"We choose this CV because we choose diversity" - What do eye movements say about the choices recruiters make?

Introduction: A large body of research has established a consensus that racial discrimination in CV screening occurs and persists. Nevertheless, we still know very little about how recruiters look at the CV and how this is connected to the discriminatory patterns. This article examines the way recruiters view and select CVs and how they reason about their CV selection choices, as a first step in unpacking the patterns of hiring discrimination. Specifically, we explore how race and ethnicity signaled through the CV matter, and how recruiters reason about the choices they make. Methods: We recorded data from 40 respondents (20 pairs) who are real-life recruiters with experiences in recruitment of diverse employees in three large Swedish-based firms in the finance and retail sector in two large cities. The participating firms all value diversity, equity and inclusion in their recruitment. Their task was to individually rate 10 fictious CVs where race (signaled by face image) and ethnicity (signaled by name) were systematically manipulated, select the top three candidates, and then discuss their choices in pairs to decide on a single top candidate. We examined whether respondents' choices were associated with the parts of the CV they looked at, and how they reasoned and justified their choices through dialog. Results: Our results show that non-White CVs were rated higher than White CVs. While we do not observe any statistically significant differences in the ratings between different racial groups, we see a statistically significant preference for Chinese over Iraqi names. There were no significant differences in time spent looking at the CV across different racial groups, but respondents looked longer at Polish names compared to Swedish names when presented next to a White face. The dialog data reveal how respondents assess different CVs by making assumptions about the candidates' job and organizational fit through limited information on the CVs, especially when the qualifications of the candidates are evaluated to be equal.

What is a blink? Classifying and characterizing blinks in eye openness signals.

Blinks, the closing and opening of the eyelids, are used in a wide array of fields where human function and behavior are studied. In data from video-based eye trackers, blink rate and duration are often estimated from the pupil-size signal. However, blinks and their parameters can be estimated only indirectly from this signal, since it does not explicitly contain information about the eyelid position. We ask whether blinks detected from an eye openness signal that estimates the distance between the eyelids (EO blinks) are comparable to blinks detected with a traditional algorithm using the pupil-size signal (PS blinks) and how robust blink detection is when data quality is low. In terms of rate, there was an almost-perfect overlap between EO and PS blink (F1 score: 0.98) when the head was in the center of the eye tracker's tracking range where data quality was high and a high overlap (F1 score 0.94) when the head was at the edge of the tracking range where data quality was worse. When there was a difference in blink rate between EO and PS blinks, it was mainly due to data loss in the pupil-size signal. Blink durations were about 60 ms longer in EO blinks compared to PS blinks. Moreover, the dynamics of EO blinks was similar to results from previous literature. We conclude that the eye openness signal together with our proposed blink detection algorithm provides an advantageous method to detect and describe blinks in greater detail.

Large eye-head gaze shifts measured with a wearable eye tracker and an industrial camera.

We built a novel setup to record large gaze shifts (up to 140[Formula: see text]). The setup consists of a wearable eye tracker and a high-speed camera with fiducial marker technology to track the head. We tested our setup by replicating findings from the classic eye-head gaze shift literature. We conclude that our new inexpensive setup is good enough to investigate the dynamics of large eye-head gaze shifts. This novel setup could be used for future research on large eye-head gaze shifts, but also for research on gaze during e.g., human interaction. We further discuss reference frames and terminology in head-free eye tracking. Despite a transition from head-fixed eye tracking to head-free gaze tracking, researchers still use head-fixed eye movement terminology when discussing world-fixed gaze phenomena. We propose to use more specific terminology for world-fixed phenomena, including gaze fixation, gaze pursuit, and gaze saccade.

GlassesValidator: A data quality tool for eye tracking glasses.

