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Articles about model of eye movements and trajectories

Articles about model of eye movements and trajectories


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I need to work with data from an eye tracker. I have raw data about eye coordinates. I need to reconstruct trajectories of eye movements. Since I know modern science has several methods for this task, I already googled for them, but I don't know which ones are serious and authority works.


I am not sure what you may have looked up previously, but here are a couple of referenced articles that may be of help to you: "Identifying Fixations and Saccades in Eye-Tracking Protocols" (Salvucci and Goldberg, 2000), from their abstract:

In this paper we propose a taxonomy of fixation identification algorithms that classifies algorithms in terms of how they utilize spatial and temporal information in eye-tracking protocols.

In terms of Human-computer interaction:

"Commentary on Section 4. Eye tracking in human-computer interaction and usability research: Ready to deliver the promises." (Jacob and Karn), this article makes reference to several more specific references.


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S. Jay Samuels, The University of Minnesota Tim Rasinski, Kent State University Elfrieda H. Hiebert, TextProject, Inc. & University of California, Berkeley

Book Chapter
Published

Samuels, S.J., Rasinski, T., & Hiebert, E.H. (2011). Eye movements and reading: What teachers need to know. In A. Farstrup & S.J. Samuels (Eds.), What research has to say about reading instruction (4th Ed. pp.25-50). Newark, DE: IRA.

Abstract

From time to time, students in teacher training programs express curiosity about the course work they are required to take in preparation for being credentialed as teachers. Why, for example, some students would like to know, are they being asked to take courses in child development or the psychology of reading? Why not simply take methods courses that focus directly on erasing the achievement gap in reading? In truth, this is an important question that the students are asking because the answer to this question relates directly to how one prepares professionals in disciplines such as medicine, law, and education. Our best colleges of education are in the business of developing professionals. This being the case, what are the most important characteristics of a profession? The answer to this question is that to be considered a professional it is assumed that the practitioner possess a body of theoretical knowledge that can be used to assist in solving the problems encountered in pursuit of that profession. For example, if some students are unmotivated to learn in a classroom setting, is there a body of knowledge that the teacher can use to enhance student engagement with the learning process? Or, if despite the use of efficient reading methods, a student still has continued difficulty learning how to read, does the teacher have the theoretical knowledge necessary to diagnose the problem and resolve it? Highly educated teaching professionals understand the multifaceted nature of motivation and the complex nature of learning disability such that they can help students who are experiencing problems in learning. Of equal importance to theoretical knowledge, it is assumed the professionally trained teacher has mastered the applied skills required to help students achieve the instructional goals of the classroom. In today’s educational market place, the demands placed on the teachers have increased enormously and it is becoming increasingly common to expect that every teacher will be able to move students along a skill trajectory that leads to reading proficiency. To meet the increasing demands of the marketplace, teachers need to know more than what methods seem to work. They also need theoretical background knowledge that may prove to be useful as they work with students who are experiencing difficulty learning. For example, they should know how to motivate reluctant readers and they need to know about the work of the eye in reading In addition, if there is a problem that relates to the eyes or to faulty eye movements teachers should be aware of the symptoms so that the problem can be identified and corrected. In essence, course work that students take is designed to help them pursue their work with competency. Consequently this chapter will explain the role of eye movements in reading and it will also explain what teaches can do to help students who are experiencing difficulties with the eye movements that are essential to the reading process.

For more information about this edited volume, please visit the publisher's (International Reading Association) website.

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A New Type of Eye Movement Model Based on Recurrent Neural Networks for Simulating the Gaze Behavior of Human Reading

1 National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an 710072, China

2 Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK

Abstract

Traditional eye movement models are based on psychological assumptions and empirical data that are not able to simulate eye movement on previously unseen text data. To address this problem, a new type of eye movement model is presented and tested in this paper. In contrast to conventional psychology-based eye movement models, ours is based on a recurrent neural network (RNN) to generate a gaze point prediction sequence, by using the combination of convolutional neural networks (CNN), bidirectional long short-term memory networks (LSTM), and conditional random fields (CRF). The model uses the eye movement data of a reader reading some texts as training data to predict the eye movements of the same reader reading a previously unseen text. A theoretical analysis of the model is presented to show its excellent convergence performance. Experimental results are then presented to demonstrate that the proposed model can achieve similar prediction accuracy while requiring fewer features than current machine learning models.

1. Introduction

Using computers to simulate humans or to reproduce certain intelligent behaviors related to human vision is a typical computer vision task [1], such as simulating eye movements in reading. However, reading is complex cognitive behavior and the underlying cognitive process occurs only in the brain [2]. Modeling such behavior requires obtaining some explicit indicators via such methods as eye tracking.

When reading a text, the eyes of a skilled reader do not move continuously over the lines of text. Instead, reading proceeds by alternating between fixations and rapid eye movements called saccades [3]. This behavior is determined by the physiological structure of the human retina. Most of the optic nerve cells are concentrated in the fovea and only when the visual image falls in this area can it be “seen” clearly. Unfortunately, the fovea only provides about a 5-degree field of view [4]. Therefore, the reader needs to change the fixation point through successive saccades so that the next content falls on the fovea region of the retina. By analyzing eye movements during reading, we can quantify the reader’s actions and model for reading. Eye tracking helps researchers to determine where and how many times subjects focus on a certain word, along with their eye movement sequences from one word to another [5]. Figure 1 shows an example eye movement trajectory from an adult reader.

Models of eye movement control have been studied in cognitive psychology [6–9]. Researchers integrated a large amount of experimental data and proposed a variety of eye movement models such as easy-rider (E-Z) reader [10] and saccade generation with inhibition by foveal targets (SWIFT) [11]. Although these eye movement models typically have parameters that are fit to empirical data, their predictions are rarely tested on unseen data [12]. Moreover, their predictions are usually averaged over a group of readers, while eye movement patterns vary significantly between individuals [13]. Predicting the actual eye movements that an individual will make while reading a new text is arguably a challenging problem.

Some recent work has studied eye movement patterns from a machine learning perspective [14–17]. These studies were inspired by recent work in natural language processing (NLP) and are less tied to psychophysiological assumptions about the mechanisms that drive eye movements. The work presented in [14] was the first to apply machine learning methods to simulate human eye movements. The authors used a transformation-based model to predict word-based fixation of unseen text. Reference [17] applied a conditional random field (CRF) model to predict which words in a text are fixated by a reader. However, traditional supervised learning requires more features and preprocessing of data, which may lead to high latency in human-computer interaction applications.

Aided by their parallel distributed processing paradigm, neural networks have been widely used in pattern recognition and language processing because of their parallel distribution [18]. In 2010, Jiang [19] studied how to apply neural networks to multiple fields and provided a review of the key literature on the development of neural networks in computer-aided diagnosis. In 2011, Ren [20] proposed an improved neural network classifier that introduced balanced learning and optimization decisions, enabling efficient learning from unbalanced samples. In 2012, Ren [21] proposed a new balanced learning strategy with optimal decision making that enables effective learning from unbalanced samples and is further used to evaluate the performance of neural networks and support vector machines (SVMs).

In recent years, deep neural networks (DNN) have become a popular topic in the field of machine learning. DNN has successfully improved the recognition rate and some excellent optimization algorithms and frameworks have been proposed and applied. Guo (2018) proposed a novel robust and general vector quantization (VQ) framework to enhance both robustness and generalization of VQ approaches [22]. Liu (2018) presented an efficient bidirectional Gated Recurrent Unit (GRU) network to explore the feasibility and the potential of mid-air dynamic gesture based user identification [23]. Liu (2019) presented an end-to-end multiscale deep encoder (convolution) network, which took both the reconstruction of image pixels’ intensities and the recovery of multiscale edge details into consideration under the same framework [24]. Wu (2018) proposed an unsupervised deep hashing framework and adopted an efficient alternating approach to optimize the objective function [25]. Wu (2019) proposed a Self-Supervised Deep Multimodal Hashing (SSDMH) method and demonstrated the superiority of SSDMH over state-of-the-art cross-media hashing approaches [26]. Luan (2018) developed a new type of deep convolutional neural networks (DCNNs) to reinforce the robustness of learned features against the orientation and scale changes [27]. Besides, some methods based on recurrent networks have been proposed, developed, and studied for natural language processing [28–30].

In this paper, we formalize the problem of simulating the gaze behavior of human reading as a word-based sequence labeling task (which is a classic NLP application). In the proposed method, the eye movement data of a reader reading some texts is used as training data and a bidirectional Long Short-Term Memory-Conditional Random Field (bi-LSTM-CRF) neural network architecture is used to predict the eye movement of the same reader reading a previously unseen text. The model is focused on achieving similar prediction accuracy while requiring fewer features than existing methods. However, it is worth emphasizing that in this study we focus only on models of where the eyes move during reading, and we will not be concerned with the temporal aspect of how long the eyes remain stationary at fixated words.

The remainder of this paper is organized into the following sections. Section 2 introduces the problem formulation. Section 3 proves the convergence of the model. Section 4 describes the layers of our neural network architecture. Section 5 discusses the training procedure and parameter estimation. Section 6 demonstrates the superiority of the proposed method with verification experiments. Section 7 concludes the paper with final remarks. Before ending the current section, it is worth pointing out the main contributions of the paper as follows. (i) This paper proposes and tests a new type of eye movement model based on recurrent neural networks, which is quite different from previous research on eye movement model. (ii) The convergence of the RNN-based model for predicting eye movement of human reading is proved in this paper. (iii) An experiment of foveated rendering further demonstrates the novelty and effectiveness of recurrent neural networks for solving the problem of simulating the gaze behavior of human reading.

