how to perform a systematic visual search

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Visual Search Patterns for Safe Driving: Proactive Scanning

When it comes to collecting visual information while driving, there is a right way and a wrong way to go about it. Knowing where to look and how long for can be confusing for new drivers, particularly when there is so much to keep track of inside your car, right in front of the vehicle and 20 seconds ahead of you on the roadway. To drive safely, you need to adopt a systematic and efficient method of visually scanning your environment .

Immediate range

Secondary range, target area range, proactive scanning.

Never allow yourself to stay focused on one point on the roadway while driving. Sure, you need to monitor the situation on the road directly in front of your vehicle, but you also need to monitor the gauges inside your car and the situation on the roadway some distance ahead. Organizing a visual search of the roadway is easier and more effective when we sort the things we need to monitor into ranges and switch our attention between these points at regular intervals.

Everything in the area 4 to 6 seconds ahead of you, including the car’s dashboard, falls into your immediate range . The rear of the car in front of you should mark the far end of your immediate range when you are driving at a safe following distance . All the visual information you receive with this range will tell you how to set your speed and position the vehicle within the lane . Therefore, by the time a point at which you need to stop, turn or merge has entered your immediate range, you should already have started the necessary maneuver.

Beyond your immediate range is the secondary range , which covers everything from the end of the immediate range up to 12 to 15 seconds ahead of your vehicle. Any visual information gathered from your secondary range will help you make decisions about your speed and lane position. At this distance, you may prepare to execute a maneuver, respond to an upcoming situation and communicate with oncoming traffic .

The farthest range you must scan is your target area range (also known as the visual lead area ) which covers the area about 20 to 30 seconds ahead of your vehicle. When directing your attention to the target area range , you should look for all key visual information relating to potential hazards and changes in the roadway environment that may require action as the target area enters your secondary range. You should aim to select a target area as far ahead as possible while maintaining a clear line of sight. This will give you the maximum amount of time to absorb and react to visual information.

While driving, you must always keep your eyes moving between these three ranges so that no important information is neglected. The vehicle immediately in front of you may obstruct your view to some extent, though you can maximize your view of the secondary and target area ranges by maintaining a safe following distance and positioning your vehicle appropriately within the lane. Allow a greater following distance when traveling behind very large vehicles , as they may completely obscure your line of sight and hide other smaller vehicles.

In addition to alternating between the immediate, secondary and target area ranges, you must glance at your mirrors every three to five seconds and visually check the space to the sides of your vehicle. Remember that in doing so, you must not take your attention away from the roadway for more than about half a second at a time.

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Safe Following Distance

It is impossible to drive safely and attentively without leaving enough space between your vehicle and the car ahead of you. Maintaining an adequate following distance is crucial to maximize your view of the roadway up ahead.

The Effects of Speed

Keeping speed to a minimum is one of the best risk-reducing tactics you can employ as an attentive driver. As the speed you are traveling at increases, so too does the danger you are exposed to and the challenges you face.

Interacting With Other Drivers

Without effective communication between motorists, it would be impossible to predict the movement of other vehicles and negotiate the roadway safely. Attentive, conscientious motorists think about how their actions will affect other drivers and endeavor to behave considerately, at all times. It is not simply a matter of being polite, it is a matter of safety.

The Importance of Paying Attention

Your ability to fully and consistently focus your attention on the environment around your vehicle is every bit as important as your road rule knowledge and vehicle control skills. Paying attention while driving is an important skill which must not be overlooked while you’re learning to drive.

The Importance of Good Vision

No sense is more important to a driver than vision. As your eyes are responsible for 90% of the information you receive while driving, good vision is essential in making safe and appropriate driving decisions.

Vision Impairments

People with less than 20/40 vision do not qualify for an unrestricted driver’s license in most states. However, there are vast numbers of people with poorer than 20/40 vision who can drive safely and legally under a restricted license, providing they wear corrective glasses or contact lenses. Only in extreme cases of vision impairment or blindness will a person be refused a driving license altogether.

Mental Skills for Driving

The “vision, memory and understanding” trinity allows you to assess and make decisions based on all the information your eyes receive while you’re driving. If you do not receive accurate visual information due to a vision impairment, or do not have relevant memory information stored in your brain to help you make sense of what you have seen, you may not respond to roadway hazards appropriately.

Proprioception and Kinesthesia

While most of the information we receive while driving is visual, our other senses are important too. In addition to sight, our brains collect information about the world around us via hearing, smell, taste and touch.

Visual Targeting

Visual targeting is the practice of focusing your attention on a stationary object which is 12 to 20 seconds ahead of your vehicle. As you move closer to your visual target, you should then select a new fixed object within that 12 to 20-second window, repeating this process continually as you move along the roadway.

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Safe driving depends on your ability to notice many things at once. Our eyes provide two types of visions: central vision and peripheral or side vision. Central vision allows us to make very important judgments like estimating distance and understanding details in the path ahead, whereas peripheral vision helps us detect events to the side that are important to us, even when we're not looking directly at them. Most driving mistakes are caused by bad habits in the way drivers use their eyes.

IPDE (Identify, Predict, Decide, and Execute) is an important concept in defensive driving. To know more about its principles, read carefully the following section.

In order to avoid last minute moves and spot possible traffic hazards, you should always look down the road ahead of your vehicle. Start braking early if you see any hazards or traffic ahead of you slowing down. Also check the space between your car and any vehicles in the lane next to you. It is very important to check behind you before you change lanes, slow down quickly, back up, or drive down a long or steep hill. You should also glance at your instrument panel often to ensure there are no problems with the vehicle and to verify your speed.

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• Review Article
• Published: 08 March 2017

Five factors that guide attention in visual search

• Jeremy M. Wolfe 1 &
• Todd S. Horowitz 2  

Nature Human Behaviour volume  1 , Article number:  0058 ( 2017 ) Cite this article

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• Human behaviour
• Visual system

How do we find what we are looking for? Even when the desired target is in the current field of view, we need to search because fundamental limits on visual processing make it impossible to recognize everything at once. Searching involves directing attention to objects that might be the target. This deployment of attention is not random. It is guided to the most promising items and locations by five factors discussed here: bottom-up salience, top-down feature guidance, scene structure and meaning, the previous history of search over timescales ranging from milliseconds to years, and the relative value of the targets and distractors. Modern theories of visual search need to incorporate all five factors and specify how these factors combine to shape search behaviour. An understanding of the rules of guidance can be used to improve the accuracy and efficiency of socially important search tasks, from security screening to medical image perception.

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BRIEF RESEARCH REPORT article

The visual search strategies underpinning effective observational analysis in the coaching of climbing movement.

• 1 Human Sciences Research Centre, College of Life and Natural Sciences, University of Derby, Derby, United Kingdom
• 2 Lattice Training Ltd., Chesterfield, United Kingdom

Despite the importance of effective observational analysis in coaching the technical aspects of climbing performance, limited research informs this aspect of climbing coach education. Thus, the purpose of the present research was to explore the feasibility and the utility of a novel methodology, combining eye tracking technology and cued retrospective think-aloud (RTA), to capture the cognitive–perceptual mechanisms that underpin the visual search behaviors of climbing coaches. An analysis of gaze data revealed that expert climbing coaches demonstrate fewer fixations of greater duration and fixate on distinctly different areas of the visual display than their novice counterparts. Cued RTA further demonstrated differences in the cognitive–perceptual mechanisms underpinning these visual search strategies, with expert coaches being more cognizant of their visual search strategy. To expand, the gaze behavior of expert climbing coaches was underpinned by hierarchical and complex knowledge structures relating to the principles of climbing movement. This enabled the expert coaches to actively focus on the most relevant aspects of a climber’s performance for analysis. The findings demonstrate the utility of combining eye tracking and cued RTA interviewing as a new, efficient methodology of capturing the cognitive–perceptual processes of climbing coaches to inform coaching education/strategies.

Introduction

Climbing’s acceptance as an Olympic event in Tokyo 2020 is recognition of the sports’ increasing popularity and professionalization ( Bautev and Robinson, 2019 ). As demand increases, so too will the need for effective coaching, thus requiring coach educators to consider how coaching expertise is developed ( Sport England, 2018 ). Climbing coaches employ a range of complex and inter-related strategies to facilitate physical, technical, mental, and tactical improvements ( Currell and Jeukendrup, 2008 ). However, to date, climbing research has predominantly focused on the physiological and the psychological aspects of performance, somewhat neglecting the importance of the technical components of climbing ( Taylor et al., 2020 ). Furthermore, the process by which climbing coaches facilitate technical improvements in their athletes is wholly under-researched.

