What makes a microsaccade? A review of 70 years of research prompts a new detection method

Unstable foveation's impact on reading, object tracking, and its implications for diagnosing and intervening in reading difficulties

Foveation, the ability to focus on a point for clear vision, is crucial for reading smoothly and tracking moving objects. Research shows that unstable foveation disrupts reading by causing more frequent eye movements. However, it's unclear if this also affects smooth tracking of moving targets in slow readers. This study therefore investigates the link between foveation during reading and the tracking of moving targets, in a large group of participants displaying variable reading speeds in a virtual reality environment. Our analysis shows that slower readers have more intrusive fixational eye movements characterized by greater spatial and temporal variability during reading. Additionally, these readers display less efficient tracking of moving targets. Interestingly, the rate and spatial variability of catch-up saccades during object tracking correlates with fixational eye movement rates and spatial variability during reading. Our findings thus demonstrate that poor foveation stability in slow readers also affects other eye movement activities, such as during object tracking. We conclude with a discussion on the limitations of virtual reality-based reading assessment and the shared mechanisms of foveal stabilization, which might be altered under conditions of reading impairment.

Microsaccades reflect attention shifts: a mini review of 20 years of microsaccade research

Microsaccades are small, involuntary eye movements that occur during fixation. Since the 1950s, researchers have conducted extensive research on the role of microsaccades in visual information processing, and found that they also play an important role in human advanced visual cognitive activities. Research over the past 20 years further suggested that there is a close relationship between microsaccades and visual attention, yet lacking a timely review. The current article aims to provide a state-of-the-art review and bring microsaccades studies into the sight of attention research. We firstly introduce basic characteristics about microsaccades, then summarized the empirical evidence supporting the view that microsaccades can reflect both external (perception) and internal (working memory) attention shifts. We finally conclude and highlight three promising avenues for future research.

Evaluating Eye Movement Event Detection: A Review of the State of the Art

Detecting eye movements in raw eye tracking data is a well-established research area by itself, as well as a common pre-processing step before any subsequent analysis. As in any field, however, progress and successful collaboration can only be achieved provided a shared understanding of the pursued goal. This is often formalised via defining metrics that express the quality of an approach to solving the posed problem. Both the big-picture intuition behind the evaluation strategies and seemingly small implementation details influence the resulting measures, making even studies with outwardly similar procedures essentially incomparable, impeding a common understanding. In this review, we systematically describe and analyse evaluation methods and measures employed in the eye movement event detection field to date. While recently developed evaluation strategies tend to quantify the detector's mistakes at the level of whole eye movement events rather than individual gaze samples, they typically do not separate establishing correspondences between true and predicted events from the quantification of the discovered errors. In our analysis we separate these two steps where possible, enabling their almost arbitrary combinations in an evaluation pipeline. We also present the first large-scale empirical analysis of event matching strategies in the literature, examining these various combinations both in practice and theoretically. We examine the particular benefits and downsides of the evaluation methods, providing recommendations towards more intuitive and informative assessment. We implemented the evaluation strategies on which this work focuses in a single publicly available library: https://github.com/r-zemblys/EM-event-detection-evaluation .

Microsaccades, Drifts, Hopf Bundle and Neurogeometry

The first part of the paper contains a short review of the image processing in early vision is static, when the eyes and the stimulus are stable, and in dynamics, when the eyes participate in fixation eye movements. In the second part, we give an interpretation of Donders’ and Listing’s law in terms of the Hopf fibration of the 3-sphere over the 2-sphere. In particular, it is shown that the configuration space of the eye ball (when the head is fixed) is the 2-dimensional hemisphere SL+, called Listing hemisphere, and saccades are described as geodesic segments of SL+ with respect to the standard round metric. We study fixation eye movements (drift and microsaccades) in terms of this model and discuss the role of fixation eye movements in vision. A model of fixation eye movements is proposed that gives an explanation of presaccadic shift of receptive fields.

The retinal and perceived locus of fixation in the human visual system

Due to the dramatic difference in spatial resolution between the central fovea and the surrounding retinal regions, accurate fixation on important objects is critical for humans. It is known that the preferred retinal location (PRL) for fixation of healthy human observers rarely coincides with the retinal location with the highest cone density. It is not currently known, however, whether the PRL is consistent within an observer or is subject to fluctuations and, moreover, whether observers' subjective fixation location coincides with the PRL. We studied whether the PRL changes between days. We used an adaptive optics scanning laser ophthalmoscope to project a Maltese cross fixation target on an observer's retina and continuously imaged the exact retinal location of the target. We found that observers consistently use the same PRL across days, regardless of how much the PRL is displaced from the cone density peak location. We then showed observers small stimuli near the visual field location on which they fixated, and the observers judged whether or not the stimuli appeared in fixation. Observers' precision in this task approached that of fixation itself. Observers based their judgment on both the visual scene coordinates and the retinal location of the stimuli. We conclude that the PRL in a normally functioning visual system is fixed, and observers use it as a reference point in judging stimulus locations.

Emulated retinal image capture (ERICA) to test, train and validate processing of retinal images

High resolution retinal imaging systems, such as adaptive optics scanning laser ophthalmoscopes (AOSLO), are increasingly being used for clinical research and fundamental studies in neuroscience. These systems offer unprecedented spatial and temporal resolution of retinal structures in vivo. However, a major challenge is the development of robust and automated methods for processing and analysing these images. We present ERICA (Emulated Retinal Image CApture), a simulation tool that generates realistic synthetic images of the human cone mosaic, mimicking images that would be captured by an AOSLO, with specified image quality and with corresponding ground-truth data. The simulation includes a self-organising mosaic of photoreceptors, the eye movements an observer might make during image capture, and data capture through a real system incorporating diffraction, residual optical aberrations and noise. The retinal photoreceptor mosaics generated by ERICA have a similar packing geometry to human retina, as determined by expert labelling of AOSLO images of real eyes. In the current implementation ERICA outputs convincingly realistic en face images of the cone photoreceptor mosaic but extensions to other imaging modalities and structures are also discussed. These images and associated ground-truth data can be used to develop, test and validate image processing and analysis algorithms or to train and validate machine learning approaches. The use of synthetic images has the advantage that neither access to an imaging system, nor to human participants is necessary for development.