2. Problem Formulation

Experimental findings in eye movement and reading research suggest that eye movements in reading are both goal-directed and discrete [6]. This means that the saccadic system selects visual targets on a nonrandom basis and that saccades are directed towards particular words rather than being sent a particular distance. Under this view, there are a number of candidate words during any fixation, with each having a certain probability of being selected as the target for the subsequent saccade. For our purposes we will assume that a probabilistic saccade model assigns a probability to fixation sequences resulting from saccade generation over the words in a text. Let us use the following simple representations of a text and fixation sequence.


Background & Summary

By 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 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. One of the key challenges for understanding the neural basis of selecting saccade targets is therefore to establish behavioral models of viewing behavior. Such models depend on an appropriate task for sampling viewing behavior from observers. One natural possibility is free-viewing of pictures and other stimuli. We define free-viewing as a task that imposes no external constraints on what locations or parts of a stimulus should be looked at. Instead, what locations are interesting or rewarding are defined internally by the observer. The lack of external constraints has two important advantages. 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 . 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 . These properties make free-viewing ideally suited for the study of complex oculomotor control behavior.

Yet, because observers might select different viewing strategies, the analysis of free-viewing data requires data across many observers and stimuli. Presently, a number of datasets are publicly available. Specifically, this includes datasets that document viewing behavior of a rather small number of subjects on a large number of images 5,6 . However, studies combining a sizable set of stimuli and a larger number of subjects are sparse 7 . A more complete list of different contributions can be found at http://saliency.mit.edu/datasets.html. Here, we present a dataset of eye-movement recordings from 949 observers who freely viewed images from different categories to address this issue. We believe that this dataset will be a valuable resource for investigating behavioral and neural models of oculomotor control. First, computational modeling of viewing behavior is a challenging research field that depends on a gold standard for model evaluation and comparison. With 2.7 million fixations, the presented dataset will significantly increase the size of the corpus of available eye tracking data. Second, the size of this dataset allows fine-grained analysis of spatial and temporal characteristics of eye-movement behavior. 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. 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.

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). 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. We also give a short overview of basic properties of the dataset to allow other researchers to assess its usefulness for their own research questions.


Eye movement trajectories in active visual search: Contributions of attention, memory, and scene boundaries to pattern formation

We relate the roles of attention, memory, and spatial constraints to pattern formation in eye movement trajectories previously measured in a conjunctive visual search task. Autocorrelations and power spectra of saccade direction cosines confirm a bias to progress forwardly, while turning at the display boundaries, plus a long-range memory component for the search path. Analyses of certain measures of circulation and imbalance in the eye trajectories, and their relations with the display area correspondingly subtended, bear signatures of spiraling or circulating patterns. We interpret their prevalence as mainly due to the interactions between three basic psychoneural mechanisms (conspicuity area, forward bias, long-range memory) and two task-specific geometric- spatial constraints on the eye trajectories (central start and display confinement). Conversely, computer simulations of random walks in which all psychoneural mechanisms are eliminated, while geometric-spatial constraints are maintained, show no prevalence of circulating patterns by those measures. We did find certain peculiarities of some individual participants in their pattern selections, but they appear too casual and incidental to suggest more systematic or complex search strategies in our randomized displays of uninformative stimuli.

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Acknowledgments

We thank Ann Lablans, Lindsey Duck, Donald Brien, Sean Hickman, and Mike Lewis for outstanding technical assistance. We also thank Stefan van der Stigchel, Thomas Trappenberg, Zhiguo Wang, and the members of the Munoz Lab for insightful comments on earlier versions of this work. The project was funded by the Human Frontiers Science Program (Grant RGP0039-2005-C) and the Canadian Institutes of Health Research (Grants MOP-77734 and CNS-90910). D. P. M. was supported by the Canada Research Chair Program.


References

Ernst UA, Mandon S, Schinkel-Bielefeld N, Neitzel SD, Kreiter AK, Pawelzik KR: Optimality of human contour integration. PLoS Comp Biol. in review

Field DJ, Hayes A, Hess RF: Contour integration by the human visual system: Evidence for a local “association field.”. Vision Research. 1993, 33: 173-193. 10.1016/0042-6989(93)90156-Q.

Van Humbeeck N, Hermens F, Wagemans J: Eye movement strategies during contour integration. Perception. 2011, 40 (ECVP Abstract Supplement): 192-


Introduction

When we open our eyes, we experience seeing and acting in the present. However, due to the delays inherent in neuronal transmission, the brain needs time to process what we see. Our awareness of visual events therefore lags behind the occurrence of those events in the world. Nevertheless, we are usually unaware of this delay, and are able to interact with even rapidly moving objects with surprising accuracy (Smeets et al., 1998). One explanation for how the brain might achieve this is that it overcomes its own delays through prediction. By using what it knows about how objects move in the world, the brain can work ahead to compensate for known delays, essentially predicting the present. In visual motion, for example, the future position of a moving object can be extrapolated based on previous samples (Nijhawan, 1994), and we have recently demonstrated that such neural mechanisms do indeed reduce the lag with which the brain represents the position of a moving object (Hogendoorn and Burkitt, 2018). A rapidly moving ball, which would be mislocalized if the brain did not compensate for processing delays, can be caught because its future location can be extrapolated given enough information about its past trajectory. Accurately catching the moving ball further requires that the brain compensates not only for the delays inherent in the processing of the incoming visual information, but also for the additional delays incurred by the planning and execution of the hand and arm movement. Effectively functioning in the present therefore requires that a predictive mechanism accurately encodes the time lost in the transmission and processing of sensory information, as well as the expected time that will be lost in preparing the next motor program, transmitting the associated motor commands, and actually moving the corresponding effectors.

That the brain is able to solve this computational challenge is readily apparent in the saccadic eye-movement system. Short duration, saccadic eye movements in the healthy observer are effectively open-loop, ballistic motor acts that can bring moving objects into foveal vision with remarkable precision (Becker, 1989 van Beers, 2007). Although the saccadic system is thought to program upcoming saccades based on target locations defined by retinal input, our ability to make saccades to moving objects reveals that target encoding in the saccadic system incorporates additional information about the target's anticipated position (Robinson, 1965 Barmack, 1970 Keller and Johnsen, 1990 Cassanello et al., 2008). Even when the moving object is only very briefly presented, monkeys trained to make an eye-movement to the target make saccades that land at or close to the location where the target would have been, had it still been visible (Quinet and Goffart, 2015). This shows that the additional information used by the saccadic system is predictive, and that the brain is capable of using that information to direct the eyes toward a moving object's future location.

Of course, the execution of eye movements has consequences for the visual information that lands on the retina and accordingly for what we see that is their primary purpose. Nevertheless, our visual experience is stable across eye movements, and one of the neural mechanism responsible for integrating successive fixations is saccadic remapping (Duhamel et al., 1992). In saccadic remapping, directly before a saccade, visual cells start responding to stimuli that will soon fall in their receptive fields, anticipating the future positions of objects on the retina. Essentially, these cells respond as if the eyes had already moved. An efference copy signal that encodes the magnitude and direction of the intended eye movement allows the visual system to predict the spatial consequences of the saccade (Sommer and Wurtz, 2002, 2006, 2008). Similarly, just before a saccade, visual attention shifts to those locations on the retina that attended objects will occupy after the movement (Rolfs et al., 2011 Jonikaitis et al., 2013), a process that might give rise to a continuous visual experience.

Importantly, the study of saccadic remapping has focused on the spatial parameters of the eye movement and the consequences for static stimuli. However, when executing a saccade to a moving object, the direction of an accurate saccade necessarily depends on its timing: a saccade made with a long latency must be directed further along the anticipated trajectory than a saccade made with a short latency. The fact that we are generally very good at making saccades to rapidly moving objects suggests that the efference copy signal that informs the visual system about imminent saccades encodes not only the direction and amplitude of those eye movements, but also their anticipated timing. The oculomotor system could then use the expected timing and duration of an imminent saccade to extrapolate the locations of moving objects at saccade landing.

Like the oculomotor system, perception also acts as if it extrapolates the position of moving objects, possibly to keep perception aligned with eye movements, or perhaps because perception depends on the eye movement system for target locations. Indeed, there is a whole class of motion-induced position illusions that has been argued to be a direct or indirect consequence of motion extrapolation, including the flash-lag (Nijhawan, 1994), flash-drag (Krekelberg et al., 2000 Whitney and Cavanagh, 2000), flash-jump (Cai and Schlag, 2001), and flash-grab (Cavanagh and Anstis, 2013) effects, as well as the Fröhlich effect (for review, see Kerzel, 2010). We recently investigated the neural basis of the flash-grab effect, and reported a strikingly early locus of interaction between visual motion and position information (Hogendoorn et al., 2015). In the flash-grab effect, an object is briefly flashed on a moving background that abruptly reverses direction. When the object is flashed concurrently with the motion reversal of the background, the result is a large shift of the flashed object's perceived position in the direction of the background's new direction of motion (Cavanagh and Anstis, 2013). One interpretation of this illusion is that the unexpected reversal of the background violates its predicted trajectory, necessitating a corrective signal of some kind. Because the object is flashed concurrently with the reversal, the object is also shifted by this corrective signal (Cavanagh and Anstis, 2013 Hogendoorn et al., 2015). We have previously postulated that this prediction-correction might occur in the superior colliculus (SC), because SC is known to play a crucial role in the preparation and execution of saccadic eye movements (Lee et al., 1988), and is specifically involved in extrapolating the future positions of moving saccade targets (Fleuriet and Goffart, 2012 Goffart et al., 2017). Although the cortical frontal eye fields have also been implicated in extrapolation (Cassanello et al., 2008) we observed the neural signature of extrapolation at posterior, rather than frontal electrodes in our EEG study (Hogendoorn et al., 2015). The hypothesis is therefore that this perceptual illusion (in which no actual eye movements are made) recruits the same neural mechanisms that are responsible for extrapolating the future positions of saccade targets.

