Minimal reporting guideline for research involving eye tracking (2023 edition)

Dunn, Matthew; Alexander, Robert G.; Amiebenomo, Onyekachukwu Mary-Anne; Arblaster, Gemma; Atan, Denize; Erichsen, Jonathan Thor; Giardini, Mario; Gilchrist, Iain D.; Hamilton, Ruth; Hessels, Roy S.; Hodgins, S.; Hooge, Ignace T. C.; Jackson, Brooke S.; Lee, Helena; Macknik, Stephen L.; Martínez-Conde, Susana; McIlreavy, Lee; Muratori, Lisa M.; Nyström, Maria; Otero‐Millan, Jorge; Schlüssel, Michael Maia; Self, James; Singh, Tarkeshwar; Smyrnis, Nikolaos; Sprenger, Andreas

doi:10.3758/s13428-023-02187-1

Cited by 22 publications

(5 citation statements)

References 18 publications

(15 reference statements)

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“…Reporting this information could benefit future use of the datasets, e.g., focusing on use cases for certain genders or age groups. We recommend using checklists such as the one from Dunn et al ( 2023 ), which provides a list of information that should be included when reporting eye tracking studies and the reasons behind their inclusion.…”

Section: Discussionmentioning

confidence: 99%

A review of machine learning in scanpath analysis for passive gaze-based interaction

Mohamed Selim,

Barz,

Bhatti

et al. 2024

Front. Artif. Intell.

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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.

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Section: Discussionmentioning

confidence: 99%

A review of machine learning in scanpath analysis for passive gaze-based interaction

Mohamed Selim,

Barz,

Bhatti

et al. 2024

Front. Artif. Intell.

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“…During each day, participants completed a battery of eye movement tasks to assess different aspects of SPEM. Details on eye movement recording and processing are reported according to the recommendations by Dunn et al 45 (see supplement). In short, eye movements were recorded using a video-based eye tracking system (Eyelink 1000Plus, SR Research Ltd., Ontario, Canada).…”

Section: Methodsmentioning

confidence: 99%

Normative tDCS over V5 and FEF reveals practice-induced modulation of extraretinal smooth pursuit mechanisms, but no specific stimulation effect

Radecke,

Sprenger,

Stöckler

et al. 2023

Sci Rep

Self Cite

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The neural networks subserving smooth pursuit eye movements (SPEM) provide an ideal model for investigating the interaction of sensory processing and motor control during ongoing movements. To better understand core plasticity aspects of sensorimotor processing for SPEM, normative sham, anodal or cathodal transcranial direct current stimulation (tDCS) was applied over visual area V5 and frontal eye fields (FEF) in sixty healthy participants. The identical within-subject paradigm was used to assess SPEM modulations by practice. While no specific tDCS effects were revealed, within- and between-session practice effects indicate plasticity of top-down extraretinal mechanisms that mainly affect SPEM in the absence of visual input and during SPEM initiation. To explore the potential of tDCS effects, individual electric field simulations were computed based on calibrated finite element head models and individual functional localization of V5 and FEF location (using functional MRI) and orientation (using combined EEG/MEG) was conducted. Simulations revealed only limited electric field target intensities induced by the applied normative tDCS montages but indicate the potential efficacy of personalized tDCS for the modulation of SPEM. In sum, results indicate the potential susceptibility of extraretinal SPEM control to targeted external neuromodulation (e.g., personalized tDCS) and intrinsic learning protocols.

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“…The indicators used in the study to measure participants' focus on the areas of interest included the number of fixation points (NF), number of visits (NV), total fixation duration (TFD), total glance duration (TGD), proportions of fixation time (PFD), and proportions of glance duration (PGD); these indicators fall into four broad categories, the meaning of which has been interpreted by existing research [39][40][41][42].…”

Section: Eye Tracker 231 Real Scene Eye Trackermentioning

confidence: 99%

“…The study met the minimum criteria for experiments involving eye trackers [40,41]. We used the Tobii Pro Glasses 2, which is a widely used eye tracker that has 4 eye cameras (2 per eye) and 12 illuminators (6 per eye), which are integrated into the frame of the glasses below and above the eyes [42].…”

Section: Eye Tracker 231 Real Scene Eye Trackermentioning

confidence: 99%

Visual Perception Optimization of Residential Landscape Spaces in Cold Regions Using Virtual Reality and Machine Learning

Li,

Huang,

Zhang

et al. 2024

Land

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The visual perception of landscape spaces between residences in cold regions is important for public health. To compensate for the existing research ignoring the cold snow season’s influence, this study selected two types of outdoor landscape space environments in non-snow and snow seasons as research objects. An eye tracker combined with a semantic differential (SD) questionnaire was used to verify the feasibility of the application of virtual reality technology, screen out the gaze characteristics in the landscape space, and reveal the design factors related to landscape visual perception. In the snow season, the spatial aspect ratio (SAR), building elevation saturation (BS), and grass proportion in the field of view (GP) showed strong correlations with the landscape visual perception scores (W). In the non-snow season, in addition to the above three factors, the roof height difference (RHD), tall-tree height (TTH), and hue contrast (HC) also markedly influenced W. The effects of factors on W were revealed in immersive virtual environment (IVE) orthogonal experiments, and the genetic algorithm (GA) and k-nearest neighbor algorithm (KNN) were combined to optimize the environmental factors. The optimized threshold ranges in the non-snow season environment were SAR: 1.82–2.15, RHD: 10.81–20.09 m, BS: 48.53–61.01, TTH: 14.18–18.29 m, GP: 0.12–0.15, and HC: 18.64–26.83. In the snow season environment, the optimized threshold ranges were SAR: 2.22–2.54, BS: 68.47–82.34, and GP: 0.1–0.14.

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Minimal reporting guideline for research involving eye tracking (2023 edition)

Cited by 22 publications

References 18 publications

A review of machine learning in scanpath analysis for passive gaze-based interaction

A review of machine learning in scanpath analysis for passive gaze-based interaction

Normative tDCS over V5 and FEF reveals practice-induced modulation of extraretinal smooth pursuit mechanisms, but no specific stimulation effect

Visual Perception Optimization of Residential Landscape Spaces in Cold Regions Using Virtual Reality and Machine Learning

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