The task of judging perceived suspiciousness was an inherently ambiguous one. For example, footage of individuals "loitering" in a car park at night may be judged as suspicious to a greater or lesser extent by different individuals depending on their interpretation of the events depicted. In fact, it is has been previously shown for the same task that mood state can alter these judgements whilst monitoring CCTV (Cooper et al., 2013) reinforcing the subjective nature of these judgements. Therefore, even for the trained CCTV operators, there can be no objectively "correct" rating. We did not attempt to provide a benchmark of "correct" responses for this reason, though anecdotally while watching the videos, higher ratings of suspiciousness were associated with behavior such as that mentioned above in a car park, similar "loitering" around the entrance to nightclubs after dark or in an urban shopping area pedestrian underpass. A formal analysis of the content of video that is judged to be more or less suspicious is possible though it is beyond the scope of the work presented here. One can identify those periods of time in different videos that were given the highest suspiciousness ratings, and locate areas of the screen that were fixated just before the ratings were given, allowing for the time lag between eye movements and ratings. Whilst the spread measure is relative in that it describes eye positions only in terms of how close they are to other observers' fixations, one could identify the location of the centroid of these between-observer fixations to locate the most goal relevant areas of the screen whilst suspiciousness ratings are high. Characterising the content of such activity might be done either qualitatively by coding different behavior-environment interactions, or more quantitatively by looking for physical qualities of these video events.

The fact that analysis of video content is not necessary (though it is possible) for this technique makes this a powerful new tool for examining eye movements to dynamic scenes. One of the main challenges in studying eye movements to complex moving stimuli, is how to associate eye movements with different aspects of the stimuli, and therefore how to compare eye movements between observers. For simple stimuli such as a single target or a relatively small number of moving targets, dynamic areas of interest are a common method of analysis. One can use such a moving area to calculate fixations and dwell times etc., to particular stimuli of interest. However, this technique becomes unwieldy when there are very many targets, complex motion, shape changes, occlusion events or where stimuli are complex enough (such as in real-world scenes) that defining what is a target becomes non-trivial. There are additional problems with the use of dynamic areas of interest, such as how to define saccadic overshooting or undershooting, catch-up saccades and extrapolatory eye movements. The current technique circumvents all these problems.

We find that perception of dynamic scenes in this task proceeds relatively slowly: observers' responses lag behind eye movement convergence by a minimum of several hundred milliseconds. This is considerably longer than the typical time periods required for rapid evaluations of static scenes such as the "gist" which can be extracted effectively in around 100 ms (e.g., Biederman et al., 1974; Rousselet et al., 2005; Fei-Fei et al., 2007). The task here was very different from typical gist perception studies in several respects. Typical gist perception studies present stimuli only for a limited time and often measure a threshold of simple scene judgements. Here, however, the judgement was continuous and required semantic processing beyond simple scene-type judgements. More complex perceptual representations of scenes have been studied in the context of encoding images into memory. For example, Tatler et al. (2003) showed that memory representations of gist formed very rapidly. However, other judgements about more detailed aspects of the scene, like shapes, colours and positions of the scene elements benefitted from very many more seconds exposure up to 10 seconds. From this and later similar findings (Melcher, 2006) one might assume that the time course of semantic scene perception is as slow as this. However it is entirely possible that the limit in these memory studies may have occurred only at the stage of encoding and not perceptual processing. The results of the current study indicate a slow time course for semantic perception of dynamic scenes that ranges from several hundred milliseconds to several seconds.

Our findings here are somewhat consistent with earlier findings for a similar task but with a different stimulus (Howard et al., 2010) where observers watched a real videotaped football match and made continuous judgements about imminent goal likelihood. In this sports evaluation task observers' manual responses lagged behind gaze convergence by 1360 ms (non-experts) or 2260 ms (experts). The reason for the longer lags seen in the sports task than the CCTV task is not clear, but there are several differences between the two studies. The sports task is more constrained in terms of likely events. The CCTV task, by contrast, contains several different scenes, and several different types of events that are relevant to the suspiciousness judgement e.g., loitering in a car park, activity in a city shopping street, around a cashpoint etc. The CCTV task involved viewing ten different urban scenes with frequent changes between scenes. In contrast, the sports task stimulus was a single football match with a continuous shot from the same camera. Hence, the CCTV task contains more uncertainty in terms of what counts as the relevant perceptual variable, "suspiciousness", than does the sports task where the relevant variable is "goal likelihood". There may also be a greater social perception component inherent in the CCTV stimulus since the task involves making judgements about individuals' intentions and interactions with one another. Nonetheless, some comparison can be made between the two tasks since both require continuous semantic evaluation of moving scenes and appear to incur processing delays over several hundred milliseconds.

Two factors are likely to make our estimates here for the time course of dynamic scene perception longer than that previously reported for static scene evaluations. First, our method includes the time it takes to prepare and execute a response to the visual stimulus. However, reaction times to produce a manual response

to stimuli tend to be in the order of 200–250 ms (Goldstone, 1968; Green and von Gierke, 1984) and the magnitude of the lags here implicates additional contributing processes. Second, and most interestingly, the nature of the continuous task itself is likely to have caused these large time lags. Here we used a continuous video stimulus within which events unfold over time. One reason why perception of dynamic scenes may lag behind visual events is that the visual system often integrates information over a temporal window of at least 100 ms (e.g., Gorea, 1986; Watamaniuk and Sekuler, 1992) which is a physical necessity for information with a temporal component such as stimulus change or motion. Hence any temporal averaging may serve to increase these lags. Lags are also likely to be increased by the complex perceptual demands inherent in making decisions about these dynamic stimuli. For example, attending to the biological motion of humans and making judgements about their intentions is attentionally demanding and particularly important when the signal is degraded, ambiguous or subject to competition from other attentionally demanding stimuli (Thompson and Parasuraman, 2012). We also know that attending to multiple regions of a scene in terms of their visual features is attentionally demanding and can incur costs in terms of temporal lags (Howard and Holcombe, 2008; Lo et al., 2012). especially under conditions of competition for attention by different stimuli with similar features. In addition, whilst attending to video stimuli, one must use sustained attention, the nature of which is known to be different from that of transient attention (Ling and Carrasco, 2006) and it is possible that processing using sustained attention proceeds relatively slowly compared to the more transient attention that can be used for more briefly presented stimuli. Hence the complex and continuous nature of the stimulus and the task likely comprise a large component of the time course of this type of dynamic scene processing.

Howard et al. (2011) showed that observers could monitor four CCTV screens at once for suspicious events. Whilst this is very different from a traditional visual search task where the search array is typically static and the target is typically very well defined, these results and the results in the current study can be considered evidence that observers can perform visual search for extremely high level semantic targets. In the current study, this high level semantic target is "suspiciousness" which may take many different visual forms. This extends the literature from more traditional visual search tasks showing that observers need not be given complete or fully determined target template information to perform visual search in scenes (e.g., Malcolm and Henderson, 2009; Schmidt and Zelinsky, 2009; Bravo and Farid, 2012). In this task, for moving, complex scenes, attention can be guided by very high-level semantic interpretation of scenes.

Trained observers showed a greater lag between eye movements and manual responses than untrained observers. This is consistent with previous data for a similar task but when making judgements about a sports match (Howard et al., 2010). In the sports task, it appeared that expert observers were more able than non-experts to move their eyes to the goal relevant areas of the scene earlier, thus allowing them more time to produce their response. A similar mechanism could be operating here if CCTV operators "know what to look for" in the scenes. This would also explain the fact that CCTV operators showed greater between-observer consistency in eye position and longer periods of between-observer eye position convergence than our untrained observers. The fact that trained observers' ratings were more similar to one another at any given time than was the case for the untrained observers is also consistent with this picture. It is worth noting that the CCTV operators were familiar with the scenes presented in the videos and hence their expertise may lie both in the task of CCTV monitoring itself and also in their specific knowledge of the scenes and environments depicted in the video footage. The CCTV operators' reduced level of eye movement variability compared to untrained observers may account for why the relationship between eye position spread and responses was less highly correlated than it was for untrained observers. It might also help explain why the lag curve for expert observers was flatter and less pronounced than for untrained observers.