This hypothesis makes the intriguing prediction that the timing of an imminent saccade can affect the perceived position of a moving object that the saccade is targeting. Although this prediction might seem to violate intuitive causality (i.e., we know where to move our eyes because we see where the object is), it is a logical consequence of a shared neural extrapolation mechanism that compensates for both sensory and motor delays: we perceive a moving object in the position that it will occupy by the time we have made an eye movement to it.

In support of this hypothesis, it has been reported that when observers execute saccades to objects that are perceptually shifted due to the flash-drag illusion (Whitney and Cavanagh, 2000), the degree of shift depends on the latency of the saccade (de'Sperati and Baud-Bovy, 2008). Although the authors interpret the results in terms of a perception-action dissociation (Goodale and Milner, 1992 Goodale and Westwood, 2004), with early saccades driven by an accurate dorsal “vision for action” system, and later saccades drawing on ventral “vision for perception” representations that are fooled by the illusion, the results are also consistent with a predictive signal that compensates for anticipated saccade latency.

Here, we test the hypothesis that the perceived position of an object is correlated with the latency of saccades aimed at it. Using the flash-grab effect, we first replicate the relationship between saccade latency and saccade landing previously reported for the flash-drag illusion (de'Sperati and Baud-Bovy, 2008). We show that the pattern of results is explained equally well, and with fewer free parameters, by a direct, linear relationship between shift in the landing position and saccade latency than by a gradual transition from an accurate vision for action system to a vision for perception system that is susceptible to the motion-induced position shift. Altogether, we show that the visuomotor system uses both the spatial and temporal characteristics of the upcoming saccade to localize visual objects.


Discussion and Summary of Evidence

Vision science is the most coherent, integrated and prosperous branch of cognitive science. 29, 30, 98 Eye gaze metrics, probing human underlying believes, intentions, behavior and choices, spawn the field of neurocognition, where the merging of vision science and cognitively informative paradigms produces an array of scientifically recognized paradigms to study cognition. Eye movements promote understanding of how patterns of retinal activation are transformed into meaningful visual experiences, which in turn may be labeled with a specific reaction (behavior). However, while capturing mental processes of interest in cognitive psychology progresses at a relatively fast pace, the progress made in understanding the underlying mechanisms of psychiatric disorders has been surprisingly slow. In this scoping review, the authors identified journal articles published between 2010 and 2020 that addressed eye movement measurement methodology in SZ research and diagnostic field.

Smooth pursuit, free viewing and fixation stability tests offer an individual contribution to future studies of the neurobiological disturbances not only in SZ, but in other disorders. 282 Data from multiple gaze metrics have confirmed the existence of significant differences between patients with SZ and HC. 42, 57, 112, 115-118, 237 Furthermore, measurements of individuals’ exploratory eye-movements (while comparing stationary displayed ‘S’ shaped figures) provide important results. For example, in comparison with HC, patients with SZ show fewer eye fixations, longer mean duration of fixation and shorter mean scanning length (narrower range of eye movements). 121, 221, 283 It has also been reported that the results of their parents were mid-way between those of HC and SZ patients. 193, 216 Several studies concluded that EEMs are a potential biomarker of SZ. 113, 115, 121, 193, 214, 284 Importantly, among the five commonly used parameters obtained from exploratory eye movement tests, including the NEFs, mean eye scanning length, TESL, cognitive search score and RSS only the RSS, which measures the pattern of eye fixations after the question, (‘Are there any other differences?’, has been pointed out to be vulnerable to SZ. 115, 121, 219, 224

Furthermore, free viewing tests that involved various types of stimuli (i.e. landscapes, situations of interpersonal communication, normal and degraded faces) have shown that patients with SZ are characterized with abnormal scan path length and restricted scanning style. Thus, atypical scan path deficits and saccadic impairments are considered a trait marker of SZ. 53, 285

Given that eye movement assessments are non-invasive for patients, a promising future clinical research area is developing to evaluate potential relationships between disease characteristics and social functioning in patients with SZ. 1, 2, 20, 61 The growing number of reports aiming to understand the relationship between eye movement characteristics, intellectual functioning and differences in brain structures across patients with SZ provides valuable conclusions regarding cognitive impairments and social skills deficits, 221, 222, 224-230, 286 which in turn may be useful for therapeutics. 17, 20, 182, 225, 228, 287, 288

Consequently, the next decade of SZ research is likely to witness eye movement measurements being actively used as potential biomarkers and applied to cognitively informative experimental paradigms. Future research questions will require a more profound analysis of gaze metrics during viewing tasks rather than quantification of stabilized fixations. For this reason, more complex experimental paradigms (competitive cognitive tasks embracing attention-demanding components) should be translated from the well-grounded neuroscientific methods and carried out to study visual exploration among individuals with SZ. Cognitively informative paradigms, which (i) demand to act on and manipulate a given problem/task/information (ii) provide information about patients’ behavior (in laboratory and real-life environment) and (iii) disclose patients’ visual scanning patterns, are likely to offer an individual contribution to future studies of the neurobiological disturbances involved in SZ.

Until now, clinicians cannot draw a solid line between patients with different underlying pathologies, experiencing differing types and degrees of impairment depending on the nature of given task. With this in mind, multidisciplinary contributions of cutting-edge experimental paradigms that use eye-tracking methodologies are required. This will require a coordinated effort of multiple scientific disciplines, including psychiatry, psychology, neuroscience and cognitive science. Such joined effort will enable us to illustrate the characteristics of one's information processes and gaze metrics patterns under various environmental conditions. Such combination will be particularly useful in gaining insights into cognitive deficits and visual abnormalities among patients with different dimensions of SZ and individuals in the high-risk state for psychosis.


Design of Eye Movement Interactive Interface and Example Development

With the unceasing development of HCI (Human Computer Interaction) technology, natural interactive products are valued by more and more people. The next generation of man-machine interface may use touch, hearing or vision, etc. Among them, the visual sense is the most important channel to get information, but researches are mostly focused on the measurement of eye movement or data processing. In the future, the interaction of eyes with machines will have a very wide prospect. After concluding the pluses and minuses of available eye control systems, we puts forth the design principle, system concept and the state transition model of eyes interaction from the consideration of user-centered design principle. The system simplifies the measurement process, focuses on the fast tracking, calibration and accurate eye movement identification. At the same time, we use context information to reduce interaction time and combines with keys to eliminate eye movements’ ambiguity. Based on the design scheme, we develop a system-EyeHUD(Eye Head Up Diaplay) which can be applied in driving. It includes an eye preset interface, an eye control interface and a virtual vehicle scene. Before starting cars, a driver uses the eye tracker to track and calibrate eyes, then chooses an interactive function, like one blink zoom in or two blink chick. When the eye control interface is opened, the driver can use auto eye control to operate it, or choose eyes+keys way to simulate a click. The evaluation results shows: EyeHUD can successfully achieve all the default function and participants don’t need long time to understand the information provided by the interface. Compared with the eyes+keys way, the auto eye control has no obvious difference in recognition success rate.

Fang Zhi-Gang, Kong Xiang-Zong and Xu Jie, 2013. Design of Eye Movement Interactive Interface and Example Development. Information Technology Journal, 12: 1981-1987.

As is known to all, the world has entered into "radio" era, wireless Internet access, wireless charging, wireless mouse, wireless keyboard, etc. All these reflect the future of human-computer interaction is toward "anytime and anywhere" in the direction of the development. So, it can be indicated that the new interactive way must be convenient to carry without additional equipment. For example, the population of iPhone, Microsoft company Kinect system is complied with this trend, they contain touch control, sound and motion control and all of these will be generally used in interactive way in the future. However, human vision is the most important way to obtain information. Information processing depends heavily on vision, about 80-90% of the external information is obtained through the people’s eyes (Wang, 2008). And because the line of sight of people with naturalness and bidirectional which other information is unable to achieve (Jacob and Karn, 2003), people have strong interest in the research of the line of sight, with the hope that it will become the new interactive channel.

Eye Movement Technology as the main method of a visual channel information collection, has been an important role in the study of human-computer interaction, especially the Eye Tracking Technology and the line of sight Control Technology research, has had great development in recent years. At present, the line of sight tracking technology is relatively common, for its research mainly concentrated in the eye movement measurement method and the line of sight on the processing of data. And the research of regarding the intercept to the line of sight as an input channel to control program is developed in recent years and has a very wide prospects. This study puts forward an eye movement interaction system model based on an eye movement instrument, developing an eye movement interaction example EyeHUD applied to driving environment.

