The importance of search strategy for finding targets in open terrain

Charlotte A. Riggs; Katherine Cornes; Hayward J. Godwin; Simon P. Liversedge; Richard Guest; Nick Donnelly

doi:10.1186/s41235-017-0049-4

Original article
Open Access

The importance of search strategy for finding targets in open terrain

Charlotte A. Riggs¹Email author,
Katherine Cornes¹,
Hayward J. Godwin¹,
Simon P. Liversedge¹,
Richard Guest² and
Nick Donnelly¹

Cognitive Research: Principles and ImplicationsPrinciples and Implications20172:14

https://doi.org/10.1186/s41235-017-0049-4

Received: 15 April 2016
Accepted: 10 January 2017
Published: 20 February 2017

Abstract

A number of real-world search tasks (i.e. police search, detection of improvised explosive devices (IEDs)) require searchers to search exhaustively across open ground. In the present study, we simulated this problem by asking individuals (Experiments 1a and 1b) and dyads (Experiment 2) to search for coin targets pseudo-randomly located in a bounded area of open grassland terrain. In Experiment 1a, accuracy, search time, and the route used to search an area were measured. Participants tended to use an ‘S’-shaped pattern with a common width of search lane. Increased accuracy was associated with slower, but also variable, search speed, though only when participants moved along the length (as opposed to across the width) of the search area. Experiment 1b varied the number of targets available within the bounded search area and in doing so varied target prevalence and density. The results confirmed that the route taken in Experiment 1a generalizes across variations in target prevalence/density. In Experiment 2, accuracy, search time, and the search strategy used by dyads was measured. While dyads were more accurate than individuals, dyads that opted to conduct two independent searches were more accurate than those who opted to split the search space. The implications of these results for individuals and dyads when searching for targets in open space are discussed.

Keywords

Visual search
Large-scale search
Search strategy
Paired searching
Foraging
Multiple targets

Significance

The current work was inspired by an effort to inform and improve training and procedures in two search tasks: first, for police teams combing open ground for clues to a crime; and second, the detection of improvised explosive devices (IEDs) for soldiers on patrol in theatre. These tasks are related in that both require an exhaustive search and are often performed by teams as well as individuals. Despite being an important class of search problem, currently little is known about how searchers maximize the chances of finding targets in these situations. The present study is a first effort to understand this important class of search problem. It does so by recording route maps and information about participants’ speed of movement, as well as accuracy and response times, to describe how searches were conducted. These searches were conducted by individuals (in relation to search route and speed) and dyads (in relation to decisions about splitting search over space). While only a first step, a thorough understanding of what underlies accurate searching in this class of search problem will have profound implications for those involved in training and developing procedures for search teams involved in finding targets situated in open spaces.

Background

What do we know about how exhaustively police search teams comb open ground for clues to a crime or how soldiers patrolling high-risk routes search for IEDs? The unfortunate answer is that, at present, we understand very little. With a few exceptions (e.g. Foulsham, Chapman, Nasiopoulos, & Kingstone, 2014; Gilchrist, North, & Hood, 2001; Jiang, Won, Swallow, & Mussack, 2014), previous studies on human search ability have primarily explored visual search (see Eckstein, 2011 for a recent review) or foraging (Cain, Vul, Clark, & Mitroff, 2012; Wolfe, 2013) using experimental stimuli presented on computer screens.

There are three major differences that should be noted between computer-based tasks and the task of searching for small targets placed in open space. First, the spatial scale is sufficiently different that small targets in open spaces are unlikely to be detected through pre-attentive or attentive vision alone (e.g. Treisman & Gelade, 1980; Wolfe, 2007; Wolfe, Cave, & Franzel, 1989) but will also require head and body movement (i.e. physical foraging behavior). Second, unlike terminating search following the detection of a single target (Tuddenham, 1962) or when the rewards of continuing foraging in one area are less than those that might follow from moving to a new area (Cain et al., 2012; Wolfe, 2013), searchers must attempt to search spatial locations exhaustively (by which we mean try their best to search as many possible target locations as they can). Third, if we consider real-world situations, such as police searching for clues to a crime or soldiers searching for IEDs, searchers have to consider all potential instances of all potential target types (Godwin et al., 2015), rather than finding well-defined targets matching a simple template. The present study is a first effort in trying to understand how exhaustive search is in these types of task. In this study, our focus is limited to understanding the search strategies that maximize the exhaustiveness of search. In the present case, we explore this issue by examining search for an unknown number of targets, which are distributed across an area of open space.

