Eye tracking | EPFL Graph Search

Are you an EPFL student looking for a semester project?

Work with us on data science and visualisation projects, and deploy your project as an app on top of GraphSearch.

Eye tracking is the process of measuring either the point of gaze (where one is looking) or the motion of an eye relative to the head. An eye tracker is a device for measuring eye positions and eye movement. Eye trackers are used in research on the visual system, in psychology, in psycholinguistics, marketing, as an input device for human-computer interaction, and in product design. In addition, eye trackers are increasingly being used for assistive and rehabilitative applications such as controlling wheelchairs, robotic arms, and prostheses. There are several methods for measuring eye movement, with the most popular variant using video images to extract eye position. Other methods use search coils or are based on the electrooculogram. In the 1800s, studies of eye movement were made using direct observations. For example, Louis Émile Javal observed in 1879 that reading does not involve a smooth sweeping of the eyes along the text, as previously assumed, but a series of short stops (called fixations) and quick saccades. This observation raised important questions about reading, questions which were explored during the 1900s: On which words do the eyes stop? For how long? When do they regress to already seen words? Edmund Huey built an early eye tracker, using a sort of contact lens with a hole for the pupil. The lens was connected to an aluminum pointer that moved in response to the movement of the eye. Huey studied and quantified regressions (only a small proportion of saccades are regressions), and he showed that some words in a sentence are not fixated. The first non-intrusive eye-trackers were built by Guy Thomas Buswell in Chicago, using beams of light that were reflected on the eye, then recording on film. Buswell made systematic studies into reading and picture viewing. In the 1950s, Alfred L. Yarbus performed eye tracking research, and his 1967 book is often quoted. He showed that the task given to a subject has a very large influence on the subject's eye movement. He also wrote about the relation between fixations and interest: All the records .

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related publications (81)

Local plasticity rules can learn deep representations using self-supervised contrastive predictions

Wulfram Gerstner, Bernd Albert Illing, Jean Ventura

Learning in the brain is poorly understood and learning rules that respect biological constraints, yet yield deep hierarchical representations, are still unknown. Here, we propose a learning rule that takes inspiration from neuroscience and recent advances in self-supervised deep learning. Learning minimizes a simple layer-specific loss function and does not need to back-propagate error signals within or between layers. Instead, weight updates follow a local, Hebbian, learning rule that only depends on pre-and post-synaptic neuronal activity, predictive dendritic input and widely broadcasted modulation factors which are identical for large groups of neurons. The learning rule applies contrastive predictive learning to a causal, biological setting using saccades (ie rapid shifts in gaze direction). We find that networks trained with this self-supervised and local rule build deep hierarchical representations of images, speech and video.

2021

Eye-tracking and artificial intelligence to enhance motivation and learning

Pierre Dillenbourg, Kshitij Sharma

The interaction with the various learners in a Massive Open Online Course (MOOC) is often complex. Contemporary MOOC learning analytics relate with click-streams, keystrokes and other user-input variables. Such variables however, do not always capture users' learning and behavior (e.g., passive video watching). In this paper, we present a study with 40 students who watched a MOOC lecture while their eye-movements were being recorded. We then proposed a method to define stimuli-based gaze variables that can be used for any kind of stimulus. The proposed stimuli-based gaze variables indicate students' content-coverage (in space and time) and reading processes (area of interest based variables) and attention (i.e., with-me-ness), at the perceptual (following teacher's deictic acts) and conceptual levels (following teacher discourse). In our experiment, we identified a significant mediation effect of the content coverage, reading patterns and the two levels of with-me-ness on the relation between students' motivation and their learning performance. Such variables enable common measurements for the different kind of stimuli present in distinct MOOCs. Our long-term goal is to create student profiles based on their performance and learning strategy using stimuli-based gaze variables and to provide students gaze-aware feedback to improve overall learning process. One key ingredient in the process of achieving a high level of adaptation in providing gaze-aware feedback to the students is to use Artificial Intelligence (AI) algorithms for prediction of student performance from their behaviour. In this contribution, we also present a method combining state-of-the-art AI technique with the eye-tracking data to predict student performance. The results show that the student performance can be predicted with an error of less than 5%.