EYE MOVEMENT INTERACTIVE SYSTEM MODEL

Eye movement instrument: Eye movement instrument is the important instrument of psychology research which can also complete the work of capturing the line of sight in human-computer interaction. It is an equipment based on hardware which is used in the line of sight tracking. Its basic working principle is to use the image processing technology and a special camera to focus on eyes and record the line of sight. So, as to achieve the purpose of recording eye movement trajectory characteristics of visual information processing (Guo and Jin, 2005). At present, eye movement instrument is widely used in note, vision, reading and other areas of research, generally divided into wearable and non-wearable, contact and non-contact cent. The existing different manufacturer production of various types of eye movement instrument, such as Eye Link movement instrument, EVM3200 eye movement instrument, faceLAB4 eye movement instrument, Eye Trace XY 1000 eye movement meter and so on, the different eye movement instrument for different ways of eye tracking and calibration (Zhang et al ., 2009). In order to obtain a better user experience, making the auxiliary equipment cause the less interference during the interactive process. This study chooses Sweden Tobii company’s X60 eye movement apparatus to follow the line of sight. The eye movement tracking system adopts advanced wide-angle wireless telemetry tracking technology, without wearing any head holder or helmet, allowing the head moving in a wide range of 3d and acquiring accurate data at the same time. Also Tobii company also provides open source supporting software toolkits, helping engineer to develop their own applications or eye movement control program. This instrument is shown in Fig. 1.

Eye movement interactive system framework: According to the eye movement interaction design principles, this study puts forward a scalable eye movement interaction system framework, as shown in Fig. 2.

This Eye movement interaction system mainly includes two major module, the first is the line of sight pre-setting Module (Eye Preset Module), the user completes eye tracking, checking and interactive function selection through it. Eye movement instrument marks out the line of sight which goes into the interactive region from a wide range of view and then calibrate the position of interactive objects and the line of sight. If calibration succeeds, then activate follow-up function selection and recognition module, avoiding the whole system being active and analyzing all eye movement.

After calibration, the user can choose to open the line of sight Control Module (Eye Control Module), then control the top application through the multichannel input. System scalability reflects in:

Interaction function can be expanded: In addition to the commonly use such as click, rolling but also can be enlarged, turning the page and other function. As long as the user builds a one-to-one mapping relation between the selected eye movement type and interactive function, then can complete the function such as amplification through the eyes
Eye movement type extensible: Eye movement types are divided into static eye movement (watch, blink, etc.) and dynamic eye movement (cross line, draw circle, etc.), can be chosen for customers. In the definition of eye movement set, developers should choose some kinds of low correlation and conform to the mainstream user interaction habits of eye movement type, when the system switches into the recognition process, can only start one of the two modes ,static recognition or dynamic recognition. so as to decrease the system calculation load. On the other hand, due to the system focusing on a particular type of recognition and thus be able to the exclusion of other unconscious eye movement interference to reduce the recognition error rate
Identification method can be extended: In addition to judge fixation time or blink frequency outside, system can also use context information to reduce recognition time. For example, in two blink corresponding click interaction. First, keep the user first blinking location information, then remind the user that the current target has been chosen once. If the user doesn’t choose any other target in the next any moment, after the second eye blinking toward the original target then it can be selected. This will reduce the repetition time caused by midway interrupt
Higher application can be expanded: According to different application scene, the line of sight control interface can be different. In accordance with the eye movement interaction design principle, the same preliminary settings interface can be corresponding to different line of sight control interface. So that, the visual professional development personnel can not only focus on the line of sight tracking, calibration, identification of bottom technology, but also accelerate the development of the prototype system effectively. In addition, it can be expanded multiple input channels (Bloch and Hammoud, 2009), constraint the ambiguity of eye movement

Eye movement interactive state transition model: According to the eye movement interaction framework and the contact type and non-contact type interactive state transition model (Wu et al ., 2009). This study proposes a kind of eye movement interactive state transition model as shown in Fig. 3, standardizing the eye movement interaction process and reducing the effect of MIDAS contact.

In Fig. 3, the state 0, state 1 and state 2 state belongs to the line of sight pre-setting module, state 0 means not in the tracking area, the line of sight will not have any influence on the interface. State 1 means the line of sight have entered the tracking area but not yet calibrated, the line of sight fluctuations will not affect subsequent calibration and identification. State 2 means the line of sight has been correct tracked, the cursor in the interface will be moved according to the line of sight, at this time choose magnification or click to complete the function of interactive After the set into the line of sight control module, the whole process has been tracked the line of sight, the state 4 means the system will judge whether the current state exists and which predefined type of eye movement exists according to the context.

If it is static initial eye movement then goes into the state 5, or into the state 6. Only the eye movement type has been defined then interacts with the top application. It can be chosen other interactive function in the interaction process at any time, such as the "click" into "amplification". The state transition model provides a standard software development process for the eye control programming, the detail eye movement interface examples can develop software flow chart in this state transfer model basis easily.

EYE MOVEMENT INTERACTIVE INTERFACE EXAMPLE EYEHUD

In order to improve the driver’s user experience in vehicle with such various equipments, automobile manufacturers have begun to shorten the time of user’s access to information and operating equipment. The driver may lower the head to watch instrument display or regulation floor console audio air conditioning equipment vehicle at high speed, especially the night high-speed traffic, it may cause accidents in case of an emergency (Ting, 2008). More and more high-grade vehicle assembly a HUD (Head Up Display car looked Up Display) system to reduce the line of sight sloshing, so as to avoid dangerous situation happen. It will display the information about the car in front wind shield and its display position, display brightness are adjustable, so it can avoid looking down at instrument or adjusting the equipment by body rotation while driving, also shorten the time that the eye to the visual blind area ahead. It has a great application value to reduce the traffic accident (Hao, 2009).

At present HUD which most of the cars used is located in instrument desk back end projection screen, it display important information (such as vehicle speed, navigation remind, etc.) onto the bottom of the front windshield for drivers to watch (Kumar et al ., 2007). As is known to all, when people are driving a car, they can not use their hands for any other thing, if they can obtain information through the eye at the same time with the use of visual operation in the HUD of information. It can be regarded as a kind of alternative touch screen or function key, as a method to solve information saturation.

Due to the line of sight is uncertain and also everybody’s eye movement process is not the same, so it is necessary to study the advantages and disadvantages and the application range of the total line of sight control and the combination of the line of sight and key control. Which needs to explain is that eye control interface is not a traditional way which uses button to open or close a electronic equipment but needs a special key which has an effect to any equipment in any interface (Tullis and Albert, 2009).

At present the laboratory has the development of Eye movement interaction prototype system software and hardware conditions, according to the above proposed Eye movement interaction system model, this study will develop an eye control interface which is applicative in automobile.

According to the above mentioned, the concept of EyeHUD models such as Fig. 4 shows. In the development of the prototype system, there needs two block screens to simulate the real driving environment, one of them simulates the first block glass vision, another one simulates the car HMI (Human Machine Interface) operation interface.

EyeHUD is developed as an interactive interface of sight for driving environment. Vehicle electronic equipment which is commonly used can be divided into safety equipment, comfortable model equipment and recreational equipment according to the function (Zhuang, 2009), as shown in Table 1. Because the influence of eye control on the traffic safety haven’t got evaluation and its original purpose is assisting the driver controlling car HMI to reduce head-down operation. It is about 0.8s between two blink, in car environment eye movement is not suitable for controlling functions which needs rapid reaction, such as brake accelerator pedal, light system, etc., (Li, 2009).

Table 1: Common car device classification

Finally, with basis of eye movement control development and function analysis of automobile equipment, this topic selects seven commonly used equipment which make up the major element of function interactive interface, including navigation, radio, astern back sight, music, light, air conditioner and defrosting, the whole EyeHUD function hierarchy block diagram as shown in Fig. 5. Taking music as a representative, it has developed music function example interface longitudinal, interface element with the most commonly used music player function, other functional interface development may refer to the model.

According to the above Eye movement interaction system framework and EyeHUD conceptual model and the function requirement, the Eye Control prototype EyeHUD will include three main Interface: line of sight preliminary Settings Interface (Eye Preset Interface, EPI), Eye movement Control Interface (Eye Control Interface, ECI) and Virtual Car scene (Car Virtual Reality, CarVR). Three interface structure model as shown in Fig. 6.

The user first completes eye tracking, check interactive function selection through the EPI and eye movement instrument. After the success of calibration, the users in the CarVR open the line of sight control interface ECI, then use the line of sight to open the function.

First, this study summarizes the research background of eye movement interactive. Then it puts forward the eye movement interaction design principles and extensible system framework with the basis of the advantages and disadvantages of the predecessor’s eye control system. Finally, it designs an eye movement interactive state transition model on the basis of non-contact type interactive state transition model. For eye movement interaction system, it has been already developed the design principle and the interaction framework and developed the various kinds of functions of EyeHUD preliminary but there still exist some deficiency which needs to improve.

This Research Project was fully sponsored by Bureau of Hangzhou city science and technology project, Hall of Zhejiang province science and technology public welfare project and Zhejiang University City College cross research foundation project with grant number 20120633B32, 2012C21038 and J-13027.Thanks for the sponsorships.

2: Wu, H.Y., F.J. Zhang and Y.J. Liu, 2009. Research in key technology of gesture interface based on vision. Comput. J., 10: 2030-2041.

3: Hao, C., 2009. Vehicle-mounted FPD HUD system. Lightweight Car Technol., 5: 34-35.

4: Zhuang, Y.X., 2009. The application and development trend of automotive electronic technology. Agric. Equipment Vehicle Eng., 2: 51-52.
Direct Link |

5: Wang, W., 2008. Remote control of eye movement: Blink to iPod. Practical Media Technol., 5: 20-21.

6: Zhang, L.C., H.D. Li. and L.Z. Ge, 2009. Tobii eye movement the application in man-machine interaction. Human Eng., 2: 67-69.

7: Kumar, M., A. Paepcke and T. Winograd, 2007. Gaze-enhanced Scrolling Techniques. ACM Press, San Jose, CA., USA., pp: 2531-2536.

8: Li, S.D., 2009. Interactive Design Introduction. Tsinghua University Press, Beijing, pp: 276-277.