Search strategies have previously been explored in studies of eye movements, in terms of fixation patterns in active vision tasks (e.g. Gilchrist & Harvey, 2006; Keech & Resca, 2010). They have also been explored in terms of the approach undertaken by typical individuals and brain-damaged patients when performing computerized versions of pencil and paper cancellation tasks (Dalmaijer, Van der Stigchel, Nijboer, Cornelissen, & Husain, 2014; Donnelly et al., 1999). Despite differences in tasks and goals across these studies, a common conclusion is that detecting targets is better when search follows specific, structured paths.

The utility of using systematic paths for search is also apparent in mathematical models aimed at optimizing real-world search. Search Theory (Koopman, 1946, 1980) was developed in World War II to facilitate maritime search, rescue, and detection operations. The theory is an application of probability theory to search such that the likelihood of targets being found at specific locations can be computed, enabling searching of areas likely to contain targets to be prioritized. On the basis of these calculations, a search path is defined over which a plane or ship can pass in search of targets, with an improved chance of detecting them. Search Theory is still being developed, with recent applications for search and rescue on land (Koester et al., 2014; Robe & Frost, 2002). Search Theory can be utilized for simple through to complex search scenarios. In the simplest of cases, the likelihood of targets appearing within a search area might follow a uniform distribution. More complex cases must take account of Gaussian distribution of probabilities for factors such as initial target locations, drift patterns caused by air and water currents, and perhaps a desire for targets to remain hidden.

Considering the simplest of cases, optimal paths should minimize the distance of the route that allows all locations to be searched (using body, head, and eye movements) without making revisits. Evidence can be found in support of humans using such a strategy in Gilchrist et al. (2001). They explored whether the principles from visual search studies could be applied to large-scale search tasks. Using a task where all potential target locations were set in a regular array and clearly visible on the ground, they examined how the search for marble targets hidden in film canisters was conducted. The important result was that, in contrast to visual search, rechecking locations was rare, with participants making fewer revisits to canisters they had already searched. Gilchrist et al. (2001) suggested that there was a higher cost (in terms of the physical effort to cross the room) associated with forgetting in their task relative to standard visual search and that the increased effort required to search led to a higher likelihood of participants remembering searched locations. A similar result was reported by Smith, Hood, and Gilchrist (2008) in the same large-scale experimental setting and also by Ruddle and Lessels (2006). In all these tasks, minimizing re-checking must have involved determining a route through the search array.

Gilchrist et al. (2001) used a regular, visible search array (although targets were hidden in film canisters and required checking, these canisters were clearly laid out on the floor), but what happens when targets are hard to find and potential target locations are not arranged in a regular fashion? Critical to understanding how humans might conduct effective target search in such circumstances is what is meant by trying to search exhaustively across space. Consider the case where a given search area is wider than that which can be searched in a single pass. Within Search Theory (Koopman, 1946, 1980), the distance to the left and right of each location that still allows target detection defines an area known as the effective search width (ESW). The technical limit of sensors (human vision, radar, etc.) determines the width of the ESW and therefore how close together neighboring passes of a single path should be. With respect to humans, we refer to the width of the search corridor rather than the ESW as human search is not deterministic. Whether a particular setting of search width is considered effective for target search is dependent on knowing the accuracy of target detection within a search corridor.