The characteristics that define expertise in the coaching of climbing movement, and the process by which expertise is developed, have yet to be explored. Wider expertise research has sought to identify the key characteristics of expert performance; among others, one of the key hallmarks that define expert performance is superior visual search behavior ( Ericsson, 2017 ). Research in a variety of sporting contexts (i.e., athletes, officials, and coaches) has demonstrated that experts have a superior ability to pick up on salient postural cues and detect patterns of movement and can more accurately predict the probabilities of likely event occurrences ( Williams et al., 2018 , p. 663). The superior visual search behavior of expert coaches is thought to be due to more refined domain-specific knowledge and memory structures ( Williams and Ward, 2007 ). Declarative and procedural knowledge, acquired through extensive deliberate practice, enables expert coaches to extract the most salient information from the visual display to identify the key aspects of the athlete’s performance that can subsequently be targeted for improvement ( Hughes and Franks, 2004 ).

Yet without a systematic approach to observational analysis, coaches potentially threaten the validity of their analysis ( Knudson, 2013 ). To understand how coaches analyze and evaluate climbing performance, it is argued that a fundamental step in this process is characterizing the underlying cognitive–perceptual mechanisms that underpin expertise ( Spitz et al., 2016 ). To enable this, the study of expertise in sport has commonly adopted the “Expert Performance Approach” (EPA) ( Ericsson and Smith, 1991 ). In EPA, the superior performance of experts is captured, identifying the mediating mechanisms underlying their performance by recording process-tracing measures such as eye movements and/or verbalizations ( Ford et al., 2009 ). Such advances have begun to enable significant insight into the cognitive–perceptual mechanisms underlying expert performance ( Gegenfurtner et al., 2011 ). For example, lightweight mobile eye tracking devices provide a precise, non-intrusive, millisecond-to-millisecond measurement of where, for how long, and in what sequence coaches focus their visual attention when viewing athlete performance ( Duchowski, 2007 ).

Gegenfurtner et al. (2011) conducted a meta-analysis of 65 eye tracking studies to identify the common characteristics of expert performance. They concluded that the superior performance of experts, across a variety of different domains (sport, medicine, aviation, etc.), could be explained by a combination of three factors: First, experts develop specific long-term working memory skills because of accumulated deliberate practice. Second, expert coaches can optimize the amount of processed information by ignoring task-irrelevant information. This allows for a greater proportion of their attentional resources to be allocated to more task-relevant areas of the visual display ( Haider and Frensch, 1999 ). Finally, they suggest that expert–novice performance differences in visual search are explained by an enhanced ability among experts to utilize their peripheral vision.

To date, however, there has been no eye tracking studies conducted on the visual search strategies of climbing coaches. Yet in other sports, eye tracking technology has yielded insight into differences between expert and novice coaches, which can be used to inform coaching strategies. Here eye tracking research conducted with coaches in basketball ( Damas and Ferreira, 2013 ), tennis ( Moreno et al., 2006 ), gymnastics ( Moreno et al., 2002 ), and football ( Iwatsuki et al., 2013 ) has demonstrated that expert coaches focus on distinctly different locations. Experts fixate their attention on the most salient areas of the visual display as compared to novices ( Williams et al., 1999 ). Additionally, experts demonstrate fewer fixations of greater duration in relatively static tasks/sports ( Mann et al., 2007 ; Gegenfurtner et al., 2011 ).

Most eye tracking research has, nonetheless, been conducted in laboratory settings, leading some researchers to challenge the ecological validity of the approach ( Hüttermann et al., 2018 ). Adding to this, Mann et al.(2007 ; see also Gegenfurtner et al., 2011 ) argue that the more realistic the experimental design is to the realities of the sporting context, the more likely it is that experts will be able to demonstrate their enhanced cognitive–perceptual skills afforded by their increased context-specific knowledge ( Travassos et al., 2013 ). Thus, some researchers have cast doubt on whether the results of laboratory studies can be transferred beyond their immediate context into the complex realities of the coaching environment ( Renshaw et al., 2019 ). Moving forward, therefore, the use of mobile eye tracking technology potentially enables researchers to capture the expert performance of coaches in naturalistic coaching environments, thus enhancing ecological validity and ensuring transferability to coaching practice.

Although eye tracking enables researchers to investigate the processes of visual attention, the relevance of specific gaze location biases to the coaching process still requires elaboration, that is, eye tracking gaze data can tell us where someone is looking, but importantly not why. Over-reliance on averaged and uncontextualized gaze data potentially oversimplifies and limits our understanding of the coaching process ( Dicks et al., 2017 ). Indeed one of the main conceptual concerns with sports expertise research is the relative neglect of the cognitive processes underpinning expert performance ( Moran et al., 2018 ). As Abernethy (2013) identifies, there remains a lack of evidence on the defining characteristics of sports expertise and how such characteristics are developed. Hence, additional methodological approaches are needed to complement eye tracking if the mechanisms underpinning the superior cognitive–perceptual skills of expert coaches are to be captured.

Currently, two such methodologies are proposed. These are concurrent think-aloud (CTA)—and retrospective think-aloud (RTA). In CTA, the participants verbalize their thought process during the actual task (e.g., Ericsson et al., 1993 ), whereas in RTA, the participants verbalize their thought process immediately after the task (e.g., Afonso and Mesquita, 2013 ). In critique, as we can mentally process visual stimuli much faster than we can verbalize our observations, it is argued that, when using CTA, verbalizations are often incomplete ( Wilson, 1994 ). Furthermore, attempting to verbalize complex cognitively demanding tasks while simultaneously performing them affects the user’s task performance and associated gaze behavior ( Holmqvist et al., 2011 ). The alternative, to record participants thinking aloud after the task, circumvents this disruption to the participants’ performance in the primary task. However, due to the time-lag between the primary task and RTA, a “loss of detail from memory or fabulation” may occur ( Holmqvist et al., 2011 , p. 104). The limitations of RTA are, however, potentially negated when it is combined with eye tracking technology.

Cued RTA utilizes eye tracking gaze data, as an objective reference point to stimulate memory recall, and structure RTA, reducing loss of detail from memory and fabulation ( Hyrskykari et al., 2008 ). Furthermore, cued RTA provides explicit detail as to the declarative and procedural knowledge that underpin the coach’s visual search strategies, adding depth and meaning to otherwise uncontextualized gaze data ( Gegenfurtner and Seppänen, 2012 ). Cued RTA can therefore be adopted for both empirical and theoretical reasons. First, cued RTA is confirmatory in that RTA data enable the researcher to verify the gaze data for accuracy (e.g., fixation location and allocation of attention), and gaze data provide an objective location to reduce memory loss and fabulation when conducting RTA. Second, cued RTA enables the researcher to elicit a greater level of insight into the cognitive–perceptual mechanisms that underpin the visual search strategies of coaches. It is therefore proposed that cued RTA is potentially more effective than either eye tracking or RTA methodologies applied in isolation.

Thus, in the present study, we explored the feasibility and the utility of a novel methodology, combining eye tracking technology with cued RTA, to capture the cognitive–perceptual mechanisms underpinning the visual search behaviors of climbing coaches. As this was a first trial of the combined methodology, three expert and three novice coaches were asked to observe and analyze the live climbing performances of intermediate boulderers in a naturalistic and ecologically valid setting.

Materials and Methods

Participants.

A total of six UK climbing coaches were recruited for the present study based on their level of expertise (see Moreno et al., 2002 ). The “expert” group (successful elite, as defined by Swann et al., 2015 ) consisted of three national team coaches with a minimum of 5 years of professional coaching experience (three males; 8.3 ± 1.5 years). The “novice” group ( Nash and Sproule, 2011 ) consisted of three club-level coaches, with a minimum of 1 year of coaching experience (one female, two males; 3.6 ± 2.1 years). All the participants had normal or corrected-to-normal vision and voluntarily agreed to participate following the local University of Derby ethical approval.

Climber/Bouldering Problems

The coaches were asked to observe the same intermediate (V4/F6B) climber (male; 21 years) climb four different boulder problems (2 × vertical, 1 × slab, 1 × roof) at a grade of V4/F6B ( Draper et al., 2016 ) at a national center climbing wall. Each boulder problem was repeated three times, requiring the coach to view a total of 12 attempts lasting approximately 16 s each (15.87 ± 0.81 s). The boulder problems were of a maximum height of 4 m and ranged from six to eight moves for each problem. The problems were selected in consultation with an independent national-level coach to ensure that they were judged to be of an appropriate level for the grade and representative of a normal coaching setting.

Visual Gaze Behavior

Mobile eye tracking glasses (SMI ETG 2.0; SensoMotoric Instruments, Tetlow, Germany; binocular, 60 Hz) were used to record the coaches’ visual gaze behavior. The gaze data were collected via a lightweight smart recorder (Samsung Galaxy 4) using SMI IViewX software. This enabled the recording of visual gaze data in a real-world setting. Prior to capturing eye tracking data, a three-point calibration procedure was implemented by placing three targets in a triangular configuration at a distance of 5 m. The coaches were placed 5 m away from the base of each boulder problem; i.e., at the optimum viewing angle for each specific problem (as decided by an independent national-level coach), and instructed to remain stationary. However, they could move their heads to ensure that the climber remained in the eye-tracker’s recordable visual field. To validate the accuracy, a nine-point calibration grid was placed on each boulder problem, with the markers placed at the outermost areas of the visual field that the coach would be required to observe. This ensured that the gaze data were accurate across the entire visual field. The dependent variable data collected included fixation count, fixation duration, and fixation location.

