The scanpath is an important concept in eye tracking. It refers to a person\'s eye movements over a period of time, commonly represented as a series of alternating fixations and saccades. Machine learning has been increasingly used for the automatic interpretation of scanpaths over the past few years, particularly in research on passive gaze-based interaction, i.e., interfaces that implicitly observe and interpret human eye movements, with the goal of improving the interaction. This literature review investigates research on machine learning applications in scanpath analysis for passive gaze-based interaction between 2012 and 2022, starting from 2,425 publications and focussing on 77 publications. We provide insights on research domains and common learning tasks in passive gaze-based interaction and present common machine learning practices from data collection and preparation to model selection and evaluation. We discuss commonly followed practices and identify gaps and challenges, especially concerning emerging machine learning topics, to guide future research in the field.

Eye tracking has been used for decades in attempt to understand the cognitive processes of individuals. From memory access to problem-solving to decision-making, such insight has the potential to improve workflows and the education of students to become experts in relevant fields. Until recently, the traditional use of microscopes in pathology made eye tracking exceptionally difficult. However, the digital revolution of pathology from conventional microscopes to digital whole slide images allows for new research to be conducted and information to be learned with regards to pathologist visual search patterns and learning experiences. This has the promise to make pathology education more efficient and engaging, ultimately creating stronger and more proficient generations of pathologists to come. The goal of this review on eye tracking in pathology is to characterize and compare the visual search patterns of pathologists. The PubMed and Web of Science databases were searched using \'pathology\' AND \'eye tracking\' synonyms. A total of 22 relevant full-text articles published up to and including 2023 were identified and included in this review. Thematic analysis was conducted to organize each study into one or more of the 10 themes identified to characterize the visual search patterns of pathologists: (1) effect of experience, (2) fixations, (3) zooming, (4) panning, (5) saccades, (6) pupil diameter, (7) interpretation time, (8) strategies, (9) machine learning, and (10) education. Expert pathologists were found to have higher diagnostic accuracy, fewer fixations, and shorter interpretation times than pathologists with less experience. Further, literature on eye tracking in pathology indicates that there are several visual strategies for diagnostic interpretation of digital pathology images, but no evidence of a superior strategy exists. The educational implications of eye tracking in pathology have also been explored but the effect of teaching novices how to search as an expert remains unclear. In this article, the main challenges and prospects of eye tracking in pathology are briefly discussed along with their implications to the field.

This narrative review explores the integration of eye-tracking technology in dentistry, aiming to provide a comprehensive overview of its current applications and potential benefits. The review begins by elucidating the fundamental principles of eye tracking, encompassing the various eye-tracking methods and devices commonly used in dental research. It then delves into the role of eye tracking in dental education, where the technology offers a unique perspective on students\' visual attention during training and skill acquisition. Moreover, the review examines how eye tracking can aid in assessing and improving dental practitioners\' clinical performance, shedding light on areas of improvement and expertise. In patient care, the application of eye-tracking technology offers significant potential. By analyzing patients\' gaze patterns and visual focus during dental procedures, clinicians can gain valuable insights into their experiences, identifying sources of anxiety and discomfort. This newfound understanding can pave the way for more patient-centric care and optimized treatment plans. The review also explores the application of eye-tracking technology in designing and evaluating dental interfaces and equipment. By assessing visual ergonomics and usability, researchers can develop user-friendly instruments that enhance dental professionals\' workflow and efficiency. However, despite its promise, integrating eye tracking in dentistry is not without challenges. Technical limitations, data analysis complexities, and ethical considerations require careful attention to ensure this technology\'s ethical and responsible use. In conclusion, this narrative review highlights the growing significance of eye-tracking technology in dentistry. Its applications span dental education, clinical practice, and patient care, holding immense potential to revolutionize how dental procedures are conducted, evaluated, and experienced. Nevertheless, further research and collaboration between dental professionals and eye-tracking experts are necessary to unlock the technology\'s benefits and ensure its seamless integration into dental practices.