For humans, without making head and eye movements to the left and right, the search width will be determined by the limits of foveal and parafoveal vision to discriminate targets. However, head and eye movements can be made to overcome the limits of foveal vision so long as they are calibrated with the forward speed of travel (along the search corridor being ‘swept’). If the speed of sweeping along a search path is too fast to allow search of adjoining spaces to the left, right, and center, then search will be incomplete. We conclude that the likelihood of search being conducted in a fine-grained and exhaustive manner will depend on a range of factors. In the absence of existing data, it seems likely that search will be subject to the strategic decisions made by searchers regarding systematicity of search and the trade-off of their chosen width of search corridor for sweeping, with their forward speed of search. Experiment 1a was designed to reveal evidence of individuals using a search strategy (in terms of the forward speed of search, the width of the corridor being swept and consistency of a chosen strategy) and to assess the influence of this strategy on search accuracy and overall speed.

Experiment 1a

In Experiment 1a, individual participants searched for an unknown number of coin targets placed in open grassland terrain. We were interested in how search strategy was related to both target detection accuracy and search time for the task. The grassland was 75 m² in size. Participants searched the open space in any manner they chose and for however long they wished to search. Accuracy was measured as the number of targets detected and search time was calculated as the time from when the participants started the task until they told the experimenters they had finished.

It is important to understand that it was not possible for participants to detect all but very few targets from their starting point. Target detection required moving through the search area. More than the detection of targets per se, or the type of targets searched for, it is the need to conduct an exhaustive search through a search area for targets that are very hard to find that connects this task most directly with that of finding clues to a crime or to the presence of an IED. Analysis of the systematicity of search was enhanced by using data extracted from a Total Station theodolite system. These data allow for visual representation of the routes taken by participants (henceforth ‘route maps’: see Fig. 3). They can also be processed using Fourier analysis to provide some quantitative evidence for general trends apparent when inspecting representations of routes taken by participants. This approach is helpful as participants are free to move in any direction and it captures the underlying spatio-temporal properties of their movements overall.

The Fourier analysis transforms the data from the time domain to the frequency domain and enables calculation of multiple measures derived from the frequency components of movement along the x-axes and y-axes of the search area: (1) The dominant frequency component is an index of the modal speed of movement along each axis. The reciprocal of the dominant frequency component (1/dominant frequency) converts this to seconds and can be used as an approximate measure of participant’s modal time before changing direction; (2) Dividing participant’s overall search time by the modal time before changing direction gives an estimate of the number of changes of direction. Plotting the number of changes of direction along the x-axis against those on the y-axis allows determination of whether participants tended to move systematically along x-axes or y-axes (i.e. left to right or top to bottom) or use a hybrid strategy. Furthermore, by dividing the number of changes of direction by the length of the axis being travelled across provides an estimate of the width of the search corridor used by participants; and finally (3) dividing participants’ modal time before changing direction by the next most commonly occurring time before changing direction indexes variability in speed of searching.

In addition to basic data around search accuracy and time, we show typical route maps and analyze the width of search corridors used, modal movement speed, and variability in speed of movement. We predicted that more accurate search would be reliant on: (1) increased regularity, systematicity of search following a structured path (Dalmaijer et al., 2014; Donnelly et al., 1999; Gilchrist & Harvey, 2006; Keech & Resca, 2010); (2) narrower search corridors (Koopman, 1946, 1980); and (3) slower, more consistently paced movement (Koopman, 1946, 1980).

Method

Participants

Thirty participants (7 men and 23 women, mean age = 23.5 years, SD = 4.73) recruited from the University of Southampton community, with normal or corrected-to-normal color vision, took part in the study for course credit. Participants were screened to ensure visual acuity and normal color vision using Snellen (1862) chart for visual acuity and the Ishihara (1917) color plates. The study was performed in accordance with the Declaration of Helsinki and was approved by the University of Southampton, School of Psychology ethics committee. Informed consent was obtained from all participants.

Apparatus

Experimenters used a grid representing the larger-scale search space to record accuracy. When participants found a target, they pointed to the target and informed the experimenter, who then marked off the corresponding target on the grid. Overall search time was recorded using a stopwatch. Search was deemed to have finished once participants reported to the experimenter that they were done.