According to the proposal for a minimum reporting guideline for an eye tracking study by Holmqvist et al. (2022), the accuracy (in degrees) of eye tracking data should be reported. Currently, there is no easy way to determine accuracy for wearable eye tracking recordings. To enable determining the accuracy quickly and easily, we have produced a simple validation procedure using a printable poster and accompanying Python software. We tested the poster and procedure with 61 participants using one wearable eye tracker. In addition, the software was tested with six different wearable eye trackers. We found that the validation procedure can be administered within a minute per participant and provides measures of accuracy and precision. Calculating the eye-tracking data quality measures can be done offline on a simple computer and requires no advanced computer skills.

Precise localization of corneal reflections in eye images using deep learning trained on synthetic data.

We present a deep learning method for accurately localizing the center of a single corneal reflection (CR) in an eye image. Unlike previous approaches, we use a convolutional neural network (CNN) that was trained solely using synthetic data. Using only synthetic data has the benefit of completely sidestepping the time-consuming process of manual annotation that is required for supervised training on real eye images. To systematically evaluate the accuracy of our method, we first tested it on images with synthetic CRs placed on different backgrounds and embedded in varying levels of noise. Second, we tested the method on two datasets consisting of high-quality videos captured from real eyes. Our method outperformed state-of-the-art algorithmic methods on real eye images with a 3-41.5% reduction in terms of spatial precision across data sets, and performed on par with state-of-the-art on synthetic images in terms of spatial accuracy. We conclude that our method provides a precise method for CR center localization and provides a solution to the data availability problem, which is one of the important common roadblocks in the development of deep learning models for gaze estimation. Due to the superior CR center localization and ease of application, our method has the potential to improve the accuracy and precision of CR-based eye trackers.

Effects of stochastic vestibular stimulation on cognitive performance in children with ADHD.

Previous work has shown that exposure to auditory white noise (WN) can improve cognitive performance in children with ADHD, but it is unknown whether this improvement generalizes to other sensory modalities. To address this knowledge gap, we tested the effect of Stochastic Vestibular Stimulation (SVS) on cognitive performance and reaction time (RT) variability in two groups: children with ADHD and typically developing children (TDC). Children with ADHD (N=42) and TDC (N=28) performed three cognitive tasks (Spanboard, Word Recall and N-back tasks) at two different occasions, with and without exposure to SVS, in a double blinded design. The results showed no main effects of SVS on neither performance nor RT variability for children in any of the groups, and no interactions between SVS and group. Based on these results we conclude that, using our stimulation protocol, the positive effects of WN exposure on cognition in children with ADHD do not generalize to Stochastic Vestibular Stimulation.

Minimal reporting guideline for research involving eye tracking (2023 edition).

A guideline is proposed that comprises the minimum items to be reported in research studies involving an eye tracker and human or non-human primate participant(s). This guideline was developed over a 3-year period using a consensus-based process via an open invitation to the international eye tracking community. This guideline will be reviewed at maximum intervals of 4 years.

Eye movements in visual impairment.

This Special Issue describes the impact of visual impairment on visuomotor function. It includes contributions that examine gaze control in conditions associated with abnormal visual development such as amblyopia, dyslexia and neurofibromatosis as well as disorders associated with field loss later in life, such as macular degeneration and stroke. Specifically, the papers address both gaze holding (fixation), and gaze-following behavior (single saccades, sequences of saccades and smooth-pursuit) that characterize active vision in daily life and evaluate the influence of both pathological and simulated field loss. Several papers address the challenges to reading and visual search; describing how the patterns of eye movements in these real-world tasks adapt to visual impairment and highlighting how they could serve as diagnostic markers of visuomotor function.

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.

The amplitude of small eye movements can be accurately estimated with video-based eye trackers.

