Eye tracking to investigate cue processing in medical decision-making: A scoping review

Computers in Human Behavior 66 (2017) 52e66

Contents lists available at ScienceDirect

Computers in Human Behavior journal homepage: www.elsevier.com/locate/comphumbeh

Review

Eye tracking to investigate cue processing in medical decisionmaking: A scoping review Modi Owied Al-Moteri a, b, *, 1, Mark Symmons b, Virginia Plummer c, Simon Cooper d a

Nursing Department, Faculty of Applied Medical Science, University of Al-Taif, Western Region, Saudi Arabia Monash University, Victoria, Australia c Faculty of Medicine Nursing and Health Sciences, School of Nursing and Midwifery, Monash University, Frankston, Victoria, Australia d Emergency Care and Research Development, School of Nursing and Midwifery and Healthcare, Federation University, Victoria, Australia b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 31 August 2016 Accepted 12 September 2016

Eye-tracking techniques have been adopted as a research tool for a wide range of applications in healthcare studies. Recently, healthcare researchers have started to show interest in using eye-tracking techniques to study medical decision-making. Mapping the literature pertaining to eye tracking using a systematic approach is valuable at this point to bring together all the studies to date on how medical decision-makers make decisions, and the results may contribute to clinical training. This review follows Arksey and O'Malley's scoping review framework to improve our understanding of visual cue processing in medical decision-making. A diverse range of studies was identified, and the results are presented descriptively to develop a more coherent understanding of different aspects of cue processing and errors in medical decision-making. The review shows the need for more extensive investigations of cue processing and medical decision-making. Crown Copyright © 2016 Published by Elsevier Ltd. All rights reserved.

Keywords: Eye tracking Cue processing Cognition Decision-making

Contents 1. 2.

3.

4. 5. 6. 7.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 2.1. Identifying the review question . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 2.2. Study identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 2.3. Study selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 2.4. Data charting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 3.1. Eye-tracking metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 3.2. Task focus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 3.3. Task scene . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 3.4. Holistic view of the scene . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 3.5. Focal processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 3.6. Pattern matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 Review limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

* Corresponding author. South Yarra, VIC, Australia. E-mail addresses: [email protected], [email protected] (M.O. AlMoteri). 1 Permanent address: Faculty of Applied Medical Science, University of Al-Taif, Western Region, Saudi Arabia. http://dx.doi.org/10.1016/j.chb.2016.09.022 0747-5632/Crown Copyright © 2016 Published by Elsevier Ltd. All rights reserved.

M.O. Al-Moteri et al. / Computers in Human Behavior 66 (2017) 52e66

53

Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

1. Introduction Decision-making is fundamental to medical practice (Buckingham & Adams, 2000). The ability of medical decisionmakers to notice pertinent cues, deliberately seek out additional information, and efficiently discard or ignore non-informative or distracting material before making an informed decision can have life or death consequences (Croskerry, 2003; Donald & Barnard, 2012; Hassebrock & Prietula, 1992). Accordingly, each element of this process has been the subject of much research over an extended period in an attempt to improve outcomes for patients (Shaban, 2015). For example, a simple study that requires the detection of a lesion in a diagnostic image can determine the level of decision-making accuracy (Donald & Barnard, 2012), but it sheds little light on which part of the process has failed. The inability to report the presence of the lesion may have been the result of a failure to detect the visual cue within the scene, or it may have been noticed, but its significance not recognized (Orquin & Loose, 2013). One technique that provides a more objective investigation of the visual-cognitive aspects of the decision-making process is eye €f, Wallin, Dewhurst, & Holmqvist, 2013; Henderson, tracking (Gidlo 2003). Eye tracking is a technique in human computer interaction which enables researchers to follow medical providers’ eye movements and identify where they were looking as they were performing their assigned tasks (Srinath, 2015). While it is a relatively new technique, eye tracking has been used extensively across many fields, including sport, transportation, marketing, and aviation €ljo €, 2011). Recently, eye-tracking (Gegenfurtner, Lehtinen, & Sa techniques have been adopted as a research tool for a wide range of applications in healthcare studies. Indeed, there have already been a number of reviews. For instance, Tien et al. (2014) systematically reviewed studies that utilized eye tracking for the purpose of skills assessment and training of health professionals. Hermens, Flin, and Ahmed (2013) also reviewed the literature and noted that most of the published studies that use eye tracking in surgery focus on investigating differences between expert and novice surgeons to (1) understand task performance (2) identify experienced surgeons and, (3) establish training approaches. Because eye tracking is a new technique, its utilization in the medical field to study decision-making has been limited (Blondon, Wipfli, & Lovis, 2014). Some researchers have relied on collecting subjective data by asking the participant to talk during the task (concurrent think-aloud approach) or after the task (retrospective think-aloud approach) (Aitken, Marshall, Elliott, & McKinley, 2011; Balatsoukas et al., 2012). However, as most of the sensory and cognitive processes involved are sub-conscious, such introspective techniques are minimally useful in gaining a better understanding of the reasons for errors and devising evidence-based techniques for correcting them (Clark, Huddleston-Casas, Churchill, Green, & Garrett, 2008; Eger, Ball, Stevens, & Dodd, 2007). In more recent years, as the technology has advanced in the application of eyebased human computer interaction (Downing & Haladyna, 2006), researchers have started to show interest in using these means to study human cognition in medical decision-making (Blondon et al., 2014). Utilizing eye tracking has transferred the focus of medical decision-making studies from being purely focussed on the outcomes, to include a more cognitive approach that focuses on the

decision processes (Glaholt & Reingold, 2011). Indeed, the momentto-moment, real-time nature of the data and the opportunities for micro-level analyses are particularly valuable for the investigation of visual cognition processes in decision-making (Schotter, Gerety, & Rayner, 2012). Eye-tracking measures can indicate whether the participant's gaze coincides with the location of the pertinent element of the visual scene, how long they focus on various components of the scene, the relative fixation time on the particular item of interest, how often they return to particular locations, whether their searching is strategic, and so on (Glaholt & Reingold, 2011). To date, no scoping review of studies using eye-tracking techniques to investigate decision-making has been done in the healthcare field. A review using a systematic approach is valuable at this point to bring together all the studies pertaining to eye tracking. Therefore, a review has been done to investigate how decisions are made by medical professionals, and the results may contribute to clinical training (Loveday, Wiggins, Festa, Schell, & Twigg, 2013). Moreover, a review can provide researchers with valuable information on the conduct of a study in this area, in terms of what eye-tracking metrics are commonly used, what assumptions lie behind these metrics, and how they have been interpreted. This review may serve as a guide for researchers who are interested in performing eye-tracking studies to avoid variations in approaches to data analysis, and consequently, interpretation of the findings in medical decision-making research (Blondon et al., 2014). 2. Methods The method and structure of this review conforms to the framework offered by Arksey and O'Malley (2005) for scoping reviews. Scoping reviews are considered to be as rigorous as systematic reviews, but they do not seek to evaluate study quality (Arksey & O'Malley 2005). According to the Canadian Institute of Health Research, a scoping approach is preferred when the relevant literature is thought to vary in quantity and discipline (Levac, Colquhoun, & O'Brien, 2010). The focus in this instance is the medical industry, with the aim of including a wide range of medical professionals, such as physicians, surgeons, neurologists, nurses, anesthesiologists, radiologists, and other specialists. The Arksey and O'Malley (2005) framework calls for the identification of specific research questions, the systematic selection of studies, data charting, and summarizing of the outcomes. 2.1. Identifying the review question The aim of this review is to map the literature pertaining to eye tracking as an assessment or research tool for understanding decisions by medical professionals. Specific questions to be addressed include: (1) What eye-tracking metrics are used to investigate visual cue processing? (2) How are conclusions drawn from the eyetracking data in respect of decision-making? 2.2. Study identification In order to find papers related to eye tracking as a tool for investigating visual perception, the keywords used were “eye* OR

54

M.O. Al-Moteri et al. / Computers in Human Behavior 66 (2017) 52e66

recogni* OR gaz* OR vis*”, where the asterisk signifies a wildcard that can be filled with one or multiple letters. For example, “eye*” should hit on eyetracking, eye-tracking and eye tracking as well as eye movement, and so on. In order to target only papers related to the healthcare field, the following set of keywords was employed: “anaesthe* OR anesthe* OR surg* OR radiolog* OR doctor* OR nurs* OR care* OR physician* OR medic*”. Thus the search phrase was (eye* OR recogni* OR gaz* OR vis*) AND (anaesthe* OR anesthe* OR surg* OR radiolog* OR doctor* OR nurs* OR care* OR physician* OR medic*). The databases ProQuest, PubMed, CINAHL Plus, Medline, and Ovid were interrogated, along with the GoogleScholar search engine. To ensure that the search was as a broad as possible, no limits were put on the date of publication. However, the review was limited to those which were published in full, were peer reviewed, and in English. 2.3. Study selection In the first instance 1055 hits were returned. The hits were then culled to remove 549 duplicate papers. Based on a reading of the title and abstract, a further 138 papers were removed because they were outside the medical domain, along with 279 papers in which the participants were not medical professionals (some studies involved patients, and others focused on “ordinary” individuals, even though the topic related to healthcare). Nineteen of the remaining papers were either reviews, editorials, abstracts of conferences or documentaries. The author assessed the full texts of the remaining 70 studies to identify whether they clearly identified the purpose of the use of eye tracking as a method. Since the primary interest of the current review was decision-making, studies that served other interests, such as attention on task elements (e.g., Koh, Park, Wickens, Teng Ong, & Noi Chia, 2011), usability testing (Forsman, Anani, Eghdam, Falkenhav, & Koch, 2013), vigilance (e.g., Zheng et al., 2011), workload (e.g., Kataoka, Sasaki, & Kanda, 2011), distraction and interruption (e.g. Grundgeiger, Sanderson, MacDougall, & Venkatesh, 2010), and eye-hand coordination (e.g., Atkins, Tien, Khan, Meneghetti, & Zheng, 2013) were excluded. The final collection of papers numbered 27. Fig. 1 summarizes the process to identify the final collection of the most relevant papers. 2.4. Data charting The 27 selected papers are summarized, or charted (Arksey & O'Malley, 2005), in Table 1. The table briefly notes the task and stimulus required of the participants, the type of healthcare professional, the eye-tracking metrics analysed, and the resulting conclusions drawn, as they relate to the decision-making process.