Participant movement over space and time was recorded using a Total Station theodolite (Leica TPS1200, Heerbrugg, Switzerland). The Total Station used electronic distance measurement technology and an angle-measurement system to calculate the coordinate of an unknown point relative to a known coordinate point. A signal was sent from a fixed recording station to a reflector prism mounted on a 1.8-m staff held by the participants. A coordinate was recorded every 2 s and accuracy was within 3 mm per km of distance (SD = 1.5). The output of time-stamped coordinates was processed using Environmental Systems Research Institute’s ArcGIS software (ESRI, 2011).

Stimuli

The experiment was conducted on an open space of grassland (see Fig. 1). The perimeter of a 15 × 5 m search area was marked out at 1-m intervals using 40 colored cones. The position of the cones was calibrated with the Total Station prior to testing. The relative positions of cones allowed definition of 75 m² grid cells. Testing took place over four consecutive days. The location of the grid was moved each day to avoid excessive trampling of grass. Across days, the conditions of the grass remained broadly similar.

Twenty-five UK sterling two pence coins were used as targets (see Fig. 2). The two pence coins were of a copper color and were 25 mm in diameter. The coins were all matt rather than shiny in appearance. The coins were placed so that they could be detected from standing height, although they were sufficiently small that detection required active exploration of the search grid.

Coins were distributed within the grid so that five coins were placed pseudo-randomly within each 1-m ‘search lane’ (i.e. five coins were placed along each 1-m search lane across the 5-m width of the grid). On average, there was one coin per 3 m². The targets were placed in the same grid cell locations for all participants. Distractors were not added within the search area but leaves and other natural materials did form naturally occurring distractors and were not removed from the search space.

Procedure

Following the screening, participants were given a brief explanation of the Total Station and were instructed to hold the staff upright and close to their body while searching. The staff was lightweight and easy to carry. Participants were not told how many coins could be found but instructed to search until they were confident they had completed their search (i.e. they had found all the targets). Participants were told not to pick coins up but to point and tell the experimenter that a coin had been found. Participants were not penalized for reporting the same coin on more than one occasion (as the task simulates a task where a conservative approach to finding targets is encouraged) but each target was only counted once when calculating response accuracy. Once participants had completed their search, they were asked to give a score on a ten-point scale to indicate how confident they were that they had found all the coins. A higher score implied high confidence while a lower score indicated lower levels of confidence.

Results

Participants were excluded from data analysis if they failed to detect any coins. While detecting no coins might reflect their best performance, they were removed on the basis that it is impossible to differentiate poor performance from failing to engage with the task. We therefore took a conservative position on removing from analysis. This resulted in the removal of two participants (6.7% of the data) meaning data analysis was conducted on the data from 28 participants. However, the removal of the two participants did not influence the pattern of significance of results. Correlational tests were used to examine if there was a relationship between two variables, simple linear regressions were used to examine whether one variable predicted a second variable, t-tests were used to examine whether the mean scores of two participant groups differed and a Fisher’s exact test was used to test how likely it was that observed distributions were due to chance. All regressions reporting time or frequency use log-transformed data to reduce skew, though this did not, however, affect the underlying pattern of results. For the regressions, significance levels were adjusted for multiple comparisons as regressions compared the same measure across x-axes and y-axes. Only effects reaching a p value of 0.025 were considered significant. The statistical package used to analyze the data was R version 3.3.0 (R Core Team, 2016).

Behavioral data

Basic measures of accuracy and total search time are presented in Table 1. On average, participants found just under half the available targets, despite spending an average of 7.5 min on the task. On average, participants reported a confidence score of 6.89 on a ten-point scale. On average, the first target was found after 39 s and the last target was found 40 s before terminating search. For each participant, a regression was carried out exploring the linear relationship between the time of finding each target against the ordinal number of that target as found by the participant (i.e. 1^st, 2^nd, 3^rd, etc.). This measure explores whether targets were found consistently throughout search or were found more easily at the beginning than the end of search. The range of adjusted R-squared values was 0.842–0.995 (all ps < 0.052) across participants. The result suggests that targets were found at a fixed rate throughout search. Accuracy was predicted by confidence ratings (β₁ = 2.448, F(1,26) = 5.195, p = 0.031, adj R² = 0.135): participants who gave a high confidence rating were more accurate in their search.