9: Tullis, T. and B. Albert, 2009. Measuring the User Experience. Academic Press, Inc., San Diego.

10: Ting, N., 2008. New development in man-machine interface. Electron. Des. Appl., 6: 16-28,62.

11: Bloch, R. and M. Hammoud, 2009. Man-machine interface design in car. Electron. World, 3: 17-18.


Discussion and Summary of Evidence

Vision science is the most coherent, integrated and prosperous branch of cognitive science. 29, 30, 98 Eye gaze metrics, probing human underlying believes, intentions, behavior and choices, spawn the field of neurocognition, where the merging of vision science and cognitively informative paradigms produces an array of scientifically recognized paradigms to study cognition. Eye movements promote understanding of how patterns of retinal activation are transformed into meaningful visual experiences, which in turn may be labeled with a specific reaction (behavior). However, while capturing mental processes of interest in cognitive psychology progresses at a relatively fast pace, the progress made in understanding the underlying mechanisms of psychiatric disorders has been surprisingly slow. In this scoping review, the authors identified journal articles published between 2010 and 2020 that addressed eye movement measurement methodology in SZ research and diagnostic field.

Smooth pursuit, free viewing and fixation stability tests offer an individual contribution to future studies of the neurobiological disturbances not only in SZ, but in other disorders. 282 Data from multiple gaze metrics have confirmed the existence of significant differences between patients with SZ and HC. 42, 57, 112, 115-118, 237 Furthermore, measurements of individuals’ exploratory eye-movements (while comparing stationary displayed ‘S’ shaped figures) provide important results. For example, in comparison with HC, patients with SZ show fewer eye fixations, longer mean duration of fixation and shorter mean scanning length (narrower range of eye movements). 121, 221, 283 It has also been reported that the results of their parents were mid-way between those of HC and SZ patients. 193, 216 Several studies concluded that EEMs are a potential biomarker of SZ. 113, 115, 121, 193, 214, 284 Importantly, among the five commonly used parameters obtained from exploratory eye movement tests, including the NEFs, mean eye scanning length, TESL, cognitive search score and RSS only the RSS, which measures the pattern of eye fixations after the question, (‘Are there any other differences?’, has been pointed out to be vulnerable to SZ. 115, 121, 219, 224

Furthermore, free viewing tests that involved various types of stimuli (i.e. landscapes, situations of interpersonal communication, normal and degraded faces) have shown that patients with SZ are characterized with abnormal scan path length and restricted scanning style. Thus, atypical scan path deficits and saccadic impairments are considered a trait marker of SZ. 53, 285

Given that eye movement assessments are non-invasive for patients, a promising future clinical research area is developing to evaluate potential relationships between disease characteristics and social functioning in patients with SZ. 1, 2, 20, 61 The growing number of reports aiming to understand the relationship between eye movement characteristics, intellectual functioning and differences in brain structures across patients with SZ provides valuable conclusions regarding cognitive impairments and social skills deficits, 221, 222, 224-230, 286 which in turn may be useful for therapeutics. 17, 20, 182, 225, 228, 287, 288

Consequently, the next decade of SZ research is likely to witness eye movement measurements being actively used as potential biomarkers and applied to cognitively informative experimental paradigms. Future research questions will require a more profound analysis of gaze metrics during viewing tasks rather than quantification of stabilized fixations. For this reason, more complex experimental paradigms (competitive cognitive tasks embracing attention-demanding components) should be translated from the well-grounded neuroscientific methods and carried out to study visual exploration among individuals with SZ. Cognitively informative paradigms, which (i) demand to act on and manipulate a given problem/task/information (ii) provide information about patients’ behavior (in laboratory and real-life environment) and (iii) disclose patients’ visual scanning patterns, are likely to offer an individual contribution to future studies of the neurobiological disturbances involved in SZ.

Until now, clinicians cannot draw a solid line between patients with different underlying pathologies, experiencing differing types and degrees of impairment depending on the nature of given task. With this in mind, multidisciplinary contributions of cutting-edge experimental paradigms that use eye-tracking methodologies are required. This will require a coordinated effort of multiple scientific disciplines, including psychiatry, psychology, neuroscience and cognitive science. Such joined effort will enable us to illustrate the characteristics of one's information processes and gaze metrics patterns under various environmental conditions. Such combination will be particularly useful in gaining insights into cognitive deficits and visual abnormalities among patients with different dimensions of SZ and individuals in the high-risk state for psychosis.


Design of Eye Movement Interactive Interface and Example Development

With the unceasing development of HCI (Human Computer Interaction) technology, natural interactive products are valued by more and more people. The next generation of man-machine interface may use touch, hearing or vision, etc. Among them, the visual sense is the most important channel to get information, but researches are mostly focused on the measurement of eye movement or data processing. In the future, the interaction of eyes with machines will have a very wide prospect. After concluding the pluses and minuses of available eye control systems, we puts forth the design principle, system concept and the state transition model of eyes interaction from the consideration of user-centered design principle. The system simplifies the measurement process, focuses on the fast tracking, calibration and accurate eye movement identification. At the same time, we use context information to reduce interaction time and combines with keys to eliminate eye movements’ ambiguity. Based on the design scheme, we develop a system-EyeHUD(Eye Head Up Diaplay) which can be applied in driving. It includes an eye preset interface, an eye control interface and a virtual vehicle scene. Before starting cars, a driver uses the eye tracker to track and calibrate eyes, then chooses an interactive function, like one blink zoom in or two blink chick. When the eye control interface is opened, the driver can use auto eye control to operate it, or choose eyes+keys way to simulate a click. The evaluation results shows: EyeHUD can successfully achieve all the default function and participants don’t need long time to understand the information provided by the interface. Compared with the eyes+keys way, the auto eye control has no obvious difference in recognition success rate.

Fang Zhi-Gang, Kong Xiang-Zong and Xu Jie, 2013. Design of Eye Movement Interactive Interface and Example Development. Information Technology Journal, 12: 1981-1987.

As is known to all, the world has entered into "radio" era, wireless Internet access, wireless charging, wireless mouse, wireless keyboard, etc. All these reflect the future of human-computer interaction is toward "anytime and anywhere" in the direction of the development. So, it can be indicated that the new interactive way must be convenient to carry without additional equipment. For example, the population of iPhone, Microsoft company Kinect system is complied with this trend, they contain touch control, sound and motion control and all of these will be generally used in interactive way in the future. However, human vision is the most important way to obtain information. Information processing depends heavily on vision, about 80-90% of the external information is obtained through the people’s eyes (Wang, 2008). And because the line of sight of people with naturalness and bidirectional which other information is unable to achieve (Jacob and Karn, 2003), people have strong interest in the research of the line of sight, with the hope that it will become the new interactive channel.

Eye Movement Technology as the main method of a visual channel information collection, has been an important role in the study of human-computer interaction, especially the Eye Tracking Technology and the line of sight Control Technology research, has had great development in recent years. At present, the line of sight tracking technology is relatively common, for its research mainly concentrated in the eye movement measurement method and the line of sight on the processing of data. And the research of regarding the intercept to the line of sight as an input channel to control program is developed in recent years and has a very wide prospects. This study puts forward an eye movement interaction system model based on an eye movement instrument, developing an eye movement interaction example EyeHUD applied to driving environment.

EYE MOVEMENT INTERACTIVE SYSTEM MODEL

Eye movement instrument: Eye movement instrument is the important instrument of psychology research which can also complete the work of capturing the line of sight in human-computer interaction. It is an equipment based on hardware which is used in the line of sight tracking. Its basic working principle is to use the image processing technology and a special camera to focus on eyes and record the line of sight. So, as to achieve the purpose of recording eye movement trajectory characteristics of visual information processing (Guo and Jin, 2005). At present, eye movement instrument is widely used in note, vision, reading and other areas of research, generally divided into wearable and non-wearable, contact and non-contact cent. The existing different manufacturer production of various types of eye movement instrument, such as Eye Link movement instrument, EVM3200 eye movement instrument, faceLAB4 eye movement instrument, Eye Trace XY 1000 eye movement meter and so on, the different eye movement instrument for different ways of eye tracking and calibration (Zhang et al ., 2009). In order to obtain a better user experience, making the auxiliary equipment cause the less interference during the interactive process. This study chooses Sweden Tobii company’s X60 eye movement apparatus to follow the line of sight. The eye movement tracking system adopts advanced wide-angle wireless telemetry tracking technology, without wearing any head holder or helmet, allowing the head moving in a wide range of 3d and acquiring accurate data at the same time. Also Tobii company also provides open source supporting software toolkits, helping engineer to develop their own applications or eye movement control program. This instrument is shown in Fig. 1.

Eye movement interactive system framework: According to the eye movement interaction design principles, this study puts forward a scalable eye movement interaction system framework, as shown in Fig. 2.

This Eye movement interaction system mainly includes two major module, the first is the line of sight pre-setting Module (Eye Preset Module), the user completes eye tracking, checking and interactive function selection through it. Eye movement instrument marks out the line of sight which goes into the interactive region from a wide range of view and then calibrate the position of interactive objects and the line of sight. If calibration succeeds, then activate follow-up function selection and recognition module, avoiding the whole system being active and analyzing all eye movement.