(Giovinco et al., 2015; Kundel et al., 2007). From these two basic metrics additional metrics are derived, as follows: (1) fixationderived metrics, (2) saccade-derived metrics and (3) fixationsaccade-derived metrics (Poole & Ball, 2006). Fixation-derived metrics. Some of the metrics derived from fixation are time to first fixation (Wood, Knapp et al., 2013; Kundel et al., 2008), fixation number and aggregation (Donovan & Litchfield, 2013; Krupinski et al., 2006) and dwell time (Manning et al., 2004; Mello-Thoms et al., 2005; Timberg et al., 2013). Time to first fixation refers to the time from trial onset to the first fixation on the AoI (Wood & Batt et al., 2013). Time to first fixation per se cannot reveal much. However, when it is compared to other AoIs, it can show which AoI attracts attention first. The studies included in this review use various terms to describe time to first fixation, such as hit time and entry time. Accumulation of fixation on an AoI reflects how this area is of interest to the participant. The reviewed studies use different terms to describe fixation aggregation, such as fixation cluster, zoom location, fixation points, fixation density, culmination of saccadic data points and gaze points. The length of dwell time reflects the depth of processing. It is measured by summing all fixations on an AoI during the scanning time. Krupinski (1996) claimed that dwell time was a good predictor of perception. Dwell time refers to the length of time the observer looks at an AoI (Krupinski et al., 2006). Terms used to describe dwell time in the reviewed studies were visual dwell time, cumulative gaze duration, percentage of time looking at AoIs and summed fixation time. Saccade-derived metrics. Saccade length is a good indicator of visual search strategies (Giovinco et al., 2015; Kundel et al., 2007). Based on the length of saccades, they can be labelled as either focal views (short saccades) or global views (long saccades) (Kundel et al., 2007). Metrics derived from saccades include saccadic amplitude (Manning et al. 2006) and global saccade ratio (Sibbald et al., 2015). Different terms were used to describe saccades in the reviewed studies: transition between fixations and jumps between fixations. Fixation-saccade-derived metrics. The most common metric derived from fixation saccade is the scan path. Scan path is a series of fixations on AoIs (Giovinco et al., 2015; Kundel et al., 2007). Researchers who are interested in identifying the distribution of an observer's attention focus on fixation-saccade-based dwell time within AoIs (Karn, 2006), since saccades and attention are tightly coupled. Similar to the previous metrics, varying terms were noted in the studies to describe scan path: transitions of eye fixation, gaze pattern behaviour, sequences of fixations, gaze scan path, fixation pattern, scan pattern, distribution of eye-fixation positions, and direction and speed of eye fixation. 3.2. Task focus

3. Results 3.1. Eye-tracking metrics The conduct of eye-tracking research requires prior recognition and definition of AoIs that relate to the study objectives (Balslev et al., 2012; Sibbald et al., 2015). An AoI is an important metric for analysis. In the studies reviewed, different terms were used to describe AoIs, including look zone, region of interest (RoI), and useful visual field. There are two basic eye-tracking metrics: fixations and saccades. Fixation refers to the duration of time during which the eyes are at a halt for encoding data (Manning et al., 2004; Krupinski et al., 2006; Kundel et al., 2007; Manning et al. 2006; Cooper et al., 2009). The rapid movements of the eye between fixations are called saccades, and no encoding takes place during saccades

Twenty seven studies were retained. Of these studies, 23 pertained to searching for abnormalities for the purpose of making a clinical diagnosis (Wood & Batt et al., 2013; Cooper et al., 2009;  et al., Manning et al., 2004; Mello-Thoms et al., 2005; Brunye 2014; Krupinski et al., 2006; O'Neill et al., 2011; Berbaum et al., 2001; Kundel et al., 2007; Manning et al., 2006; Timberg et al., 2013; Donovan & Litchfield, 2013; Wood & Knapp et al., 2013; Cooper et al., 2010; Litchfield, Ball, Donovan, Manning, & Crawford, 2010; Krupinski, 1996; Matsumoto et al., 2011; Rubin et al., 2014; Nodine et al., 2002; Kundel et al., 2008; Tiersma et al., 2003; Sibbald et al., 2015; Giovinco et al., 2015). Of the four remaining, one study assessed the ability of physicians and nurses to recognize errors before deciding upon action (Henneman et al., 2008). The second assessed nurses' and physicians' abilities to diagnose seizure events (Fogarasi et al., 2012). The third study examined

M.O. Al-Moteri et al. / Computers in Human Behavior 66 (2017) 52e66

55

Fig. 1. Flow diagram of the study selection process.

clinicians' decision-making as they diagnosed an authentic paediatric video case (Balslev et al., 2012) and the last assessed anesthesiologists' decision-making as they managed a patient who was being prepared for an inguinal lymph node biopsy during a critical incident (Jungk et al., 2000). In addition, 11 of these 27 studies investigated the impact of experience on decision-making (Wood & Knapp et al., 2013; Cooper et al., 2009; Donovan & Litchfield, 2013; Manning et al., 2004;  et al., 2014; O'Neill et al., 2011; Krupinski et al., 2006; Brunye Wood, Batt et al., 2013; Nodine et al., 2002; Giovinco et al., 2015; Balslev et al., 2012). Two investigated the influence of knowing about the clinical history of the case on decision-making (Cooper et al., 2009; Wood & Batt et al., 2013). Further, the influence of scene presentations and attributes on decision-making was studied in six studies (Cooper et al., 2010; Timberg et al., 2013; Jungk et al., 2000; Krupinski et al., 2006; Manning et al., 2004; Mello-Thoms et al., 2005). The impact of training on decision-making was investigated in three studies (Litchfield et al., 2010; Fogarasi et al., 2012; Manning et al., 2004). Finally, one study investigated the influence of using the decisional assistant method on the accuracy of decision-making (Sibbald et al., 2015).

Of those studies which utilized dynamic scenes, Balslev et al. (2012) and Fogarasi et al., (2012) used two-dimensional (2-D) visual scene. Balslev et al. (2012) used video recordings of paediatric patients showing epileptic signs in an attempt to investigate physicians' search patterns along with the associated cognitive processes. A similar scene was used by Fogarasi et al., (2012) in an attempt to guide medical professionals’ attention to notice epileptic cues. While, Henneman et al. (2008) and Jungk et al. (2000) used three-dimensional (3-D) visual scene. Henneman et al. (2008) utilized three simulated patients in an authentic environment, in which an error was introduced in the form of a different date of birth and medical record number than the identity information on the label. Jungk et al. (2000) also utilized a simulated patient in an authentic environment to compare the decision-making of anaesthesiologists under unexpected critical events when using only a traditional monitor on which vital parameters were displayed in a traditional fashion in the form of trends along a timeline and when using the traditional monitor in combination with a redesigned monitor in which all of the information necessary for decisionmaking was displayed in a single display. The unexpected critical events were blood loss and cuff leakage.

3.3. Task scene

3.4. Holistic view of the scene

A static two-dimensional (2-D) visual scene was used by most studies included in the current review (Wood & Batt et al., 2013; Cooper et al., 2009; Manning et al., 2004; Mello-Thoms et al.,  et al., 2014; Krupinski et al., 2006; O'Neill et al., 2011; 2005; Brunye Berbaum et al., 2001; Kundel et al., 2007; Manning et al., 2006; Timberg et al., 2013; Donovan & Litchfield, 2013; Wood & Knapp et al., 2013; Cooper et al., 2010; Litchfield et al., 2010; Krupinski, 1996; Matsumoto et al., 2011; Rubin et al., 2014; Nodine et al., 2002; Kundel et al., 2008; Tiersma et al., 2003; Sibbald et al., 2015; Giovinco et al., 2015). Only four studies utilized dynamic scenes, (Balslev et al., 2012; Fogarasi et al., 2012; Henneman et al., 2008; Jungk et al., 2000).

