Fixation classification: how to merge and select fixation candidates

Strategies for enhancing automatic fixation detection in head-mounted eye tracking

Moving through a dynamic world, humans need to intermittently stabilize gaze targets on their retina to process visual information. Overt attention being thus split into discrete intervals, the automatic detection of such fixation events is paramount to downstream analysis in many eye-tracking studies. Standard algorithms tackle this challenge in the limiting case of little to no head motion. In this static scenario, which is approximately realized for most remote eye-tracking systems, it amounts to detecting periods of relative eye stillness. In contrast, head-mounted eye trackers allow for experiments with subjects moving naturally in everyday environments. Detecting fixations in these dynamic scenarios is more challenging, since gaze-stabilizing eye movements need to be reliably distinguished from non-fixational gaze shifts. Here, we propose several strategies for enhancing existing algorithms developed for fixation detection in the static case to allow for robust fixation detection in dynamic real-world scenarios recorded with head-mounted eye trackers. Specifically, we consider (i) an optic-flow-based compensation stage explicitly accounting for stabilizing eye movements during head motion, (ii) an adaptive adjustment of algorithm sensitivity according to head-motion intensity, and (iii) a coherent tuning of all algorithm parameters. Introducing a new hand-labeled dataset, recorded with the Pupil Invisible glasses by Pupil Labs, we investigate their individual contributions. The dataset comprises both static and dynamic scenarios and is made publicly available. We show that a combination of all proposed strategies improves standard thresholding algorithms and outperforms previous approaches to fixation detection in head-mounted eye tracking.

Best practices for cleaning eye movement data in reading research

One challenge that comes with studying eye movement behavior is deciding how to clean the eye movement data (e.g., fixation durations) before conducting analyses. Reading researchers must decide which data cleaning methods they will use and which thresholds they will set to remove eye movements that are not reflective of lexical processing. The purpose of this project was to determine what data cleaning methods are typically used and if there are any consequences of using different data cleaning methods. In the first study, an analysis of 192 recently published articles indicated that there is inconsistency in the reporting and application of data cleaning methods. In the second study, three different data cleaning methods were applied based on the literature analysis in the first study. Analyses were conducted to determine the impact of different data cleaning methods on three commonly studied effects in reading research (frequency, predictability, and length). Overall, standardized estimates decreased for each effect when more data were removed; however, removing more data also resulted in decreased variance. As a result, effects remained significant with each data cleaning method, and simulated power remained high for both a moderate and small sample size. Effect sizes remained consistent for most effects but decreased for the length effect as more data were removed. Seven suggestions are provided that are based on open science practices with the intention of helping researchers, reviewers, and the field as a whole.

Maximizing valid eye-tracking data in human and macaque infants by optimizing calibration and adjusting areas of interest

Remote eye tracking with automated corneal reflection provides insights into the emergence and development of cognitive, social, and emotional functions in human infants and non-human primates. However, because most eye-tracking systems were designed for use in human adults, the accuracy of eye-tracking data collected in other populations is unclear, as are potential approaches to minimize measurement error. For instance, data quality may differ across species or ages, which are necessary considerations for comparative and developmental studies. Here we examined how the calibration method and adjustments to areas of interest (AOIs) of the Tobii TX300 changed the mapping of fixations to AOIs in a cross-species longitudinal study. We tested humans (N = 119) at 2, 4, 6, 8, and 14 months of age and macaques (Macaca mulatta; N = 21) at 2 weeks, 3 weeks, and 6 months of age. In all groups, we found improvement in the proportion of AOI hits detected as the number of successful calibration points increased, suggesting calibration approaches with more points may be advantageous. Spatially enlarging and temporally prolonging AOIs increased the number of fixation-AOI mappings, suggesting improvements in capturing infants' gaze behaviors; however, these benefits varied across age groups and species, suggesting different parameters may be ideal, depending on the population studied. In sum, to maximize usable sessions and minimize measurement error, eye-tracking data collection and extraction approaches may need adjustments for the age groups and species studied. Doing so may make it easier to standardize and replicate eye-tracking research findings.

How robust are wearable eye trackers to slow and fast head and body movements?

