Best practices in eye tracking research

Existing event detection algorithms for eye-movement data almost exclusively rely on thresholding one or more hand-crafted signal features, each computed from the stream of raw gaze data. Moreover, this thresholding is largely left for the end user. Here we present and develop gazeNet, a new framework for creating event detectors that do not require hand-crafted signal features or signal thresholding. It employs an end-to-end deep learning approach, which takes raw eye-tracking data as input and classifies it into fixations, saccades and post-saccadic oscillations. Our method thereby challenges an established tacit assumption that hand-crafted features are necessary in the design of event detection algorithms. The downside of the deep learning approach is that a large amount of training data is required. We therefore first develop a method to augment hand-coded data, so that we can strongly enlarge the data set used for training, minimizing the time spent on manual coding. Using this extended hand-coded data, we train a neural network that produces eye-movement event classification from raw eye-movement data without requiring any predefined feature extraction or post-processing steps. The resulting classification performance is at the level of expert human coders. Moreover, an evaluation of gazeNet on two other datasets showed that gazeNet generalized to data from different eye trackers and consistently outperformed several other event detection algorithms that we tested.

/pdf/gazenet-end-to-end-eye-movement-event-detection-with-deep-3pmpjo9oos.pdf

gazeNet: End-to-end eye-movement event detection with deep neural networks

Evaluating Eye Movement Event Detection: A Review of the State of the Art

The results of two experiments are analyzed to find out how artistic expertise influences visual search. Experiment I comprised survey data of 1,065 students on self-reported visual memory skills and their ability to find three targets in four images of artwork. Experiment II comprised eye movement data of 50 Visual Literacy (VL) experts and non-experts whose eye movements during visual search were analyzed for nine images of artwork as an external validation of the assessment tasks performed in Sample I. No time constraint was set for completion of the visual search task. A latent profile analysis revealed four typical solution patterns for the students in Sample I, including a mainstream group, a group that completes easy images fast and difficult images slowly, a fast and erroneous group, and a slow working student group, depending on task completion time and on the probability of finding all three targets. Eidetic memory, performance in art education and visual imagination as self-reported visual skills have significant impact on latent class membership probability. We present a hidden Markov model (HMM) approach to uncover underlying regions of attraction that result from visual search eye-movement behavior in Experiment II. VL experts and non-experts did not significantly differ in task time and number of targets found but they did differ in their visual search process: compared to non-experts, experts showed greater precision in fixating specific prime and target regions, assessed through hidden state fixation overlap. Exploratory analysis of HMMs revealed differences between experts and non-experts in image locations of attraction (HMM states). Experts seem to focus their attention on smaller image parts whereas non-experts used wider parts of the image during their search. Differences between experts and non-experts depend on the relative saliency of targets embedded in images. HMMs can determine the effect of expertise on exploratory eye movements executed during visual search tasks. Further research on HMMs and art expertise is required to confirm exploratory results.

/pdf/how-do-art-skills-influence-visual-search-eye-movements-25ebq3hl2u.pdf

How Do Art Skills Influence Visual Search? – Eye Movements Analyzed With Hidden Markov Models

This chapter introduces computational models of decision making as worthy successors to the traditional, algebraic utility framework that has dominated the field. It provides an overview of several different computational modeling approaches before providing a detailed example of perhaps the most well-established of these, based on sequential sampling of information and evidence accumulation. It is shown how this approach can account for common paradoxes in decision behavior, and how it can be extended to a variety of tasks and response modes while retaining the same basic cognitive principles. The chapter concludes with an illustration of how process-tracing methods that capture the information acquisition and response processes can help to evaluate computational models of decision making and discriminate among them. 

Computational Models of Decision Making

The first stage of analyzing eye-tracking data is commonly to code the data into sequences of fixations and saccades. This process is usually automated using simple, predetermined rules for classifying ranges of the time series into events, such as "if the dispersion of gaze samples is lower than a particular threshold, then code as a fixation; otherwise code as a saccade." More recent approaches incorporate additional eye-movement categories in automated parsing algorithms by using time-varying, data-driven thresholds. We describe an alternative approach using the beta-process vector auto-regressive hidden Markov model (BP-AR-HMM). The BP-AR-HMM offers two main advantages over existing frameworks. First, it provides a statistical model for eye-movement classification rather than a single estimate. Second, the BP-AR-HMM uses a latent process to model the number and nature of the types of eye movements and hence is not constrained to predetermined categories. We applied the BP-AR-HMM both to high-sampling rate gaze data from Andersson et al. (Behavior Research Methods 49(2), 1-22 2016) and to low-sampling rate data from the DIEM project (Mital et al., Cognitive Computation 3(1), 5-24 2011). Driven by the data properties, the BP-AR-HMM identified over five categories of movements, some which clearly mapped on to fixations and saccades, and others potentially captured post-saccadic oscillations, smooth pursuit, and various recording errors. The BP-AR-HMM serves as an effective algorithm for data-driven event parsing alone or as an initial step in exploring the characteristics of gaze data sets.

/pdf/unsupervised-parsing-of-gaze-data-with-a-beta-process-vector-ncoqjerzb6.pdf

Unsupervised parsing of gaze data with a beta-process vector auto-regressive hidden Markov model

Using Process Tracing Data to Define and Test Process Models 1

The relationship between where people look and where people reach has been studied since the dawn of experimental psychology. This relationship has implications for the designs of interactive visualizations, particularly for applications involving touchscreens. We present a new visual-motor analytics dashboard for the joint study of eye movement and hand/finger movement dynamics. Our modular approach combines real-time playback of gaze and finger-dragging behavior together with statistical models quantifying the dynamics of both modalities. To aid in visualization and inference with these data, we apply Gaussian process regression models which capture the similarities and differences between eye and finger movements, while providing a statistical model of the observed functional data. Smooth estimates of the dynamics are included in the dashboard to enable visual-analytic exploration of visual-motor behaviors on touchscreen interfaces.

Capturing You Watching You: Characterizing Visual-Motor Dynamics in Touchscreen Interactions

The Lateralized Readiness Potential as a Neural Indicator of Response Competition in Binary Decision Tasks

Fluctuations in Mu Rhythm Serve as Neural Correlates of Fluctuations in Preference During Stimulus Evaluation and Choice Selection

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