Eye tracking shows that when medical decision-makers encounter the scene for the first time, they employ an initial broad visual scan of the whole scene. This perceptual strategy has been found to be specifically related to experienced medical decision-makers (Balslev et al., 2012; Cooper et al., 2009; Krupinski et al., 2006; Kundel et al., 2007, 2008; Manning et al., 2006; MelloThoms et al., 2005; Wood and Knapp et al., 2013; Tiersma et al., 2003). This broad view enables experienced medical decisionmakers to perceive large sections of the scene in a short time to enhance cue fixation. The end result of this stage is the detection of cues for further processing. If the cue is not hit by fixation in this stage, failure to detect cues

56

M.O. Al-Moteri et al. / Computers in Human Behavior 66 (2017) 52e66

Table 1 Details of reviewed papers. Paper

Task and stimulus

Participants

Balslev et al. (2012)

To watch how infants behave/ move in authentic paediatric video cases and make a diagnosis.

Clinicians

▪ Fixation duration on areas of interest (AOIs

Berbaum et al. (2001)

To make diagnostic decision about the presence or absence of abnormalities in computerbased radiographic images.

Radiologists

▪ Number of fixations ▪ Dwell time

 et al. (2014) Brunye

To examine static digitized breast biopsy for lesions.

Pathologists

▪ Visual saliency maps (computing maps that encode the saliency of visual features on each image). ▪ Scan path ▪ AoIs ▪ Number of fixations ▪ Dwell time ▪ Number of regressions back to the region of interest ROI

Cooper et al. (2010)

To make diagnostic decision regarding the presence or absence of an abnormality in multidimensional brain CT or MRI scans.

Second year radiology trainees and experienced radiology readers

▪ Scan path ▪ Number of fixations ▪ Dwell time

Specialist registrars, consultant radiologists

▪ Mean time for first fixation on AOI ▪ Dwell time

Cooper, Gale, Darker, Toms, and To examine CT & MR for Saada (2009) abnormality and make diagnostic decision.

Donovan and Litchfield (2013)

To look for lung nodules in chest Undergraduate radiography students, consultant x-ray and make diagnostic radiologists, reporting decision. radiographers

Eye tracker metrics

▪ Time to first fixation. ▪ Number of fixations.

Principle findings ▪ More experienced clinicians were more accurate in visual diagnosis and spent more of their time looking at relevant areas. At the same time, they explored data less, yet they developed & evaluated more diagnostic hypotheses. ▪ 22% failed to fixate abnormal region ▪ 11% failed to report a lesion that has been fixated, dwell time (0.267 ms) ▪ 67% failed to report an abnormal area that had been extensively fixated, dwell time ranged from 1.000 to 9.970s ▪ Pathologists with relatively low expertise in interpreting breast pathology more likely to fixate on, and subsequently return to, diagnostically irrelevant regions relative to experts. ▪ Repeatedly fixating on distracting content showed limited value in predicting diagnostic failure. ▪ Preliminary results suggest eye movements occurring during digital slide interpretation can characterize expertise development by demonstrating differential attraction to diagnostically relevant versus visually distracting image regions. ▪ In terms of modality differences, novice and expert readers spent longer appraising CT images than MR images, compared with trainees, who spent longer appraising MR than CT images. ▪ Image analysis trends did not appear to differ between modalities, but time spent within clinical images, accuracy and relative confidence performing the task did differ between CT & MR reader groups. ▪ Experts spent more time in challenging AOI than novices and trainees. The time to first AOI fixation differed by size, shape & clarity of lesion ▪ Time to lesion dropped significantly when recognition appeared to occur between lesions ▪ The influence of clinical information was minimal ▪ By reporting both absolute & relative errors of visual search, recognition and decision, it is clear that whilst naïve observers

M.O. Al-Moteri et al. / Computers in Human Behavior 66 (2017) 52e66

57

Table 1 (continued ) Paper

Task and stimulus

Participants

Eye tracker metrics

Principle findings

Fogarasi et al. (2012)

To review video-recorded seizures in three sessions and identify seizure signs

pediatric residents, nurses, epileptologists and EEG assistants

▪ Fixation duration ▪ Fixation number

▪

Giovinco et al. (2015)

To make diagnostic decision by classifying the radiographic image as “no bunion,” “mild bunion,” “moderate bunion,” and “severe bunion.”

Foot and ankle surgeons

▪ Heat map. ▪ Fixation time

▪

▪

Henneman et al. (2008)

To review each triage note and order tests based on the simulated patient's initial complaint (order screen).

Attending physicians and emergency medicine residents

▪ Fixation location

▪

▪

▪

▪

Jungk, Thull, Hoeft, and Rau (2000)

To anesthetize a 44-year-old Anaesthesiologists healthy male and identify critical events (blood loss & cuff leakage) and make a decision.

▪ Areas of interest ▪ Fixation on AOIs

▪

▪

Kundel, Nodine, Conant, and Weinstein (2007)

To make diagnostic decision regarding the presence or absence of malignancies in mammograms.

Mammography fellows and radiology residents

▪ Areas of interest ▪ Scan path ▪ Time to first fixation

▪

▪

indeed make more visual search errors than other experienced groups, they also make more recognition & decision-making errors overall. Fixation time values shorter for more experienced observers (experts & nurses) compared to less experienced observers (naive people & residents), however, difference diminished from session to session, reflecting the effect of learning process. Advanced surgeons spend more time looking at certain areas of radiographs, move their attention faster, & spend less time overall making a diagnosis. When a visual overlay of the data was reviewed, the areas at which the advanced surgeons spent most of their time correlated with the first metatarsophalangeal joint. In contrast, the students & junior residents spent significantly more time on the lesser metatarsals & hallux position All providers selected the correct patient when there was a second patient with the same last name. Two of 25 noted the DoB error; the remaining 23 ordered tests on an incorrect patient. Eye-tracking data were available on 21 of the participants, including the 2 that detected the DoB error. Nineteen of these participants ordered tests. Of these 19, 4 looked at patient ID information on the order screen but did not detect the different DoB and went ahead and ordered tests on the incorrect patient Subjects did not observe more critical incidents when working with the ecological interface than when working only with conventional displays. The incidents of blood loss & cuff leakage were detected significantly more quickly when the new display was available. Median time for the entire group to hit a cancer regardless of decision outcome was 1.13 s Difference between best & worst decision performance not a matter of incomplete or premature cessation of search, but is a matter of both an inability to (continued on next page)

58

M.O. Al-Moteri et al. / Computers in Human Behavior 66 (2017) 52e66

Table 1 (continued ) Paper

Kundel, Nodine, Krupinski, and Mello-Thoms (2008)

Task and stimulus

To reach a decision regarding the presence or absence of cancer in mammographic images.

Participants

Experienced mammographers, mammography fellows, and radiology residents

Eye tracker metrics

▪ Time to first fixation

Principle findings

▪

▪

Mammographers

Krupinski (1996)

To search a mammographic image and make a diagnostic decision

Krupinski et al. (2006)

To select the top 3 locations in Medical students, pathology residents, and practising histopathology glass slide digital images they would zoom pathologists onto to render a diagnostic decision.

▪ Number of fixations on AoI ▪ Dwell time

▪

▪ Dwell time ▪ Saccades

▪

▪

▪

Litchfield et al. (2010)

To search chest x-rays of the same image and identify nodules

Novice radiographers, experienced radiographers

▪ Dwell time ▪ Percentage of gaze time at AoI ▪ Average dwell time

▪

▪

▪

holistically identify perturbations that on closer examination will turn out to be cancers & an inability to recognize cancers even when they are fixated The initial detection occurs before visual scanning &, therefore, must be the result of a global analysis of the image resulting in an initial holistic, gestalt-like perception. The development of expertise in medical image analysis may consist of a shift in the recognition mechanism from scan-lookdetect to look-detect-scan. Using a 1000-msec threshold, survival analysis indicated that only 30% of the true-negative decisions were associated with gaze durations longer than 1000 msec, whereas 64%, 67%, & 59%, respectively, of the true-positive, false-positive, & false-negative decisions were associated with cumulative gaze durations longer than 1000 msec Fully trained pathologists spent significantly less time scanning virtual slides compared to pathology residents or medical students, but had relatively prolonged saccadic eye movements. Pathologists spent significantly more time than trainees dwelling on the 3 locations they subsequently chose for zooming. The medical students, residents, and the pathologists frequently chose areas for viewing at higher magnification outside of areas of foveal (central) vision. Viewing where another person looked during nodule detection improved subsequent identification of nodules; however, only novice radiographers demonstrated consistent improvements in performance First year radiographers performed at the same level as more experienced radiographers simply by observing where another person looked for nodules. Group differences in time taken to first fixate on the fracture site, with experts significantly faster than both other groups, & intermediates were significantly faster than novices.

M.O. Al-Moteri et al. / Computers in Human Behavior 66 (2017) 52e66

59

Table 1 (continued ) Paper

Task and stimulus

Participants

Eye tracker metrics

Manning, Ethell, and Donovan (2004)

To make diagnostic decision on the presence or absence of pulmonary nodules in chest radiograph images.

Radiographers

▪ Median total dwell ▪ Fixation on AoI

Manning, Ethell, Donovan, and Crawford (2006)

To decide on a nodule's presence and its location on chest images.