Retrospective Think-Aloud Data Capture

Retrospective think-aloud was conducted using gaze data to cue responses from the coaches: i.e., the coaches were asked to explain individual fixation locations during their analysis of the climber’s performance, verbalizing their relevance to their coaching process. The gaze data were presented to the coach as video replay with the coach’s own visual gaze scan-path super-imposed (see Figure 1 ). This scan-path showed the most recent 2-s of gaze data appearing to the coaches as a connected string of fixations (circles) and saccades (connecting lines). Each attempt was replayed at 100% speed and then slowed down to 25%.

Figure 1. Example of how the gaze data were presented to coaches to cue retrospective think-aloud: visual gaze data super-imposed as 2-s scan path [a connected string of fixations (circles) and saccades (connecting lines)] to cue verbal responses (i.e., why coaches focus on specific fixation locations).

Other Materials

A demographic questionnaire captured the coaches’ prior experience: i.e., highest level of coaching experience, accumulated coaching experience, and current coaching role/responsibilities.

Once the participants had completed the demographic questionnaire, they were fitted with mobile eye tracking glasses and undertook the calibration process. The coaches were then instructed to observe the climber to assess their quality of movement and identify movement errors. It was further explained that they would be required to verbalize their analysis of the climber’s performance later in the experiment. Each coach observed the same climber climb four different boulder problems at a grade of V4, viewing three attempts for each problem. Once each coach had observed all 12 attempts, gaze data were downloaded for further review using SMI BeGaze (V3.2, SensoMotoric Instruments, Tetlow, Germany) analysis software. Using the BeGaze RTA function, the cued RTA interviews were conducted immediately after the collection of gaze data using video replay with the gaze data super-imposed to cue verbal responses. After viewing the gaze data in real time, the participants were asked to scroll through gaze data at 25% speed, explaining why they focused on specific fixation locations and their relevance to the analyses. The fixations discussed were self-selected by the participant in order to reduce researcher bias. The gaze data were replayed until each coach had exhausted all fixations they could recall.

The eye tracking metrics analyzed were: (a) “fixation rate” (i.e., average number of fixations per second), (b) “average fixation duration” (i.e., average fixation duration of all fixations throughout the entire viewing period), and (c) “total fixation duration” (i.e., total duration of a viewer’s fixations landing on a given visual element throughout the entire viewing period) within pre-defined areas of interest. Visual fixations were defined as periods where the eye remained stable in the same location (within 1° degree of tolerance) for a minimum of 120 ms ( Catteeuw et al., 2009 ). The visual gaze data were analyzed using the “semantic gaze mapping” function of SMI BeGaze to manually code fixations against three predefined areas of interest. These were the hands, the feet, and the core regions. Only the gaze data collected while the climber was attempting the problem were included in analysis. As the length of recordings differed for individual coach’s visual gaze behavior due to small variations (±5%) in the athlete’s performance, the data were normalized by cropping the recordings so that each trial was of equal duration to the shortest trial. This enabled the eye tracking metrics (e.g., “total fixation duration”) to be analyzed for comparison between coaches/groups. To enable comparison in visual search strategy, the aggregated gaze data as a function of expert or novice group were used to produce heat maps ( Holmqvist et al., 2011 ). Additional analysis was pursued using Microsoft Excel (Version 15.37, Santa Rosa, CA, United States). Due to the small sample size, the magnitude of differences was determined using Cohen’s d ( Cohen, 1988 ).

The cued RTA data were recorded concurrently, ensuring that the interview responses were not separated from the context of the coaches’ individual gaze data. The cued RTA data were transcribed verbatim, and inductive thematic analysis was conducted in accordance to the six-step process outlined by Braun and Clarke (2006) . Two members of the research team initially conducted thematic analyses independently before comparing and auditing the analysis process (i.e., first- and second-level codes and final themes). Issues of credibility and transferability were addressed by a process of member checking to ensure a good “fit” between the coaches’ views and the researchers’ final interpretation of themes, as well as ensuring that the themes transfer to the wider coaching context ( Tobin and Begley, 2004 ).

The eye tracking data quality was 98.6% (±0.9), i.e., 98.6% of the samples were captured. An analysis of the gaze data revealed distinct differences between expert and novice groups. The experts demonstrated slower fixation rates (experts 2.23 ± 0.20/s, novices 2.44s ± 0.37/s; d = 0.71) and greater average fixation durations (experts 315 ± 30 ms, novices 261 ± 59 ms; d = 1.07) than their novice counterparts. In other words, the experts demonstrated fewer fixations but of greater duration.

Furthermore, distinct differences were identified in the locations that the groups allocated attentional resources to. The experts allocated a greater proportion of their attention to the proximal (core) features of the climber’s body, demonstrating a greater number of fixations (experts 58.7 ± 24.5, novices 17.4 ± 1.4; d = 2.4) and longer total fixation durations to core body areas (experts 23.6 ± 14.5 s, novices 4.5 ± 1.2 s; d = 1.9). The experts additionally placed less attention on the climber’s hand placements than the novices did, with fewer total fixations (experts 41.0 ± 25.9, novices 69.5 ± 27.6; d = 1.1) and shorter total fixation durations (experts 16.6 ± 11.6 s, novices 25.8 ± 0.4 s; d = 1.1) toward hand placements. Finally, the experts spent more time fixating their attention on the climber’s foot placements than the novices did, with greater numbers of total fixations (experts 44.7 ± 14.6, novices 38.5 ± 14.9; d = 0.4) and longer total fixation durations (experts 20.2 ± 4.7 s, novices 11.1 ± 1.4 s; d = 2.6) toward foot placements. These differences between the expert and the novice coaches’ visual search strategy were evident from the aggregated heat maps ( Figure 2 ), which illustrate that the experts focused more attention on proximal features (e.g., hips, lumbar region, and center of back), whereas the novices almost solely focused on distal features (e.g., feet and hands).

Figure 2. Aggregated heat maps of expert (A) and novice (B) coaches’ gaze behavior over 12 boulder problems illustrate notable differences in the allocation of visual attention to different regions of the climber’s body.

Retrospective Think-Aloud Data

The interview durations (min) differed noticeably between the expert and the novice coaches (experts 75.3 ± 12.3, novices 38.0 ± 11.5; d = 3.1), reflecting the level of detail that each group was able to provide while explaining their visual gaze data. The thematic analyses revealed three themes: “cognizance of visual search behavior,” “knowledge in the principles of movement and their application,” and “systematic visual search strategy.” Table 1 illustrates the first- and second-level codes that contribute to the three main themes.

Table 1. Organization of data codes from the thematic analysis.

In respect to the first theme, the expert coaches were far more cognizant of their visual search behavior, being able to verbalize their thought process and provide rationale that explains how the gaze data relate to their coaching process. For example, one expert coach stated:

“I can tell immediately these are my eye movements… You can see I am going through my standard functional movement screening process here. This point here, I am looking at whether hip mobility limiting the climber’s ability to rock-over.” (participant E3)

The novice group, by comparison, was often unable to make any link between their gaze data and their coaching process, simply passing no comment or stating: “I’m not sure why I was looking there” (participant N2). One coach was particularly candid by stating:

“To be honest, I don’t really know what I’m looking for when I’m coaching. I know to look for messy footwork, so that’s what I look for. Beyond that, I don’t know what to look for.” (participant N3)

Considering the second theme, the expert coaches demonstrated a far greater understanding of the principles of movement and their application. Here they demonstrated more complex frameworks and principles of movement that applied to the nature and the angle of the problem. For example, one expert coach succinctly described their process as follows:

“Climbing is a really complex 3D interrelationship between the climber and infinitely varied points of contacts, at differing and changing angles. I try to think of how those points of contact can be used in conjunction, so that the climber can move their center of mass into the optimal position for that particular situation. When the climber is not achieving that position, I try to diagnose secondary factors that may be prohibiting them.” (participant E2)

By comparison, the novice coaches often discussed specific aspects of technique in isolation. For example, participant N1 stated: “So I’m looking for bad footwork here, then I’m looking for if they are holding the hold in the right way.” Comments relating to isolated aspects of technique were common among the novice group with little to no reference to the complex interrelationships between the components of the movement system and their interaction with the environment.

Finally, in reference to the third theme, the expert coaches eluded to a hierarchy of skills that guided their priorities for analysis. Participant E2 observed that:

“If you can see, I am looking at completely different areas during each attempt…looking at different aspects of their performance. I start by looking at the most basic aspects of technique, building up a picture of their ability, working through to more complex skills. When I start to see errors creeping in, I look to see if it is a consistent pattern or just a one-off. If there is a consistent pattern, that is usually the aspect of their climbing I look to address first.”