There is evidence that expertise affects visual processing efficiency in a range of different tasks. Hershler and Hochstein (2009) examined the influence of expertise during visual search. They found that experts in specific recognition of either categories of "cars" or "birds" appeared to be able to process visual information in their area of expertise from a wider portion of the search display with each fixation. This is consistent with an explanation of expertise on the grounds of a greater capacity for information processing across the spatial domain. These results are reminiscent of similar results in the vision-for-action literature. For example, Underwood et al. (2002) report that expert drivers scan a wider portion of the road scene than novices. In musical sight reading, Furneaux and Land (1999) find that experts and non-experts tended to look at positions in the musical text that are approximately one second ahead of the notes they are currently playing. However the expert musicians appeared to be able to store more information in this visual information memory buffer. In their information reduction hypothesis of skill acquisition, Haider and Frensch (1996) point towards selective attention to goal relevant information as the cause of improved performance in experts. This selection of task relevant information over non-relevant information could be operating here and elsewhere, though it is also possible that processing is more efficient even once the most critical areas of the scene are selected by attention. These two aspects of efficiency i.e., selection and post-selection processing, are difficult to tease apart in the data presented here. However, the effect of expertise in these very different types of tasks, visual search and vision for action, are likely to reside in efficiency of visual information processing, albeit at potentially different cognitive and perceptual stages.

Any visual processing efficiency differences may plausibly reduce the cognitive or mnemonic load for experts. Since the task presented here involves a high degree of perceptual, cognitive and mnemonic load, this may be especially beneficial to the experts in the current study. Cognitive complexity and memory load have been shown to influence fixation patterns with observers using fixations to regain goal relevant information under conditions of high load (Droll and Hayhoe, 2007; Hardiess et al., 2008). Hence experts may have needed to use fewer re-fixations in this manner, contributing to the difference in eye movement patterns observed between our two groups.

One further possibility for the locus of expertise in this and in our previously reported sports task (Howard et al., 2010) is that experts are better able to anticipate upcoming visual events. Some evidence that this may be the case is given by Didierjean and Marmèche (2005) who showed anticipatory representations in expert basketball players. This was evidenced by the fact that experts' comparisons between pairs of gameplay configurations was poorer when making comparisons about pairs that moved forwards in time rather than backwards. It appeared that their representations had already moved the events on in time when presented with the future configuration. Perhaps the experts in the current study and in our previous sports monitoring task were more able to predict near future events and hence use their eye movements more efficiently.

At first glance it is not clear how these visual processing differences might account for the longer lags reported here for CCTV operators than untrained observers between eye movements and manual responses. However in the football task, experts appeared to move their eyes to the relevant parts of the scene earlier, and this could have been facilitated by superior visual processing. The longer lag between eye movement convergence and manual responses may be a result of experts deliberately adopting an accuracy-over-speed strategy, perhaps as a direct result of more confidence about making goal relevant fixations. Trained operators may choose to undertake more processing before reaching a decision about manual responses. Additional time observing events is likely to result in increased visual information and decreased ambiguity about events being displayed, and it is possible that operators use a waiting strategy to minimise the number of false alarms. Indeed in the real CCTV control room, operators use a similar type of joystick to zoom in to events in real time, zooming in and out as desired depending on the unfolding events. Zooming in to more closely examine a particular stimulus incurs some information cost since it narrows the field of view in that particular camera and carries the risk of missing events occurring at other locations in the scene. Hence experts may have learned to use a conservative criterion for making suspiciousness judgements. One factor to note here is that our two groups of observers differed along many dimensions including training and experience, knowledge and expectations of the scenes presented, gender, age, socio-economic background and specific instruction in the task. Of course any or all of these factors could have contributed to this difference.

In the CCTV task presented here, our experts were trained professional CCTV operators, compared to untrained psychology undergraduates. In the football task, all the observers were undergraduate psychology students, but they differed in their level of self-reported experience watching football matches. Hence, there was a greater difference in expertise level in this CCTV monitoring task than in the football task and this may account for some of the differences in the results. Other differences between the CCTV and football tasks include the greater level of constraint about events in the football match (i.e., events typical of a football match such as passes, tackles, goal attempts, etc.) than the CCTV task, which is video footage of several different types of urban scene. Additionally, the CCTV video is potentially much more of a task of social perception than the football task, since it requires judgements of intentions, potential future behavior and interactions between individuals. Therefore the data we present here are a second example of the successful application of this DAF method of measuring eye-hand lags in two very different contexts. The method enables the use of tasks with moving video

#### **REFERENCES**

stimuli from real-life scenarios, as well as on-line continuous judgements about these stimuli. We demonstrate that cognitive evaluation of these moving scenes is a somewhat slow process. The ramifications of this processing time when multiple screens must be monitored, as in CCTV monitoring, may be particularly severe.

behaviour viewed through CCTV cameras. *Perception* 33, 87–101. doi: 10.1068/p3402

**Conflict of Interest Statement:** The 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.

*Received: 12 April 2013; accepted: 18 July 2013; published online: 22 August 2013. Citation: Howard CJ, Troscianko T, Gilchrist ID, Behera A and Hogg DC (2013) Suspiciousness perception in dynamic scenes: a comparison of CCTV operators and novices. Front. Hum. Neurosci. 7:441. doi: 10.3389/fnhum.2013.00441*

*This article was submitted to the journal Frontiers in Human Neuroscience.*

*Copyright © 2013 Howard, Troscianko, Gilchrist, Behera and Hogg. 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) or licensor 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.*

## Looking for trouble: a description of oculomotor search strategies during live CCTV operation

#### *Matthew J. Stainer 1,2, Kenneth C. Scott-Brown3 \* and Benjamin W. Tatler 1*

*1 Active Vision Lab, School of Psychology, University of Dundee, Dundee, Angus, UK*

*2 Department of Optometry and Vision Science, University of Melbourne, Melbourne, VIC, Australia*

*3 Department of Psychology, University of Abertay Dundee, Dundee, Angus, UK*

#### *Edited by:*

*Elena Rusconi, University College London, UK*

#### *Reviewed by:*

*Evan F. Risko, University of Waterloo, Canada Michele Furlan, Royal Holloway University of London, UK Matteo Valsecchi, Justus-Liebig Universität Giessen, Germany*

#### *\*Correspondence:*

*Kenneth C. Scott-Brown, Department of Psychology, University of Abertay Dundee, Bell Street, Dundee, Angus, DD1 1HG, UK e-mail: k.c.scott-brown@ abertay.ac.uk*

Recent research has begun to address how CCTV operators in the modern control room attempt to search for crime (e.g., Howard et al., 2011). However, an often-neglected element of the CCTV task is that the operators have at their disposal a multiplexed wall of scenes, and a single spot-monitor on which they can select any of these feeds for inspection. Here we examined how 2 trained CCTV operators used these sources of information to search from crime during a morning, afternoon, and night-time shift. We found that they spent surprisingly little time viewing the multiplex wall, instead preferentially spending most of their time searching on the single-scene spot-monitor. Such search must require a sophisticated understanding of the surveilled environment, as the operators must make their selection of which screen to view based on their prediction of where crime is likely to occur. This seems to be reflected in the difference in the screens that they selected to view at different times of the day. For example, night-clubs received close monitoring at night, but were seldom viewed in mid-morning. Such narrowing of search based on a contextual understanding of an environment is not a new idea (e.g., Torralba et al., 2006), and appears to contribute to operator's selection strategy. This research prompts new questions regarding the nature of representation that operators have of their environment, and how they might develop expectation-based search strategies to countermand the demands of the large influx of visual information. Future research should ensure not to neglect examination of operator behavior "in the wild" (Hutchins, 1995a), as such insights are difficult to gain from laboratory based paradigms alone.