Experienced radiologists, radiographers before and after six months' training, undergraduate radiography students

▪ Mean saccadic amplitude per image ▪ Number of zones hit by fixations ▪ Number of fixations per image ▪ Total duration of film scrutiny in seconds ▪ Total cumulative dwell time

Matsumoto et al. (2011)

Neurologists To interpret brain computed tomography (CT) images to give a radiographic diagnosis with regard to cerebrovascular accidents

Mello-Thoms et al. (2005)

To search for malignant masses on breast mammographic image.

Mammographers

▪ Median total dwell

Nodine, Mello-Thoms, Kundel, and Weinstein (2002)

To evaluate two digital mammograms for abnormal findings.

Radiology trainees and three mammographers

▪ Dwell time ▪ Median dwell time ▪ Scan path

▪ ROI ▪ Dwell time ▪ Heat map

Principle findings ▪ Experts also spent relatively longer fixating on the fracture site (dwell time) than novices ▪ 80% of true negative (TN) decisions were made within 1s ▪ False negative decisions (FN) were fixated for greater than 1000 ms in 65% of cases. ▪ Some missed lesions visually fixated & dwelt on for an average time of 3.1 s. ▪ Expert radiologists made larger visual sweeps from one point of interest to the next in a more global sense. Despite the wide saccades, their visual fixation behaviour revealed systematic coverage ▪ The mean saccadic amplitudes of all the nonradiologists showed that they sampled information on a more local scale. ▪ Heat maps revealed that areas on which control subjects frequently fixated often coincided with areas identified as outstanding in saliency maps, while the areas on which neurologists frequently fixated often did not. ▪ Although dwell time on large lesions did not differ between the two groups, dwell time in clinically important areas with low salience was longer for neurologists than controls. ▪ Neurologists intentionally scan clinically important areas when reading brain CT images showing cerebrovascular accidents. ▪ Misses of lung nodules due to lack of fixation were 30% in the current diagnosis (the mammogram in which the cancer was reported in the clinical practice) & 47% in the prior diagnosis (the most recent prior diagnosis). ▪ Misses of lung nodules due to perceptual error were 40% in the current, dwell time was 0.394 ms, & 33% in the prior, dwell time was 0.507 ms. ▪ Misses of lung nodules due to decisional error were 30% in the current, dwell time was 1.696 ms, & 20% in the prior, dwell time was 2.024 ms. ▪ A fixation dwell time of 1000 msec was associated with the detection of true lesions for the mammographers but not for the trainees. ▪ Mammographers detected most breast lesions by (continued on next page)

60

M.O. Al-Moteri et al. / Computers in Human Behavior 66 (2017) 52e66

Table 1 (continued ) Paper

Task and stimulus

Participants

Eye tracker metrics

▪ AoIs ▪ Total dwell time ▪ Scan path

O'Neill et al. (2011)

To examine glaucomatous optic Glaucoma sub-specialists and disc photographs. ophthalmology trainees

Tiersma, Peters, Mooij, and Fleuren (2003)

To grade the lesion in a histopathology glass slide digital images

Pathologists

▪ Scan path

Rubin et al. 2014

To examine lung CT images for lung nodules

Radiologists

▪ Scan path ▪ Dwell time

Sibbald, de Bruin, Yu, and van Merrienboer (2015)

To Interpret each ECG trace and Cardiology residents to verify their interpretation in one of two conditions: checklist (CH) and analytic prompt (AP).

▪ AoIs ▪ Dwell time

Timberg et al., (2013)

To detect lesions in breast tomosynthesis (BT) image

Radiologist and medical physicists

▪ ▪ ▪ ▪

Wood, Batt, Appelboam, Harris & Wilson(2013) )

To make a diagnostic decision on each presented ECG trace and report their level of diagnostic confidence.

Medical students and consultant emergency doctors

▪ AoIs ▪ Total dwell time ▪ Number of fixations second.

AoIs Total dwell time Time to first fixation Saccade length

Principle findings global recognition, but trainees took more time. ▪ Prolonging search beyond the global recognition phase yielded few new lesions & increased the risk of error. ▪ Overall, trainees spent more time looking at disc images than glaucoma subspecialists. ▪ Experienced viewers demonstrated more systematic & ordered gaze behaviour patterns & spent longer observing areas with the greatest likelihood of pathology compared with trainees. ▪Experts adapted their viewing habits according to disc morphology. ▪ Two types of scanning patterns were distinguished: 1.A scanning type of search, whereby the pathologists focused on many points within the image but only for a short moment; 2. A selective type of search, whereby the pathologists limited their search to specific points within the lesion which they studied for a relatively long time. ▪ Radiologists appear to actively search less than half of the lung parenchyma ▪ There was substantial interreader variation in volume searched, fraction of nodules included within the search volume, sensitivity for nodules within the search volume, & overall detection rate. ▪ Participants were more likely to find & fix errors when verifying with a checklist compared with an analytic prompt. ▪ Participants re-examined individual components of the ECG and the primary data involved in interpretation decisions, potentially addressing errors related to premature diagnostic closure. ▪ Observed differences in detection performance were not statistically significant between any reading conditions (horizontally- & vertically-orientated BT). ▪ Horizontally-orientated BT image volumes were read faster than verticallyorientated. ▪ Experts were significantly quicker at locating the leads of critical importance. per ▪ Clinical history had no significant effect on the reader's ability to detect the

M.O. Al-Moteri et al. / Computers in Human Behavior 66 (2017) 52e66

61

Table 1 (continued ) Paper

Wood et al., (2013)

Task and stimulus

To report any fractures in skeletal radiograph images as accurately and as quickly as possible.

Participants

Eye tracker metrics

Undergraduate radiography students, prefellowship radiology trainees, post-fellowship radiologists

will result and may influence the accuracy of the decision-making outcome. Regardless of experience, cue detection errors are attributed to the lack of effective searching strategies (Berbaum et al., 2001; Henneman et al., 2008; Kundel et al., 2007; Donovan & Litchfield, 2013; Krupinski, 1996; Manning et al., 2006; Nodine et al., 2002). Examples of ineffective searching strategies include random scan path (Henneman et al., 2008), searching bias (MelloThoms et al., 2005), search satisfaction, in which the medical decision-maker stops searching early and is satisfied with the findings (Berbaum et al., 2001), and prolonged initial search (Nodine et al., 2002). However, effective visual searching does not always ensure recognition (Rubin et al., 2014), perhaps because detection involves only knowing about the presence or the absence of something, without entailing its meaning, while recognition involves allocating meaning (Nodine & Kundel, 1987). Detection error can be overcome by training, in particular, if the training targets the visual search strategies (Fogarasi et al., 2012; Manning et al., 2004). Scene attributes such as conspicuity and the way the scene is presented can also influence scene interpretation, and ultimately, cue detection. Cue detection error increases if the scene is less obvious (Mello-Thoms et al., 2005; Manning et al., 2004). Timberg et al., (2013), Cooper et al. (2010), Krupinski et al. (2006) and Jungk et al. (2000) found that changing the graphic presentations or introducing different modalities appear to enhance the detection of informative information required for decision-making. Further, it might be expected that knowing the clinical history of the case would enhance detection of the abnormalities, enhancing the accuracy of diagnosis. Nevertheless, Wood and Batt et al. (2013) found no effect for clinical history on physicians’ ability to detect the abnormality or make an accurate diagnosis, while Cooper et al. (2009) found a minimal effect. 3.5. Focal processing Eye tracking also shows that following the interpretation of the whole scene, localized fixation on the areas of concern mainly takes place (Cooper et al. 2009; Kundel et al., 2007; Manning et al., 2006; Mello-Thoms et al., 2005). This stage of cue processing involves a high level of selectivity to attend to or ignore a specific cue for processing. Two mechanisms control attention selectivity to process cues: goal-driven fixation (Wood, Batt et al., 2013; Wood & Knapp et al., 2013; Litchfield et al., 2010; Matsumoto et al., 2011;  et al., 2014; O'Neill et al., 2011; Giovinco et al., 2015) and Brunye  et al. 2014; Krupinski et al., 2006; stimulus-driven fixation (Brunye

▪ Dwell time ▪ Time for the first fixation

Principle findings abnormality or make an accurate diagnosis. ▪ Experts more accurate & faster in their diagnoses than intermediates or novices ▪ Experts faster to fixate site of the fracture & spend relatively more time fixating the fracture than intermediates or novices ▪ The time to fixate the fracture was inversely related to diagnostic accuracy & explained 34% of the variance in this variable