By contrast, the process of the novice coaches was continually described as a process of search for foot placement errors and search for hand placement errors, continually repeating this cycle. Thus, while both groups eluded to the skills that they prioritized, the above quote highlights how expert verbalizations were more comprehensive and demonstrated a logical/systematic progression in skill complexity. By comparison, novice verbalizations demonstrated a limited and rudimentary grasp of the critical factors that underpin the climbing movement.

Despite the importance of observational analysis in the coaching of climbing movement, the cognitive–perceptual mechanisms underpinning the visual search behavior of climbing coaches have not previously been explored. This study sets out to explore the feasibility and the utility of a previously underutilized methodology within sports expertise research, namely, if mobile eye tracking data, captured in a naturalistic and ecologically valid coaching environment, combined with cued RTA interviews can effectively capture the mechanisms that underpin the visual search behavior in expert and novice coaches. Here the results revealed that the gaze behavior of expert climbing coaches is characterized by fewer fixations, but fixations that were of longer duration than those of novice coaches. Additionally, that experts coaches tend to focus a greater proportion of their attention on proximal regions, whereas the novice coaches typically focused on distal regions. Finally, the RTA analysis revealed that the experts were more cognizant of their visual search strategy, detailing how their visual gaze behavior is guided by a systematic hierarchical process underpinned by complex knowledge structures relating to the principles of climbing movement.

A major finding of the current research was that visual attentional strategies differed between expert and novice climbing coaches. We observed that the expert coaches demonstrated fewer fixations—but these were of greater duration, suggesting that the accumulated context-specific experience of the expert coaches enables them to develop a more efficient visual search behavior. The expert coaches selectively attend to only the most task-relevant areas of the visual display, requiring them to make fewer fixations (of longer duration) to efficiently extract relevant information from specific gaze locations ( Ericsson and Kintsch, 1995 ; Haider and Frensch, 1999 ). These findings accord with previous studies investigating the visual search strategies of coaches in similar self-paced individual sports (e.g., coaching a tennis serve; Moreno et al., 2006 ).

The current research further highlighted the relevance of specific fixation locations to more efficient visual search. The proportion of attentional resources that coaches allocated to specific locations varied distinctly between experts and novices. The experts spent nearly five times as long focusing on the proximal regions of the climber’s body (or core) as compared to the novice coaches (refer again to Figure 2 ), supporting Lamb et al. (2010) notion that the observational strategies of coaches may be overly influenced by the motion of distal segments due to the greater range of motion and velocities than that of proximal segments. It is therefore proposed that the climber’s core represents one of the most salient areas upon which to analyze a climbing performance. Fluency of the center of mass, as defined by the geometric index of entropy, has been shown to be an important performance characteristic ( Cordier et al., 1994 ; Taylor et al., 2020 ). Identifying the most salient areas to analyze a climbing performance may provide a viable means to inform future coach training, helping novice coaches make their visual search behaviors more efficient ( Spitz et al., 2018 ). However, identifying gaze location alone is of limited practical value to developing coaches unless its relevance is made explicit ( Nash et al., 2011 ).

The addition of cued RTA to the eye tracking methodology revealed three themes that provide insight into the cognitions underpinning the visual attentional strategies of novice vs . expert coaches. First, the expert coaches were far more cognizant of their visual search behavior, providing a far more explicit rationale for how their gaze data related to their coaching process. The inability of novice coaches to recall and elaborate on their visual gaze data suggests a randomized and inefficient visual search strategy, that is, they were unclear as to why they fixated on specific locations or what information they hoped to acquire by doing so. Second, the experts were able to provide rich descriptions of the critical factors that underpin successful movement and relate such principles to their gaze data. Here they demonstrated more complex frameworks and principles of movement applied to the nature and the angle of the problem. Comparatively, the novice coaches provided very little detail on how principles of movement guide their visual search, suggesting that a lack of knowledge regarding the critical factors that underpin climbing movement may be a key factor that limits the effectiveness of their observational analysis. Finally, the experts were more proactive and systematic in their analysis, with their visual search strategy underpinned by a hierarchy of skills ( Gegenfurtner et al., 2011 ). It is likely that the lack of a systematic approach to observational analysis observed among novice coaches potentially limits the validity and the effectiveness of their analysis ( Knudson, 2013 ).

Based on the insights above, it is proposed that the use of cued RTA interviews potentially offers a deeper insight into the cognitive–perceptual process of coaches than the use of eye tracking or think-aloud methodologies employed in isolation. By capturing the declarative and the procedural knowledge that expert coaches utilize to guide their visual search strategy, valuable insight is acquired as to the systematic processes that expert coaches employ to analyze a climbing performance, that is, where the most salient areas of the visual display are and why they are important to the analysis of a climbing performance. Coach educators may be able to utilize such insights to provide developing coaches with a more explicit rationale to guide their visual search, enhancing the efficiency and the quality of their observational analysis.

In sum, the present results demonstrate the utility of combining eye tracking technology and cued RTA as a methodology for capturing the cognitive–perceptual processes of climbing coaches. In combining these methods, a range of different cognitions and perceptual behaviors were observed as a consequence of coaching expertise. Combining these technologies potentially offers a valid and a reliable method to capture the processes underpinning the observational analysis of a climbing movement. Indeed the same methodological approach could be applied in a variety of coaching contexts. This stated, a number of limitations and recommendations for future research are highlighted. Despite the ecological validity of the present research, the results must be interpreted tentatively given the small sample size. Furthermore, viewing the live performance of a single athlete presents challenges to study repeatability. Researchers will need to weigh the benefits of ecological validity against replicability. Future research would also benefit from exploring whether the visual search strategies of coaches remain consistent with a greater number of athletes of varying ability, anthropometrics, and style. This will help a comprehensive framework for the observational analysis of a climbing movement to be developed.

Data Availability Statement

The datasets generated for this study are available on request to the corresponding author.

Ethics Statement

The studies involving human participants were reviewed and approved by the Human Sciences Research Ethics Committee, University of Derby. The participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this manuscript.

Author Contributions

JM, DG, and NT contributed to the design of the study and in data collection. JM and NT performed data analysis and wrote the first draft of the manuscript. JM, FM, DS, DG, and NT revised the manuscript to produce the final draft, which was subsequently reviewed by all the authors.

Funding for open access fees is being provided internally through research budgets associated with the Human Sciences Research Centre, College of Life and Natural Sciences, University of Derby.

Conflict of Interest

DG was employed by the company Lattice Training Ltd.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords : eye tracking, think-aloud, sport, education, expertise, gaze behavior, coaching

Citation: Mitchell J, Maratos FA, Giles D, Taylor N, Butterworth A and Sheffield D (2020) The Visual Search Strategies Underpinning Effective Observational Analysis in the Coaching of Climbing Movement. Front. Psychol. 11:1025. doi: 10.3389/fpsyg.2020.01025

Received: 02 December 2019; Accepted: 24 April 2020; Published: 28 May 2020.

Reviewed by:

Copyright © 2020 Mitchell, Maratos, Giles, Taylor, Butterworth and Sheffield. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: James Mitchell, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

January 17, 2013

Searching Science: How the Brain Finds What You're Looking for

A target-locating test from Science Buddies

By Science Buddies

Key concepts Visual search Perception Distractions Reaction time

Introduction Have you ever wondered what makes you notice a certain person or object when you're rushing along in a crowd? Why do some things stand out whereas others melt into the background? In this activity you can explore the psychology of how things get noticed by studying how our brains help us perform a visual search. Specifically, you'll look at how changing the number and type of visual distractions affects a person's ability to find what they're looking for.

Background Have you ever looked for something you really need to find—quickly? The classic example is when someone loses a set of keys. This frustrating situation is the perfect example of performing what cognitive psychologists call a visual search. During a visual search, an observer (the person who is searching for the keys, for example) looks for a target (the keys) in the midst of distracters (all the other stuff in a home). By making the target easier to see, such as by putting the keys on a big, bright red key chain, the observer could improve on their visual search and improve its chances of success.

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What properties are important for performing a successful visual search? Consider the following exercise to help you think about the variables: If you had a printed page full of letter L’s in blue ink and just one letter T in red ink, it would be pretty easy to find the red T, right? However, what if half of the L’s were blue and half were red? In the latter situation, there are more complex distracters, making finding the target (the red letter T) more difficult.