After calibration, the user can choose to open the line of sight Control Module (Eye Control Module), then control the top application through the multichannel input. System scalability reflects in:

Interaction function can be expanded: In addition to the commonly use such as click, rolling but also can be enlarged, turning the page and other function. As long as the user builds a one-to-one mapping relation between the selected eye movement type and interactive function, then can complete the function such as amplification through the eyes
Eye movement type extensible: Eye movement types are divided into static eye movement (watch, blink, etc.) and dynamic eye movement (cross line, draw circle, etc.), can be chosen for customers. In the definition of eye movement set, developers should choose some kinds of low correlation and conform to the mainstream user interaction habits of eye movement type, when the system switches into the recognition process, can only start one of the two modes ,static recognition or dynamic recognition. so as to decrease the system calculation load. On the other hand, due to the system focusing on a particular type of recognition and thus be able to the exclusion of other unconscious eye movement interference to reduce the recognition error rate
Identification method can be extended: In addition to judge fixation time or blink frequency outside, system can also use context information to reduce recognition time. For example, in two blink corresponding click interaction. First, keep the user first blinking location information, then remind the user that the current target has been chosen once. If the user doesn’t choose any other target in the next any moment, after the second eye blinking toward the original target then it can be selected. This will reduce the repetition time caused by midway interrupt
Higher application can be expanded: According to different application scene, the line of sight control interface can be different. In accordance with the eye movement interaction design principle, the same preliminary settings interface can be corresponding to different line of sight control interface. So that, the visual professional development personnel can not only focus on the line of sight tracking, calibration, identification of bottom technology, but also accelerate the development of the prototype system effectively. In addition, it can be expanded multiple input channels (Bloch and Hammoud, 2009), constraint the ambiguity of eye movement

Eye movement interactive state transition model: According to the eye movement interaction framework and the contact type and non-contact type interactive state transition model (Wu et al ., 2009). This study proposes a kind of eye movement interactive state transition model as shown in Fig. 3, standardizing the eye movement interaction process and reducing the effect of MIDAS contact.

In Fig. 3, the state 0, state 1 and state 2 state belongs to the line of sight pre-setting module, state 0 means not in the tracking area, the line of sight will not have any influence on the interface. State 1 means the line of sight have entered the tracking area but not yet calibrated, the line of sight fluctuations will not affect subsequent calibration and identification. State 2 means the line of sight has been correct tracked, the cursor in the interface will be moved according to the line of sight, at this time choose magnification or click to complete the function of interactive After the set into the line of sight control module, the whole process has been tracked the line of sight, the state 4 means the system will judge whether the current state exists and which predefined type of eye movement exists according to the context.

If it is static initial eye movement then goes into the state 5, or into the state 6. Only the eye movement type has been defined then interacts with the top application. It can be chosen other interactive function in the interaction process at any time, such as the "click" into "amplification". The state transition model provides a standard software development process for the eye control programming, the detail eye movement interface examples can develop software flow chart in this state transfer model basis easily.

EYE MOVEMENT INTERACTIVE INTERFACE EXAMPLE EYEHUD

In order to improve the driver’s user experience in vehicle with such various equipments, automobile manufacturers have begun to shorten the time of user’s access to information and operating equipment. The driver may lower the head to watch instrument display or regulation floor console audio air conditioning equipment vehicle at high speed, especially the night high-speed traffic, it may cause accidents in case of an emergency (Ting, 2008). More and more high-grade vehicle assembly a HUD (Head Up Display car looked Up Display) system to reduce the line of sight sloshing, so as to avoid dangerous situation happen. It will display the information about the car in front wind shield and its display position, display brightness are adjustable, so it can avoid looking down at instrument or adjusting the equipment by body rotation while driving, also shorten the time that the eye to the visual blind area ahead. It has a great application value to reduce the traffic accident (Hao, 2009).

At present HUD which most of the cars used is located in instrument desk back end projection screen, it display important information (such as vehicle speed, navigation remind, etc.) onto the bottom of the front windshield for drivers to watch (Kumar et al ., 2007). As is known to all, when people are driving a car, they can not use their hands for any other thing, if they can obtain information through the eye at the same time with the use of visual operation in the HUD of information. It can be regarded as a kind of alternative touch screen or function key, as a method to solve information saturation.

Due to the line of sight is uncertain and also everybody’s eye movement process is not the same, so it is necessary to study the advantages and disadvantages and the application range of the total line of sight control and the combination of the line of sight and key control. Which needs to explain is that eye control interface is not a traditional way which uses button to open or close a electronic equipment but needs a special key which has an effect to any equipment in any interface (Tullis and Albert, 2009).

At present the laboratory has the development of Eye movement interaction prototype system software and hardware conditions, according to the above proposed Eye movement interaction system model, this study will develop an eye control interface which is applicative in automobile.

According to the above mentioned, the concept of EyeHUD models such as Fig. 4 shows. In the development of the prototype system, there needs two block screens to simulate the real driving environment, one of them simulates the first block glass vision, another one simulates the car HMI (Human Machine Interface) operation interface.

EyeHUD is developed as an interactive interface of sight for driving environment. Vehicle electronic equipment which is commonly used can be divided into safety equipment, comfortable model equipment and recreational equipment according to the function (Zhuang, 2009), as shown in Table 1. Because the influence of eye control on the traffic safety haven’t got evaluation and its original purpose is assisting the driver controlling car HMI to reduce head-down operation. It is about 0.8s between two blink, in car environment eye movement is not suitable for controlling functions which needs rapid reaction, such as brake accelerator pedal, light system, etc., (Li, 2009).

Table 1: Common car device classification

Finally, with basis of eye movement control development and function analysis of automobile equipment, this topic selects seven commonly used equipment which make up the major element of function interactive interface, including navigation, radio, astern back sight, music, light, air conditioner and defrosting, the whole EyeHUD function hierarchy block diagram as shown in Fig. 5. Taking music as a representative, it has developed music function example interface longitudinal, interface element with the most commonly used music player function, other functional interface development may refer to the model.

According to the above Eye movement interaction system framework and EyeHUD conceptual model and the function requirement, the Eye Control prototype EyeHUD will include three main Interface: line of sight preliminary Settings Interface (Eye Preset Interface, EPI), Eye movement Control Interface (Eye Control Interface, ECI) and Virtual Car scene (Car Virtual Reality, CarVR). Three interface structure model as shown in Fig. 6.

The user first completes eye tracking, check interactive function selection through the EPI and eye movement instrument. After the success of calibration, the users in the CarVR open the line of sight control interface ECI, then use the line of sight to open the function.

First, this study summarizes the research background of eye movement interactive. Then it puts forward the eye movement interaction design principles and extensible system framework with the basis of the advantages and disadvantages of the predecessor’s eye control system. Finally, it designs an eye movement interactive state transition model on the basis of non-contact type interactive state transition model. For eye movement interaction system, it has been already developed the design principle and the interaction framework and developed the various kinds of functions of EyeHUD preliminary but there still exist some deficiency which needs to improve.

This Research Project was fully sponsored by Bureau of Hangzhou city science and technology project, Hall of Zhejiang province science and technology public welfare project and Zhejiang University City College cross research foundation project with grant number 20120633B32, 2012C21038 and J-13027.Thanks for the sponsorships.

2: Wu, H.Y., F.J. Zhang and Y.J. Liu, 2009. Research in key technology of gesture interface based on vision. Comput. J., 10: 2030-2041.

3: Hao, C., 2009. Vehicle-mounted FPD HUD system. Lightweight Car Technol., 5: 34-35.

4: Zhuang, Y.X., 2009. The application and development trend of automotive electronic technology. Agric. Equipment Vehicle Eng., 2: 51-52.
Direct Link |

5: Wang, W., 2008. Remote control of eye movement: Blink to iPod. Practical Media Technol., 5: 20-21.

6: Zhang, L.C., H.D. Li. and L.Z. Ge, 2009. Tobii eye movement the application in man-machine interaction. Human Eng., 2: 67-69.

7: Kumar, M., A. Paepcke and T. Winograd, 2007. Gaze-enhanced Scrolling Techniques. ACM Press, San Jose, CA., USA., pp: 2531-2536.

8: Li, S.D., 2009. Interactive Design Introduction. Tsinghua University Press, Beijing, pp: 276-277.

9: Tullis, T. and B. Albert, 2009. Measuring the User Experience. Academic Press, Inc., San Diego.

10: Ting, N., 2008. New development in man-machine interface. Electron. Des. Appl., 6: 16-28,62.

11: Bloch, R. and M. Hammoud, 2009. Man-machine interface design in car. Electron. World, 3: 17-18.


References

Ernst UA, Mandon S, Schinkel-Bielefeld N, Neitzel SD, Kreiter AK, Pawelzik KR: Optimality of human contour integration. PLoS Comp Biol. in review

Field DJ, Hayes A, Hess RF: Contour integration by the human visual system: Evidence for a local “association field.”. Vision Research. 1993, 33: 173-193. 10.1016/0042-6989(93)90156-Q.

Van Humbeeck N, Hermens F, Wagemans J: Eye movement strategies during contour integration. Perception. 2011, 40 (ECVP Abstract Supplement): 192-


A New Type of Eye Movement Model Based on Recurrent Neural Networks for Simulating the Gaze Behavior of Human Reading

1 National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an 710072, China

2 Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK

Abstract

Traditional eye movement models are based on psychological assumptions and empirical data that are not able to simulate eye movement on previously unseen text data. To address this problem, a new type of eye movement model is presented and tested in this paper. In contrast to conventional psychology-based eye movement models, ours is based on a recurrent neural network (RNN) to generate a gaze point prediction sequence, by using the combination of convolutional neural networks (CNN), bidirectional long short-term memory networks (LSTM), and conditional random fields (CRF). The model uses the eye movement data of a reader reading some texts as training data to predict the eye movements of the same reader reading a previously unseen text. A theoretical analysis of the model is presented to show its excellent convergence performance. Experimental results are then presented to demonstrate that the proposed model can achieve similar prediction accuracy while requiring fewer features than current machine learning models.