Table 1

Behavioral data

	Accuracy					Total search time (min:s)
	Experiment 1a	Experiment 1b			Experiment 2	Experiment 1a	Experiment 1b			Experiment 2
	Individuals	5 targets	15 targets	25 targets	Dyads	Individuals	5 targets	15 targets	25 targets	Dyads
Mean	0.45 (0.04)	0.76 (0.19)	0.84 (0.16)	0.80 (0.11)	0.68 (0.13)	07:33 (02:32)	03:51 (02:49)	03:41 (02:00)	03:41 (01:40)	06:15 (04:13)
Minimum	0.12	0.40	0.47	0.52	0.40	02:32	01:19	01:38	01:05	01:13
Maximum	0.80	1	1	0.96	0.92	25:08	07:35	08:32	11:53	18:05

The mean, minimum, and maximum scores for accuracy and total search time for the individuals in Experiment 1a, the individuals in the three target frequency conditions of Experiment 1b and the dyads in Experiment 2

Parentheses indicate Standard Deviation

Search strategy

Examples of typical route maps are shown in Fig. 3. Visual inspection of these route maps reveals some commonalities across participants. These commonalities were explored using Fourier analysis.

Systematicity of search path

To quantify regularity of search path, a Fourier transformation was carried out for each participant and along both the x- and y- axes (see Donnelly et al., 1999). Our first prediction was that search would be more accurate when participants used a regular, systematic search strategy following a structured path. To explore this prediction, we examined the number of changes of direction (calculated by dividing participant’s overall search time by the modal time before changing direction).

The number of changes of direction was plotted across both axes (see Fig. 4). High values on one axis were associated with low values on the other axis (r(26) = –0.558, p = 0.002). Participants turning top to bottom tended to search along fewer, longer corridors and participants turning left to right tending to search along more, shorter corridors. These data are consistent with all participants searching systematically, using an ‘S’-shaped route to cover the search area (as shown in Fig. 3). Their fundamental pattern of movement (the ‘S’ shape) was consistent irrespective of whether they primarily moved top to bottom or left to right. The important result is that all participants exhibited regularity in the path taken to search. Given this, it was not possible to explore variations in accuracy as a function of the presence or absence of regularity (as per our first prediction).

Width of search corridors

Our second prediction was that search would be more accurate as search width narrowed. This hypothesis was based on the idea that a narrow search width would better facilitate the search of close space using the fine-grained spatial acuity of foveal vision. To explore this prediction, participants were split into two groups – those who primarily moved left to right (n = 11) and those who primarily moved top to bottom (n = 16). There was one participant who did not fall into either category, conducting a hybrid strategy, and was therefore removed from subsequent analysis. We took the number of changes of direction made by each participant along their dominant axis (i.e. whether they were in the top-to-bottom or left-to-right group), added 1 (to take into account the number of sweeps both up and down, i.e. five turns would mean six sweeps), and divided the length of the axis being travelled across by this figure (i.e. for those in the top-to-bottom group, the length of the axis being travelled across in meters, which was 5, would be divided by the first figure calculated). This normalized the data, as calculating the search width took into account the length of each axis.

These data are shown in Fig. 5. The striking result is that the search width for the majority of participants lies between 1 and 2 m (alternatively between 50 cm and 1 m to both the left and right of the center). This suggests that there is commonality in search width irrespective of whether participants search top to bottom or left to right across the search grid. Given the limited range of width of search corridors, there is no evidence of search width predicting either accuracy (β₁ = –0.534, F(1,25) = 0.356, p = 0.556, adj R² = –0.025) or total search time (β₁ = 0.446, F(1,25) = 2.11, p = 0.159, adj R² = 0.041). Irrespective of outcome for accuracy or time, participants searched along their ‘S’-shaped path, using a common search width.