Matsumoto et al., 2011). For researchers to identify the control mechanism being exerted and the cue processing mechanism, they need to determine eye fixation on an AoI. Goal-driven fixation is related to experienced medical decisionmakers. They tend to ignore the salient but irrelevant cues and focus on the information that serves the immediate task. Experienced medical decision-makers are sometimes attracted to the salient cues. However, they immediately switch from stimulus-to goal-driven fixation (Matsumoto et al., 2011). This is in contrast to less experienced decision-makers, who fixate on areas that immediately capture their attention but have no diagnostic value.  et al. (2014) and Krupinski et al. (2006) found that less Brunye experienced pathologists fixated on salient cues, and returned repeatedly to these areas, indicating that they were susceptible to distraction. Balslev et al. (2012) found that experienced physicians use relatively more of their time looking at areas with high diagnostic value, and with decreasing level of clinical experience, clinicians use less and less of their time looking at relevant areas, and more and more of their time searching other areas. Jungk et al. (2000) found that the use of a new display helped anaesthesiologists in detecting the critical event faster by supporting goaloriented strategic decision-making behaviour and helped them to find relevant problem-specific information. As indicated previously, experienced medical decision-makers devote longer fixation time to the diagnostically relevant cues (Wood & Knapp et al. 2013; Matsumoto et al. 2011). In this review, some studies define perceptual failure as any failure to recognize a lesion with a dwell time of less than 1000 ms (Donovan & Litchfield, 2013; Krupinski et al., 2006; Mello-Thoms et al., 2005; Manning et al., 2004; Berbaum et al., 2001). Experienced medical decisionmakers exhibit superior perceptual abilities relative to less experienced (Wood, Batt et al., 2013). Krupinski (1996) found that less experienced radiologists made more recognition errors than more experienced ones. This is due to the presence of internal presentations in the long-term memory. Exposure to a variety of lesions may overcome recognition failure among less experienced medical decision-makers, a typical learning pattern to enlarge the “mental library” of diseases (Mello-Thoms et al., 2005; Wood, Batt et al., 2013; Litchfield et al., 2010). It worth noting that the 1000 ms threshold differs between contexts: Krupinski (1996) revealed a 200 ms fixation difference between lesions in a mammographic image and nodules in a chest x-ray. Perceptual failure in a dynamic context was investigated by Henneman et al. (2008), who assessed health care professionals’ ability to notice visual cues in a dynamic setting in emergency

62

M.O. Al-Moteri et al. / Computers in Human Behavior 66 (2017) 52e66

departments to assess their accuracy to verify patient identity before executing care tasks. The health care providers were blinded to the focus of the study. The results showed that of the 19 health care providers ordered tests, 4 completed the steps of verifying patient information on the wrong patient; in fact, they fixated on the error, but failed to recognize it. Such failure may reveal the degree to which clinicians may be unaware of visual information, despite their efforts to provide safe care. This inattention to a simple but significant cue may result in serious but preventable adverse outcomes. 3.6. Pattern matching If cues are fixated sufficiently for perception to take place, this leads to pattern matching for a particular response. Pattern matching refers to recognizing previously encountered cues. In general, medical decision-makers with more experience have an internal schema that is activated within the very first moments of exposure to the scene and guides the recognition of the cues (Balslev et al., 2012; Cooper et al., 2009; Donovan & Litchfield, 2013; Wood & Knapp et al., 2013; Mello-Thoms et al., 2005; Wood, Batt et al., 2013). Not only the length of experience matters, but also the variety of cues to which medical decision-makers are exposed during their years of experience. Rubin et al. (2014) found that nodule recognition varies considerably among experienced radiologists, indicating differences in depth of experience. Other studies have found that medical decision-makers with less experience tend to spend more time analyzing unambiguous lesion areas, indicating that they are trying to construct their own pattern recognition (Cooper et al., 2010). However, with training greater improvement in the performance of novices has been reported (Litchfield et al., 2010). Sometimes, the visual target is not reported, even though dwell time should have been sufficient for recognition to take place. Among those who fixated on an area that contained lesions but failed to report one, some had a dwell time greater than 1000 ms (Berbaum et al., 2001; Donovan & Litchfield, 2013; Manning et al., 2006; Mello-Thoms et al., 2005). The fixated areas were detected

and raised concern for potential reporting. Long dwell time reflects decisional activities, but for some reason medical professionals may decide that the area is free of lesions (Berbaum et al. 2001; Manning et al. 2006). This is a decisional error and may be due to uncertainty (Cooper et al., 2009, 2010), or the similarity between normal tissue features and pathological features that makes reporting of lesions difficult (Manning et al., 2006). Sibbald et al. (2015) found that using the decisional assistance method that allows medical decision-makers to review their judgment outcomes could help in reducing decisional errors. Rubin et al. (2014) found considerable variations among expert radiologists in terms of their sensitivity to nodules. Krupinski (1996) compared experienced with inexperienced radiologists and found that the experienced group made more decisional errors compared to the inexperienced group. 4. Discussion This review included 27 studies that used eye tracking to investigate medical decision-makers’ cognitive process in decisionmaking. The most common metrics were fixation, scan path, and dwell time. Researchers focus on scan paths if they are interested in identifying how medical decision makers search for cues (Karn, 2006), because saccades and attention are closely linked (Holmqvist et al., 2011). Researchers interested in cue processing focus on fixation-based dwell time (Van der Lans, Wedel, & Pieters, 2011). The review shows that visual cue processing in decisionmaking involves three interrelated processes, starting with scene perception (understanding the whole), focal processing (selecting specific cues for deeper processing), and pattern matching (binding knowledge to long-term memory) (Lindsay & Norman, 2013; McGuiness & Axford, 1995). Further, the review identifies three types of errors that can lead to failure to report cues: (1) detection errors, which occur when cues are not fixated upon; (2) recognition errors, which occur when cues are fixated upon, but insufficiently for pattern matching to take place and (3) judgmental errors, when cues are sufficiently and sometimes extensively fixated (processed) but not reported. Fig. 2 shows the cue processing types and the

Fig. 2. The underlying medical professionals' cognition processes and the associated errors in decision-making as revealed by eye tracker studies.

M.O. Al-Moteri et al. / Computers in Human Behavior 66 (2017) 52e66

associated errors in decision-making. Conducting the study in an authentic setting is preferable, however, there are limitations of using a mobile eye tracker in an authentic area. Browning et al. (2016) claim that although the use of mobile eye trackers in healthcare settings has potential, the limitations are the low percentage of attention capturing (gaze location), which may significantly influence the reliability and the interpretation of the results. Therefore, the simplest solution is to substitute the mobile eye tracker for a screen-based eye tracker in a high-fidelity computer-based simulated task. Most of the studies reviewed used a static 2-D scenes, researchers into cue processing in decision-making encourage the use of dynamic 2 or 3-D scenes (Mallett et al., 2014; Stuijfzand et al., 2016). According to Krupinski et al. (2012), a static 2-D scene requires less visual information processing and human-computer interaction compared to a dynamic 3-D scene. Therefore, decisions and errors related to perceptual limitations might not be adequately assessed. The dwell-time threshold of 1s, established in medical imaging decades ago (Nodine & Kundel, 1987), has since been frequently investigated, and has produced the same level of certainty. In general, dwell time is a powerful indicator of cognitive cue processing errors, such as perception and recognition (Krupinski, Nodine, & Kundel, 1998). The threshold varies in different contexts. In contrast to the cues in static 2-D medical images, some clinical decision-making requires the analysis of extremely dataintensive, complex, dynamic or attention-disrupting contexts, and these contexts potentially lead to poor decisions (Poli, Cinel, Sepulveda, & Stoica, 2013). However, to date, the thresholds for visual cue processing have not been established. Future studies are required to bridge this gap, as deeper understanding of cue processing errors can help improve clinical education (Hoffman, Aitken, & Duffield, 2009). Notably, different fixation durations indicate different processing (Follet, Le Meur, & Baccino, 2011; Holmqvist et al. 2011). The first exposure to a given scene tends to prompt short fixation and long saccades (Rayner, 2009) in an attempt to produce an overview of the scene. The initial overview is followed by longer, focused fixation for deep processing of the details (Unema, Pannasch, Joos, & Velichkovsky, 2005). This perceptual ability allows the identification of areas with high information value for subsequent focus and deeper processing (Bond et al., 2014). Perception seems to facilitate decision-making by limiting the number of cues requiring fixation, and enhancing the recognition of fixated cues (Orquin & Loose, 2013). In the current review, most of the studies reported perceptual ability as characteristic of experienced medical decision makers. This may provide a basis for the development of training approaches, by mirroring experienced medical decision-makers’ cognitive processes (Fogarasi et al., 2012). Selectivity of cue processing is a highly competitive process (Knudsen, 2007) and tends to be controlled by two distinct mechanisms: bottom-up (stimulus-driven) and top-down (goaldriven). Bottom-up processing is driven by stimuli present in the external environment, while top-down processing is driven by internal mental stimuli that come from the mind (Awh, Belopolsky, & Theeuwes, 2012). However, interaction can occur between stimulus-driven and goal-driven processing (Orquin & Loose, 2013). For instance, when searching for myocardial infarction electro-cardiograph (ECG) features, attention is increased to all segment elevations in the entire ECG strip. Although the possibility of such interaction exists, the included studies have not investigated its influence on eye movement behaviour. Task goal is a typical top-down process and has a crucial role in making a decision (Orquin & Loose, 2013). Regardless of the task goal, research indicates that task instructions (Castelhano, Mack & Henderson, 2009) and individual preferences (Atalay, Bodur, &

