Almost all eye-movement researchers use algorithms to parse raw data and detect distinct types of eye movement events, such as fixations, saccades, and pursuit, and then base their results on these. Surprisingly, these algorithms are rarely evaluated. We evaluated the classifications of ten eyemovement event detection algorithms, on data from an SMI HiSpeed 1250 system, and compared them to manual ratings of two human experts. The evaluation focused on fixations, saccades, and post-saccadic oscillations. The evaluation used both event duration parameters, and sample-by-sample comparisons to rank the algorithms. The resulting event durations varied substantially as a function of what algorithm was used. This evaluation differed from previous evaluations by considering a relatively large set of algorithms, multiple events, and data from both static and dynamic stimuli. The main conclusion is that current detectors of only fixations and saccades work reasonably well for static stimuli, but barely better than chance for dynamic stimuli. Differing results across evaluation methods make it difficult to select one winner for fixation detection. For saccade detection, however, the algorithm by Larsson, Nyström and Stridh (IEEE Transaction on Biomedical Engineering, 60(9): [2484][2485][2486][2487][2488][2489][2490][2491][2492][2493]2013) outperforms all algorithms in data from both static and dynamic stimuli. The data also show how improperly selected algorithms applied to dynamic data misestimate fixation and saccade properties.

Recording eye movement data with high quality is often a prerequisite for producing valid and replicable results and for drawing well-founded conclusions about the oculomotor system. Today, many aspects of data quality are often informally discussed among researchers but are very seldom measured, quantified, and reported. Here we systematically investigated how the calibration method, aspects of participants' eye physiologies, the influences of recording time and gaze direction, and the experience of operators affect the quality of data recorded with a common tower-mounted, video-based eyetracker. We quantified accuracy, precision, and the amount of valid data, and found an increase in data quality when the participant indicated that he or she was looking at a calibration target, as compared to leaving this decision to the operator or the eyetracker software. Moreover, our results provide statistical evidence of how factors such as glasses, contact lenses, eye color, eyelashes, and mascara influence data quality. This method and the results provide eye movement researchers with an understanding of what is required to record high-quality data, as well as providing manufacturers with the knowledge to build better eyetrackers.

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Eye movements have been extensively studied in a wide range of research fields. While new methods such as mobile eye tracking and eye tracking in virtual/augmented realities are emerging quickly, the eye-movement terminology has scarcely been revised. We assert that this may cause confusion about two of the main concepts: fixations and saccades. In this study, we assessed the definitions of fixations and saccades held in the eye-movement field, by surveying 124 eye-movement researchers. These eye-movement researchers held a variety of definitions of fixations and saccades, of which the breadth seems even wider than what is reported in the literature. Moreover, these definitions did not seem to be related to researcher background or experience. We urge researchers to make their definitions more explicit by specifying all the relevant components of the eye movement under investigation: (i) the oculomotor component: e.g. whether the eye moves slow or fast; (ii) the functional component: what purposes does the eye movement (or lack thereof) serve; (iii) the coordinate system used: relative to what does the eye move; (iv) the computational definition: how is the event represented in the eye-tracker signal. This should enable eye-movement researchers from different fields to have a discussion without misunderstandings.

Male Sprague-Dawley rats were randomly divided into five groups in which group 1 received a sham operation (controls), groups 2-5 underwent common bile duct ligation and transection 14 days before the experiments. Two days prior to the studies, animals in groups 1 and 2 received saline orally, while groups 3–5 received an oral administration of either cholic acid, deoxycholic acid or whole bile. Specimens were taken for bacterial culture, and blood was collected for endotoxin assay. The rate of positive bacterial cultures from mesenteric lymph nodes in jaundiced salinetreated animals was significantly higher (p < 0.05) as compared with both controls and the other jaundiced animals treated with either bile or bile acids. Assays were positive for endotoxin in the jaundiced salinetreated group, whereas they were negative in both controls and bile- or bile-acid-treated animals. We conclude that oral administration of cholic acid, deoxycholic acid or whole bile inhibited bacterial translocation and endotoxin absorption in obstructive jaundice in the rat.

Manual classification is still a common method to evaluate event detection algorithms. The procedure is often as follows: Two or three human coders and the algorithm classify a significant quantity of data. In the gold standard approach, deviations from the human classifications are considered to be due to mistakes of the algorithm. However, little is known about human classification in eye tracking. To what extent do the classifications from a larger group of human coders agree? Twelve experienced but untrained human coders classified fixations in 6 min of adult and infant eye-tracking data. When using the sample-based Cohen's kappa, the classifications of the humans agreed near perfectly. However, we found substantial differences between the classifications when we examined fixation duration and number of fixations. We hypothesized that the human coders applied different (implicit) thresholds and selection rules. Indeed, when spatially close fixations were merged, most of the classification differences disappeared. On the basis of the nature of these intercoder differences, we concluded that fixation classification by experienced untrained human coders is not a gold standard. To bridge the gap between agreement measures (e.g., Cohen's kappa) and eye movement parameters (fixation duration, number of fixations), we suggest the use of the event-based F1 score and two new measures: the relative timing offset (RTO) and the relative timing deviation (RTD).

Eye tracking has become a valuable tool for investigating infant looking behavior over the last decades. However, where eye‐tracking methodology and achieving high data quality have received a much attention for adult participants, it is unclear how these results generalize to infant research. This is particularly important as infants behave different from adults in front of the eye tracker. In this study, we investigated whether eye physiology, positioning, and infant behavior affect measures of eye‐tracking data quality: accuracy, precision, and data loss. We report that accuracy and precision are lower, and more data loss occurs for infants with bluish eye color compared to infants with brownish eye color. Moreover, accuracy was lower for infants positioned in a high chair or in the parents' lap compared to infants positioned in a baby seat. Finally, precision decreased and data loss increased as a function of time. We highlight the importance of data quality when comparing multiple groups, as differences in data quality can affect eye‐tracking measures. In addition, we investigate how two different measures to quantify infant movement influence eye‐tracker data quality. These findings might help researchers with data collection and help manufacturers develop better eye‐tracking systems for infants.

The effect of language-driven eye movements in a visual scene with concurrent speech was examined using complex linguistic stimuli and complex scenes. The processing demands were manipulated using speech rate and the temporal distance between mentioned objects. This experiment differs from previous research by using complex photographic scenes, three-sentence utterances and mentioning four target objects. The main finding was that objects that are more slowly mentioned, more evenly placed and isolated in the speech stream are more likely to be fixated after having been mentioned and are fixated faster. Surprisingly, even objects mentioned in the most demanding conditions still show an effect of language-driven eye-movements. This supports research using concurrent speech and visual scenes, and shows that the behavior of matching visual and linguistic information is likely to generalize to language situations of high information load.