Search speed and search speed variability

Our third prediction was that search would be more accurate when participants searched with slow, consistently paced movement (Search Theory; Koopman, 1946, 1980). To examine this prediction, we examined the modal time before changing direction on each axis and variability in time before changing direction (by dividing the modal time before changing direction by the next most commonly occurring time before changing direction). For the left-to-right group, accuracy was not predicted by the modal time before changing direction, nor variability in time before changing direction (β₁ = –0.41, F(1,9) = 0.065, p = 0.805, adj R² = –0.103; β₁ = –3.437, F(1,9) = 1.478, p = 0.255, adj R² = 0.046; see Fig. 6a and b). Total search time was predicted by modal time before changing direction but not variability in time before changing direction (β₁ = 1.133, F(1,9) = 8.712, p = 0.016, adj R² = 0.435; β₁ = 0.094, F(1,9) = 0.009, p = 0.928, adj R² = –0.11; see Fig. 6c and d).

Fig. 6
Search speed to predict Accuracy and Total Search Time for left-to-right participants on the *x-axis*. On the basis of the Fourier analysis, we computed an approximation of participants’ modal time before changing direction in seconds (s) on each axis, using their dominant frequency component. By dividing the modal time before changing direction by the next most commonly occurring time before changing direction we computed the variability in time before changing direction in seconds (s). Accuracy was the proportion of coins detected and Total Search Time indicates the total amount of time spent searching in seconds (s). Figure 6 shows the modal time before changing direction (a) and the variability in time before changing direction (b) to predict Accuracy, plus the modal time before changing direction (c) and the variability in time before changing direction (d) to predict Total Search time. Note: *lines of fit* are shown when a statistically significant relationship is present

For the top-to-bottom group, there was a very strong trend for accuracy to be predicted by modal time before changing direction and variability in time before changing direction (β₁ = 1.229, F(1,14) = 5.926, p = 0.029, adj R² = 0.247; β₁ = 1.982, F(1,14) = 5.716, p = 0.031, adj R² = 0.239; see Fig. 7a and b). Participants were more accurate when they took longer before changing direction and varied their time before changing direction. Total search time was predicted by modal time before changing direction but not variability in time before changing direction (β₁ = 0.956, F(1,14) = 27.21, p < 0.001, adj R² = 0.636; β₁ = 0.507, F(1,14) = 1.047, p = 0.324, adj R² = 0.003; see Fig. 7c and d). Participants took longer overall to search when they took longer before changing direction. As predicted, increased accuracy was associated with slow search (i.e. longer before changing direction, i.e. turning), but surprisingly, it was variable search speed that was associated with increased accuracy rather than a consistent pace as predicted.

Fig. 7
Search speed to predict Accuracy and Total Search Time for top-to-bottom-participants on the *y-axis*. On the basis of the Fourier analysis, we computed an approximation of participants’ modal time before changing direction in seconds (s) on each axis, using their dominant frequency component. By dividing the modal time before changing direction by the next most commonly occurring time before changing direction we computed the variability in time before changing direction in seconds (s). Accuracy was the proportion of coins detected and Total Search Time indicates the total amount of time spent searching in seconds (s). Figure 7 shows the modal time before changing direction (a) and the variability in time before changing direction (b) to predict Accuracy, plus the modal time before changing direction (c) and the variability in time before changing direction (d) to predict Total Search time. Note: *lines of fit* are shown when a statistically significant relationship is present

Discussion

In Experiment 1a, we examined how searching for an unknown number of coins in an open space of grassland terrain was conducted. We predicted that accurate search would be reliant on: (1) a regular, systematic search strategy following a structured path (Dalmaijer et al., 2014; Donnelly et al., 1999; Gilchrist & Harvey, 2006; Keech & Resca, 2010); (2) narrow search corridors (Koopman, 1946, 1980); and (3) slow, consistently paced movement (Koopman, 1946, 1980).

The basic behavioral data showed the task to be extremely challenging, with task accuracy being at 45%. Many targets were missed despite participants taking a significant amount of time to explore the search area (on average, 7 min 33 s). Targets were, however, detected at a fixed rate throughout search. Furthermore, accuracy was predicted by the confidence ratings given by the participants, suggesting participants had some idea of how accurate they had been in the task.