Materials • Computer with Internet connection • Piece of paper and pen or pencil • At least three volunteers (including yourself)

Preparation • In your Web browser, go to the Cognitive Science Visual Search Web page developed by cognitive psychologist Tom Busey of Indiana University Bloomington. (Depending on what Internet browser you have and whether Java is enabled on your computer, you will either need to run the Java applet directly from the Web page or download the software to your computer. To use the Java applet, simply select the "Run Applet" button. To download the software, click on the movie link and follow the instructions to download the software to your desktop.) • In the Visual Search applet or downloaded program, select the "Targets" tab at the top. In the "Target 1" section click on the large drop-down menu and select the image of a hot dog (the images are arranged alphabetically). The hot dog image should appear in the box after selecting it. Make sure the box next to "Display Target 1" is checked. • Now click on the "Distracters" tab at the top. In the "Distracter 1" section, click on the large drop-down menu and select the image of a burger. The burger image should appear in the box. Make sure the box next to "Display Distracter 1" is checked. • Next click on the "Do Experiment" tab. Make sure the "Use Circular Display" box is unchecked ! Click on the "Start Experiment" button when you're ready and follow the instructions, pressing "f" if you see the hotdog or "j" if you do not see it. Tip: Make sure nothing distracting is going on when you do the experiment! • After you are done it will tell you to click on the button below to quit and view your results, which will show up on the "Do Experiment" screen. Do not worry about your results quite yet. Instead, do the experiment one or two more times, or until you feel comfortable with the system, and then move on to the procedure below.

Procedure • After you have tried doing the experiment with one distracter a few times and feel comfortable with it, do the experiment again. • This time when you get the results of the experiment, write down the numbers in the "Mean Reaction Times for Target Present Trials" and "Mean Reaction Times for Target Absent Trials" boxes under "All Trials." (Ignore the numbers in parentheses.) • Go back to the "Distracters" tab at the top. In the "Distracter 2" section, click on the large drop-down menu and select the image of a pizza slice. Check the box next to "Display Distracter 2." • Click on the "Do Experiment" tab and run it by clicking "Start Experiment" and following the instructions. • When you get the results of the experiment, again write down the numbers in boxes in the "All Trials" section. Did it take you more or less time to react with two distracters compared with one? • Go back to the "Distracters" tab and add a third distracter, this time a peach. Do the experiment and again write down the relevant reaction time numbers. Did it take you more or less time to react with three distracters compared with one or two? • Go back to the "Distracters" tab and add a fourth distracter, this time a carrot. Run the experiment again and write down the relevant reaction time numbers. Did it take you more or less time to react with four distracters compared with fewer ones? • Overall, how did the reaction time change as more distracters were added? Is this what you would have expected? Did it take longer for someone to react when there was a target present or absent? • Repeat this activity with at least two other volunteers so that you have tested it with a total of three different people. For each person, be sure to let him or her try the experiment with only one distracter a few times before you start collecting the numbers from their experiments. For each volunteer, did you see the same correlation between reaction time and number of distracters? • Extra: Repeat this activity but try changing some different variables and see how it affects reaction time and the percent of the answers that are correct. You could try changing the number of targets instead of distracters. Is it easier or harder to spot multiple targets? You could also measure percent correct instead of response time. Do people give fewer correct answers as more distracters are present? Try changing the number of images by changing the number of rows and columns. Is it harder to find the target when there are more images? You could also try changing the images, such as using symbols, letters or numbers instead of types of food, or change the colors of the target and distracter. Is it harder to find the target as its similarity to the distracters increases? • Extra: This cognitive test has real-world applications that you could investigate. Look into how logos and brand names are designed to be noticed, the way Web sites are designed to be easy to navigate, how points of interest on a map are marked or other data are displayed in a way that highlights what's important. How are visual search properties used in these different areas?

Observations and results Did the reaction time increase as more distracters were added? Did it take longer for volunteers to answer when the target was absent compared with when it was present?

You should have seen that, in general, the reaction time needed to do the visual search increased as more distracters were added. (There may have been some exceptions, such as a person taking only slightly longer to do a visual search with three distracters present in comparison with four, but it should have clearly taken a good deal more time to find the target when there were four distracters compared with when there was just one.) When more distracters are present, it makes finding the target more difficult. (Think of the example with the red letter T target and letter L distracters that became more distracting when they changed from all blue to half red.) This makes people take more time in their visual search, even if the target is not there. In fact, you should have seen that people actually take more time when the target is absent compared with when it is present, as they may spend more time checking and rechecking to make sure that the target is really not there.

More to explore Cognitive Science Software: Visual Search , from Tom Busey at Indiana University Bloomington Research Explains How the Brain Finds Waldo , from ScienceDaily The Truth Behind Where's Waldo? , from ScienceDaily The Brains Behind Where's Waldo?, from Science Buddies

This activity brought to you in partnership with  Science Buddies

Visual Search in Real-World and Applied Contexts

Call for papers.

Co-organizers:

CRPI Editor in Chief:   Jeremy M. Wolfe, Brigham & Women’s Hospital and Harvard Medical School

Guest Editor: Trafton Drew, University of Utah, [email protected]  

Assistant Guest Editor : Lauren H. Williams, University of Utah, [email protected]

Visual search tasks are an everyday part of the human experience - ranging from hunting for a specific recipe ingredient in the pantry to monitoring for road hazards and informational signs while driving. Due to its ubiquity in everyday life, visual search has been extensively studied in the laboratory for decades even if laboratory tasks are unable to capture the full complexity of real-world visual search tasks. In recent years, there has been a growing body of research seeking to narrow the gaps in our understanding of visual search behavior between the laboratory and the real world. The purpose of this special issue in Cognitive Research: Principles and Implications (CRPI) is to bring together this important research. 

We anticipate one set of submissions involving studies of visual search behavior in applied situations, such as driving, data visualization, website design, and expert visual search tasks (e.g., baggage screening, radiology). These papers should make an effort to connect specific applied settings to basic principles in the study of visual attention. A second set of papers will use more general laboratory search tasks to further our understanding of visual search behavior in real world situations. Both types of papers fit CRPI’s mission to publish “use-inspired basic research”; research that starts from a problem in the world and illuminates fundamental properties of perception and cognition.

Potential topics of interest include but are not limited to: individual differences in search performance, scene grammar, search in three-dimensional (3D) environments, visual & motor system interactions in visual search (e.g., rummaging or foraging tasks), search for imprecise or categorical search targets, quitting behavior, visual search in dynamic environments, real-world search errors and their laboratory analogues (the low prevalence effect, subsequent search misses, inattentional blindness), hybrid visual search, and interventions designed to improve visual search performance.  We invite you to contribute.

CRPI is the open access journal of the Psychonomic Society. Its mission is to publish use-inspired basic research : fundamental cognitive research that grows from hypotheses about real-world problems. As with all Psychonomic Society journals, submissions to CRPI are subject to rigorous peer review.

For manuscripts accepted for the special issue, the publication fee may be fully or partially waived depending on the number of manuscripts accepted for the special issue. The authors should indicate when they submit a manuscript if they are requesting a waiver of the publication fee. Please email any of the editors with questions about submissions.

Deadline: manuscripts should be submitted before August 1, 2020

You can find manuscript submission details at http://cognitiveresearchjournal.springeropen.com/submission-guidelines/preparing-your-manuscript

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Visual Search: How Do We Find What We Are Looking For?

Affiliations.

• 1 Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts 02115, USA.
• 2 Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115, USA.
• 3 Visual Attention Lab, Brigham & Women's Hospital, Cambridge, Massachusetts 02139, USA; email: [email protected].
• PMID: 32320631
• DOI: 10.1146/annurev-vision-091718-015048

In visual search tasks, observers look for targets among distractors. In the lab, this often takes the form of multiple searches for a simple shape that may or may not be present among other items scattered at random on a computer screen (e.g., Find a red T among other letters that are either black or red.). In the real world, observers may search for multiple classes of target in complex scenes that occur only once (e.g., As I emerge from the subway, can I find lunch, my friend, and a street sign in the scene before me?). This article reviews work on how search is guided intelligently. I ask how serial and parallel processes collaborate in visual search, describe the distinction between search templates in working memory and target templates in long-term memory, and consider how searches are terminated.

Keywords: foraging; parallel processing; serial processing; visual attention; visual search; working memory.

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Annual Review of Vision Science

Volume 6, 2020, review article, visual search: how do we find what we are looking for.

• Jeremy M. Wolfe 1,2,3
• View Affiliations Hide Affiliations Affiliations: 1 Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts 02115, USA 2 Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115, USA 3 Visual Attention Lab, Brigham & Women's Hospital, Cambridge, Massachusetts 02139, USA; email: [email protected]
• Vol. 6:539-562 (Volume publication date September 2020) https://doi.org/10.1146/annurev-vision-091718-015048
• First published as a Review in Advance on April 22, 2020
• Copyright © 2020 by Annual Reviews. All rights reserved

In visual search tasks, observers look for targets among distractors. In the lab, this often takes the form of multiple searches for a simple shape that may or may not be present among other items scattered at random on a computer screen (e.g., Find a red T among other letters that are either black or red.). In the real world, observers may search for multiple classes of target in complex scenes that occur only once (e.g., As I emerge from the subway, can I find lunch, my friend, and a street sign in the scene before me?). This article reviews work on how search is guided intelligently. I ask how serial and parallel processes collaborate in visual search, describe the distinction between search templates in working memory and target templates in long-term memory, and consider how searches are terminated.

