Understanding the quality of eye-tracking recordings, often characterized using accuracy, precision, and data loss, is crucial for the interpretation of eye tracking data. Eye-tracking data quality can furthermore place fundamental limits on what studies can be conducted with an eye tracker, and one may be required to report eye-tracking data quality when publishing a study. However, how does one determine the quality of eye-tracking data? This article provides an overview of operationalizations of accuracy, precision, and data loss and practical advice for determining eye-tracking data quality. Furthermore, the programming code for calculating various quality metrics for a segment of eye-tracking data is provided in MATLAB, Python, and R. Also provided is ETDQualitizer, a tool designed to enable anyone to easily determine the data quality of their recordings. We provide a version that is browser-based ( https://dcnieho.github.io/ETDQualitizer ) and enables determining eye-tracking data quality without installation or programming, while ensuring data privacy by running entirely locally. ETDQualitizer is further provided as a MATLAB, Python, and R library ( https://github.com/dcnieho/ETDQualitizer ) that can be integrated in one’s analysis scripts. We hope that this article enables any researcher to determine, critically evaluate, and report on eye-tracking data quality, and that it spurs researchers to adopt a data quality perspective in all their future eye-tracking studies.

This study investigated whether saccadic search exists on a micro scale. Using a high-speed, confocal retinal eye tracker (FET), participants searched for tiny targets within dense displays subtending less than 4 deg2. The participants exhibited goal-directed, exploratory scanning behavior with tiny saccades (median amplitude < 0.5°); saccade amplitude scaled with inter-element distance, fixation duration increased in denser displays, and consecutive saccades followed typical directional dependencies (saccadic momentum and facilitation of return). Search performance and scanpaths resembled those found in large-scale visual search tasks, suggesting functional equivalence across spatial scales. These findings support the view that microsaccades serve the same perceptual and attentional roles as larger saccades. We argue that the distinction between microsaccades and saccades is unnecessary when describing natural, task-driven visual scanning behavior.

Researchers use area of interest (AOI) analyses to interpret eye-tracking data. This article addresses four key aspects of AOI use: 1) how to report AOIs to support replicable analyses, 2) how to interpret AOI-related statistics, 3) methods for generating both static and dynamic AOIs, and 4) recent developments and future directions in AOI use. The article underscores the importance of aligning AOI design with the study’s conceptual and methodological foundations. It argues that critical decisions, such as the size, shape, and placement of AOIs, should be made early in the experimental design process and should involve eye-tracking data quality, the research question, participant tasks, and the nature of the visual stimulus. It also evaluates recent advances in AOI automation, outlining both their benefits and limitations. The article’s main message is that researchers should plan AOIs carefully and explain their choices openly so others can replicate the work.

The eyeball is not rigid and deforms during saccades. As a consequence, the saccade waveform recorded by an eye tracker may depend on which structure of the eye is used to estimate eyeball rotation. Here, we systematically describe and compare signals co-recorded from the retina, the cornea (corneal reflection, CR), the pupil, and the lens (fourth Purkinje reflection, P4) during saccades. We found that several commonly used parameters for saccade characterization differ systematically across the signals. For instance, saccades in the retinal signal had earlier onsets compared to saccades in the pupil and the P4 signals. The retinal signal had the smallest saccade amplitude and reached the peak saccade velocity earlier compared to the other signals. At the end of saccades, the retinal signal came to a stop faster than the other signals. We discuss possible explanations that may account for the relationship between the retinal signal and the other signals.

Researchers using eye tracking are heavily dependent on software and hardware tools to perform their studies, from recording eye tracking data and visualizing it, to processing and analyzing it. This article provides an overview of available tools for research using eye trackers and discusses considerations to make when choosing which tools to adopt for one's study.

In this article, we discuss operationalizations and examples of experimental design in eye-tracking research. First, we distinguish direct operationalization for entities like saccades, which are closely aligned with their original concepts, and indirect operationalization for concepts not directly measurable, such as attention or mind-wandering. The latter relies on selecting a measurable proxy. Second, we highlight the variability in algorithmic operationalizations and emphasize that changing parameters can affect outcome measures. Transparency in reporting these parameters and algorithms is crucial for comparisons across studies. Third, we provide references to studies for common operationalizations in eye-tracking research and discuss key operationalizations in reading research. Fourth, the IO-model is introduced as a tool to help researchers operationalize difficult concepts. Finally, we present three example experiments with useful methods for eye-tracking research, encouraging readers to consider these examples for inspiration in their own experiments.

