A generative learning model for saccade adaptation.

Wolfgang Einhäuser,Martin Rolfs,Carlos R Cassanello,Florian Ostendorf

doi:10.1371/journal.pcbi.1006695

Abstract

Plasticity in the oculomotor system ensures that saccadic eye movements reliably meet their visual goals—to bring regions of interest into foveal, high-acuity vision. Here, we present a comprehensive description of sensorimotor learning in saccades. We induced continuous adaptation of saccade amplitudes using a double-step paradigm, in which participants saccade to a peripheral target stimulus, which then undergoes a surreptitious, intra-saccadic shift (ISS) as the eyes are in flight. In our experiments, the ISS followed a systematic variation, increasing or decreasing from one saccade to the next as a sinusoidal function of the trial number. Over a large range of frequencies, we confirm that adaptation gain shows (1) a periodic response, reflecting the frequency of the ISS with a delay of a number of trials, and (2) a simultaneous drift towards lower saccade gains. We then show that state-space-based linear time-invariant systems (LTIS) represent suitable generative models for this evolution of saccade gain over time. This state-equation algorithm computes the prediction of an internal (or hidden state-) variable by learning from recent feedback errors, and it can be compared to experimentally observed adaptation gain. The algorithm also includes a forgetting rate that quantifies per-trial leaks in the adaptation gain, as well as a systematic, non-error-based bias. Finally, we study how the parameters of the generative models depend on features of the ISS. Driven by a sinusoidal disturbance, the state-equation admits an exact analytical solution that expresses the parameters of the phenomenological description as functions of those of the generative model. Together with statistical model selection criteria, we use these correspondences to characterize and refine the structure of compatible state-equation models. We discuss the relation of these findings to established results and suggest that they may guide further design of experimental research across domains of sensorimotor adaptation.

Highlights

The accuracy of saccadic eye movement is maintained through mechanisms of saccade adaptation, which adjust the amplitude [1,2,3] or direction [4,5,6] of subsequent movements in response to targeting errors
This choice was supported by the fact that we had confirmed using statistical model selection criteria (i.e., Akaike information criterion (AIC) and BIC, [38,39,40,41,45]) that this functional dependence was the best descriptor of the oculomotor response among the set of models tested in Cassanello et al [11]
Having adequate generative models that describe eye movements have been stressed before [80,82,83,84,85,86] as an important tool to assess, at the single patient level, a variety of movement abnormalities that have been identified as markers of neurological conditions or disorders at a group level

Summary

Introduction

The accuracy of saccadic eye movement is maintained through mechanisms of saccade adaptation, which adjust the amplitude [1,2,3] or direction [4,5,6] of subsequent movements in response to targeting errors. To induce saccade adaptation in the laboratory [1], participants are instructed to follow a step of a target stimulus with their eyes and this visual cue is displaced further during the saccade eye movement. This second, intra-saccadic step (ISS) is constant across trials and directed along the initial target vector towards smaller or larger saccade amplitudes. In phenomenological analyses of such saccade adaptation data, the amount of adaptation is usually quantified by comparing saccade gain values before and after the adapting block and interpolating an exponential fit in between [1,2,3,10]

Methods

Results

Discussion

Conclusion