Estimating the gaze direction with a digital video-based pupil and corneal reflection (P-CR) eye tracker is challenging partly since a video camera is limited in terms of spatial and temporal resolution, and because the captured eye images contain noise. Through computer simulation, we evaluated the localization accuracy of pupil-, and CR centers in the eye image for small eye rotations (≪ 1 deg). Results highlight how inaccuracies in center localization are related to 1) how many pixels the pupil and CR span in the eye camera image, 2) the method to compute the center of the pupil and CRs, and 3) the level of image noise. Our results provide a possible explanation to why the amplitude of small saccades may not be accurately estimated by many currently used video-based eye trackers. We conclude that eye movements with arbitrarily small amplitudes can be accurately estimated using the P-CR eye-tracking principle given that the level of image noise is low and the pupil and CR span enough pixels in the eye camera, or if localization of the CR is based on the intensity values in the eye image instead of a binary representation.

Eye tracking: empirical foundations for a minimal reporting guideline.

In this paper, we present a review of how the various aspects of any study using an eye tracker (such as the instrument, methodology, environment, participant, etc.) affect the quality of the recorded eye-tracking data and the obtained eye-movement and gaze measures. We take this review to represent the empirical foundation for reporting guidelines of any study involving an eye tracker. We compare this empirical foundation to five existing reporting guidelines and to a database of 207 published eye-tracking studies. We find that reporting guidelines vary substantially and do not match with actual reporting practices. We end by deriving a minimal, flexible reporting guideline based on empirical research (Section "An empirically based minimal reporting guideline").

Eye-movement replay supports episodic remembering.

When we bring to mind something we have seen before, our eyes spontaneously unfold in a sequential pattern strikingly similar to that made during the original encounter, even in the absence of supporting visual input. Oculomotor movements of the eye may then serve the opposite purpose of acquiring new visual information; they may serve as self-generated cues, pointing to stored memories. Over 50 years ago Donald Hebb, the forefather of cognitive neuroscience, posited that such a sequential replay of eye movements supports our ability to mentally recreate visuospatial relations during episodic remembering. However, direct evidence for this influential claim is lacking. Here we isolate the sequential properties of spontaneous eye movements during encoding and retrieval in a pure recall memory task and capture their encoding-retrieval overlap. Critically, we show that the fidelity with which a series of consecutive eye movements from initial encoding is sequentially retained during subsequent retrieval predicts the quality of the recalled memory. Our findings provide direct evidence that such scanpaths are replayed to assemble and reconstruct spatio-temporal relations as we remember and further suggest that distinct scanpath properties differentially contribute depending on the nature of the goal-relevant memory.

Fixation classification: how to merge and select fixation candidates.

Eye trackers are applied in many research fields (e.g., cognitive science, medicine, marketing research). To give meaning to the eye-tracking data, researchers have a broad choice of classification methods to extract various behaviors (e.g., saccade, blink, fixation) from the gaze signal. There is extensive literature about the different classification algorithms. Surprisingly, not much is known about the effect of fixation and saccade selection rules that are usually (implicitly) applied. We want to answer the following question: What is the impact of the selection-rule parameters (minimal saccade amplitude and minimal fixation duration) on the distribution of fixation durations? To answer this question, we used eye-tracking data with high and low quality and seven different classification algorithms. We conclude that selection rules play an important role in merging and selecting fixation candidates. For eye-tracking data with good-to-moderate precision (RMSD < 0.5∘), the classification algorithm of choice does not matter too much as long as it is sensitive enough and is followed by a rule that selects saccades with amplitudes larger than 1.0∘ and a rule that selects fixations with duration longer than 60 ms. Because of the importance of selection, researchers should always report whether they performed selection and the values of their parameters.

Replacing eye trackers in ongoing studies: A comparison of eye-tracking data quality between the Tobii Pro TX300 and the Tobii Pro Spectrum.