**Keywords: CCTV, surveillance, visual search, spatial selection, eye guidance, multiplex**

#### **INTRODUCTION**

The task of the CCTV operator is to find and, if possible, prevent crime in public spaces. Research has shown that when asked to predict whether the events presented in a single video will turn violent, naive observers perform as well as trained CCTV operators (Troscianko et al., 2004; Grant and Williams, 2011). In control rooms, however, the CCTV operator is tasked with searching for such undefined targets across not one, but a vast number of screens displaying dynamic information from locations across a wide geographical area. For example, in a survey of 11 local authority and private security CCTV control rooms, operators were faced with a range of 27–520 cameras per operator, with up to 175 feeds displayed simultaneously across a bank of monitors (Gill and Spriggs, 2005; Gill et al., 2005). As such, the visually rich layout of the modern CCTV control room seems unnatural, complex, and ill-suited to the perceptual and cognitive constraints of the human operator (Scott-Brown and Cronin, 2008). It is well characterized that when searching for a target, the number of distractors that are present can dramatically influence search time (see review by Wolfe, 1998), including when a target's identity is not known (Rensink, 2000). Thus, performance skill in CCTV operation may be better characterized by their ability to find a "target" scene (i.e., containing information for the task) amongst a large number of "distractor" scenes (e.g., see Howard et al., 2009).

#### **MULTIPLE SCENE SURVEILLANCE**

Tickner and Poulton (1973) demonstrated the behavioral costs when faced with increasing numbers of scenes in a surveillancebased task. These authors showed that when monitoring simultaneous feeds from cameras in a prison, the accuracy with which participants detected suspicious events was lower when the number of simultaneously-viewed camera feeds was high; with 83% for 4 monitors, 84% for 9 monitors, and 64% for 16 monitors. Wallace et al. (1997) examined observers' target detection across multiple scenes and found decreases in performance when increasing the number of town center scenes in the display. Correspondingly, this difficulty is reflected in CCTV operators confidence of multi-scene detection. When interviewed, 82% of CCTV operators interviewed only reported confidence with monitoring up to sixteen screens, with 50% reporting that they felt comfortable monitoring up to four screens simultaneously (Wallace and Diffley, 1998). This is considerably less than the number of screens that can be displayed in the modern control room. In another study, Howard et al. (2011) presented participants with a series of quadraplex displayed CCTV clips and recorded their perceived suspiciousness of the video by means of a joystick. Participants moved the joystick forward to indicate their belief that an event was likely to happen. Viewers eye-gaze in these conditions, where multiple different video streams compete for attention, was allocated according to the relative suspiciousness of each video clip.

The overriding message from what is known about visual information load and visual search performance (e.g., Wolfe, 1998), and the performance in multiple-scene detection tasks (Tickner and Poulton, 1973; Howard et al., 2011) is that efficient search for crime among a large number of scenes is likely to be poor. However, while simultaneous display of a large number of camera feeds in multiplexes is an integral feature of CCTV control rooms, operators also have at their disposal individual spot monitors that can be used to selectively view the information from a single camera at a time (**Figure 1**). The selection of content in this way is an often-neglected element in studies of the CCTV task and it is important to characterize the relative use of multiplex and spot monitor for real surveillance situations.

Not only is it important to understand the manner in which the multiplex and spot monitor are used in surveillance, but it is also important to consider the different cognitive demands associated with the use of each of these display formats. In the multiplex, all visual content is displayed at one time. However, skilled and strategic use of the spot monitor relies on an understanding of the camera array and geographical area under surveillance, (Hillstrom et al., 2008). For example, tracking a suspect across an extended area of space requires selection of geographically adjacent cameras, even though they may neither be spatially adjacent in the multiplex nor visually continuous in content. Thus, selection on the spot monitor is not simply based on visual guidance, but rather by the representation of the environment or a mixture of the two. The multiplex and the spot monitor therefore present rather different challenges and opportunities for the operator and potentially rely on rather different underlying knowledge. Moreover, these two display types may be differentially suited to particular aspects of the surveillance task: the multiplex might be well suited for detecting unexpected or suspicious events as

**FIGURE 1 | Prototypical layout of modern provincial CCTV control room layout similar to that used by the Tayside Police.** 3D model adapted from Google SketchUp program by artist "STUFF & STUFF." Each operator has their own controllable spot monitor, an additional monitor, a computer keyboard a camera control keypad and a telephone headset to wear. Metropolitan area control rooms may feature more operators and a larger array of screens on the wall.

these might occur in any of a number of different locations in the environment at any time. On the other hand, detailed information of unfolding events at a particular location might be better accessed via the spot monitor, where potential distraction from other camera feeds can be avoided.

One relatively unexplored aspect of the surveillance task is the extent to which the task demands vary over a 24-h period and how this impacts on operator behavior. For example, flash-point outbreaks of violence are a more prominent feature of the task at night than during the day in many urban settings (Felson and Poulsen, 2003). Not only do the likely types of events differ over the 24-h period, but also the likely locations at which these events occur changes: night-clubs are a likely venue for fights at night, but not during the day. During a visual search task, when people are told the area of a scene that contains the target, performance is related to the size of that area, rather than the whole scene (Zelinsky and Schmidt, 2009). In the control room, and idea of where to look for different targets would likely serve to reduce the load of the observer. Similarly contextual understanding of scenes has been shown to influence where people search for items (Torralba et al., 2006). Given the intimate link between vision and task demands in real world activity (see Land and Tatler, 2009) it seems likely that visual strategies of CCTV operators will vary depending upon the time of day or night during which they are working.

The cognitive ethology (Kingstone et al., 2008; or ethnography e.g., Hutchins, 1995a; Hollan et al., 2000) approach to understanding how a system functions can provide otherwise hidden insights into how tasks are completed, such as how drivers navigate corners (Land and Lee, 1994). The purpose of this paper is to offer a first step toward understanding the nature of the surveillance task as it exists in a real CCTV control room. While in doing so we sacrifice some of the control which laboratory paradigms afford, such studies are essential to ensure that the questions we can ask in the lab are valid to the task (see also Hutchins, 1995a and Weibel et al., 2012 for a recent example including eye-tracking).

The first question we examined was to look at what extent the operators use the multiplex or the spot monitor. Research has addressed both single scene (e.g., Troscianko et al., 2004) and multiplex surveillance (e.g., Tickner and Poulton, 1973) viewing conditions, but a systematic analysis of their use in day-to-day Control Room operation has yet to be conducted. The second question that this paper addresses is to what extent is selection based on the monitoring task, and, by extension, to what extent is selection based on the viewing preference of the individual operator? If the task dictates spatial selection, then we would expect there to be larger differences in selection of content between shifts of operation. However, if selection is more related to the preferences of the individual operator, we would expect selection to be more different between the operators, and similar across different sessions.

#### **MATERIALS AND METHODS PARTICIPANTS**

The observers were two trained CCTV Control Room operators from Tayside Police (now "Police Scotland") Control Room. Operator 1 had been working as an operator for approximately 10 years, whereas Operator 2 had been in the position for approximately 2 years (and was trained by Operator 1).

#### **TAYSIDE CONTROL ROOM**

Tayside Police Control Room receives live feeds from around 100 CCTV cameras in the Dundee City area at any one time. These camera feeds are displayed on a multiplexed bank of 47 CRT monitors (**Figure 2**). Several of the monitors are used to simultaneously display four camera feeds in split-screen (usually low-activity scenes such as car parks), and some automatically scroll through up to five different cameras, showing each one at a time for a period of several seconds. Many of the cameras are also on a set "walk" pattern, whereby they automatically pan across the area in a pre-set manner. Both operators that we recorded reported being able to comfortably see detail on the multiplex from their viewing position.

Operators in Tayside Control room work in teams of two (although they may occasionally be joined by a third person who will review footage on a separate station). This research was authorized by the Force Executive of Tayside Police.