Objectives: The purpose of this article is to review research that has been conducted over the past five years on language development, reading skills and word learning with the use of the eye tracking machine as regards the population with autism spectrum disorder (ASD) in comparison to typically developed population. Materials and methods: A combination of relevant terms from Google Scholar, Research Gate and PsychINFO databases was used and as a result 24 studies emerged. The total number of studies that met the inclusion criteria was 21. Results: Studies focusing on the language development of people with ASD have presented significant findings regarding vocabulary processing and the preferential focus on specific stimuli (images, audio) over a wide range of ages that in some cases have not been investigated until then. Furthermore, studies on reading have found that adults with ASD showed a strong preference for images and symbols over texts, longer reading time and performances similar to the typical population in vocabulary processing. Studies on word learning demonstrated that adults with ASD are able to rely on gaze cues in order to learn a new word and they have the ability to use syntactic bootstrapping. For preschool and early school-aged children with ASD the results showed that they are capable of cross-situational learning. Conclusions: This review provides information on the effectiveness of the eye tracking method as a tool that can contribute to the identification of deficits in language processing on the part of individuals with ASD from early childhood to adulthood, and more specifically as regards the domains of language development, reading and word learning.

Background: Assistive Robotic Arms are designed to assist physically disabled people with daily activities. Existing joysticks and head controls are not applicable for severely disabled people such as people with Locked-in Syndrome. Therefore, eye tracking control is part of ongoing research. The related literature spans many disciplines, creating a heterogeneous field that makes it difficult to gain an overview. Objectives: This work focuses on ARAs that are controlled by gaze and eye movements. By answering the research questions, this paper provides details on the design of the systems, a comparison of input modalities, methods for measuring the performance of these controls, and an outlook on research areas that gained interest in recent years. Methods: This review was conducted as outlined in the PRISMA 2020 Statement. After identifying a wide range of approaches in use the authors decided to use the PRISMA-ScR extension for a scoping review to present the results. The identification process was carried out by screening three databases. After the screening process, a snowball search was conducted. Results: 39 articles and 6 reviews were included in this article. Characteristics related to the system and study design were extracted and presented divided into three groups based on the use of eye tracking. Conclusion: This paper aims to provide an overview for researchers new to the field by offering insight into eye tracking based robot controllers. We have identified open questions that need to be answered in order to provide people with severe motor function loss with systems that are highly useable and accessible.

X-ray imaging plays a crucial role in diagnostic medicine. Yet, a significant portion of the global population lacks access to this essential technology due to a shortage of trained radiologists. Eye-tracking data and deep learning models can enhance X-ray analysis by mapping expert focus areas, guiding automated anomaly detection, optimizing workflow efficiency, and bolstering training methods for novice radiologists. However, the literature shows contradictory results regarding the usefulness of eye-tracking data in deep-learning architectures for abnormality detection. We argue that these discrepancies between studies in the literature are due to (a) the way eye-tracking data is (or is not) processed, (b) the types of deep learning architectures chosen, and (c) the type of application that these architectures will have. We conducted a systematic literature review using PRISMA to address these contradicting results. We analyzed 60 studies that incorporated eye-tracking data in a deep-learning approach for different application goals in radiology. We performed a comparative analysis to understand if eye gaze data contains feature maps that can be useful under a deep learning approach and whether they can promote more interpretable predictions. To the best of our knowledge, this is the first survey in the area that performs a thorough investigation of eye gaze data processing techniques and their impacts in different deep learning architectures for applications such as error detection, classification, object detection, expertise level analysis, fatigue estimation and human attention prediction in medical imaging data. Our analysis resulted in two main contributions: (1) taxonomy that first divides the literature by task, enabling us to analyze the value eye movement can bring for each case and build guidelines regarding architectures and gaze processing techniques adequate for each application, and (2) an overall analysis of how eye gaze data can promote explainability in radiology.

This systematic literature review presents an update on developments in 3D visualization techniques and analysis tools for eye movement data in 3D environments. With the introduction of affordable and non-intrusive eye-tracking solutions to the mass market, access to users\' gaze is now increasingly possible. As a result, the adoption of eye-tracking in virtual environments using head-mounted displays is expected to increase since the trend is to incorporate gaze tracking as part of new technical solutions. The systematic literature review presented in this paper was conducted using the Scopus database (using the period 2017 to 17th of May 2022), which after analysis, resulted in the inclusion of 15 recent publications with relevance in eye-tracking visualization techniques for 3D virtual scenes. First, this paper briefly describes the foundations of eye-tracking and traditional 2D visualization techniques. As background, we also list earlier 3D eye-tracking visualization techniques identified in a previous review. Next, the systematic literature review presents the method used to acquire the included papers and a description of these in terms of eye-tracking technology, observed stimuli, application context, and type of 3D gaze visualization techniques. We then discuss the overall findings, including opportunities, challenges, trends, and present ideas for future directions. Overall the results show that eye-tracking in immersive virtual environments is on the rise and that more research and developments are needed to create novel and improved technical solutions for 3D gaze analysis.