The Tobii Pro TX300 is a popular eye tracker in developmental eye-tracking research, yet it is no longer manufactured. If a TX300 breaks down, it may have to be replaced. The data quality of the replacement eye tracker may differ from that of the TX300, which may affect the experimental outcome measures. This is problematic for longitudinal and multi-site studies, and for researchers replacing eye trackers between studies. We, therefore, ask how the TX300 and its successor, the Tobii Pro Spectrum, compare in terms of eye-tracking data quality. Data quality-operationalized through precision, accuracy, and data loss-was compared between eye trackers for three age groups (around 5-months, 10-months, and 3-years). Precision was better for all gaze position signals obtained with the Spectrum in comparison to the TX300. Accuracy of the Spectrum was higher for the 5-month-old and 10-month-old children. For the three-year-old children, accuracy was similar across both eye trackers. Gaze position signals from the Spectrum exhibited lower proportions of data loss, and the duration of the data loss periods tended to be shorter. In conclusion, the Spectrum produces gaze position signals with higher data quality, especially for the younger infants. Implications for data analysis are discussed.

Quiet Eye and Computerized Precision Tasks in First-Person Shooter Perspective Esport Games.

The gaze behavior in sports and other applied settings has been studied for more than 20 years. A common finding is related to the "quiet eye" (QE), predicting that the duration of the last fixation before a critical event is associated with higher performance. Unlike previous studies conducted in applied settings with mobile eye trackers, we investigate the QE in a context similar to esport, in which participants click the mouse to hit targets presented on a computer screen under different levels of cognitive load. Simultaneously, eye and mouse movements were tracked using a high-end remote eye tracker at 300 Hz. Consistent with previous studies, we found that longer QE fixations were associated with higher performance. Increasing the cognitive load delayed the onset of the QE fixation, but had no significant influence on the QE duration. We discuss the implications of our results in the context of how the QE is defined, the quality of the eye-tracker data, and the type of analysis applied to QE data.

The pupil-size artefact (PSA) across time, viewing direction, and different eye trackers.

The pupil size artefact (PSA) is the gaze deviation reported by an eye tracker during pupil size changes if the eye does not rotate. In the present study, we ask three questions: 1) how stable is the PSA over time, 2) does the PSA depend on properties of the eye tracker set up, and 3) does the PSA depend on the participants' viewing direction? We found that the PSA is very stable over time for periods as long as 1 year, but may differ between participants. When comparing the magnitude of the PSA between eye trackers, we found the magnitude of the obtained PSA to be related to the direction of the eye-tracker-camera axis, suggesting that the angle between the participants' viewing direction and the camera axis affects the PSA. We then investigated the PSA as a function of the participants' viewing direction. The PSA was non-zero for viewing direction 0∘ and depended on the viewing direction. These findings corroborate the suggestion by Choe et al. (Vision Research 118(6755):48-59, 2016), that the PSA can be described by an idiosyncratic and a viewing direction-dependent component. Based on a simulation, we cannot claim that the viewing direction-dependent component of the PSA is caused by the optics of the cornea.

Waveform characterisation and comparison of nystagmus eye-tracking signals.

OBJECTIVE: Pathological nystagmus is a symptom of oculomotor disease where the eyes oscillate involuntarily. The underlying cause of the nystagmus and the characteristics of the oscillatory eye movements are patient specific. An important part of clinical assessment in nystagmus patients is therefore to characterise different recorded eye-tracking signals, i.e. waveforms. APPROACH: A method for characterisation of the nystagmus waveform morphology is proposed. The method extracts local morphologic characteristics based on a sinusoidal model, and clusters these into a description of the complete signal. The clusters are used to characterise and compare recordings within and between patients and tasks. New metrics are proposed that can measure waveform similarity at different scales; from short signal segments up to entire signals, both within and between patients. MAIN RESULTS: The results show that the proposed method robustly can find the most prominent nystagmus waveforms in a recording. The method accurately identifies different eye movement patterns within and between patients and across different tasks. SIGNIFICANCE: In conclusion, by allowing characterisation and comparison of nystagmus waveform patterns, the proposed method opens up for investigation and identification of the underlying condition in the individual patient, and for quantifying eye movements during tasks.