63

Rasolofoarison, 2012) are both related to top-down processing that might direct the visual attention. Cues may be valued differently from one medical decision maker to another, based on individual preferences. Certain cues might not be interesting at all and would not attract attention. Researchers cannot tell if the medical decision-maker is not aware of these cues, as eye metrics per se do not explore individuals' preferences, interests or goals (Guan, Lee, Cuddihy, & Ramey, 2006). To enable researchers to have a complete understanding of these complex cognitive processes, they need to study this aspect of decision-making from a different perspective, by utilizing different approaches, for example, gaze path-cued retrospective think-aloud interviews. In relation to decision-making, if a cue is not fixated, it is more likely that it will not be processed and is, therefore, unavailable to the medical decision-maker. Understanding the effect of missing cues on decision-making in dynamic patient areas is lacking. This is an interesting area for future research to identify the link between missing cues and cognitive and decisional errors using eye movement measurements. Experienced medical decision-makers are driven by the task goal, while less experienced visual search is directed by external salient stimuli that appear different from the rest of the scene (Foulsham & Underwood, 2008). This difference likely makes novice medical decision-maker more prone to search errors when compared to experts. However, with training (Brockmole & Henderson, 2006), less experienced medical decision-makers become less concerned about the salient features and more focused on the task goal. The transition most likely results from the development of pattern recognition, as test subjects become better able to distinguish relevant from irrelevant stimuli. According to Henderson (2003), visual searching to acquire knowledge switches from salience-driven to knowledge-driven with training, practice, and frequent exposure. A lesion may be both detected and perceived, but a medical decision-maker may decide that it is normal. Interpreting medical images is extremely subjective. Therefore, the concept of variation has emerged in diagnostic imaging, which is highly prone to decisional error (Pescarini & Inches, 2006). Regardless of the medical speciality, medical decision-makers express certainty about a disease by searching for patterns. However, pattern matching is conditioned by the length and the depth of experience (Croskerry, 2009). This strategy lends efficacy to the decisionmaking process, but when the disease presentation is contaminated with noise (distracting, irrelevant cues) and when the medical decision-makers fail to differentiate the noise from the actual disease presentation, they may wrongly analyse and interpret these cues (Croskerry, 2008). Judgmental skills would be needed in this case to differentiate noise from the actual disease signs to extract all available possibilities. 5. Implications Although no assessment of methodological quality was undertaken as part of this scoping review, reliable results still emerged that may be useful for improving clinical training. The review suggests that perceptual and recognition ability, and hence decision-making, improve as clinical experience increases. This offers the potential for the development of perceptual training techniques to train less experienced medical decision makers (e.g., by using attention guides). Although the non-deliberate (pattern matching) thinking process seems to be the focus of most of the studies in the current review, the investigation of eye movement behaviour in deliberate (analytical) decision-making modes does not appear to be a priority in eye tracking studies in the medical field. This is an important areas for future research. There is also a

64

M.O. Al-Moteri et al. / Computers in Human Behavior 66 (2017) 52e66

need to investigate visual cognition and to establish a threshold for cognition errors in decision-making in dynamic patient-care contexts. On the one hand, understanding visual cognition skills may allow clinical instructors to tailor training programs that provide maximum benefit. On the other hand, identifying cognition errors may allow clinical instructors to isolate the errors in cue processing that should be given priority in clinical training. 6. Review limitations The studies in this review indicate that eye tracking has potential for assessing the cognitive aspect of decision-making, but there are limitations. Technical difficulties hinder the use of eye trackers in dynamic settings, such as medical professionals moving from one place to another. This contrasts with the medical imaging context, because the medical professional remains in place. This is probably the reason why the review did not locate many studies conducted in a dynamic context, such as in-patient areas. Further, the reviewed studies were conducted in laboratories. Findings from laboratory studies may differ from those obtained from clinical practice. In addition, most of the included studies had a small sample size, and some potential participants could not work with eye tracking calibration, which restricted participant eligibility. 7. Conclusion This scoping review demonstrates the potential to identify distinct aspects of cue processing and errors. Cue recognition in decision-making starts with an initial perception of the scene, followed by a stage in which cues are processed in depth for pattern recognition. The review findings support the assertion that cognitive errors in decision-making can be distinguished by identifying the length of dwell time. These findings encourage investigations that are more extensive. Contribution Review Design: MM, MS, VP, SC. Data Collection and Analysis: MM. Manuscript Writing: MM, MS. Manuscript reviewing: MS, VP, SC. References Aitken, L. M., Marshall, A., Elliott, R., & McKinley, S. (2011). Comparison of ‘think aloud’and observation as data collection methods in the study of decision making regarding sedation in intensive care patients. International Journal of Nursing Studies, 48(3), 318e325. http://dx.doi.org/10.1016/ j.ijnurstu.2010.07.014. Arksey, H., & O'Malley, L. (2005). Scoping studies: Towards a methodological framework. International Journal of Social Research Methodology, 8(1), 19e32. http://dx.doi.org/10.1080/1364557032000119616. Atalay, A. S., Bodur, H. O., & Rasolofoarison, D. (2012). Shining in the center: Central gaze cascade effect on product choice. Journal of Consumer Research, 39(4), 848e866. http://dx.doi.org/10.1086/665984. Atkins, M. S., Tien, G., Khan, R. S., Meneghetti, A., & Zheng, B. (2013). What do surgeons see capturing and synchronizing eye gaze for surgery applications. Surgical Innovation, 20(3), 241e248. http://dx.doi.org/10.1177/ 1553350612449075. Awh, E., Belopolsky, A. V., & Theeuwes, J. (2012). Top-down versus bottom-up attentional control: A failed theoretical dichotomy. Trends in Cognitive Sciences, 16(8), 437e443. http://dx.doi.org/10.1016/j.tics.2012.06.010. Balatsoukas, P., Ainsworth, J., Williams, R., Carruthers, E., Davies, C., McGrath, J., et al. (2012). Verbal protocols for assessing the usability of clinical decision support: The retrospective sense making protocol. Studies in Health Technology and Informatics, 192, 283e287. http://europepmc.org/abstract/med/23920561. Balslev, T., Jarodzka, H., Holmqvist, K., de Grave, W., Muijtjens, A. M., Eika, B., et al. (2012). Visual expertise in paediatric neurology. European Journal of Paediatric Neurology, 16(2), 161e166. http://dx.doi.org/10.1016/j.ejpn.2011.07.004. Berbaum, K. S., Brandser, E. A., Franken, E. A., Dorfman, D. D., Caldwell, R. T., & Krupinski, E. A. (2001). Gaze dwell times on acute trauma injuries missed

