Once a month we discuss a particular research method that may be of interest to people working in health economics. We’ll consider widely used key methodologies, as well as more novel approaches. Our reviews are not designed to be comprehensive but provide an introduction to the method, its underlying principles, some applied examples, and where to find out more. If you’d like to write a post for this series, get in touch. This month’s method is eye-tracking.
Eye-tracking methods can be used to analyse how individuals acquire information and how they make decisions. The method has been extensively used by psychologists in a variety of applications, from identifying cases of dyslexia in children to testing aviation pilots’ awareness. It was made popular by Keith Rayner, but its growing use reflects changes in the availability and affordability of technology. The textbook ‘Eye Tracking: A Comprehensive Guide to Methods and Measures’ provides a great introduction and complements the course offered by Lund University.
Eye-tracking analyses typically depend on the ‘eye-mind hypothesis’ which states “there is no appreciable lag between what is fixated on and what is processed”. In addition to fixing their gaze, individuals make rapid eye movements called ‘saccades’ when they are searching for information or items of interest. There is also research into ‘pupilometery’ which relates pupil size to cognitive burden, where ‘hard’ tasks are hypothesised to cause dilation.
There is a growing interest in using quantitative elicitation methods to understand individuals’ preferences for healthcare goods or services. Quantitative methods, such as the standard gamble, time trade-off, contingent valuation or discrete choice experiments, often employ surveys which are increasingly self-completed and administered online. These valuation methods are often underpinned by economic theories either for utility (random utility theory, expected utility theory) or, in the case of attribute-based approaches, Lancaster’s Theory of consumer demand. If respondents do not answer in line with the supporting theories, the valuations derived from the survey data may be biased. Therefore, various approaches have been employed to understand whether people complete surveys in line with the analysts’ expectations – from restricted models to test for attribute non-attendance to qualitative ‘think-aloud’ interviews. Eye-tracking offers an alternative method of testing these hypotheses.
The research question will dictate the eye-tracking study design, if it’s a reading study or a survey then accuracy will be key. If the experiment seeks to understand how participants respond to large visual stimuli, then it may be preferable to have comfortable equipment which can be used in a setting the participant is familiar with.
In its most basic form, eye-tracking research has involved researcher-individual observation of a participants’ eyes and manual notes on pupil dilation. However, more sophisticated methods have since developed in line with changes to, and availability of, technology. In the 1950s, magnetic search coils were used to track people’s eye movements which involved placing two coils on the eye, with one circling the iris on a contact lens. Nowadays, most eye-tracking involves less invasive equipment, commonly with a camera recording data on a computer and complex algorithms to calculate the location of the individual’s gaze.
To track eyes, almost all modern devices record the corneal reflection on a camera positioned towards the individual’s pupil. The corneal reflection is a glint, usually in the iris, which allows the machine to calculate the direction of the gaze using the distance from 1) the camera to the eye and; 2) the eye to the screen. From the corneal reflection, the X and Y (horizontal and vertical) coordinates, which provide the location of current focus on the screen, are then recorded. The number of times this is logged a second is referred to as the speed (or ‘frequency’) of the tracker. As the eye moves from one position to another, the magnitude of the movement is measured in visual degrees (θ), rather than millimetres, as studies may involve moving stimulus and so the distance between eye and object could change.
Popular manufacturers include Tobii, SensoMotoric Instruments (SMI) and SR Research. Eye-trackers are usually distinguished by their speed and, as a general rule, a ‘good’ eye-tracker has a high frequency and high-resolution camera. A higher frequency allows a more accurate estimation of the fixation duration, as the start of the fixation is revealed earlier and the end revealed later. There is some consensus that a sampling frequency of 500 Hz is sufficiently powerful to accurately determine fixations and saccades. Another determinant of a ‘good’ eye-tracking device is its ‘latency’, which is the time taken for the computer to make a recording. A substantial volume of processing from the headset to screen to recording is required and, for some devices, there is a measurable delay in this process.
There are three broad categories of modern eye-tracking devices.
Head-mounted eye-trackers, such as smart glasses or helmet cameras, offer participants some freedom but are harder to calibrate and can be cumbersome to wear. These eye-trackers are often used to understand how objects are attended to in a dynamic situation, for example, whilst the participant is engaged in a shopping activity.
Remote eye-trackers let the participant move freely but in front of a screen, with algorithms used to detect non-eye movements [PDF]. However, the additional calculations to distinguish head and eye movements are a burden to the processing capacity of the computer and, generally, result in a lower frequency and, as a consequence, have decreased precision.
Head-supported towers involve the use of a forehead and chin-rest. Whilst being contactless, these can be uncomfortable and unnatural for some participants. These devices are also often immobile, due to their heavy processing power, and require stability of the head because of their high frequency. However, head-supported towers are the most accurate and precise equipment available for researchers. For studies where the individual is not required to move and the stimuli are stationary (such as a survey), a head-supported tower eye-tracker provides the best quality data. Head-supported towers also offer the most accurate recording of pupil size.