#### **EYE MOVEMENT RECORDING**

Eye movements were recorded using a lightweight Positive Science LLC mobile eye tracking system built by Jason Babcock (Babcock and Pelz, 2004). The system samples eye position at a 30 Hz and creates a video overlay of the scene viewed from a first person perspective with a gaze-cursor cross. Two cameras were mounted on a spectacle frame, simultaneously recording the scene and the observer's eye. The key benefit of this system is its unobtrusive qualities. Thanks to its small visual footprint and low-weight construction, operators can enjoy full freedom of movement in their normal seated position. As viewing behavior may be influenced by the process of wearing an eye-tracker (e.g., an "eyetracker awareness"; Risko and Kingstone, 2011), operators were given no instruction other than to carry out their task as usual to attempt to minimize experimenter effects.

The video from the cameras was captured live into the Yarbus software package (version 2.2.2) on a MacBook Pro (4 GB Memory, 2.4 GHz Intel Core 2 Duo), where eye position was estimated based on detection of the pupil (with accuracy within a degree of visual angle). Observers calibrated live using a 9-point grid made up of the corners of monitors on the data wall, and the four corners around their spot monitor.

Data were exported as videos from the scene camera overlaid with eye position (**Figure 3**). The videos were then hand-coded to extract where the operators were looking throughout each session in terms of the type of display (multiplex and spot-monitor), and the camera feed that was shown on that display. Data during blinks were excluded from analysis.

#### **PROCEDURE**

Recordings were made during live system operation from each observer at each of their three shifts of work (afternoon, morning, and late night). Each recording session for each operator was 15 min in duration. Care was taken to ensure fitting and removal of the equipment from the operator was performed at convenient times within the surveillance task so as not to interrupt actions *en train*. Operators were told that they could remove the glasses at any time if they felt it was hindering their work (although neither chose to at any point).

#### **ANALYSIS**

Our approach to examining the question of how operators search for crime is not a traditional experimental design, but rather an observational approach. There are potential issues with overgeneralizing the data from such observations (particularly given the low number of operators). However, our aim is to describe behavior as it occurs. Thus, we applied traditional quantitative techniques of analysis to attempt to quantify this behavior, and describe the operators' use of the multiplex and the spot monitor in their search for crime. As such, some data presented are simply numerical (such as the number of cameras that an operator viewed on a particular session).

When examining differences between operators based on continuous variables, we used linear mixed-effect modeling (for example, to examine the difference in scanning time per scene between operators). Linear-mixed effects models have become increasingly used to examine non-normally distributed data (e.g., see Druker and Anderson, 2010). They allow for modeling of fixed factors, and random factors, with all data included (rather than condensing the data to a single mean). Thus, it considers the variance within a random factor (such as participant), as well as the variance between fixed factors. However, here we consider operator as a fixed factor. Conventionally the fixed factor in an analysis must be repeatable (Baayen, 2007, p263). However, we include

**FIGURE 3 | Examples of eye gaze videos with gaze position crosshair overlaid.**

operator here as a fixed factor, as we do not intend to generalize our data beyond differences between our operators (and simply to try to quantify if they *were* different).

Here, we analyzed the data using the *lme4* (Bates, 2005) and *languageR* (Baayen, 2007) packages in *R* (R Development Core Team, 2009). We follow the reporting style of Druker and Anderson (2010), who used similar modeling, to report the mean difference between conditions with highest 95% posterior density intervals from Markov Chain Monte Carlo mean estimates, with approximated *p* values generated with the *pval.fnc* function (Baayen, 2007; Baayen et al., 2008).

When looking at categorical differences between operators, we employed Kullback-Leibler divergence analysis (for example, to analyze whether there was a difference in the cameras selected between operators, and between sessions). Kullback-Leibler divergence is an information theoretic measure that allows us to quantify the difference between two probability distributions in terms of the number of bits of code that is required to describe one distribution based on another. We present these probability distributions in graph form, with camera number being a categorical factor, plotted against probability of fixation. Thus, the Kullback-Leibler divergence score can be used a measure of the difference between two categorical distributions (for similar use see Tatler et al., 2005). This allows us to quantify whether differences in selection are greater between shifts of operation, or between operators, with higher scores representing larger differences.

#### **RESULTS**

Across all sessions, we found that operators spent the majority of their time selecting content on their individual spot monitor (*>*90% across both observers in all sessions; **Table 1**). Operator 1 did not use the multiplex at all in the morning, or evening sessions, with the highest proportion of time spent on the multiplex being the afternoon session for both operators.

#### **SPOT MONITOR SCANNING**

We looked at four principle measures of spot monitor use that are summarized in **Figure 4**. First, **Figure 4A** reveals that in the afternoon and morning sessions, Operator 1 viewed around half the total number of scenes compared to Operator 2. However, in the night session Operator 1 viewed more scenes in total than Operator 2 (although this total was less than the number of scenes viewed by Operator 2 other two sessions).

Per scan (a viewing session on the spot monitor that was uninterrupted by looks at the multiplex), Operator 2 was relatively

**Table 1 | The proportion of time spent by each operator viewing their spot monitor, and the multiplex.**

consistent, viewing around 2–3 scenes between looks to the multiplex in all three shifts (**Figure 4B**). However, as Operator 1 did not view the multiplex at all in the morning and evening recording sessions, they viewed more scenes per scan than Operator 2. In the only session that Operator 1 did use the multiplex (the afternoon session), the number of scenes per scan was similar to Operator 2 (2–3 scenes). Correspondingly, **Figure 4C** shows that the Operator 1 had longer periods of spot monitor scanning than Operator 2 in all sessions.

Finally, we looked at how long Operators would view each scene before selecting to view content from a different camera. To examine scanning time per scene, a linear mixed effect model was carried out with operator included as a fixed factor, and session included as a random factor. **Figure 4D** demonstrates that Operator 2 inspected each scene for significantly less time than Operator 1 (Markov-Chain Monte Carlo (MCMC) mean difference = −35.19 s, 95% *CI* = −49*.*86 to −21*.*55 s, *p <* 0*.*0001).

#### **SPOT MONITOR SELECTION**

The amount of time that operators spent on each selected scene viewed on the spot monitor across the three recording sessions is illustrated in **Figure 5**. Operators' selection of content was most similar between the afternoon and night sessions (**Figure 6** center bar of panels 1 and 2). Operators showed the greatest difference in the scenes that they chose to view on the spot monitor in the morning compared to the night shift (right bar of panels 1 and 2). The scenes that were selected at night were most similar between operators (right bar of panel 3), and least similar in the afternoon.

#### **MULTIPLEX SCANNING**

As discussed previously, Operator 1 used their spot monitor for the entire morning and night session. **Figure 7A** reveals that Operator 2 viewed just over 3 times as many scenes in the afternoon session compared to Operator 1. Operator 2 also viewed more scenes per scan (**Figure 7B**), and had longer periods of multiplex scanning (**Figure 7C**). However, **Figure 7D** reveals that when Operator 1 did look at scenes on the multiplex, the operator spent more time on average viewing each scene before moving to another.

#### **MULTIPLEX SELECTION**

The distributions of time spent viewing scenes on the multiplex can be seen in **Figure 8**. As Operator 1 did not use the multiplex on either the morning or afternoon session, only selection by Operator 2 was examined using Kullback-Leibler divergence. **Figure 9** shows that there was much less variance in selection on the multiplex between sessions compared to the content viewed on the spot monitor (which yielded higher Kullback-Leibler scores). However, when compared across sessions, selection followed a similar pattern as on the spot monitor. Selection of content was most similar between the afternoon and night sessions.

#### **DISCUSSION**

In what we believe to be the first study of visual strategies for expert CCTV surveillance in a public space control room under normal working conditions, we report the results of a mobile eye-tracking study of CCTV operator performance during day

**FIGURE 4 | (A)** Total number of scenes viewed on the spot monitor on each session by each operator. **(B)** The number of scenes selected by each operator per scan. This figure shows that Operator 1 selected more screens than Operator 2 (and this was unbroken in

the morning and night session with no looks at the multiplex—hence lack of ±SE). **(C)** Mean length of each spot monitor scanning session. **(D)** Mean scanning time per scene on the spot monitor (with ±SE).

and night shift team-based surveillance. Spot monitor scanning and selection was compared with multiplex scanning and selection data along with a comparison of inter-operator differences in screen inspections.

