect to displacement) for both Smooth (red line) and Noisy (blue line) conditions. Note that being behind the target entails being above the black line in the first half of the cycle and below it in the second. It is clear that the pattern of eye movements in each condition is very similar in both experiments; the major difference is an increase in lag in the Smooth condition in the second experiment, especially in the first quarter cycle (please see the main text for discussion of this phenomenon). The graphs on the second row show the position errors predicted by the generative model in Fig. 1, using the posterior expectations of the parameters in the lower two rows: in both experiments, the models fit the data well. The previous experiment (left panels) used two different speeds and hence the plots on the left have been normalised with respect to time, but those on the right – using only one speed – have not.The graphs on the third and fourth rows depict the parameters used to generate the predicted position errors on the second row. The graphs on the third row display the posterior expectations of the model parameters (averaged over conditions), plotted as the changes from prior expectations listed in Table 1. The graphs on the fourth row display the changes in parameters due to the noise of target motion. The changes in kinetic parameters (θ1, …, θ6) are absolute, but the changes in precision parameters (teal) and prior parameters are log scaled. The pink bars correspond to 90% Bayesian confidence intervals. The posterior expectations in each dataset are remarkably similar: please see the text for a discussion of their minor differences.
Mentions: The empirical eye trajectories in Smooth and Noisy conditions are plotted on the left of Fig. 5 and the eye velocities (excluding saccades) are shown on the right. When the target was occluded, smooth pursuit eye movement (SPEM) velocity in both conditions decreased to a steady ‘residual predictive velocity’ of either − 3°/s (decelerating target) or 6°/s (accelerating target). The corresponding position errors between eye and target in each condition are shown on the top right of Fig. 6, together with the predictions of the pursuit model below (second row, right column). For comparison, the results of an earlier behavioural experiment (Adams et al., 2015) conducted with the same stimuli are displayed on the top left of Fig. 6, with the model predictions following DCM inversion below it. The model predictions of these independent data are reasonably accurate in both cases and consistent with each other (see the Discussion for comments on the few inconsistencies).

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: The Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3BG, UK. Electronic address: rick.adams@ucl.ac.uk.

No MeSH data available.


Related in: MedlinePlus