SPRINGER HEIDELBERG2020

Predicting learners' effortful behaviour in adaptive assessment using multimodal data

Kshitij Sharma, Jennifer Kaitlyn Olsen

Many factors influence learners' performance on an activity beyond the knowledge required. Learners' on-task effort has been acknowledged for strongly relating to their educational outcomes, reflecting how actively they are engaged in that activity. However, effort is not directly observable. Multimodal data can provide additional insights into the learning processes and may allow for effort estimation. This paper presents an approach for the classification of effort in an adaptive assessment context. Specifically, the behaviour of 32 students was captured during an adaptive self-assessment activity, using logs and physiological data (i.e., eye-tracking, EEG, wristband and facial expressions). We applied k-means to the multimodal data to cluster students' behavioural patterns. Next, we predicted students' effort to complete the upcoming task, based on the discovered behavioural patterns using a combination of Hidden Markov Models (HMMs) and the Viterbi algorithm. We also compared the results with other state-of-the-art classification algorithms (SVM, Random Forest). Our findings provide evidence that HMMs can encode the relationship between effort and behaviour (captured by the multimodal data) in a more efficient way than the other methods. Foremost, a practical implication of the approach is that the derived HMMs also pinpoint the moments to provide preventive/prescriptive feedback to the learners in real-time, by building-upon the relationship between behavioural patterns and the effort the learners are putting in.

ASSOC COMPUTING MACHINERY2020

Related people (36)

Pierre Dillenbourg, Patrick Jermann, Daniel Thalmann, Touradj Ebrahimi, Jean-Marc Odobez, Marilyne Andersen, Frédéric Kaplan, Jan Wienold, Bruno Herbelin, Jean-Philippe Thiran

Related units (16)

Related concepts (8)

Related courses (3)

CS-213: Human computer interaction

La discipline de l'Interaction Homme-Machine (ou HCI : Human-Computer Interaction) vise à systématiquement placer le facteur humain dans la conception de systèmes interactifs.

CS-444: Virtual reality

The goal of VR is to embed the users in a potentially complex virtual environment while ensuring that they are able to react as if this environment were real. The course provides a human perception-ac

EE-519: Bioelectronics and biomedical microelectronics

The course covers the fundaments of bioelectronics and integrated microelectronics for biomedical and implantable systems. Issues and trade-offs at the circuit and systems levels of invasive microelec

Related lectures (37)

Related MOOCs (3)

Instructional Design with Orchestration Graphs

Discover a visual language for designing pedagogical scenarios that integrate individual, team and class wide activities.

Instructional Design with Orchestration Graphs

Discover a visual language for designing pedagogical scenarios that integrate individual, team and class wide activities.

Venice Time Machine

Eye tracking

Human–computer interaction

Human–computer interaction (HCI) is research in the design and the use of computer technology, which focuses on the interfaces between people (users) and computers. HCI researchers observe the ways humans interact with computers and design technologies that allow humans to interact with computers in novel ways. A device that allows interaction between human being and a computer is known as a "Human-computer Interface (HCI)".

Eye movement in reading

Eye movement in reading involves the visual processing of written text. This was described by the French ophthalmologist Louis Émile Javal in the late 19th century. He reported that eyes do not move continuously along a line of text, but make short, rapid movements (saccades) intermingled with short stops (fixations). Javal's observations were characterised by a reliance on naked-eye observation of eye movement in the absence of technology.

Collaborative Tools: Enhancing Teamwork and Learning

Explores the development of interactive tools and eye tracking technology to enhance teamwork and learning experiences.

Multi-Object Tracking and Person Re-Identification CIVIL-459: Deep learning for autonomous vehicles

Covers Multi-Object Tracking challenges, tracking paradigms, Person Re-Identification, and state-of-the-art methods.

Neuroprosthetics: Challenges and Brain-Computer Interface

Explores challenges in neuroprosthetics and brain-computer interface technologies.