The route maps showed participants tended to search using an ‘S’-shaped strategy, though sometimes embedded within a more complex pattern (see Fig. 3). This was confirmed in the analysis of data extracted using the Fourier analysis. Given that all participants exhibited regularity in their search path, we were unable to explore whether a regular search path predicted higher accuracy or not. However, the regularity of the ‘S’-shaped path made it possible for participants to define a search path that rarely contained crossovers and where the search width varied between 1 and 2 m. Following an ‘S’-shaped path minimized the memory demands inherent in the task relative to if a more irregular path was followed (Gilchrist et al., 2001). Presumably this width of search corridor adopted by participants was set according to their beliefs about the salience of targets in the context of the environment in which they were being sought. More or less salient targets would, respectively, lead to use of a wider or narrower search corridor.

Given that participants opted to search using a common search width, an important question is, how exhaustive is each participants’ search within their search corridor? Accuracy varied markedly across participants despite using the common search width and so using the common search width did not ensure that search was exhaustive. At least for some participants, the failure to search exhaustively was associated with a faster movement time reflected in the reduced modal time before turning. The implication of speeded search is that areas of the search corridor were left unexplored as forward body motion occurred at a rate too fast for the sweep of left-to-right head movements and associated eye movements. The fact that accuracy was associated with confidence is consistent with participants having some insight into the likelihood of the success or failure of their attempt at an exhaustive search (metacognitive awareness).

One might think of the relationship between time before changing direction and accuracy as reflecting a speed-accuracy trade-off but its observation is important. An observer tasked with ensuring or judging the quality of a search is unlikely to be able to make such a determination from the search path but it may follow from measuring differences in the time taken for search. Interestingly, slowed search distinguishes experts from novices in airport baggage-screening tasks (Biggs, Cain, Clark, Darling, & Mitroff, 2013). Calibrating how long a search task requires is, we suggest, a skill to be learnt both in complex visual searches and searches for targets placed in a more complex physical environment.

Variable search speed was also associated with increased accuracy, rather than a consistent pace, as predicted. This variability of search speed and accuracy found for the top-to-bottom participants was unexpected. On reflection, however, it is likely to be an effect associated with the task itself. Careful searchers slowed to ensure targets were clearly identified and marked as detected by the experimenters leading to variability in search speed as being identified with increased accuracy.

It is possible that the failure to find a relationship in the left-to-right group for time before changing direction and variability in time before changing direction for accuracy may be accounted for by the shorter time and distance between turns that participants had to make when moving left to right than top to bottom. Given that participants searching top to bottom were more accurate when they took longer before changing direction, the shorter distance before having to turn when searching left to right may have led to less efficient search. Measures based on participants movement through space may require sufficient movement time along axes, unfettered by the noise introduced by the slowing and speeding of turning itself, to become reliable indices of performance. In other words, the failure to find a relationship for accuracy for the participants moving left to right is likely to be a form of signal-to-noise problem.

Experiment 1b

One concern is about the generality of the conclusions that can be drawn from Experiment 1a. It is possible that target conditions used may have forced a specific search strategy where participants searched along an ‘S’-shaped path for reasonably densely packed targets. The data were generated in response to a single grid and a fixed set of 25 targets. Within the limits of the search area, these 25 targets had a specific configuration and density. Many studies have shown that target prevalence influences the conduct of visual search (e.g. Fleck & Mitroff, 2007; Godwin et al., 2010b; Godwin, Menneer, Cave, & Donnelly, 2010a; Wolfe, Horowitz, & Kenner, 2005, Wolfe et al., 2007) and variations in target density are known to influence foraging (Cain et al., 2012; Wolfe, 2013). Might it be that the definition of search path and the width of the search corridors being searched are subject to change as target prevalence (along with target density and target configuration) varies? In Experiment 1b we repeated Experiment 1a, but had participants search three different search grids, each with a different number of targets present.