because of satisfaction of search. Academic Radiology, 8(4), 304e314. http:// dx.doi.org/10.1016/S1076-6332(03)80499-3. Blondon, K., Wipfli, R., & Lovis, C. (2014). Use of eye-tracking technology in clinical reasoning: A systematic review. Studies in Health Technology and Informatics, 210, 90e94. https://books.google.com.au. Bond, R. R., Zhu, T., Finlay, D. D., Drew, B., Kligfield, P. D., Guldenring, D., et al. (2014). Assessing computerized eye tracking technology for gaining insight into expert interpretation of the 12-lead electrocardiogram: An objective quantitative approach. Journal of Electrocardiology, 47(6), 895e906. http://dx.doi.org/ 10.1016/j.jelectrocard.2014.07.011. Brockmole, J. R., & Henderson, J. M. (2006). Recognition and attention guidance during contextual cueing in real-world scenes: Evidence from eye movements. The Quarterly Journal of Experimental Psychology, 59(7), 1177e1187. http:// dx.doi.org/10.1080/17470210600665996. Browning, M., Cooper, S., Cant, R., Sparkes, L., Bogossian, F., Williams, B., et al. (2016). The use and limits of eye-tracking in high-fidelity clinical scenarios: A pilot study. International Emergency Nursing, 25, 43e47. http://dx.doi.org/ 10.1016/j.ienj.2015.08.002. , T. T., Carney, P. A., Allison, K. H., Shapiro, L. G., Weaver, D. L., & Elmore, J. G. Brunye (2014). Eye movements as an index of pathologist visual expertise: A pilot study. Plos One, 9(8), e103447. http://dx.doi.org/10.1371/journal.pone.0103447. Buckingham, C. D., & Adams, A. (2000). Classifying clinical decision making: A unifying approach. Journal of Advanced Nursing, 32(4), 981e989. http:// dx.doi.org/10.1046/j.1365-2648.2000.t01-1-01565.x. Castelhano, M. S., Mack, M. L., & Henderson, J. M. (2009). Viewing task influences eye movement control during active scene perception, 6e6 Journal of Vision, 9(3). http://dx.doi.org/10.1167/9.3.6. Clark, V. L. P., Huddleston-Casas, C. A., Churchill, S. L., Green, D. O. N., & Garrett, A. L. (2008). Mixed methods approaches in family science research. Journal of Family Issues, 29(11), 1543e1566. http://dx.doi.org/10.1177/0192513X08318251. Cooper, L., Gale, A., Darker, I., Toms, A., & Saada, J. (2009). Radiology image perception and observer performance: How does expertise and clinical information alter interpretation? Stroke detection explored through eye-tracking. In International society for optics and photonics (Vol. 7263)Bellingham: SPIE. http:// dx.doi.org/10.1117/12.811098 (pp. 72630Ke72630K-12). Cooper, L., Gale, A., Saada, J., Gedela, S., Scott, H., & Toms, A. (2010). The assessment of stroke multidimensional CT and MR imaging using eye movement analysis: Does modality preference enhance observer performance?. In International society for optics and photonics. Bellingham: SPIE. http://dx.doi.org/10.1117/ 12.843680 (pp. 76270B-76270B). Croskerry, P. (2003). The importance of cognitive errors in diagnosis and strategies to minimize them. Academic Medicine, 78(8), 775e780. http://ovidsp.tx.ovid. com.ezproxy.lib.monash.edu.au. Croskerry, P. (2008). Context is everything or how could I have been that stupid? Healthcare Quarterly, 12, e171ee176. http://europepmc.org/abstract/med/ 19667765. Croskerry, P. (2009). A universal model of diagnostic reasoning. Academic Medicine, 84(8), 1022e1028. http://dx.doi.org/10.1097/ACM.0b013e3181ace703. Donald, J. J., & Barnard, S. A. (2012). Common patterns in 558 diagnostic radiology errors. Journal of Medical Imaging and Radiation Oncology, 56(2), 173e178. http:// dx.doi.org/10.1111/j.1754-9485.2012.02348.x. Donovan, T., & Litchfield, D. (2013). Looking for cancer: Expertise related differences in searching and decision making. Applied Cognitive Psychology, 27(1), 43e49. http://DOI:10.1002/acp.2869. Downing, S. M., & Haladyna, T. M. (2006). Handbook of test development. US: Lawrence Erlbaum Associates Publishers. http://psycinfo/2006-01815-000. Eger, N., Ball, L. J., Stevens, R., & Dodd, J. (2007). Cueing retrospective verbal reports in usability testing through eye-movement replay. In Proceedings of the 21st British HCI group annual conference on people and computers: HCI But not as we know it (Vol. 1, pp. 129e137). British Computer Society. http://10.1145/1540000/ 1531312/p129-eger.pdf. k, G., & Lorincz, A. (2012). Improving seizure recognition Fogarasi, A., Szirtes, G., Zsia by visual reinforcement. Neurology, Psychiatry and Brain Research, 18(1), 1e7. http://dx.doi.org.ezproxy.lib.monash.edu.au/10.1016/j.npbr.2011.10.002. Follet, B., Le Meur, O., & Baccino, T. (2011). New insights into ambient and focal visual fixations using an automatic classification algorithm. Perception, 2(6), 592. http://dx.doi.org/10.1068/i0414. Forsman, J., Anani, N., Eghdam, A., Falkenhav, M., & Koch, S. (2013). Integrated information visualization to support decision making for use of antibiotics in intensive care: Design and usability evaluation. Informatics for Health and Social Care, 38(4), 330e353. http://dx.doi.org/10.3109/17538157.2013.812649. Foulsham, T., & Underwood, G. (2008). What can saliency models predict about eye movements? Spatial and sequential aspects of fixations during encoding and recognition. Journal of Vision, 8(2), 1e17. http://dx.doi.org/10.1167/8.2.6. € , R. (2011). Expertise differences in the Gegenfurtner, A., Lehtinen, E., & S€ aljo comprehension of visualizations: A meta-analysis of eye-tracking research in professional domains. Educational Psychology Review, 23(4), 523e552. http:// dx.doi.org/10.1007/s10648-011-9174-7. € f, K., Wallin, A., Dewhurst, R., & Holmqvist, K. (2013). Using eye tracking to Gidlo trace a cognitive process: Gaze behaviour during decision making in a natural environment. Journal of Eye Movement Research, 6(1), 1e14. http://dx.doi.org/ 10.16910/jemr.6.1.3. Giovinco, N. A., Sutton, S. M., Miller, J. D., Rankin, T. M., Gonzalez, G. W., Najafi, B., et al. (2015). A passing Glance? Differences in eye tracking and gaze patterns between trainees and experts reading plain film bunion radiographs. The

M.O. Al-Moteri et al. / Computers in Human Behavior 66 (2017) 52e66 Journal of Foot and Ankle Surgery, 54(3), 382e391. http://dx.doi.org/10.1053/ j.jfas.2014.08.013. Glaholt, M. G., & Reingold, E. M. (2011). Eye movement monitoring as a process tracing methodology in decision making research. Journal of Neuroscience, Psychology, and Economics, 4(2), 125. http://dx.doi.org/10.1037/a0020692. Grundgeiger, T., Sanderson, P., MacDougall, H. G., & Venkatesh, B. (2010). Interruption management in the intensive care unit: Predicting resumption times and assessing distributed support. Journal of Experimental Psychology: Applied, 16(4), 317. http://dx.doi.org/10.1037/a0021912. Guan, Z., Lee, S., Cuddihy, E., & Ramey, J. (2006). The validity of the stimulated retrospective think-aloud method as measured by eye tracking (April). In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 1253e1262). ACM. http://dx.doi.org/10.1145/1124772.1124961. Hassebrock, F., & Prietula, M. J. (1992). A protocol-based coding scheme for the analysis of medical reasoning. International Journal of Man-Machine Studies, 37(5), 613e652. http://dx.doi.org/10.1016/0020-7373(92)90026-H. Henderson, J. M. (2003). Human gaze control during real-world scene perception. Trends in Cognitive Sciences, 7(11), 498e504. http://dx.doi.org/10.1016/ j.tics.2003.09.006. Henneman, P. L., Fisher, D. L., Henneman, E. A., Pham, T. A., Mei, Y. Y., Talati, R., et al. (2008). Providers do not verify patient identity during computer order entry. Academic Emergency Medicine, 15(7), 641e648. http://dx.doi.org/10.1111/j.15532712.2008.00148.x. Hermens, F., Flin, R., & Ahmed, I. (2013). Eye movements in surgery: A literature review. Journal of Eye Movement Research, 6(4), 1e11. http://dx.doi.org/10.16910/ jemr.6.4.4. Hoffman, K. A., Aitken, L. M., & Duffield, C. (2009). A comparison of novice and expert nurses' cue collection during clinical decision-making: Verbal protocol analysis. International Journal of Nursing Studies, 46(10), 1335e1344. http:// dx.doi.org/10.1016/j.ijnurstu.2009.04.001. €m, M., Andersson, R., Dewhurst, R., Jarodzka, H., & Van de Holmqvist, K., Nystro Weijer, J. (2011). Eye tracking: A comprehensive guide to methods and measures. Oxford University Press. http://bitstream/1820/3441/1/Holmqvist%20et%20al% 20(2011).pdf. Jungk, A., Thull, B., Hoeft, A., & Rau, G. (2000). Evaluation of two new ecological interface approaches for the anesthesia workplace. Journal of Clinical Monitoring and Computing, 16(4), 243e258. http://dx.doi.org/10.1023/A:1011462726040. Karn, K. S. (2006). Eye tracking for usability testing, You’ve got to know your strengths and weaknesses. In Proceedings of CHI 2006. Kataoka, J., Sasaki, M., & Kanda, K. (2011). Effects of mental workload on nurses' visual behaviors during infusion pump operation. Japan Journal of Nursing Science, 8(1), 47e56. http://dx.doi.org/10.1111/j.1742-7924.2010.00158.x. Knudsen, E. I. (2007). Fundamental components of attention. Annual Reviews Neuroscience, 30, 57e78. http://dx.doi.org/10.1146/ annurev.neuro.30.051606.094256. Koh, R. Y., Park, T., Wickens, C. D., Teng Ong, L., & Noi Chia, S. (2011). Differences in attentional strategies by novice and experienced operating theatre scrub nurses. Journal of Experimental Psychology-Applied, 17(3), 233. http://dx.doi.org/ 10.1037/a0025171. Krupinski, E. A. (1996). Visual scanning patterns of radiologists searching mammograms. Academic Radiology, 3(2), 137e144. http://dx.doi.org/10.1016/S10766332(05)80381-2. Krupinski, E. A., Berbaum, K. S., Caldwell, R. T., Schartz, K. M., Madsen, M. T., & Kramer, D. J. (2012). Do long radiology workdays affect nodule detection in dynamic CT interpretation? Journal of the American College of Radiology, 9(3), 191e198. http://dx.doi.org/10.1016/j.jacr.2011.11.013. Krupinski, E. A., Nodine, C. F., & Kundel, H. L. (1998). Enhancing recognition of lesions in radiographic images using perceptual feedback. Optical Engineering, 37(3), 813e818. http://dx.doi.org/10.1117/1.601914. Krupinski, E. A., Tillack, A. A., Richter, L., Henderson, J. T., Bhattacharyya, A. K., Scott, K. M., et al. (2006). Eye-movement study and human performance using telepathology virtual slides. Implications for medical education and differences with experience. Human Pathology, 37(12), 1543e1556. http://dx.doi.org/ 10.1016/j. Kundel, H. L., Nodine, C. F., Conant, E. F., & Weinstein, S. P. (2007). Holistic component of image perception in mammogram interpretation: Gaze-tracking Study1. Radiology, 242(2), 396e402. http://dx.doi.org/10.1148/ radiol.2422051997. Kundel, H. L., Nodine, C. F., Krupinski, E. A., & Mello-Thoms, C. (2008). Using gazetracking data and mixture distribution analysis to support a holistic model. Academic Radiology, 15, 881e886. http://dx.doi.org/10.1016/j.acra.2008.01.023. Levac, D., Colquhoun, H., & O'Brien, K. K. (2010). Scoping studies: Advancing the methodology. Implement Science, 5(1), 1e9. http://dx.doi.org/10.1186/17485908-5-69. Lindsay, P. H., & Norman, D. A. (2013). Human information processing: An introduction to psychology. NY: Academic Press. https://books.google.com.au/books? Humanþinformationþprocessing. Litchfield, D., Ball, L. J., Donovan, T., Manning, D. J., & Crawford, T. (2010). Viewing another person’s eye movements improves identification of pulmonary nodules in chest x-ray inspection. Journal of Experimental Psychology: Applied, 16(3), 251. http://dx.doi.org/10.1037/a0020082. Loveday, T., Wiggins, M., Festa, M., Schell, D., & Twigg, D. (2013). Pattern recognition as an indicator of diagnostic expertise. In Pattern recognition-applications and methods (pp. 1e11). Berlin Heidelberg: Springer. http://link.springer.com/ chapter/10.1007/978-3-642-36530-0_1.