For the operators we studied, spot monitor observation took up more than 90% of inspection time in the control room during the periods of observation (afternoon, morning, and evening). The data demonstrate that during our recording spatial selection in the control room differed dramatically both between operators, and between different shifts of operation. For example, Operator 1 spent more time viewing content on the spot monitor than Operator 2, and spent longer on each scene before transitioning. These differences between operators may reflect different idiosyncratic styles for surveillance or the differing experience of the two operators. However, the operators work as a team and these differences may reflect the different roles that each operator took in their collaborative effort. For example, Operator 1 might take the role of monitoring the night clubs, while Operator 2 monitors at the suburbs. Such distribution of cognition has been previously demonstrated, for example, between pilots in the cockpit of

**FIGURE 6 | Kullback-Leibler divergence score in screens selected for viewing on the spot monitor by session for Operator 1 (panel 1), Operator 2 (panel 2) and across all sessions (panel 3).** ±SE are included, and represent that Kullback-Leibler divergence analysis gives two scores for each comparison (the probability of distribution A/B, and the probability of distribution B/A).

an airplane (Hutchins, 1995b). While the question of how operators work together to efficiently detect crime was not the aim of this study, this would likely be an informative and interesting direction for future research.

Despite the data showing that during three 15-min recording sessions the operators spent little time viewing content on the multiplex, when operators did use the multiplex, they were more similar to each other in what they chose to view compared to their selections for inspection on the spot monitor. Short scans of the multiplex lasting approximately 1–4 s punctuate the longer spot monitor views, and inspection times for individual scenes are extremely short when viewed on the multiplex. Thus, it appears that anything worth further inspection is probably brought to the spot monitor, and multiplex viewing may be used primarily to help identify content that should receive more detailed scrutiny. Content selection in the multiplex appears most similar in afternoon and night conditions.

These findings indicate that approaches to understanding surveillance that are based solely on multiplex detection (Tickner and Poulton, 1973) or single screen detection (Troscianko et al., 2004) may provide insights into aspects of the task. However, given the dynamic interplay between multiplex viewing and selecting single camera feeds for further inspection, these two modes of viewing need to be considered together. Moreover, single screen viewing is a very active process in which content from different cameras is actively selected, with new camera feeds being selected on average every 26.94 (Operator 2) to 62.44 (Operator 1) s while using the spot monitor. Selection of

content during spot monitor use necessarily reflects considerable use of the internal representation of the surveilled environment, including an understanding of the camera locations in external space.

#### **STRATEGIES IN SEARCHING FOR CRIME**

When searching for crime, we found that the CCTV operators spent very little time searching the multiplex. In accordance to this finding, operators of multiplex systems reported low confidence in their ability to monitor several scenes (Wallace and Diffley, 1998). This would be entirely consistent with what is understood about search of complex displays (e.g., see Wolfe, 1998). Increasing the amount of visual information in a display increases search time (with visual information measured in several methods; Rosenholtz et al., 2007; Henderson et al., 2009; Beck et al., 2010; Bi et al., 2010; Wolfe et al., 2011; Asher et al., 2013). Given the likelihood of a bottleneck of attention at some-point in the visual system (for example, see limits on the number of objects we can simultaneously track; Alvarez and Franconeri, 2007), the multiplex might present a daunting task to the visual system. Performance drops have been shown at four screens (Tickner and Poulton, 1973, or Rousselet et al., 2004), which was less than 1/10th of the screens in the multiplex of the control room examined here.

One way that operators might effectively be able to increase confidence is to use the spot monitor (i.e., reduce the task to a single scene load). If operators conduct the majority of work on their single spot monitor, it is important that they select the appropriate scenes to view. While previous research has found no effect of training on single scene detection tasks (such as Troscianko et al., 2004), it may be that expertise in the control room serves to guide operators' search for crime within the large number of scenes that they could potentially select and view. Accordingly, Howard et al. (2010) demonstrated that the difference between experts and novices watching a five-a-side football match is that experts look at the most informative locations earlier than novices. In the surveillance context, we found that operators appear to select content differently at different times of day and this seems likely to be based on both their knowledge of the environment and their experience of where events are likely to occur at different times of the day.

It is important to consider how operators are able to select a subset of appropriate content from the large array of camera feeds available. It is possible that this is based on reactive selection to events unfolding in each camera feed. However, the proactive nature of surveillance and the often subtle events that are selected for detailed monitoring suggests that the selection processes are likely to be strategic, based on prior knowledge and expectation. One plausible possibility is that operators have an understanding of the likely locations at which events will occur at different times and that they use this to constrain much of their surveillance effort to the cameras that depict these locations. In this way, suspicious events will be monitored primarily within expected locations in the surveilled environment. This suggestion is similar to the contextual selection that has been demonstrated in scene viewing paradigms, where observers appear to combine expectations of where things are likely to be in the world with low level feature information (Torralba et al., 2006; Ehinger et al., 2009). In such paradigms, it has been shown that observers primarily search regions in which targets are expected to occur, with search time being related to the area the observer has to search, rather than the whole display (Zelinsky and Schmidt, 2009). Some cameras facing night-clubs (e.g., feed X20 and X62) were not viewed at all in the morning and afternoon session, but made up a large proportion of the night-time surveillance. How operators develop their criteria for selecting appropriate content is a question that further research should seek to address.

We propose four potential ways that expectation might develop: First, expectation may simply be based on general associations of social factors (e.g., areas associated with drug use are more likely to be high violence areas, Lum, 2011). Second, expectation might be built up via reinforcement, as operators successfully experience or detect events in certain scenes (similar to the development of spatial bias in visual search, e.g., Carpenter and Williams, 1995). Third, it may be based on how the amount of activity (and hence content and motion within the camera feeds, e.g., see Howard and Holcombe, 2010) changes throughout the day. There are simply more people around night-clubs at night than anywhere else. Fourth, strategic selections may arise as a result of explicit instruction about where to look and when during operator training (e.g., Wallace and Diffley, 1998, Appendix A). We might speculate that the fourth possibility does not account for aspects of our findings because the two operators differed in the scenes they viewed, however, as previously suggested this difference might be an active choice for efficient collaboration of efforts across the control room.

#### **CONCLUSIONS**

Research has shown that when observers attempt to detect criminal activity in one scene, untrained observers are as good as trained CCTV operators (Troscianko et al., 2004; Grant and

#### **REFERENCES**

and items. *J. Mem. Lang*. 59, 390–412. doi: 10.1016/j.jml.2007. 12.005

Williams, 2011). However, this situation only captures one part of the CCTV operator's task. First, operators have to correctly select the scene to view from a large number of possibilities. As such, the task of CCTV operation is not simply a case of looking at the right place at the right time, but rather of looking at the right place at the right time *in the right scene*. To complete this task, we found that two trained CCTV operators spent more time searching for crime using a single scene spot monitor, rather than the multiplex data wall, despite the latter giving the operator more information at one time. This may, in part, reflect the difficulty of search across large amounts of visual information (e.g., Wolfe, 1998 among others). However, to be able to search with the spot monitor, operators must select screens based on their representation of the surveilled world. Moreover, this understanding of the environment seems to incorporate the monitoring demands associated with different shifts of operation, with operators selecting different screens at day compared to night, for example. This may reflect the locations of high event likelihood being different at night, compared to during the morning, which would be consistent with using contextual understanding to guide visual search to areas likely to contain a target (such as Torralba et al., 2006).

Using cognitive ethology, we can gain a more comprehensive, ecologically valid idea of how cognition functions "in the wild." We echo the sentiments of Kingstone et al. (2008) that observation of naturally occurring behavior can provide an essential complement to laboratory-based studies in generating valid hypotheses and questions, as neither alone can provide a complete picture of complex cognitive tasks such as CCTV operation.

#### **ETHICS STATEMENT**

This research was carried out in accordance with, and approval of the University of Dundee Ethics Committee.

#### **ACKNOWLEDGMENTS**

The authors would like to thank Tayside Police (now Police Scotland) and their CCTV Control Room staff for their collaboration and participation in this research. Thanks also to Sharon Scrafton for comments on the manuscript.