BACKGROUND: Visual attention is a cognitive skill related to visual perception and neural activity, and also moderated by expertise, in time-constrained professional domains (e.g., aviation, driving, sport, surgery). However, the contribution of both perceptual and neural processes on performance has been studied separately in the literature.
METHODS: We defend an integration of visual and neural signals to offer a more complete picture of the visual attention displayed by professionals of different skill levels when performing free-viewing tasks. Specifically, we propose to zoom the analysis in data related to the quiet eye and P300 component jointly, as a novel signal processing approach to evaluate professionals\' visual attention.
CONCLUSIONS: This review highlights the advantages of using portable eye trackers and electroencephalogram systems altogether, as a promising technique for a better understanding of early cognitive components related to attentional processes. Altogether, the eye-fixation-related potentials method may provide a better understanding of the cognitive mechanisms employed by the participants in natural settings, revealing what visual information is of interest for participants and distinguishing the neural bases of visual attention between targets and non-targets whenever they perceive a stimulus during free viewing experiments.

Modeling the perception and evaluation of landscapes from the human perspective is a desirable goal for several scientific domains and applications. Human vision is the dominant sense, and human eyes are the sensors for apperceiving the environmental stimuli of our surroundings. Therefore, exploring the experimental recording and measurement of the visual landscape can reveal crucial aspects about human visual perception responses while viewing the natural or man-made landscapes. Landscape evaluation (or assessment) is another dimension that refers mainly to preferences of the visual landscape, involving human cognition as well, in ways that are often unpredictable. Yet, landscape can be approached by both egocentric (i.e., human view) and exocentric (i.e., bird\'s eye view) perspectives. The overarching approach of this review article lies in systematically presenting the different ways for modeling and quantifying the two \'modalities\' of human perception and evaluation, under the two geometric perspectives, suggesting integrative approaches on these two \'diverging\' dualities. To this end, several pertinent traditions/approaches, sensor-based experimental methods and techniques (e.g., eye tracking, fMRI, and EEG), and metrics are adduced and described. Essentially, this review article acts as a \'guide-map\' for the delineation of the different activities related to landscape experience and/or management and to the valid or potentially suitable types of stimuli, sensors techniques, and metrics for each activity. Throughout our work, two main research directions are identified: (1) one that attempts to transfer the visual landscape experience/management from the one perspective to the other (and vice versa); (2) another one that aims to anticipate the visual perception of different landscapes and establish connections between perceptual processes and landscape preferences. As it appears, the research in the field is rapidly growing. In our opinion, it can be greatly advanced and enriched using integrative, interdisciplinary approaches in order to better understand the concepts and the mechanisms by which the visual landscape, as a complex set of stimuli, influences visual perception, potentially leading to more elaborate outcomes such as the anticipation of landscape preferences. As an effect, such approaches can support a rigorous, evidence-based, and socially just framework towards landscape management, protection, and decision making, based on a wide spectrum of well-suited and advanced sensor-based technologies.

BACKGROUND: Saccade is a novel and feasible method for cognition assessment and has potential to screen older people with cognitive impairment.
OBJECTIVE: To systematically summarize the evidence and determine whether different saccade parameters can effectively identify patients with mild cognitive impairment (MCI) and Alzheimer\'s disease (AD).
METHODS: English and Chinese databases were searched until 19 April 2022. Studies analyzing saccade parameters in older adults with normal cognition, MCI, or AD were included. Two researchers independently performed the screening, data extraction, and quality appraisal. Meta-analyses were conducted and standard mean differences and 95% confidence intervals were estimated with a random effects model.
RESULTS: Thirty-five studies were included, and 26 studies were pooled for the meta-analysis. The results demonstrated that patients with cognitive impairment exhibited longer latency and lower accuracy rates in the prosaccade and antisaccade tasks, along with lower corrected error rates in the antisaccade tasks. However, the pooled results for antisaccades were more stable, providing the ability to distinguish patients with cognitive impairment among older adults. The results of the subgroup analyses revealed that only the accuracy rates of the antisaccades differed significantly between people with MCI and AD. Regarding the differences between older adults with normal cognition and those with MCI, the effect sizes of latency and the accuracy rates of saccades as well as the corrected error rates of antisaccades were significant.
CONCLUSIONS: Saccades, especially antisaccades, are a potential screening and assessment tool for distinguishing older adults with MCI or AD from those with normal cognition.