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Foraging behavior in visual search: A review of theoretical and mathematical models in humans and animals

• Published: 21 March 2021
• Volume 86 , pages 331–349, ( 2022 )

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• Marcos Bella-Fernández   ORCID: orcid.org/0000-0001-6621-0199 1 , 2 ,
• Manuel Suero Suñé 1 &
• Beatriz Gil-Gómez de Liaño 3  

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Visual search (VS) is a fundamental task in daily life widely studied for over half a century. A variant of the classic paradigm—searching one target among distractors—requires the observer to look for several (undetermined) instances of a target (so-called foraging) or several targets that may appear an undefined number of times (recently named as hybrid foraging). In these searches, besides looking for targets, the observer must decide how much time is needed to exploit the area, and when to quit the search to eventually explore new search options. In fact, visual foraging is a very common search task in the real world, probably involving additional cognitive functions than typical VS. It has been widely studied in natural animal environments, for which several mathematical models have been proposed, and just recently applied to humans: Lévy processes, composite and area-restricted search models, marginal value theorem, and Bayesian learning (among others). We conducted a systematic search in the literature to understand those mathematical models and study its applicability in human visual foraging. The review suggests that these models might be the first step, but they seem to be limited to fully comprehend foraging in visual search. There are essential variables involving human visual foraging still to be established and understood. Indeed, a jointly theoretical interpretation based on the different models reviewed could better account for its understanding. In addition, some other relevant variables, such as certain individual differences or time perception might be crucial to understanding visual foraging in humans.

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Reprinted from Charnov, E. L. (1976) Optimal Foraging, the Marginal Value Theorem. Theoretical Population Biology, 9(2), p. 132. Copyright by Elsevier. Reprinted with permission

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Bella-Fernández, M., Suero Suñé, M. & Gil-Gómez de Liaño, B. Foraging behavior in visual search: A review of theoretical and mathematical models in humans and animals. Psychological Research 86 , 331–349 (2022). https://doi.org/10.1007/s00426-021-01499-1

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Five Factors that Guide Attention in Visual Search

Jeremy m wolfe.

Brigham and Women’s Hospital / Harvard Med

Todd S Horowitz

NIH, National Cancer Inst.

How do we find what we are looking for? Fundamental limits on visual processing mean that even when the desired target is in our field of view, we often need to search, because it is impossible to recognize everything at once. Searching involves directing attention to objects that might be the target. This deployment of attention is not random. It is guided to the most promising items and locations by five factors discussed here: Bottom-up salience, top-down feature guidance, scene structure and meaning, the previous history of search over time scales from msec to years, and the relative value of the targets and distractors. Modern theories of search need to specify how all five factors combine to shape search behavior. An understanding of the rules of guidance can be used to improve the accuracy and efficiency of socially-important search tasks, from security screening to medical image perception.

How can a texting pedestrian walk right into a pole, even though it is clearly visible 1 ? At any given moment, our attention and eyes are focused on some aspects of the scene in front of us, while other portions of the visible world go relatively unattended. We deploy this selective visual attention because we are unable to fully process everything in the scene at the same time. We have the impression of seeing everything in front of our eyes, but over most of the visual field we are probably seeing something like visual textures, rather than objects 2 , 3 Identifying specific objects and apprehending their relationships to each other typically requires attention, as our unfortunate texting pedestrian can attest.

Figure 1 illustrates this point. It is obvious that this image is filled with Ms and Ws in various combinations of red, blue, and yellow, but it takes attentional scrutiny to determine whether or not there is a red and yellow M.

On first glimpse, you know something about the distribution of colors and shapes but not how those colors and shapes are bound to each other. Find ‘M’s that are red and yellow.

The need to attend to objects in order to recognize them raises a problem. At any given moment, the visual field contains a very large, possibly uncountable number of objects. We can count the Ms and Ws of Figure 1 , but imagine looking at your reflection in the mirror. Are you an object? What about your eyes or nose or that small spot on your chin? If object recognition requires attention, and if the number of objects is uncountable, how do we manage to get our attention to a target object in a reasonable amount of time? Attention can process items at a rate of, perhaps, 20-50 items per second. If you were looking for a street sign in an urban setting containing a mere 1000 possible objects (every window, tire, door handle, piece of trash, etc.), it would take 20-50 seconds just to find that sign. It is introspectively obvious that you routinely find what you are looking for in the real world in a fraction of that time. To be sure, there are searches of the needle-in-a-haystack, Where’s Waldo? variety that take significant time, but routine searches for the saltshaker, the light switch, your pen, and so forth, obviously proceed much more quickly. Search is not overwhelmed by the welter of objects in the world because search is guided to a (often very small) subset of all possible objects by several sources of information. The purpose of this article is to briefly review the growing body of knowledge about the nature of that guidance.

We will discuss five forms of guidance:

• Bottom-up, stimulus-driven guidance in which the visual properties of some aspects of the scene attract more attention than others.
• Top-down, user-driven guidance in which attention is directed to objects with known features of desired targets.
• Scene guidance in which attributes of the scene guide attention to areas likely to contain targets.
• Guidance based on the perceived value of some items or features.
• Guidance based on the history of prior search.

Measuring Guidance

We can operationalize the degree of guidance in a search for a target by asking what fraction of all items can be eliminated from consideration. One of the more straight-forward methods to do this is to present observers with visual search displays like those in Figure 2 and measure the reaction time (RT) required for them to report whether or not there is a target (here a “T), as a function of the number of items ( set size ). The slope of the RT x set size function is a measure of the efficiency of search. For a search for a T among Ls ( Fig 2A ), the slope would be in the vicinity of 20-50 msec/item 4 . We believe that this reflects serial deployment of attention from item to item 5 though this need not be the case 6 .

The basic visual search paradigm. A target (here a ‘T‘) is presented amidst a variable number of distractors. Search ‘efficiency’ can be indexed by the slope of the function relating reaction time (RT) to the visual set size. If the target in 2B is a red T, the slope for 2B will be half of that for 2A because attention can be limited to just half of the items in 2B.

In Fig. 2B , the target is a red T. This search would be faster and more efficient 7 because attention can be guided to the red items. If half the items are red (and if guidance is perfect), the slope will be reduced by about half, suggesting that, at least in this straightforward case, slopes index the amount of guidance.

The relationship of slopes to guidance is not entirely simple, even for arrays of items like those in Fig 2 8 but see 9 . Matters become far more complex with real world scenes where the visual set size is not easily defined 10 , 11 . However, if the slope is cut in half when half the items acquire some property, like the color red in 2B, it is reasonable to assert that search has been guided by that property 9 .

The problem of distractor rejection

As shown in Figure 2 , a stimulus attribute can make search slopes shallower by limiting the number of items in a display that need to be examined. However, guidance of attention is not the only factor that can modulate search slopes. If observers are attending to each item in the display (in series or in parallel), the slope of the RT x set size function can also be altered by changing how long it takes to reject each distractor. Thus, if we markedly reduced the contrast of Figure 2A , the RT x set size function would become steeper, not because of a change in guidance but because it would now take longer to decide if any given item was a T or an L.

Bottom-up guidance by stimulus salience

Attention is attracted to items that differ from their surroundings, if those differences are large enough and if those differences occur in one of a limited set of attributes that guide attention. The basic principles are illustrated in Figure 3 .

Which items ‘pop-out’ of this display, and why?

Three items ‘pop-out’ of this display. The purple item on the left differs from its neighbors in color. It is identical to the purple item just inside the upper right corner of the image. That second purple item is not particularly salient even though it is the only other item in that shade of purple; its neighbors are close enough in color that the differences in color do not attract attention. The bluish item to its left is salient by virtue of an orientation difference. The square item a bit further to the left is salient because of the presence of a ‘closure’ feature 12 or the absence of a collection of line terminations 13 . We call properties like color, orientation, or closure basic (or guiding ) features, because they can guide the deployment of attention. Other properties may be striking when one is directly attending to an item, and may be important for object recognition, but they do not guide attention. For example, the one ‘plus’ in the display is not salient, even though it possesses the only X-intersection in the display, because intersection type is not a basic feature 14 . The ‘pop-out’ we see in Figure 3 is not just subjective phenomenonology. Pop-out refers to extremely effective guidance, and is diagnosed by a near-zero slope of the RT x set size function; though there may be systematic variability even in these ‘flat’ slopes 15 .