*the 28th International Conference on Human Factors in Computing Systems*, (Atlanta, GA), 65–74.

*Psychol*. 16, 307–322. doi: 10.1348/135532510X512665

*Tracking Research and Applications,* (Santa Barbara, CA: ACM), 107–114.

**Conflict of Interest Statement:** The 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.

*Received: 27 May 2013; accepted: 07 September 2013; published online: 30 September 2013.*

*Citation: Stainer MJ, Scott-Brown KC and Tatler BW (2013) Lookingfortrouble: a description of oculomotor search strategies during live CCTV operation. Front. Hum. Neurosci. 7:615. doi: 10.3389/ fnhum.2013.00615*

*This article was submitted to the journal Frontiers in Human Neuroscience.*

*Copyright © 2013 Stainer, Scott-Brown and Tatler. This is an open-access article distributed undertheterms oftheCreative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor 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.*

### Cues derived from facial appearance in security-related contexts: a biological and socio-cognitive framework

#### *Christopher D. Watkins\**

*Division of Psychology, School of Social and Health Sciences, University of Abertay, Dundee, UK \*Correspondence<EMAIL_ADDRESS>
*Edited by:*

*Elena Rusconi, University College London, UK*

#### *Reviewed by:*

*S. Craig Roberts, University of Stirling, UK*

Failures in the security process can have profound costs for both the individual and organizations (e.g., fraud costs the British economy approximately £72 billion; NFA, 2012). A biological and socio-cognitive framework may enhance our understanding of the security process, as the two perspectives collectively acknowledge that (i) competition for resources is/was an important factor in human social behavior and evolution (e.g., Bowles, 2009) and (ii) individuals differ in the ways in which they interpret information given their own traits and circumstances. Both levels of explanation (Mayr, 1963; Tinbergen, 1963) could generate novel hypotheses. For example, proximate-level explanations may clarify *how* resources are defended and extorted, and the cognitive processes underlying the "chess game" between gatekeepers and "gate crashers." Ultimate-level explanations may clarify *why* some individuals are more likely than others to succeed at securing or gaining access to resources and whether certain securityrelated outcomes can be reliably predicted given specific contexts or ecological conditions.

#### **DECISION-MAKING UNDER UNCERTAINTY**

Humans make many decisions (consciously or otherwise) based on uncertain outcomes. Error management theory proposes that cognition has evolved so that when faced with two alternate strategies, we pick the strategy that would result in the least-costly errors (Haselton and Buss, 2000; Haselton and Nettle, 2006). This "cost-benefit" approach to decisionmaking is of value in describing the nature of human conflict. For example, sex differences in aggression are said to reflect the greater net "pay-off " to the reproductive fitness of males who engage in potentially risky competition for resources (see Archer, 2009 for discussion). Local differences in income inequality are also an important predictor of violent male– male competition (Daly et al., 2001), and may "pay-off " if harsh environments promote risky behavior in light of future economic uncertainty (e.g., Wilson and Daly, 2006). Research on environmental differences in behavior could provide an evidence-base for effective investment in crime-prevention (e.g., examining the local distribution of CCTV cameras), given that current strategies may be suboptimal (see, e.g., Webster, 2009).

Psychological mechanisms also play an important role in conflict. Overconfidence, the illusion of thinking you are better than you are, is an important cause of warfare (Johnson et al., 2006) and is more likely to evolve in contexts where the perceived benefits of competition outweigh their perceived costs (Johnson and Fowler, 2011). Indeed, this is neatly illustrated by George Bush's "mission accomplished" speech aboard the USS Abraham Lincoln in May 2003. Thus, given knowledge of context, ultimate levels of explanation can aid our understanding of the maladaptive practice of warfare.

Error management theories suggest that we will tolerate "false alarms" in circumstances where they are much less costly than having no alarm in place when really needed. Given the costs of security failure, to what extent will a gatekeeper tolerate false alarms (e.g., risk making a false conviction) given their own personality or immediate environment? These issues are clearly very current, as commentators debate the "trade off " between security and civil liberties. Indeed, the extent to which human error accounts for the false conviction of suspects (e.g., the innocence project; see Jenkins and Burton, 2011 for related discussion) is a neat illustration that demonstrating scientific evidence for a given behavior (e.g., cognitive errors/biases) is not the same as morally-endorsing that behavior (see, Greene, 2003).

Contextual cues may alter the nature of the trade-off between the perceived costs and benefits of identifying, controlling and monitoring perceived threats to the security of one's resources. Across species, evidence for the "winner effect" suggests that future decisions to engage in competition are modulated by recent experience such that winners more likely to escalate a future confrontation (even with a rival of higher rank than themselves) and losers are more likely to withdraw from future confrontation [reviewed in Hsu et al. (2006)]. Recent work suggests that confrontation outcomes modulate competition-related perceptions in men in a similar way as it appears to do in other species. Men who are primed to imagine having lost a confrontation find facial cues of dominance in other men to be more salient than men who are primed to imagine having won a confrontation (Watkins and Jones, 2012). These effects may be adaptive if they function as a compensatory response to the increased vulnerability of loss of resources in light of recent experience, and are consistent with other work which demonstrates how a lack of power can predict general inhibition in behavior and greater orientation toward threat [reviewed in Keltner et al. (2003)]. Contextual factors relevant to competition may predict systematic variation in judgments toward other cues of threat, such as facial expression or movement. Differential treatment toward others based on their appearance suggests an underlying biological basis to social interactions that might be important for effective competition.

#### **A BIOLOGICAL BASIS TO SOCIAL JUDGMENTS**

Information provided by the face plays an important role in social interaction (Bruce and Young, 1986), and the categorization (e.g., Hugenberg and Bodenhausen, 2003; Mason et al., 2006) and identification (e.g., Hancock et al., 2000) of other people. We appear to be very quick to make our mind up about the character of an individual based on his or her facial appearance; trait judgments of faces made after just 100 ms of exposure are highly correlated with judgments made at longer exposure intervals (Willis and Todorov, 2006). A principal components analysis of trait judgments made toward faces revealed that differences in human face shape can be modeled on two primary dimensions, reflecting the extent to which an individual appears *intent* on causing harm to others (their perceived trustworthiness) and the extent to which an individual appears *capable* of causing harm to others [their perceived dominance; (Oosterhof and Todorov, 2008)]. Rapid judgments of traits that are important for personal safety are functionally adaptive if the costs of erring on the side of optimism are much greater than the costs of erring on the side of caution—the speed of social judgments at zero acquaintance may be more important than their accuracy [reviewed in Todorov et al. (2008)]. For example, given the potential costs of competition (Manson and Wrangham, 1991; Bowles, 2009), a rapid attribution of "threat" that turns out to be inaccurate is much less costly than an attribution of "no threat" that turns out to be inaccurate.

Perceptions of dominance and trust appear to have an underlying biological basis and are of obvious relevance to security scientists. Although the relationship between hormones and facial appearance is complex (see Pound et al., 2009), sex differences in the human face are thought to depend on exposure to gonadal steroids (see Puts et al., 2012 for discussion). Masculine physical characteristics in men are positively correlated with their perceived dominance (e.g., Perrett et al., 1998; Puts et al., 2006; Jones et al., 2010) and untrustworthiness (e.g. Perrett et al., 1998; Boothroyd et al., 2007). These attributions toward physically dominant individuals may have a "kernel of truth." For example, physically dominant men are more likely to endorse the use of physical force to resolve conflict (Sell et al., 2009), are more aggressive in certain contexts (Carré and McCormick, 2008; Carré et al., 2009) and are less likely to share resources fairly with others (Stirrat and Perrett, 2010; Price et al., 2011) than their less dominant peers. From a biological perspective, physically dominant individuals should express less concern for the welfare of others than their less dominant peers, given that dominant individuals are better-placed to exploit or forcefully acquire resources with impunity (Sell et al., 2009; Puts, 2010; Stirrat and Perrett, 2010). Indeed, the costs of conflict are rarely symmetric between two parties (Maynard Smith and Price, 1973), and recent work suggests that facial cues of dominance in potential rivals are more salient to those who are less well-equipped to "offset" these costs (Watkins et al., 2010a,b). Systematic variation in dominance perceptions may be adaptive if it functions to minimize the costs of conflict in light of the perceiver's own dominance (Watkins et al., 2010a,b; Watkins and Jones, 2012). Exploring the extent to which the gatekeeper's own dominance predicts security-related outcomes may be a practical application for this line of reasoning.

