Literature DB >> 24190910

Dynamic interaction between retinal and extraretinal signals in motion integration for smooth pursuit.

Amarender R Bogadhi1, Anna Montagnini, Guillaume S Masson.   

Abstract

Due to the aperture problem, the initial direction of tracking responses to a translating bar is biased towards the direction orthogonal to the bar. This observation offers a powerful way to explore the interactions between retinal and extraretinal signals in controlling our actions. We conducted two experiments to probe these interactions by briefly (200 and 400 ms) blanking the moving target (45° or 135° tilted bar) during steady state (Experiment 1) and at different moments during the early phase of pursuit (Experiment 2). In Experiment 1, we found a marginal but statistically significant directional bias on target reappearance for all subjects in at least one blank condition (200 or 400 ms). In Experiment 2, no systematic significant directional bias was observed at target reappearance after a blank. These results suggest that the weighting of retinal and extraretinal signals is dynamically modulated during the different phases of pursuit. Based on our previous theoretical work on motion integration, we propose a new closed-loop two-stage recurrent Bayesian model where retinal and extraretinal signals are dynamically weighted based on their respective reliabilities and combined to compute the visuomotor drive. With a single free parameter, the model reproduces many aspects of smooth pursuit observed across subjects during and immediately after target blanking. It provides a new theoretical framework to understand how different signals are dynamically combined based on their relative reliability to adaptively control our actions. Overall, the model and behavioral results suggest that human subjects rely more strongly on prediction during the early phase than in the steady state phase of pursuit.

Entities:  

Keywords:  dynamic motion integration; hierarchical recurrent Bayesian model; prediction; smooth pursuit; transient blanking

Mesh:

Year:  2013        PMID: 24190910     DOI: 10.1167/13.13.5

Source DB:  PubMed          Journal:  J Vis        ISSN: 1534-7362            Impact factor:   2.240


  13 in total

1.  Control of the strength of visual-motor transmission as the mechanism of rapid adaptation of priors for Bayesian inference in smooth pursuit eye movements.

Authors:  Timothy R Darlington; Stefanie Tokiyama; Stephen G Lisberger
Journal:  J Neurophysiol       Date:  2017-06-07       Impact factor: 2.714

2.  Temporal dynamics of retinal and extraretinal signals in the FEFsem during smooth pursuit eye movements.

Authors:  Leah Bakst; Jérome Fleuriet; Michael J Mustari
Journal:  J Neurophysiol       Date:  2017-02-15       Impact factor: 2.714

3.  Eye tracking a self-moved target with complex hand-target dynamics.

Authors:  Caroline Landelle; Anna Montagnini; Laurent Madelain; Frederic Danion
Journal:  J Neurophysiol       Date:  2016-07-27       Impact factor: 2.714

4.  Active inference and oculomotor pursuit: the dynamic causal modelling of eye movements.

Authors:  Rick A Adams; Eduardo Aponte; Louise Marshall; Karl J Friston
Journal:  J Neurosci Methods       Date:  2015-01-10       Impact factor: 2.390

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

Authors:  Rick A Adams; Markus Bauer; Dimitris Pinotsis; Karl J Friston
Journal:  Neuroimage       Date:  2016-02-24       Impact factor: 6.556

6.  FEFsem neuronal response during combined volitional and reflexive pursuit.

Authors:  Leah Bakst; Jérome Fleuriet; Michael J Mustari
Journal:  J Vis       Date:  2017-05-01       Impact factor: 2.240

7.  Execution of saccadic eye movements affects speed perception.

Authors:  Alexander Goettker; Doris I Braun; Alexander C Schütz; Karl R Gegenfurtner
Journal:  Proc Natl Acad Sci U S A       Date:  2018-02-13       Impact factor: 11.205

8.  Effect of Prior Direction Expectation on the Accuracy and Precision of Smooth Pursuit Eye Movements.

Authors:  Seolmin Kim; Jeongjun Park; Joonyeol Lee
Journal:  Front Syst Neurosci       Date:  2019-11-26

9.  Neural implementation of Bayesian inference in a sensorimotor behavior.

Authors:  Timothy R Darlington; Jeffrey M Beck; Stephen G Lisberger
Journal:  Nat Neurosci       Date:  2018-09-17       Impact factor: 24.884

10.  Humans adapt their anticipatory eye movements to the volatility of visual motion properties.

Authors:  Chloé Pasturel; Anna Montagnini; Laurent Udo Perrinet
Journal:  PLoS Comput Biol       Date:  2020-04-13       Impact factor: 4.475

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