There are two fundamental rules of bottom-up salience 16 . Salience of a target increases with difference from the distractors ( target-distractor – TD- heterogeneity ) and with the homogeneity of the distractors ( distractor-distractor –DD- homogeneity ) along basic feature dimensions. Bottom-up salience is the most extensively modeled aspect of visual guidance nicely reviewed in 17 . The seminal modern work on bottom-up salience is Koch and Ullman’s 18 description of a winner-take-all network for deploying attention. Subsequent decades have seen the development of several influential bottom-up models e.g. 19 , 20 – 22 . However, bottom-up salience is just one of the factors guiding attention. By itself, it does only modestly well in predicting the deployment of attention (usually indexed by eye fixations). Models do quite well predicting search for salience, but not as well predicting search for other sorts of targets 17 . This is quite reasonable. If you are looking for your cat in the bedroom, it would be counterproductive to have your attention visit all the shiny, colorful objects first. Thus, a bottom-up saliency model will not do well if the observer has a clear top-down goal 23 . One might think that bottom-up salience would dominate if observers free-viewed a scene in the absence of such a goal, but bottom-up models can be poor at predicting fixations even when observers “free view” scenes without specific instructions 24 . It seems that observers generate their own, idiosyncratic tasks, allowing other guiding forces to come into play. It is worth noting that salience models work better if they are not based purely on local features but acknowledge the structure of objects in the field of view 25 . For instance, while the most salient spot in an image might be the edge between the cat’s tail and the white sheet on the bed, fixations are more likely to be directed to middle of the cat 26 , 27 .

Top-down Feature Guidance

Returning to Figure 1 , if you search for Ws with yellow elements, you can guide your attention to yellow items and subsequently determine if they are Ws or Ms 7 . This is feature guidance, sometimes referred to as feature-based attention 28 . Importantly, it is possible to guide attention to more than one feature at a time. Thus, search for a big, red, vertical item can benefit from our knowledge of its color, size, and orientation 29 . Following the TD heterogeneity rule, search efficiency is dependent on the number of features shared by targets and distractors 29 , and observers appear to be able to guide to multiple target features simultaneously 30 . This finding raises the attractive possibility that search for an arbitrary object among other arbitrary objects would be quite efficient because objects would be represented sparsely in a high-dimensional space. Such sparse coding has been invoked to explain object recognition 31 , 32 . However, search for arbitrary objects turns out not to be particularly efficient 11 , 33 . By itself, guidance to multiple features does not appear to be an adequate account of how we search for objects in the real world (see the section on scene guidance, below).

What are the guiding attributes?

Feature guidance bears some metaphorical similarity to your favorite computer search engine. You enter some terms into the search box and an ordered list of places to attend is returned. A major difference between internet search engines and the human visual search engine is that human search uses only a very small vocabulary of search terms (i.e., features). The idea that there might be a limited set of features that could be appreciated “preattentively” 34 was at the heart of Treisman’s “Feature Integration Theory” 35 . She predicted that targets defined by unique features would pop-out of displays. Subsequent theorists modified this proposal to suggest that features could guide the deployment of attention 7 36 .

There are probably only a couple dozen attributes that can guide attention. The visual system can detect and identify a vast number of stimuli, but it cannot use arbitrary properties to guide attention the way that Google or Bing can use arbitrary search terms. A list of guiding attributes is found in Table 1 . This article does not list all of the citations that support each entry. Many of these can be found in older versions of the list 37 , 38 . Recent changes to the list are marked in color in Table 1 and citations are given for those.

The guiding attributes for feature search

Attributes like color are deemed to be “undoubted” because multiple experiments from multiple labs attest to their ability to guide attention. “Probable” feature dimensions may be merely probable because we are not sure how to define the feature. Shape is the most notable entry here. It seems quite clear that something about shape guides attention 49 . It is less clear exactly what that might be, though the success of deep learning algorithms in enabling computers to classify objects may open up new vistas for understanding human search for shape 50 .

The attributes described as “possible” await more research. Often these attributes only have a single paper supporting their entry on the list, as in the case of numerosity: Can you direct attention to the pile with “more” elements in it, once you eliminate size, density, and other confounding visual factors? Perhaps 51 , but it would be good to have converging evidence. Search for the magnitude of a digit (e.g. “find the highest number”) is not guided by the semantic meaning of the digits but by their visual properties 52

The list of attributes that do not guide attention is, of course, potentially infinite. Table 1 lists a few plausible candidates that have been tested and found wanting. For example, there has been considerable interest recently in what could be called “evolutionarily motivated” candidates for guidance. What would enhance our survival if we could find it efficiently? Looking at a set of moving dots on a computer screen, we can perceive that one is “chasing” another 53 . However, this aspect of animacy does not appear to be a guiding attribute 47 . Nor does “threat” (defined by association with electric shock) seem to guide search 48 .

Some caution is needed here because a failure to guide is a negative finding and it is always possible that, were the experiment done correctly, the attribute might guide after all. Thus, early research 54 found that binocular rivalry and eye-of-origin information did not guide attention, but more recent work 55 , 56 suggests that it may be possible to guide attention to interocular conflict, and our own newer data 57 indicates that rivalry may guide attention if care is taken to suppress other signals that interfere with that guidance. Thus, binocular rivalry was listed under “doubtful cases & probable non-features” in 37 , but is now listed under “possible guiding attributes” in Table 1 .

Faces remain a problematic candidate for feature status, with a substantial literature yielding conflicting results and conclusions. Faces are quite easy to find among other objects 58 , 59 but there is dispute about whether the guiding feature is “face-ness” or some simpler stimulus attribute 60 , 61 . A useful review by Frischen et al. 62 argues that “preattentive search processes are sensitive to and influenced by facial expressions of emotion”, but this is one of the cases where it is hard to reject the hypothesis that the proposed feature is modulating the processing of attended items, rather than guiding the selection of which items to attend. Suppose that, once attended, it takes 10 msec longer to disengage attention from an angry face than from a neutral face. The result would be that search would go faster (10 msec/item faster) when the distractors were neutral than when they were angry. Consequently, an angry target among neutral distractors would be found more efficiently than a neutral face among angry. Evidence for guidance by emotion would be stronger if the more efficient emotion searches were closer to pop-out than to classic inefficient, unguided searches, e.g., T among Ls, 63 . Typically, this is not the case. For example, Gerritsen et al 64 report that “Visual search is not blind to emotion” but, in a representative finding, search for hostile faces produced a slope of 64 msec/item, which is quite inefficient, even if somewhat more efficient than the 82 msec/item for peaceful target faces (p1054).

There are stimulus properties that, while they may not be guiding attributes in their own right, do modulate the effectiveness of other attributes. For example, apparent depth modulates apparent size, and search is guided by that apparent size 65 . Finally, there are properties of the display that influence the deployment of attention. These could be considered aspects of “scene guidance” (see the next major section, below). For example, attention tends to be attracted to the center of gravity in a display 66 . Elements like arrows direct attention even if they, themselves do not pop-out 67 . As discussed by Rensink 68 , these and related factors can inform graphic design and other situations where the creator of an images wants to control how the observer consumes that image.

There have been some general challenges to the enterprise of defining specific features, notably the hypothesis that many of the effects attributed to the presence or absence of basic features are actually produced by crowding in the periphery 3 . For example, is efficient search for cubes lit from one side among cubes lit from another side evidence for preattentive processing of 3D shape and lighting 69 , or merely a by-product of the way these stimuli are represented in peripheral vision 41 ? Resolution of this issue requires a set of visual search experiments with stimuli that are “uncrowded”. This probably means using low set sizes; for example, see the evidence that material type is not a guiding attribute 70 .

A different challenge to the preattentive feature enterprise is the possibility that too many discrete features are proposed. Perhaps many specific features form a continuum of guidance by a single, more broadly defined attribute. For instance, the cues to the 3D layout of the scene include stereopsis, shading, linear perspective and more. These might be part of a single attribute describing the 3D disposition of an object. Motion, onsets, and flicker might be part of a general dynamic change property 71 . Most significantly, we might combine the spatial features of line termination, closure, topological status, orientation, and so forth into a single shape attribute with properties defined by the appropriate layer of the right convolutional neural net (CNN). Such nets have shown themselves capable of categorizing objects, so one could imagine a preattentive CNN guiding attention to objects as well 72 . At this writing, such an idea remains a promissory note. Regardless of how powerful CNNs may become, humans cannot guide attention to entirely arbitrary/specific properties in order to find particular types of object 73 and it is unknown if some intermediate representation in a CNN could capture the properties of the human search engine. If it did, we might well find that such a layer represented a space with dimensions corresponding to attributes like size, orientation, line termination, vernier offset, and so forth, but this remains to be seen.

Guidance by scene properties

While the field of visual search has largely been built on search for targets in arbitrary 2D arrays of items, most real world search takes place in structured scenes, and this structure provides a source of guidance. To illustrate, try search for any humans in Figure 4 . Depending on the resolution of the image as you are viewing it, you may or may not be able to see legs poking out from behind the roses by the gate. Regardless, what should be clear is that the places you looked were strongly constrained. Biederman, Mezzanotte, and Rabinowitz 74 suggested a distinction between semantic and syntactic guidance.