Other aspects of facial appearance may predict trusting behavior in the exchange of resources. While attractive individuals are more likely to be trusted in economic exchanges (Solnick and Schweitzer, 1999; Hancock and DeBruine, 2003; Wilson and Eckel, 2006; Andreoni and Petrie, 2008), particularly attractive individuals are more likely than their less attractive peers to "shift" toward more trusting behavior when they believe that others' have the opportunity to take their appearance into account (Smith et al., 2009). Given that attractiveness is associated with a suite of positive attributions (Langlois et al., 2000) and that a positive reputation can benefit one's reproductive fitness (Fehr, 2004; Nowak and Sigmund, 2005), strategic economic behavior in light of a beautiful appearance is to be expected, particularly given the severe penalties incurred when individuals are perceived as having used their looks for nefarious purposes (e.g., in cases of fraud; see Mazzella and Feingold, 1994 for a metaanalytic review; see also Wilson and Eckel, 2006).

If visible cues play an important role in trusting behavior and the exchange of resources, the context in which we interact with others may be important for security-related outcomes. For example, while direct face-to-face combat could be described as the "traditional method" of resource competition, online theft presents an evolutionary-novel challenge that strategists might only just be coming to terms with (see Anderson et al., 2012 for discussion). Given the potential for anonymity in the extortion of resources online, individuals may be better-placed to exploit others with impunity in these contexts. Thus, overconfidence may be expected to "evolve" among hackers, and this may be particularly pronounced among those who are less physicallyequipped to inflict *immediate* costs on others during face-to-face competition. Future research could explore the relationship between personality and hacking behavior using a behavioral measure of "persistence" in "code-cracking" tasks.

#### **PRACTICAL APPLICATIONS**

Understanding how individual and environmental differences predict security outcomes could generate practical solutions to problems. The extent to which personality and appearance influence social judgments and behavior at key "barriers" to entry may enhance the overall quality of professional recruitment and training. For example, work has already demonstrated that self-rated attention to detail is predictive of security screening performance (Rusconi et al., 2012). In a highrisk, high-reliability industry, stress within the immediate environment may affect the performance of some more than others, even at basic levels of cognition. For example, while experimentally-activating feelings of power has a positive effect on performance in executive-function tasks (Smith et al., 2008) it may also promote abstract thinking at a potential cost of false recognition—broadly speaking, focussing on the bigger picture at the expense of the finer details (Smith and Trope, 2006). The possibility that both transient (following a security breach) and stable (promotion) changes in perceived power within a security role may alter task performance is worthy of further research.

Competition for resources could be investigated at the neural level by exploring the neural basis of individual differences in morality and risk-taking in contexts related to resource acquisition and defence. Testosterone is associated with both financial risk-taking (Apicella et al., 2008; Coates and Herbert, 2008; Stanton et al., 2011) and strict endorsement of utilitarian morals (Carney and Mason, 2010), and increases as feelings of power are primed experimentally (Carney et al., 2010). Individual differences in state and trait levels of testosterone may predict the nature of the "trade-off " between the costs and benefits of monitoring and controlling perceived threats to security. Imaging studies could shed light on this, given that recent work suggests a complementary role for dopamine and noradrenalin in the evaluation of benefit and cost respectively (Bouret et al., 2012).

#### **CONCLUSION**

Biology provides a unifying framework with which to understand human behavior in light of differences between individuals and their surrounding environment. An understanding of the biological basis of strategic "biases" in social judgments can potentially increase the quality of security decision-making in light of greater awareness of the contexts and environments that might mitigate or exacerbate the risk of lost resources.

#### **ACKNOWLEDGMENTS**

I would like to thank the topic Editors for encouraging me to submit to this special issue. Thanks also to Finlay Smith for helpful comments on an earlier draft.

#### **REFERENCES**

Stirrat, M., and Perrett, D. I. (2010). Valid facial cues to cooperation and trust: male facial width and trustworthiness. *Psychol. Sci.* 21, 349–354.

Todorov, A., Said, C. P., Engell, A. D., and Oosterhof, N. N. (2008). Understanding evaluation of faces on social dimensions. *Trends Cogn. Sci.* 12, 455–460.

*Received: 08 April 2013; accepted: 01 May 2013; published online: 16 May 2013.*

*Citation: Watkins CD (2013) Cues derived from facial appearance in security-related contexts: a biological and socio-cognitive framework. Front. Hum. Neurosci. 7:204. doi: 10.3389/fnhum.2013.00204*

*Copyright © 2013 Watkins. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.*

## The reasoning criminal vs. Homer Simpson: conceptual challenges for crime science

#### *Noémie Bouhana\**

*Department of Security and Crime Science, University College London, London, UK*

#### *Edited by:*

*Elena Rusconi, University College London, UK*

*Reviewed by: Elliot Berkman, University of Oregon, USA*

*Nicola Lettieri, Isfol, Italy*

#### *\*Correspondence:*

*Noémie Bouhana, Department of Security and Crime Science, University College London, 35 Tavistock Square, WC1H 9EZ, London, UK e-mail<EMAIL_ADDRESS>
A recent disciplinary offshoot of criminology, crime science (CS) defines itself as "the application of science to the control of crime." One of its stated ambitions is to act as a cross-disciplinary linchpin in the domain of crime reduction. Despite many practical successes, notably in the area of situational crime prevention (SCP), CS has yet to achieve a commensurate level of academic visibility. The case is made that the growth of CS is stifled by its reliance on a model of decision-making, the Rational Choice Perspective (RCP), which is inimical to the integration of knowledge and insights from the behavioral, cognitive and neurosciences (CBNs). Examples of salient developments in the CBNs are provided, as regards notably multiple-system perspectives of decision-making and approaches to person-environment interaction. Short and long-term benefits of integration for CS are briefly outlined.

**Keywords: crime science, situational crime prevention, rational choice, decision-making, theory**

#### **A VERY SHORT INTRODUCTION TO CRIME SCIENCE**

A recent disciplinary offshoot of criminology, crime science (CS) defines itself as "the application of science to the control of crime" (Laycock et al., 2005; Laycock, 2008:149). Problem-driven, CS is chiefly concerned with the design of social and technological systems in service to the needs of stakeholders and end-users be they industry, government, security agencies, or the general public. Underpinning CS and its preferred approach to crime reduction, situational crime prevention (SCP), is the premise that crime is best tackled by targeting its immediate causes. This focus on proximate factors is intentionally lopsided.While the necessary conditions of crime are defined as the intersection in time and space of a motivated offender and a suitable target in the absence of a capable guardian, relatively little attention has been paid to the "offender" part of the equation. CS digs its philosophical roots in the 18th Century Classical School, whereby Man is understood as an essentially self-interested animal driven by desires which he seeks to fulfill while incurring the least amount of effort. Susceptibility to temptation is thus taken as a given and CS looks to situational control—the removal of temptations—as the most promising crime reduction strategy. "Opportunity makes the thief ": remove the opportunity, increase the effort and reduce the rewards of offending, and the crime will be prevented (Clarke, 2012).

The effectiveness of this approach has been demonstrated against a diverse range of crime problems. The promise of technological solutions and an emphasis on practical problem-solving have been popular with law enforcement agencies, and the claim was made that CS would soon eclipse criminology departments within universities (Clarke, 2004). However, CS has yet to achieve commensurate visibility in the academic sphere. This paper contends that the conceptual limitations of CS's standard model of decision-making, the Rational Choice Perspective (RCP), as well as the discipline's largely "bottom-up" research programme, hold it back from fulfilling its stated ambition to act as a cross-disciplinary linchpin (Laycock et al., 2005). The case is made that CS must look to developments in the cognitive, behavioral and neurosciences (henceforth, Cognitive and Neurosciences (CBNs)) to address RCP's shortcomings. Examples of developments which suggest potential for integration are provided. In conclusion, the benefits of integration are further outlined.