1. Introduction

Using computers to simulate humans or to reproduce certain intelligent behaviors related to human vision is a typical computer vision task [1], such as simulating eye movements in reading. However, reading is complex cognitive behavior and the underlying cognitive process occurs only in the brain [2]. Modeling such behavior requires obtaining some explicit indicators via such methods as eye tracking.

When reading a text, the eyes of a skilled reader do not move continuously over the lines of text. Instead, reading proceeds by alternating between fixations and rapid eye movements called saccades [3]. This behavior is determined by the physiological structure of the human retina. Most of the optic nerve cells are concentrated in the fovea and only when the visual image falls in this area can it be “seen” clearly. Unfortunately, the fovea only provides about a 5-degree field of view [4]. Therefore, the reader needs to change the fixation point through successive saccades so that the next content falls on the fovea region of the retina. By analyzing eye movements during reading, we can quantify the reader’s actions and model for reading. Eye tracking helps researchers to determine where and how many times subjects focus on a certain word, along with their eye movement sequences from one word to another [5]. Figure 1 shows an example eye movement trajectory from an adult reader.

Models of eye movement control have been studied in cognitive psychology [6–9]. Researchers integrated a large amount of experimental data and proposed a variety of eye movement models such as easy-rider (E-Z) reader [10] and saccade generation with inhibition by foveal targets (SWIFT) [11]. Although these eye movement models typically have parameters that are fit to empirical data, their predictions are rarely tested on unseen data [12]. Moreover, their predictions are usually averaged over a group of readers, while eye movement patterns vary significantly between individuals [13]. Predicting the actual eye movements that an individual will make while reading a new text is arguably a challenging problem.

Some recent work has studied eye movement patterns from a machine learning perspective [14–17]. These studies were inspired by recent work in natural language processing (NLP) and are less tied to psychophysiological assumptions about the mechanisms that drive eye movements. The work presented in [14] was the first to apply machine learning methods to simulate human eye movements. The authors used a transformation-based model to predict word-based fixation of unseen text. Reference [17] applied a conditional random field (CRF) model to predict which words in a text are fixated by a reader. However, traditional supervised learning requires more features and preprocessing of data, which may lead to high latency in human-computer interaction applications.

Aided by their parallel distributed processing paradigm, neural networks have been widely used in pattern recognition and language processing because of their parallel distribution [18]. In 2010, Jiang [19] studied how to apply neural networks to multiple fields and provided a review of the key literature on the development of neural networks in computer-aided diagnosis. In 2011, Ren [20] proposed an improved neural network classifier that introduced balanced learning and optimization decisions, enabling efficient learning from unbalanced samples. In 2012, Ren [21] proposed a new balanced learning strategy with optimal decision making that enables effective learning from unbalanced samples and is further used to evaluate the performance of neural networks and support vector machines (SVMs).

In recent years, deep neural networks (DNN) have become a popular topic in the field of machine learning. DNN has successfully improved the recognition rate and some excellent optimization algorithms and frameworks have been proposed and applied. Guo (2018) proposed a novel robust and general vector quantization (VQ) framework to enhance both robustness and generalization of VQ approaches [22]. Liu (2018) presented an efficient bidirectional Gated Recurrent Unit (GRU) network to explore the feasibility and the potential of mid-air dynamic gesture based user identification [23]. Liu (2019) presented an end-to-end multiscale deep encoder (convolution) network, which took both the reconstruction of image pixels’ intensities and the recovery of multiscale edge details into consideration under the same framework [24]. Wu (2018) proposed an unsupervised deep hashing framework and adopted an efficient alternating approach to optimize the objective function [25]. Wu (2019) proposed a Self-Supervised Deep Multimodal Hashing (SSDMH) method and demonstrated the superiority of SSDMH over state-of-the-art cross-media hashing approaches [26]. Luan (2018) developed a new type of deep convolutional neural networks (DCNNs) to reinforce the robustness of learned features against the orientation and scale changes [27]. Besides, some methods based on recurrent networks have been proposed, developed, and studied for natural language processing [28–30].

In this paper, we formalize the problem of simulating the gaze behavior of human reading as a word-based sequence labeling task (which is a classic NLP application). In the proposed method, the eye movement data of a reader reading some texts is used as training data and a bidirectional Long Short-Term Memory-Conditional Random Field (bi-LSTM-CRF) neural network architecture is used to predict the eye movement of the same reader reading a previously unseen text. The model is focused on achieving similar prediction accuracy while requiring fewer features than existing methods. However, it is worth emphasizing that in this study we focus only on models of where the eyes move during reading, and we will not be concerned with the temporal aspect of how long the eyes remain stationary at fixated words.

The remainder of this paper is organized into the following sections. Section 2 introduces the problem formulation. Section 3 proves the convergence of the model. Section 4 describes the layers of our neural network architecture. Section 5 discusses the training procedure and parameter estimation. Section 6 demonstrates the superiority of the proposed method with verification experiments. Section 7 concludes the paper with final remarks. Before ending the current section, it is worth pointing out the main contributions of the paper as follows. (i) This paper proposes and tests a new type of eye movement model based on recurrent neural networks, which is quite different from previous research on eye movement model. (ii) The convergence of the RNN-based model for predicting eye movement of human reading is proved in this paper. (iii) An experiment of foveated rendering further demonstrates the novelty and effectiveness of recurrent neural networks for solving the problem of simulating the gaze behavior of human reading.

2. Problem Formulation

Experimental findings in eye movement and reading research suggest that eye movements in reading are both goal-directed and discrete [6]. This means that the saccadic system selects visual targets on a nonrandom basis and that saccades are directed towards particular words rather than being sent a particular distance. Under this view, there are a number of candidate words during any fixation, with each having a certain probability of being selected as the target for the subsequent saccade. For our purposes we will assume that a probabilistic saccade model assigns a probability to fixation sequences resulting from saccade generation over the words in a text. Let us use the following simple representations of a text and fixation sequence.


Background & Summary

By 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 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. One of the key challenges for understanding the neural basis of selecting saccade targets is therefore to establish behavioral models of viewing behavior. Such models depend on an appropriate task for sampling viewing behavior from observers. One natural possibility is free-viewing of pictures and other stimuli. We define free-viewing as a task that imposes no external constraints on what locations or parts of a stimulus should be looked at. Instead, what locations are interesting or rewarding are defined internally by the observer. The lack of external constraints has two important advantages. 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 . 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 . These properties make free-viewing ideally suited for the study of complex oculomotor control behavior.

Yet, because observers might select different viewing strategies, the analysis of free-viewing data requires data across many observers and stimuli. Presently, a number of datasets are publicly available. Specifically, this includes datasets that document viewing behavior of a rather small number of subjects on a large number of images 5,6 . However, studies combining a sizable set of stimuli and a larger number of subjects are sparse 7 . A more complete list of different contributions can be found at http://saliency.mit.edu/datasets.html. Here, we present a dataset of eye-movement recordings from 949 observers who freely viewed images from different categories to address this issue. We believe that this dataset will be a valuable resource for investigating behavioral and neural models of oculomotor control. First, computational modeling of viewing behavior is a challenging research field that depends on a gold standard for model evaluation and comparison. With 2.7 million fixations, the presented dataset will significantly increase the size of the corpus of available eye tracking data. Second, the size of this dataset allows fine-grained analysis of spatial and temporal characteristics of eye-movement behavior. 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. 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.

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). 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. We also give a short overview of basic properties of the dataset to allow other researchers to assess its usefulness for their own research questions.


Introduction

When we open our eyes, we experience seeing and acting in the present. However, due to the delays inherent in neuronal transmission, the brain needs time to process what we see. Our awareness of visual events therefore lags behind the occurrence of those events in the world. Nevertheless, we are usually unaware of this delay, and are able to interact with even rapidly moving objects with surprising accuracy (Smeets et al., 1998). One explanation for how the brain might achieve this is that it overcomes its own delays through prediction. By using what it knows about how objects move in the world, the brain can work ahead to compensate for known delays, essentially predicting the present. In visual motion, for example, the future position of a moving object can be extrapolated based on previous samples (Nijhawan, 1994), and we have recently demonstrated that such neural mechanisms do indeed reduce the lag with which the brain represents the position of a moving object (Hogendoorn and Burkitt, 2018). A rapidly moving ball, which would be mislocalized if the brain did not compensate for processing delays, can be caught because its future location can be extrapolated given enough information about its past trajectory. Accurately catching the moving ball further requires that the brain compensates not only for the delays inherent in the processing of the incoming visual information, but also for the additional delays incurred by the planning and execution of the hand and arm movement. Effectively functioning in the present therefore requires that a predictive mechanism accurately encodes the time lost in the transmission and processing of sensory information, as well as the expected time that will be lost in preparing the next motor program, transmitting the associated motor commands, and actually moving the corresponding effectors.

That the brain is able to solve this computational challenge is readily apparent in the saccadic eye-movement system. Short duration, saccadic eye movements in the healthy observer are effectively open-loop, ballistic motor acts that can bring moving objects into foveal vision with remarkable precision (Becker, 1989 van Beers, 2007). Although the saccadic system is thought to program upcoming saccades based on target locations defined by retinal input, our ability to make saccades to moving objects reveals that target encoding in the saccadic system incorporates additional information about the target's anticipated position (Robinson, 1965 Barmack, 1970 Keller and Johnsen, 1990 Cassanello et al., 2008). Even when the moving object is only very briefly presented, monkeys trained to make an eye-movement to the target make saccades that land at or close to the location where the target would have been, had it still been visible (Quinet and Goffart, 2015). This shows that the additional information used by the saccadic system is predictive, and that the brain is capable of using that information to direct the eyes toward a moving object's future location.