Scene Guidance: Where is attention guided if you are looking for humans? What if the target was a bird?

Syntactic guidance has to do with physical constraints. You don’t look for people on the front surface of the wall or in the sky because people typically need to be supported against gravity. Semantic guidance refers to the meaning of the scene. You don’t look for people on the top of the wall, not because they could not be there but because they are unlikely to be there given your understanding of the scene, whereas you might scrutinize the bench. Scene guidance would be quite different (and less constrained) if the target were a bird. The use of the terms “semantic” and “syntactic” should not be seen as tying scene processing too closely to linguistic processing, nor should the two categories be seen as neatly non-overlapping 75 76 . Nevertheless, the distinction between syntactic and semantic factors, as roughly defined here, can be observed in electrophysiological recordings: scenes showing semantic violations (e.g., a bar of soap sitting next to the computer on the desk) produce different neural signatures than scenes showing syntactic violations (e.g., a computer mouse on top of the laptop screen) 77 . While salience may have some influence in this task 78 , it does not appear to be the major force guiding attention here 24 , 79 . But note that feature guidance and scene guidance work together. People certainly could be on the lawn, but you do not scrutinize the empty lawn in Figure 4 because it lacks the correct target features.

Extending the study of guidance from controlled arrays of distinct items to structured scenes poses some methodological challenges. For example, how do we define the set size of a scene? Is “rose bush” an item in Figure 4 , or does each bloom count as an item? In bridging between the world of artificial arrays of items and scenes, perhaps the best we can do is to talk about the “effective set size” 80 10 , the number of items/locations that are treated as candidate targets in a scene give a specific task. If you are looking for the biggest flower, each rose bloom is part of the effective set. If you are looking for a human, those blooms are not part of the set. While any estimate of effective set size is imperfect, it is a very useful idea and it is clear that, for most tasks, the effective set size will be much smaller than the set of all possible items 11 .

Preview methods have been very useful in examining the mechanisms of scene search 81 . A scene is flashed for a fraction of a second and then the observer searches for a target. The primary data are often eye tracking records. Often, these experiments involve searching while the observer’s view of the scene is restricted to a small region around the point of fixation (“gaze-contingent” displays). Very brief exposures (50-75 msec) can guide deployment of the eyes once search begins 82 . A preview of the specific scene is much more useful than a preview of another scene of the same category, though the preview scene does not need to be the same size as the search stimulus 81 . Importantly, the preview need not contain the target in order to be effective 83 . Search appears to be more strongly guided by a relatively specific scene ‘gist’ 80 84 , an initial understanding of the scene that does not rely on recognizing specific objects 85 . The gist includes both syntactic (e.g., spatial layout) and semantic information, and this combination can provide powerful search guidance. Knowledge about the target provides an independent source of guidance 86 , 87 . These sources of information provide useful ‘priors’ on where targets might be (“If there is a vase present, it’s more likely to be on a table than in the sink”), that are more powerful than memory for where a target might have been seen 88 89 , 90 in terms of guiding search.

Preview effects may be fairly limited in search of real scenes. If the observer searches a fully visible scene rather than being limited to a gaze-contingent window, guidance by the preview is limited to the first couple of fixations 91 . Once search begins, guidance is presumably updated based on the real scene, rendering the preview obsolete. In gaze-contingent search, the effects last longer because this updating cannot occur. This updating can be seen in the work of Hwang et al. 76 , where, in the course of normal search, the semantic content of the current fixation in a scene influences the target of the next fixation.

Modulation of search by prior history

In this section, we summarize evidence showing that the prior history of the observer, especially the prior history of search, modulates the guidance of attention. We can organize these effects by their time scale, from within a trial (on the order of 100s of ms) to lifetime learning (on the order of years).

A number of studies have demonstrated the preview benefit : when half of the search array is presented a few hundred msec before the rest of the array, the effective set size is reduced, either because attention is guided away from the old “marked” items (visual marking 92 ) and/or toward the new items (onset prioritization 93 ).

On a slightly longer timescale, priming phenomena are observed from trial to trial within an experiment, and can be observed over seconds to weeks. The basic example is “priming of pop-out” 94 , in which an observer might be asked to report the shape of the one item of unique color in a display. If that item is the one red shape among green on one trial, responses will be faster if the next trial repeats red among green as compared to a switch to green among red; though the search in both cases will be a highly efficient, color pop-out search. More priming of pop-out is found if the task is harder 95 . Note that it is neither the response nor the reporting feature which is repeated in priming of pop-out, but the target-defining or selection feature.

More generally, seeing the features of the target makes search faster than reading a word cue describing the target, even for overlearned targets. This priming by target features takes about 200 msec to develop 96 . Priming by the features of a prior stimulus can be entirely incidental; simply repeating the target from trial to trial is sufficient 97 . More than one feature can be primed at the same time 97 , 98 and both target and distractor features can be primed 97 , 99 . Moreover, it is not just that observers are more ready to report targets with the primed feature; priming actually boosts sensitivity (i.e., d ’) 100 . Such priming can last for at least a week 101 .

Observers can also incidentally learn information over the course of an experiment that can guide search. In contextual cueing 102 , a subset of the displays are repeated across several blocks of trials. While observers do not notice this repetition, RTs are faster for repeated displays than for novel, unrepeated displays 103 . The contextual cueing effect is typically interpreted as an abstract form of scene guidance: just as you learn that, in your friend’s kitchen, the toaster is on the counter next to the coffeemaker, you learn that, in this configuration of rotated Ls, the T is in the bottom left corner. However, evidence for this interpretation is mixed. RT x set size slopes are reduced for repeated displays 102 in some experiments, but not in others 104 . Contextual cueing effects can also be observed in cases such as pop-out search and 105 , attentionally-cued search, 106 , where guidance is already nearly perfect. Kunar et al. 104 suggested that contextual cueing reflects response facilitation, rather than guidance. Again, the evidence is mixed. There is a shift towards a more liberal response criterion for repeated displays 107 , but this is not correlated with the size of the contextual cueing RT effect. In pop-out search, sensitivity to the target improves for repeated displays without an effect on decision criterion 105 . It seems likely that observed contextual cueing effects reflect a combination of guidance effects and response facilitation, the mix depending on the specifics of the task. Oculomotor studies show that the context is often not retrieved and available to guide attention until a search has been underway for several fixations 108 , 109 . Thus, the more efficient the search, the greater the likelihood that the target will be found before the context can be retrieved. Indeed, in simple letter displays, search does not become more efficient even when the same display is repeated several hundred times 110 , presumably because searching de novo is always faster than waiting for context to become available. Once the task becomes more complex (e.g., searching for that toaster) 111 , it becomes worthwhile to let memory guide search 112 113 .

Over years and decades, we become intimately familiar with, for example, the characters of our own written language. There is a long-running debate about whether familiarity (or, conversely, novelty) might be a basic guiding attribute. Much of this work has been conducted with overlearned categories like letters. While the topic is not settled, semantic categories like “letter” probably do not guide attention 114 , 115 , though mirror-reversed letters may stand out against standard letters 116 117 . Instead, items made familiar in LTM can modulate search 42 , 118 though there are limits on the effects of familiarity in search 119 120 .

Modulation of search by the value of items

In the past few years, there has been increasing interest in the effects of reward or value on search. Value proves to be a strong modulator of guidance. For instance, if observers are rewarded more highly for red items than for green, they will subsequently guide attention toward red, even if this is irrelevant to the task 121 . Note, color is the guiding feature; value modulates its effectiveness. The learned associations of value do not need to be task relevant or salient in order to have their effects 122 and learning can be very persistent with value-driven effects being seen half a year after acquisition 123 . Indeed, the effects of value may be driving some of the long-term familiarity effects described in the previous paragraph 42 .

Visual search is mostly effortless. Unless we are scrutinizing aerial photographs for hints to North Korea’s missile program, or hunting for signs of cancer in a chest radiograph, we typically find what we are looking for in seconds or less. This remarkable ability is the result of attentional guidance mechanisms. While thirty-five years or so of research has given us a good grasp of the mechanisms of bottom-up salience, top-down feature-driven guidance and how those factors combine to guide attention 124 , 125 , we are just beginning to understand how attention is guided by the structure of scenes and the sum of our past experiences. Future challenges for the field will include understanding how discrete features might fit together in a continuum of guidance and extending our theoretical frameworks from two-dimensional scenes to immersive, dynamic, three-dimensional environments.

Competing Interests:

JMW occasionally serves as an expert witness or consultant for which this article might be relevant. TSH has no competing interests to declare.

Contributor Information

Jeremy M Wolfe, Brigham and Women’s Hospital / Harvard Med.

Todd S Horowitz, NIH, National Cancer Inst.

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