Data collection will likely occur in a university lab if a head-supported tower tracker is chosen. Head-mounted and remote trackers are generally mobile and can, therefore, travel to people of interest.
Tracking devices can either record both eyes (binocular) or a single eye (monocular). When both eyes are recorded, an average of the horizontal and vertical coordinates from each eye are taken. However, most people generally have an ‘active’ and ‘lazy’ eye and literature suggests that the active, dominant eye should only be tracked. If a participant performs poorly in the calibration, then an alternative eye should be tried.
It is crucial that the eye-tracker is calibrated for each individual to ensure the eye-tracker is recording correctly. The calibration procedure involves collecting fixation data from simple points on the screen in order to ascertain the true gaze position of the individual before the experiment begins. The points are often shown as dots or crosses which move around the screen whilst fixation data are collected. A test of the calibration can be conducted by re-running the sequence and comparing the secondary fixations to the tracker’s prediction based on the first calibration data.
The calibration should involve points in all corners of the screen to ensure that the tracker is able to record in all areas. In the corners and edges, the corneal reflection can disappear, which therefore invalidates the computer’s calculations as well as resulting in missing data. Similarly, for individuals with visual aids (glasses, contact lenses) or heavy eye-makeup, the far corners can often induce another reflection which may confuse the recording and create anomalous data.
If a respondent is completing a survey, between-page calibration called ‘drift correction’ can also be completed. In this procedure, a small dot is presented in the centre of the screen and the next page appears once the participant has focussed on the spot. If there has been too much movement, the experiment will not progress and the tracker must be recalibrated.
Saccades are easily identifiable as the eye moves quickly in response to or in search of visual ‘stimuli’ or objects of interest. Saccadic behaviour rarely indicates information processing as the movements are so rapid that the brain is unable to consciously realise everything that is scanned, a process known as ‘saccadic suppression’. Instead, saccades most often represent a search for information. Saccades are distinctly different to ‘micro-saccades’, which are involuntary movements whilst an individual is attempting to fixate, and the involuntary movements which occur when an individual blinks. Blinks are quite easily identifiable from regular saccades as they are immediately followed by a missing pupil image on the camera as the eyelid closes.
What constitutes a fixation varies from study to study and is dependent on the stimulus presented. For example, a familiar picture may be processed quicker than text, and a new diagram may be somewhere in between. Although complex algorithms exist for the identification of fixations in eye-tracking data, most studies define a threshold for a fixation as less than one degree of movement (a measure of distance) for between 50 to 200 milliseconds. Aggregation of the total time spent fixating, including recurrent fixations, is defined as the ‘dwell time’ to a stimulus.
Eye-tracking data provide a highly detailed record of all the locations that a user has looked at, so reducing these data to a level that can be easily analysed is challenging. One common approach in the analysis of eye-tracking data involves segmenting coordinates to defined regions or ‘areas of interest’ (AOI). AOI can be defined either prior to the experiment or post-experimentally once eye-movement data have been collected.
Another approach to reducing the data is the generation of a ‘scan path’ describing the overall sequence of movements in terms of both saccades and fixations of a respondent, either imposed on a background image of the stimulus or as a colour-coded heat map.
Pupil size can be more difficult to interpret and analyse. Measurement of the pupil differs by equipment, some use an ellipse whereas others count the number of black pixels on the camera image of the eye. Pupil dilation can be calculated as the difference in pupil size, however, analysing this as a percentage increase can cause inflated estimates when the baseline pupil size is small. Pupil size can also rarely be compared across studies as it highly affected by equipment set-up and setting luminosity.
Many eye-trackers come with manufacturer written software for programming the experiment (such as the EyeLink Experiment Builder). However, access to eye-trackers and related software may be restricted. PsychoPy is an open-source software, written in Python, for eye-tracking (and other neuroscience) experiments. Similarly, data can be analysed either in specialist software such as EyeLink’s Data Viewer (which is useful for creating scan paths) or the data can be exported to other statistical programmes such as Matlab, Stata or R.
Orquin & Loose review how eye-tracking methods have been used in decision-making generally. In health, there are a few examples of survey-based choice experiments which have employed eye-tracking methods to understand more about respondents’ decision-making. Spinks & Mortimer used a remote eye-tracker to identify attribute non-attendance. Krucien et al combined eye-tracking and choice data to model information processing; and Ryan extended this analysis to focus on presentation biases in a subsequent publication. Vass et al use eye-tracking to understand how respondents complete choice experiments and if this differed with the presentation of risk. A forthcoming publication by Rigby et al. will provide an overview of approaches, including eye-tracking, to capturing decision-making in health care choice experiments.