Of course, the execution of eye movements has consequences for the visual information that lands on the retina and accordingly for what we see that is their primary purpose. Nevertheless, our visual experience is stable across eye movements, and one of the neural mechanism responsible for integrating successive fixations is saccadic remapping (Duhamel et al., 1992). In saccadic remapping, directly before a saccade, visual cells start responding to stimuli that will soon fall in their receptive fields, anticipating the future positions of objects on the retina. Essentially, these cells respond as if the eyes had already moved. An efference copy signal that encodes the magnitude and direction of the intended eye movement allows the visual system to predict the spatial consequences of the saccade (Sommer and Wurtz, 2002, 2006, 2008). Similarly, just before a saccade, visual attention shifts to those locations on the retina that attended objects will occupy after the movement (Rolfs et al., 2011 Jonikaitis et al., 2013), a process that might give rise to a continuous visual experience.

Importantly, the study of saccadic remapping has focused on the spatial parameters of the eye movement and the consequences for static stimuli. However, when executing a saccade to a moving object, the direction of an accurate saccade necessarily depends on its timing: a saccade made with a long latency must be directed further along the anticipated trajectory than a saccade made with a short latency. The fact that we are generally very good at making saccades to rapidly moving objects suggests that the efference copy signal that informs the visual system about imminent saccades encodes not only the direction and amplitude of those eye movements, but also their anticipated timing. The oculomotor system could then use the expected timing and duration of an imminent saccade to extrapolate the locations of moving objects at saccade landing.

Like the oculomotor system, perception also acts as if it extrapolates the position of moving objects, possibly to keep perception aligned with eye movements, or perhaps because perception depends on the eye movement system for target locations. Indeed, there is a whole class of motion-induced position illusions that has been argued to be a direct or indirect consequence of motion extrapolation, including the flash-lag (Nijhawan, 1994), flash-drag (Krekelberg et al., 2000 Whitney and Cavanagh, 2000), flash-jump (Cai and Schlag, 2001), and flash-grab (Cavanagh and Anstis, 2013) effects, as well as the Fröhlich effect (for review, see Kerzel, 2010). We recently investigated the neural basis of the flash-grab effect, and reported a strikingly early locus of interaction between visual motion and position information (Hogendoorn et al., 2015). In the flash-grab effect, an object is briefly flashed on a moving background that abruptly reverses direction. When the object is flashed concurrently with the motion reversal of the background, the result is a large shift of the flashed object's perceived position in the direction of the background's new direction of motion (Cavanagh and Anstis, 2013). One interpretation of this illusion is that the unexpected reversal of the background violates its predicted trajectory, necessitating a corrective signal of some kind. Because the object is flashed concurrently with the reversal, the object is also shifted by this corrective signal (Cavanagh and Anstis, 2013 Hogendoorn et al., 2015). We have previously postulated that this prediction-correction might occur in the superior colliculus (SC), because SC is known to play a crucial role in the preparation and execution of saccadic eye movements (Lee et al., 1988), and is specifically involved in extrapolating the future positions of moving saccade targets (Fleuriet and Goffart, 2012 Goffart et al., 2017). Although the cortical frontal eye fields have also been implicated in extrapolation (Cassanello et al., 2008) we observed the neural signature of extrapolation at posterior, rather than frontal electrodes in our EEG study (Hogendoorn et al., 2015). The hypothesis is therefore that this perceptual illusion (in which no actual eye movements are made) recruits the same neural mechanisms that are responsible for extrapolating the future positions of saccade targets.

This hypothesis makes the intriguing prediction that the timing of an imminent saccade can affect the perceived position of a moving object that the saccade is targeting. Although this prediction might seem to violate intuitive causality (i.e., we know where to move our eyes because we see where the object is), it is a logical consequence of a shared neural extrapolation mechanism that compensates for both sensory and motor delays: we perceive a moving object in the position that it will occupy by the time we have made an eye movement to it.

In support of this hypothesis, it has been reported that when observers execute saccades to objects that are perceptually shifted due to the flash-drag illusion (Whitney and Cavanagh, 2000), the degree of shift depends on the latency of the saccade (de'Sperati and Baud-Bovy, 2008). Although the authors interpret the results in terms of a perception-action dissociation (Goodale and Milner, 1992 Goodale and Westwood, 2004), with early saccades driven by an accurate dorsal “vision for action” system, and later saccades drawing on ventral “vision for perception” representations that are fooled by the illusion, the results are also consistent with a predictive signal that compensates for anticipated saccade latency.

Here, we test the hypothesis that the perceived position of an object is correlated with the latency of saccades aimed at it. Using the flash-grab effect, we first replicate the relationship between saccade latency and saccade landing previously reported for the flash-drag illusion (de'Sperati and Baud-Bovy, 2008). We show that the pattern of results is explained equally well, and with fewer free parameters, by a direct, linear relationship between shift in the landing position and saccade latency than by a gradual transition from an accurate vision for action system to a vision for perception system that is susceptible to the motion-induced position shift. Altogether, we show that the visuomotor system uses both the spatial and temporal characteristics of the upcoming saccade to localize visual objects.


TextProject

S. Jay Samuels, The University of Minnesota Tim Rasinski, Kent State University Elfrieda H. Hiebert, TextProject, Inc. & University of California, Berkeley

Book Chapter
Published

Samuels, S.J., Rasinski, T., & Hiebert, E.H. (2011). Eye movements and reading: What teachers need to know. In A. Farstrup & S.J. Samuels (Eds.), What research has to say about reading instruction (4th Ed. pp.25-50). Newark, DE: IRA.

Abstract

From time to time, students in teacher training programs express curiosity about the course work they are required to take in preparation for being credentialed as teachers. Why, for example, some students would like to know, are they being asked to take courses in child development or the psychology of reading? Why not simply take methods courses that focus directly on erasing the achievement gap in reading? In truth, this is an important question that the students are asking because the answer to this question relates directly to how one prepares professionals in disciplines such as medicine, law, and education. Our best colleges of education are in the business of developing professionals. This being the case, what are the most important characteristics of a profession? The answer to this question is that to be considered a professional it is assumed that the practitioner possess a body of theoretical knowledge that can be used to assist in solving the problems encountered in pursuit of that profession. For example, if some students are unmotivated to learn in a classroom setting, is there a body of knowledge that the teacher can use to enhance student engagement with the learning process? Or, if despite the use of efficient reading methods, a student still has continued difficulty learning how to read, does the teacher have the theoretical knowledge necessary to diagnose the problem and resolve it? Highly educated teaching professionals understand the multifaceted nature of motivation and the complex nature of learning disability such that they can help students who are experiencing problems in learning. Of equal importance to theoretical knowledge, it is assumed the professionally trained teacher has mastered the applied skills required to help students achieve the instructional goals of the classroom. In today’s educational market place, the demands placed on the teachers have increased enormously and it is becoming increasingly common to expect that every teacher will be able to move students along a skill trajectory that leads to reading proficiency. To meet the increasing demands of the marketplace, teachers need to know more than what methods seem to work. They also need theoretical background knowledge that may prove to be useful as they work with students who are experiencing difficulty learning. For example, they should know how to motivate reluctant readers and they need to know about the work of the eye in reading In addition, if there is a problem that relates to the eyes or to faulty eye movements teachers should be aware of the symptoms so that the problem can be identified and corrected. In essence, course work that students take is designed to help them pursue their work with competency. Consequently this chapter will explain the role of eye movements in reading and it will also explain what teaches can do to help students who are experiencing difficulties with the eye movements that are essential to the reading process.

For more information about this edited volume, please visit the publisher's (International Reading Association) website.

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Eye movement trajectories in active visual search: Contributions of attention, memory, and scene boundaries to pattern formation

We relate the roles of attention, memory, and spatial constraints to pattern formation in eye movement trajectories previously measured in a conjunctive visual search task. Autocorrelations and power spectra of saccade direction cosines confirm a bias to progress forwardly, while turning at the display boundaries, plus a long-range memory component for the search path. Analyses of certain measures of circulation and imbalance in the eye trajectories, and their relations with the display area correspondingly subtended, bear signatures of spiraling or circulating patterns. We interpret their prevalence as mainly due to the interactions between three basic psychoneural mechanisms (conspicuity area, forward bias, long-range memory) and two task-specific geometric- spatial constraints on the eye trajectories (central start and display confinement). Conversely, computer simulations of random walks in which all psychoneural mechanisms are eliminated, while geometric-spatial constraints are maintained, show no prevalence of circulating patterns by those measures. We did find certain peculiarities of some individual participants in their pattern selections, but they appear too casual and incidental to suggest more systematic or complex search strategies in our randomized displays of uninformative stimuli.

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Acknowledgments

We thank Ann Lablans, Lindsey Duck, Donald Brien, Sean Hickman, and Mike Lewis for outstanding technical assistance. We also thank Stefan van der Stigchel, Thomas Trappenberg, Zhiguo Wang, and the members of the Munoz Lab for insightful comments on earlier versions of this work. The project was funded by the Human Frontiers Science Program (Grant RGP0039-2005-C) and the Canadian Institutes of Health Research (Grants MOP-77734 and CNS-90910). D. P. M. was supported by the Canada Research Chair Program.


Watch the video: Types of Eye Movements (June 2022).


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