How well can modern wearable eye trackers cope with head and body movement? To investigate this question, we asked four participants to stand still, walk, skip, and jump while fixating a static physical target in space. We did this for six different eye trackers. All the eye trackers were capable of recording gaze during the most dynamic episodes (skipping and jumping). The accuracy became worse as movement got wilder. During skipping and jumping, the biggest error was 5.8∘. However, most errors were smaller than 3∘. We discuss the implications of decreased accuracy in the context of different research scenarios.

Assessment of oculomotor function after prolonged computer use

To analyze the specific effects of prolonged computer use on oculomotor function, we propose an oculomotor function evaluation system to analyze changes in oculomotor movement function by using an eye tracker to record eye movement data when performing gaze, smooth pursuit, and saccade under normal condition, after one hour and one and a half hours of continuous working at a computer. The captured eye movement data is pre-processed, and then data features are calculated and analyzed to understand the specific effects of continuously using the computer on the oculomotor function. The results show that the oculomotor function decreases as we gaze at the computer screen for longer periods, as evidenced by a decrease in the stability of the gaze function, a reduction in the gaze focus, a reduction in the speed of eye saccades, and a decrease in the smooth pursuit function. In short, the oculomotor function worsens after prolonged working at a computer. This paper presents the effects of continuously using the computer quantificationally for the first time. The proposed oculomotor function evaluation system could also be used to assess patients who have a disability in oculomotor function and specific individuals, e.g. pilots.

Eye-tracking indices of attention allocation and attention bias variability are differently related to trauma exposure and PTSD

Amplified attention allocation to negative information in one's environment has been implicated in posttraumatic stress disorder (PTSD). Attention bias variability (ABV), the magnitude of attention fluctuation between negative and neutral cues, has also been found to be elevated in PTSD. While eye-tracking methodology has been used in research on attention allocation in PTSD, ABV was only explored using manual reaction-time-based indices. Thirty-seven participants with PTSD, 34 trauma-exposed healthy controls (TEHC), and 30 non-exposed healthy controls (HC) completed an eye-tracking free-viewing task in which matrices comprised of neutral and negatively-valenced faces were presented. Threat-related attention allocation was calculated as the proportion of dwell time (DT%) on negatively-valenced faces. Eye-tracking-based ABV was calculated as the standard deviation of DT% across matrices. DT% on negatively-valenced faces was greater in participants with PTSD compared to both TEHC (p = .036, d = 0.50) and HC (p < .001, d = 1.03), with TEHCs showing a greater attentional bias compared to HCs (p = .001, d = 0.84). Controlling for average fixation duration, ABV was higher in both the PTSD and TEHC groups relative to the HC group (p = .004, d = 0.40), with no difference between the two trauma-exposed groups. Biased attention allocation toward negative social information is related to PTSD pathology, whereas elevated ABV measured with eye-tracking appear to be related to trauma-exposure per-se.

Employing Eye Tracking to Study Visual Attention to Live Streaming: A Case Study of Facebook Live

In recent years, the COVID-19 pandemic has led to the development of a new business model, “Live Streaming + Ecommerce”, which is a new method for commercial sales that shares the goal of sustainable economic growth (SDG 8). As information technology finds its way into the digital lives of internet users, the real-time and interactive nature of live streaming has overturned the traditional entertainment experience of audio and video content, moving towards a more nuanced division of labor with multiple applications. This study used a portable eye tracker to collect eye movement information from participants watching Facebook Live, with 31 participants who had experience using the live streaming platform. The four eye movement indicators, namely, latency of first fixation (LFF), duration of first fixation (DFF), total fixation durations (TFD), and the number of fixations (NOF), were used to analyze the distribution of the visual attention in each region of interest (ROI) and explore the study questions based on the ROIs. The findings of this study were as follows: (1) the fixation order of the ROIs in the live ecommerce platform differed between participants of different sexes; (2) the DFF of the ROIs in the live ecommerce platform differed among participants of different sexes; and (3) regarding the ROIs of participants on the live ecommerce platform, participants of different sexes showed the same attention to the live products according to the TFD and NOF eye movement indicators. This study explored the visual search behaviors of existing consumers watching live ecommerce and provides the results as a reference for operators and researchers of live streaming platforms.