There is an abundance of commercial and open-source eye trackers available for researchers interested in gaze and eye movements. Which aspects should be considered when choosing an eye tracker? The paper describes what distinguishes different types of eye trackers, their suitability for different types of research questions, and highlights questions researchers should ask themselves to make an informed choice.

The goal of this article is to demonstrate the importance of pilot studies in empirical eye-tracking research. First, we show what can go wrong when proper pilot experiments are omitted for all phases of an eye-tracking study, from testing an experiment, conducting the data collection, to building, revising, and interpreting the data analysis. Second, we describe a series of eye-tracking studies as a case study, and elaborate on all the pilot experiments that were conducted. We highlight what was learned from each pilot experiment when conceiving, designing, and conducting the research. Finally, we give practical advice for eye-tracking researchers on planning and conducting pilot experiments. This advice can be summarized as (1) take enough time, (2) be problem-oriented, (3) pilots are of an iterative nature, (4) many questions are empirical, and (5) apply the four-eyes principle. We envision that the present article helps early career researchers discover, and more established researchers rediscover, the utility of pilot experiments.

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 ( F 1 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 ( F 1 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.

Irrespective of the precision, the inaccuracy of a pupil-based eye tracker is about 0.5 ∘ . This paper delves into two factors that potentially increase the inaccuracy of the gaze signal, namely, 1) Pupil-size changes and the pupil-size artefact (PSA) and 2) the putative inability of experienced individuals to precisely refixate a visual target. Experiment 1 utilizes a traditional pupil-CR eye tracker, while Experiment 2 employs a retinal eye tracker, the FreezeEye tracker, eliminating the pupil-based estimation. Results reveal that the PSA significantly affects gaze accuracy, introducing up to 0.5 ∘ inaccuracies during calibration and validation. Corrections based on the relation between pupil size and apparent gaze shift substantially reduce inaccuracies, underscoring the PSA's influence on eye-tracking quality. Conversely, Experiment 2 demonstrates humans' precise refixation abilities, suggesting that the accuracy of the gaze signal is not limited by human refixation inconsistencies.

Eye tracking technology has become increasingly prevalent in scientific research, offering unique insights into oculomotor and cognitive processes. The present article explores the relationship between scientific theory, the research question, and the use of eye-tracking technology. It aims to guide readers in determining if eye tracking is suitable for their studies and how to formulate relevant research questions. Examples from research on oculomotor control, reading, scene perception, task execution, visual expertise, and instructional design are used to illustrate the connection between theory and eye-tracking data. These examples may serve as inspiration to researchers new to eye tracking. In summarizing the examples, three important considerations emerge: (1) whether the study focuses on describing eye movements or uses them as a proxy for e.g., perceptual, or cognitive processes, (2) the logical chain from theory to predictions, and (3) whether the study is of an observational or idea-testing nature. We provide a generic scheme and a set of specific questions that may help researchers formulate and explicate their research question using eye tracking.

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 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.

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.

Background Previous research shows that critical constructive feedback, that scaffolds students to improve on tasks, often remains untapped. The paper's aim is to illuminateat what stagesstudents provided with such feedback drop out of feedback processing. Methods In our model, students can drop out at any of five stages of feedback processing: (1) noticing, (2) decoding, (3) making sense, (4) acting upon, and (5) using feedback to make progress. Eye-tracking was used to measure noticing and decoding of feedback. Behavioral data-logging tracked students' use of feedback and potential progress. Three feedback signaling conditions were experimentally compared: a pedagogical agent, an animated arrow, and no signaling (control condition). Findings Students dropped out at each stage and few made it past the final stage. The agent condition led to significantly less feedback neglect at the two first stages, suggesting that students who are not initially inclined to notice and read feedback text can be influenced into doing so. Contribution The study provides a model and method to build more fine-grained knowledge of students' (non)processing of feedback. More knowledge on at what stages students drop out and why can inform methods to counteract drop out and scaffold more productive and fruitful responses.