65

Mallett, S., Phillips, P., Fanshawe, T. R., Helbren, E., Boone, D., Gale, A., et al. (2014). Tracking eye gaze during interpretation of endoluminal three-dimensional CT Colonography: Visual perception of experienced and inexperienced readers. Radiology, 273(3), 783e792. http://dx.doi.org/10.1148/radiol.14132896. Manning, D. J., Ethell, S. C., & Donovan, T. (2004). Detection or decision errors? Missed lung from the posteroanterior chest radiograph. British Journal of Radiology, 77(915), 231e235. http://dx.doi.org/10.1259/bjr/28883951. Manning, D., Ethell, S., Donovan, T., & Crawford, T. (2006). How do radiologists do it? The influence of experience and training on searching for chest nodules. Radiography, 12(2), 134e142. http://dx.doi.org/10.1016/j.radi.2005.02.003. Matsumoto, H., Terao, Y., Yugeta, A., Fukuda, H., Emoto, M., Furubayashi, T., et al. (2011). Where do neurologists look when viewing brain ct images? an eyetracking study involving stroke cases. Plos One, 6(12), e28928. http:// dx.doi.org/10.1371/journal.pone.0028928. McGuiness, B., & Axford, R. (1995). Digital photography. A potential documentation source for storing patient associated cues. In Hic 95: It's time for Healthcare: Proceedings of the third national health informatics conference, adelaide, Australia 16th-17th october (p. 31). Health Informatics Society of Australia. http://search. informit.com.au/documentSummary;dn¼025358881095575;res¼IELHEA. Mello-Thoms, C., Hardesty, L., Sumkin, J., Ganott, M., Hakim, C., Britton, C., et al. (2005). Effects of lesion conspicuity on visual search in mammogram reading. Academic Radiology, 12(7), 830. http://dx.doi.org/10.1016/j.acra.2005.03.068. Nodine, C. F., & Kundel, H. L. (1987). Using eye movements to study visual search and to improve tumor detection. Radiographics, 7(6), 1241e1250. http:// dx.doi.org/10.1148/radiographics.7.6.3423330. Nodine, C. F., Mello-Thoms, C., Kundel, H. L., & Weinstein, S. P. (2002). Time course of perception and decision making during mammographic interpretation. American Journal of Roentgenology, 179(4), 917e923. http://dx.doi.org/10.2214/ ajr.179.4.1790917. O'Neill, E. C., Kong, Y. X. G., Connell, P. P., Ong, D. N., Haymes, S. A., Coote, M. A., et al. (2011). Gaze behavior among experts and trainees during optic disc examination: Does how we look affect what we see? Investigative Ophthalmology & Visual Science, 52(7), 3976e3983. http://dx.doi.org/10.1167/iovs.10-6912. Orquin, J. L., & Loose, S. M. (2013). Attention and choice: A review on eye movements in decision making. Acta Psychologica, 144(1), 190e206. http://dx.doi.org/ 10.1016/j.actpsy.2013.06.003. Pescarini, L., & Inches, I. (2006). Systematic approach to human error in radiology. La Radiologia Medica, 111(2), 252e267. http://dx.doi.org/10.1007/s11547-0060026-3. Poli, R., Cinel, C., Sepulveda, F., & Stoica, A. (2013). Improving decision-making based on visual perception via a collaborative brain-computer interface. In Cognitive methods in situation awareness and decision support, international multidisciplinary conference on (pp. 1e8). IEEE. http://dx.doi.org/10.1109/CogSIMA.2013.6523816, 2013. Poole, A., & Ball, L. J. (2006). Eye tracking in HCI and usability research. Encyclopedia of Human Computer Interaction, 1, 211e219. https://books.google.com.au/books? hl¼EyeþtrackingþinþHCIþandþusabilityþresearch. Rayner, K. (2009). Eye movements and attention in reading, scene perception, and visual search. The Quarterly Journal of Experimental Psychology, 62(8), 1457e1506. http://dx.doi.org/10.1080/17470210902816461. Rubin, G. D., Roos, J. E., Tall, M., Harrawood, B., Bag, S., Ly, D. L., et al. (2014). Characterizing search, recognition, and decision in the detection of lung nodules on CT scans: Elucidation with eye tracking. Radiology, 274(1), 276e286. http://dx.doi.org/10.1148/radiol.14132918. Schotter, E. R., Gerety, C., & Rayner, K. (2012). Heuristics and criterion setting during selective encoding in visual decision making: Evidence from eye movements. Visual Cognition, 20(9), 1110e1129. http://dx.doi.org/10.1080/ 13506285.2012.735719. Shaban, R. (2015). Theories of clinical judgment and decision-making: A review of the theoretical literature. Australasian Journal of Paramedicine, 3(1), 1e2, 3081188-1-PB.pdf. Sibbald, M., de Bruin, A. B., Yu, E., & van Merrienboer, J. J. (2015). Why verifying diagnostic decisions with a checklist can help: Insights from eye tracking. Advances in Health Sciences Education, 20(4), 1053e1060. http://dx.doi.org/ 10.1007/s10459-015-9585-1. Srinath, D. K. (2015). Eye movement-based human-computer interaction techniques. International Journal of Innovation Technology Research, 156e161. http:// cs.tufts.edu/~jacob/papers/hartson.pdf. Stuijfzand, B. G., van der Schaaf, M. F., Kirschner, F. C., Ravesloot, C. J., van der Gijp, A., & Vincken, K. L. (2016). Medical students' cognitive load in volumetric image interpretation: Insights from human-computer interaction and eye movements. Computers in Human Behavior, 62, 394e403. http://dx.doi.org/ 10.1016/j.chb.2016.04.015. Tien, T., Pucher, P. H., Sodergren, M. H., Sriskandarajah, K., Yang, G. Z., & Darzi, A. (2014). Eye tracking for skills assessment and training: a systematic review. Journal of Surgical Research, 191(1), 169e178. http://dx.doi.org.ezproxy.lib. monash.edu.au/10.1080/19439342.2012.710641. Tiersma, E. S. M., Peters, A. A., Mooij, H. A., & Fleuren, G. J. (2003). Visualising scanning patterns of pathologists in the grading of cervical intraepithelial neoplasia. Journal of Clinical Pathology, 56(9), 677e680. http://dx.doi.org/ 10.1136/jcp.56.9.677. € m, M., Holmqvist, K., Wagner, P., Fo €rnvik, D., et al. Timberg, P., Lång, K., Nystro (2013). Investigation of viewing procedures for interpretation of breast tomosynthesis image volumes: A detection-task study with eye tracking. European Radiology, 23(4), 997e1005. http://dx.doi.org/10.1007/s00330-012-2675-z.

66

M.O. Al-Moteri et al. / Computers in Human Behavior 66 (2017) 52e66

Unema, P. J., Pannasch, S., Joos, M., & Velichkovsky, B. M. (2005). Time course of information processing during scene perception: The relationship between saccade amplitude and fixation duration. Visual Cognition, 12(3), 473e494. http://dx.doi.org/10.1080/13506280444000409. Van der Lans, R., Wedel, M., & Pieters, R. (2011). Defining eye-fixation sequences across individuals and tasks: The binocular-individual threshold (BIT) algorithm. Behavior Research Methods, 43(1), 239e257. http://dx.doi.org/10.3758/ s13428-010-0031-2. Wood, G., Batt, J., Appelboam, A., Harris, A., & Wilson, M. R. (2013). Exploring the

impact of expertise, clinical history, and visual search on electrocardiogram interpretation. Medical Decision Making. http://dx.doi.org/10.1177/ 0272989X13492016, 0272989X13492016. Wood, G., Knapp, K. M., Rock, B., Cousens, C., Roobottom, C., & Wilson, M. R. (2013). Visual expertise in detecting and diagnosing skeletal fractures. Skeletal Radiology, 42(2), 165e172. http://dx.doi.org/10.1007/s00256-012-1503-5. Zheng, B., Tien, G., Atkins, S. M., Swindells, C., Tanin, H., Meneghetti, A., et al. (2011). Surgeon's vigilance in the operating room. The American Journal of Surgery, 201(5), 673e677. http://dx.doi.org/10.1016/j.amjsurg.2011.01.016.