#Eye tracking data analysis how to#
In the following sections, we describe the origin of the contained data, detail pre-processing steps performed, and show how to use the overall dataset. In summary, this unique dataset of viewing behavior will allow evaluations of models of viewing behavior against a large sample of observers and stimulus categories ( Data Citation 1). Third, this dataset might act as a reference to identify changes in oculomotor control in specific subpopulations, e.g., after stroke or due to mental illness. This is an important aspect, since eye-movement trajectories are highly structured in space and time 8– 11, and increasing the temporal window of analysis requires increasing the amounts of data. Second, the size of this dataset allows fine-grained analysis of spatial and temporal characteristics of eye-movement behavior. With 2.7 million fixations, the presented dataset will significantly increase the size of the corpus of available eye tracking data. First, computational modeling of viewing behavior is a challenging research field that depends on a gold standard for model evaluation and comparison. We believe that this dataset will be a valuable resource for investigating behavioral and neural models of oculomotor control. Here, we present a dataset of eye-movement recordings from 949 observers who freely viewed images from different categories to address this issue. A more complete list of different contributions can be found at. However, studies combining a sizable set of stimuli and a larger number of subjects are sparse 7. Specifically, this includes datasets that document viewing behavior of a rather small number of subjects on a large number of images 5, 6. Presently, a number of datasets are publicly available. Yet, because observers might select different viewing strategies, the analysis of free-viewing data requires data across many observers and stimuli. These properties make free-viewing ideally suited for the study of complex oculomotor control behavior. On the other hand, it implies that the task requires almost no training and undemanding instructions, such that it can easily be executed by children 2, cognitively impaired individuals, and a variety of non-human species 3, 4. On the one hand, it naturally leads to a rich variety of viewing behavior across observers and stimulus categories that is nevertheless highly structured 1. The lack of external constraints has two important advantages. Instead, what locations are interesting or rewarding are defined internally by the observer. We define free-viewing as a task that imposes no external constraints on what locations or parts of a stimulus should be looked at. One natural possibility is free-viewing of pictures and other stimuli.
![eye tracking data analysis eye tracking data analysis](https://www.usability.gov/sites/default/files/images/eye-tracking-full-option2.jpg)
![eye tracking data analysis eye tracking data analysis](https://i.stack.imgur.com/jqbi9.jpg)
Such models depend on an appropriate task for sampling viewing behavior from observers. One of the key challenges for understanding the neural basis of selecting saccade targets is therefore to establish behavioral models of viewing behavior. The study of this selection process spans several levels of neuroscientific analysis because it requires relating behavioral models of viewing behavior to the activity of individual neurons and brain networks. This also makes the dataset a good starting point for investigating whether viewing strategies change in patient groups.īy moving our eyes in fast and ballistic movements our oculomotor system constantly selects which parts of the environment are processed with high-acuity vision. The size and variability of viewing behavior within this dataset presents a strong opportunity for evaluating and comparing computational models of overt attention, and furthermore, for thoroughly quantifying strategies of viewing behavior.
#Eye tracking data analysis for free#
All studies allowed for free eye-movements, and differed in the age range of participants (~7–80 years), stimulus sizes, stimulus modifications (phase scrambled, spatial filtering, mirrored), and stimuli categories (natural and urban scenes, web sites, fractal, pink-noise, and ambiguous artistic figures). Trained personnel recorded all studies under standard conditions with homogeneous equipment and parameter settings. This dataset aggregates and harmonizes data from 23 different studies conducted at the Institute of Cognitive Science at Osnabrück University and the University Medical Center in Hamburg-Eppendorf.
![eye tracking data analysis eye tracking data analysis](https://marissabarlaz.github.io/portfolio/EyeTracking_files/figure-html/unnamed-chunk-15-1.png)
We present a dataset of free-viewing eye-movement recordings that contains more than 2.7 million fixation locations from 949 observers on more than 1000 images from different categories.