OBJECTIVETo map out and describe an earlier response by using firefighters as medical first responders on while waiting for the ambulance and first incident person assignments focusing on frequency, event time and survival >30 days after performed cardiopulmonary resuscitation.DESIGNRetrospective descriptive design.SETTINGAmbulance service in a county of southern Sweden with a population of 200 000 inhabitants (23/km2 ).PARTICIPANTSData were collected from four data systems within different organizations; emergency medical communication centre, fire deparment, ambulance services and conty hospital analysis unit.MAIN OUTCOME MEASURE(S)Data from 600 while waiting for the ambulance assignments, whereof 120 with first incident person present, collected between 1 January 2012 and 31 December 2016. Between 1 June 2014 and 1 October 2015, the two fire departments were dually dispatched on out-of-hospital cardiac arrests.RESULTSThree main findings were made: there was a prolonged process time for dispatching fire fighters on while waiting for the ambulance assignments. Dual dispatches did not shorten the process time for dispatching full-time firefighters, and, in a majority of while waiting for the ambulance assignments where cardiopulmonary resuscitation was performed, firefighters or first incident persons arrived first on the scene.CONCLUSIONMinimising every minute that delays the performance of life-saving actions is crucial. By dispatching firefighters on while waiting for the ambulance assignments in rural areas, the response time in a majority of assignments was shortened. However, there was substantial delay in dispatching firefighters due to prolonged process time at the emergency medical communication centre. The emergency medical communication centre operator's ability to quickly assess the need for while waiting for the ambulance assignments plays a crucial role in the chain of survival.

We present Titta, an open-source toolbox for controlling eye trackers manufactured by Tobii AB from MATLAB and Python. The toolbox provides a wrapper around the Tobii Pro SDK, providing a convenient graphical participant setup, calibration and validation interface implemented using the PsychToolbox and PsychoPy toolboxes. The toolbox furthermore enables MATLAB and Python experiments to communicate with Tobii Pro Lab through the TalkToProLab tool. This enables experiments to be created and run using the freedom of MATLAB and Python, while the recording can be visualized and analyzed in Tobii Pro Lab. All screen-mounted Tobii eye trackers that are supported by the Tobii Pro SDK are also supported by Titta. At the time of writing, these are the Spectrum, Nano, TX300, T60XL, X3-120, X2-60, X2-30, X60, X120, T60 and T120 from Tobii Pro, and the 4C from Tobii Tech.

Objectives Patients ≥65 years old represent 30%–50% of all ambulance assignments (AAs), and the knowledge of which care level they are disposed to is limited and diverging. The aim of this study was therefore to describe and compare characteristics of patients’ aged ≥65 years dispositions during AA, including determining changes over time and factors associated with non-conveyance to hospitals.Design A longitudinal and comparative database study.Setting Ambulance service in a Swedish region.Participants 32 085 AAs with patients ≥65 years old during the years 2014, 2016 and 2018. Exclusion criteria: AAs with interhospital patient transfers and lack of patients’ dispositions data.Outcome measures Dependent factors: conveyance and non-conveyance to hospitals. Independent factors: age, sex, symptom, triage level, scene, time, day and season.Results The majority (n=29 060; 90.6%) of patients’ dispositions during AA were conveyance to hospitals. In total, the most common symptoms were circulatory (n=4953; 15.5%) and respiratory (n=4529; 14.1%). A significant increase, p<0.01, of non-conveyance to hospitals was shown during 2014 and 2018, from 801 (7.8%) to 1295 (11.4%). Increasing age was associated with decreasing odds of non-conveyance, 85–89 years (OR=0.85, 95 % CI=0.72 to 0.99) and 90 years or older (OR=0.80, 95 % CI=0.68 to 0.93). Several factors were associated with non-conveyance, for example, symptoms of diabetes (OR=8.57, 95 % CI=5.99 to 12.26) and mental disorders (OR=5.71, 95 % CI=3.85 to 8.48) in comparison with infections.Conclusions The study demonstrates several patient characteristics, and factors associated with non-conveyance to hospitals, such as age, symptom, triage level, scene, time, day and season. The increasing non-conveyance trend highlights the importance of further studies on optimal care levels for patients ≥65 years old.