Biology subjects

Rolfs, M.

Publications and source records attributed to Rolfs, M..

22 records · Page 2Linked to original sources

bioRxiv2021

Definition, modeling and detection of saccades in the face of post-saccadic oscillations

When analyzing eye tracking data, one of the central tasks is the detection of saccades. Although many automatic saccade detection algorithms exist, the field still debates how to deal with brief periods of instability around saccade offset, so-called post-saccadic oscillations (PSOs), which are especially prominent in todays widely used video-based eye tracking techniques. There is good evidence that PSOs are caused by inertial forces that act on the elastic components of the eye, such as the iris or the lens. As this relative movement can greatly distort estimates of saccade metrics, especially saccade duration and peak velocity, video-based eye tracking has recurrently been considered unsuitable for measuring saccade kinematics. In this chapter, we review recent biophysical models that describe the relationship between pupil motion and eyeball motion. We found that these models were well capable of accurately reproducing saccade trajectories and implemented a framework for the simulation of saccades, PSOs, and fixations, which can be used - just like datasets hand-labelled by human experts - to evaluate detection algorithms and train statistical models. Moreover, as only pupil and corneal-reflection signals are observable in video-based eye tracking, one may also be able to use these models to predict the unobservable motion of the eyeball. Testing these predictions by analyzing saccade data that was registered with video-based and search-coil eye tracking techniques revealed strong relationships between the two types of measurements, especially when saccade offset is defined as the onset of the PSO. To enable eye tracking researchers to make use of this definition, we present and evaluate two novel algorithms - one based on eye-movement direction inversion, one based on linear classifiers previously trained on simulation data. These algorithms allow for the detection of PSO onset with high fidelity. Even though PSOs may still pose problems for a range of eye tracking applications, the techniques described here may help to alleviate these.

neuroscience↗

bioRxiv2020

Intra-saccadic motion streaks jump-start gaze correction

Rapid eye movements (saccades) incessantly shift objects across the retina. To establish object correspondence, the visual system is thought to match surface features of objects across saccades. Here we show that an objects intra-saccadic retinal trace - a signal previously considered unavailable to visual processing - facilitates this match-making. Human observers made saccades to a cued target in a circular stimulus array. Using high-speed visual projection, we swiftly rotated this array during the eyes flight, displaying continuous intra-saccadic target motion. Observers saccades landed between the target and a distractor, prompting secondary saccades. Independently of the availability of object features, which we controlled tightly, target motion increased the rate and reduced the latency of gaze-correcting saccades to the initial pre-saccadic target, in particular when the targets stimulus features incidentally gave rise to efficient motion streaks. These results suggest that intra-saccadic visual information informs the establishment of object correspondence and jump-starts gaze correction.

neuroscience↗

bioRxiv2019

An adaptive algorithm for fast and reliable online saccade detection

To investigate visual perception around the time of eye movements, vision scientists manipulate stimuli contingent upon the onset of a saccade. For these experimental paradigms, timing is especially crucial, as saccade offset imposes a deadline on the display change. Although efficient online saccade detection can greatly improve timing, most algorithms rely on spatial-boundary techniques or absolute-velocity thresholds, which both suffer from their respective weaknesses: late detections and false alarms. We propose an adaptive, velocity-based algorithm for online saccade detection that surpasses both standard techniques in speed and accuracy and allows the user to freely define detection criteria. Inspired by the Engbert-Kliegl-algorithm for microsaccade detection, our algorithm computes two-dimensional velocity thresholds from variance in preceding fixation samples, while compensating for noisy or missing data samples. An optional direction criterion limits detection to the instructed saccade direction, further increasing robustness. We validated the algorithm by simulating its performance on a large saccade dataset and found that high detection accuracy (false-alarm rates of <1%) could be achieved with detection latencies of only 3 milliseconds. High accuracy was maintained even under simulated high-noise conditions. To demonstrate that purely intra-saccadic presentations are technically feasible, we devised an experimental test, in which a Gabor patch drifted at saccadic peak velocities. While this stimulus was invisible when presented during fixation, observers reliably detected it during saccades. Photodiode measurements verified that - including all system delays - stimuli were physically displayed on average 20 ms after saccade onset. Thus, the proposed algorithm provides valuable tool for gaze-contingent paradigms.

neuroscience↗

bioRxiv2018

A generative learning model for saccade adaptation

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.\n\nAuthor SummaryConstant adjustments of saccade metrics maintain oculomotor accuracy under changing environments. This error-driven learning can be induced experimentally by manipulating the targeting error of eye movements. Here, we investigate oculomotor learning in healthy participants in response to a sinusoidally evolving error. We then fit a class of generative models to the observed dynamics of oculomotor adaptation under this new learning regime. Formal model comparison suggests a richer model parameterization for such a sinusoidal error variation than proposed so far in the context of classical, step-like disturbances. We identify and fit the parameters of a generative model as underlying those of a phenomenological description of adaptation dynamics and provide an explicit link of this generative model to more established state equations for motor learning. The joint use of the sinusoidal adaption regime and consecutive model fit may provide a powerful approach to assess interindividual differences in adaptation across healthy individuals and to evaluate changes in learning dynamics in altered brain states, such as sustained by injuries, diseases, or aging.

neuroscience↗