#### **THE CASE FOR "BOUNDED" PARSIMONY**

It is not possible to leave the offender out of crime prevention altogether. In order to "increase effort" and "reduce rewards", a model of criminal decision-making is needed. For this purpose, the fathers of SCP adopted the RCP (Clarke et al., 1985; Cornish et al., 2008). As presented, RCP is not a theory *per se*, but a heuristic device, a "good enough" conceptual model which provides a schematic understanding of how offenders make decisions—evaluating, to the best of their abilities, the costs and benefits of their actions. Armed with this basic understanding, the crime controller can design an array of situational techniques to influence the offender's decisional process away from crime (Smith and Clarke, 2012).

While RCP has met with notable success as an engineering heuristic, it has fallen short as a model of offender decisionmaking (Wortley et al., 2013). Although the framework acknowledges, on the one hand, the less-than-rational aspects of offender decision-making—criminal rationality is described as "bounded"—it implies, on the other, that the problem isn't worth agonizing over: a parsimonious, *as-if* model, unencumbered by the vagaries of human affect and cognition, should serve the crime controllers well enough (Smith and Clarke, 2012). As Wortley et al. (2013) observes, this state of affairs has had the consequence of stifling theoretical development in CS, so much so that RCP has remained essentially static since the 1980s. One may take Wortley's critique further and observe that other theoretical perspectives within the "family" of opportunity theories—notably, the Routine Activity Approach (Cohen and Felson, 1979)—have likewise remained relatively untouched. Opportunity theories are still, to a large extent, axiomatic statements rather than explanations of the causal processes which bring crime about (Wikström et al., 2011). This is illustrated by the oft-repeated claim that opportunities cause crime (Felson and Clarke, 1998); for it is not, of course, the opportunity which causes the crime, but its perception by the offender (the Thomas Theorem in action), among other processes: opportunities, whether provocations or temptations, are not criminal in themselves. To address this problem, some have proposed that the ecological concept of *affordance* (Gibson, 1979) should replace opportunity in CS parlance (Pease et al., 2006). However, affordance has yet to be integrated into the wider opportunity control framework. To take affordance on board, a model of criminal action is required which explains motivation in terms of the interaction between individual and situation, instead of postulating it as a given.

The move towards a more dynamic, interactionist model has been resisted, for fear that it would compromise RCP's radical parsimony, a condition of its heuristic usefulness. Faced with evidence of the non-rational features of offender decision-making, the strategy has been to stretch the concept of "rationality" to encompass the new phenomena. Drives to criminal action are restated as factors in a cost-benefit analysis. Psychological rewards (e.g., excitement), moral emotions (e.g., guilt, shame), social inducements (e.g., status), psychobiological factors (e.g., addiction), and so on, are reinterpreted in "rational" terms (e.g., Clarke et al., 1997). This approach renders the model impregnable, but runs roughshod over Einstein's admonition that theory should "make the irreducible basic elements as simple and as few as possible *without having to surrender the adequate representation of a single datum of experience"* (Einstein, 1934:165, emphasis added). The construct which explains everything explains nothing: the more phenomena is stuffed into the construct, the emptier it becomes. "Bounded" rather than radical parsimony would seem the more reasonable option.

#### **DRAWBACKS OF "BOTTOM-UP" RESEARCH**

Calls to overhaul RCP and bring the offender back into SCP have been sounded in the past (Ekblom and Tilley, 2000; Wortley, 2001; Wortley et al., 2013), but have fallen on reluctant ears. New SCP techniques concerned with situational precipitators have been added to the catalogue (Cornish et al., 2003), falling far short of a conceptual shake-up. CS's continuing identity struggle may explain this inertia: "science" moniker aside, CS is fundamentally an engineering discipline, with a self-confessed preference for short-term problem-solving (Laycock et al., 2005). At the outset, SCP was established as the technological framework most likely to deliver returns. A number of technological rules and design principles, most of them implicated in opportunity control, were identified, which produced reliable results. The discipline's scientific programme was thus largely circumscribed to those research activities which provided a knowledge-base for the design of opportunity control technologies (broadly defined), or contributed to the testing, validation and refinement of those technological rules and design principles at the heart of the discipline.

Arguably, the crime scientist's trademark question is, "So what?" (Laycock, 2012). If the topic is not self-evidently useful to crime control, it is not worth investigating. On the upside, this instrumental approach, whereby CS's engineering ambitions dictate the discipline's research activity, has produced reliable analytical tools and prevention technologies, which have achieved concrete gains in terms of crime reduction. On the downside, this relatively narrow research agenda has done little to encourage inquiry driven by "big questions". Indeed, crime scientists have been known to take criminologists to task for studying the "wrong" kinds of causes and failing to be more problem-oriented (Clarke, 2004), as if only a finite number of scientific questions about crime were worth asking.

The concern is that this "bottom-up" research agenda has insularised CS from a wealth of knowledge in other disciplines, notably the CBNs, as much as it has impeded theoretical growth from within. Yet a field which looks to medicine as a desirable model of cross-disciplinarity (Laycock et al., 2005) needs a conceptual framework which *affords* (in Gibson's sense of the word) disciplinary integration. Medicine and its parent disciplines share the foundations of a systemic (chemical, biological, psychosocial, ecological, and so on) understanding of the human organism and its environment. To achieve its stated goal, CS needs, if not a unified framework, then conceptual models which are not inimical to neighboring research programmes. As a first step, opportunity perspectives should clarify what they mean by "bounded rationality" and formulate explicit mechanisms of person-situation interaction (which will also necessitate a clear definition of "situation"; Snyder, 2013). Examples of developments in the CBNs may illustrate the value of integration.

#### **ENTERS HOMER SIMPSON, STAGE RIGHT**

The outsider looks on with envy at the effervescence which has characterized the growth and, increasingly, the integration of the CBNs in recent years. Given the breakneck speed of research in these domains, an overview isn't attempted, but it is noteworthy that the surge of activity has often been accompanied, if not triggered, by an empirical challenge to single-factor (notably rationalist) models and theories.

 In moral psychology, dual models of moral judgment have likewise emerged which call into question the Kholbergian view of moral development, adopting instead an adaptationist perspective in which moral intuitions underpin moral judgment as much as moral reasoning, if not more so (Haidt, 2001; Greene and Gazzaniga, 2009; Cushman et al., 2010).

Of particular interest, given SCP's original borrowing of the rational perspective from economics, has been the development of behavioral economics, which built upon social psychology's insights to address commonly observed violations of the standard neo-classic model (Thaler, 1991; Mullainathan et al., 2001). As Camerer et al. (2004) put it, "At the core of behavioral economics is the conviction that increasing the realism of the psychological underpinnings of economic analysis will improve economics on its own terms—generating theoretical insights, making better predictions of field phenomena, and suggesting better policy." The scientific gain, behavioral economists feel, is worth renouncing the seductive (i.e., simple and clear-cut), but ultimately misleading, solutions proposed by standard models. While neo-classical economics would like people to think like Mr. Spock, the average human being is rather closer to Homer Simpson (Thaler and Sunstein, 2008). Policies aimed at improving anything from individual health to personal finances, road safety, energy savings, and so on, are better designed while keeping Springfield's most famous resident in mind. Boosted by these developments in behavioral economics, neuroeconomics has set out to open the "black box" of the economic brain (Camerer et al., 2005), progressively adding detail to an "emorational" organ (Oullier et al., 2010) constituted of neural systems so enmeshed it makes little sense to study decision-making without reference to emotional states (Sanfey et al., 2006), or—another fundamental revision to the standard models—without reference to the socio-physical environment.

#### **THE FUTURE'S BRIGHT, THE FUTURE'S INTERACTIVE**

The emphasis on system interaction within the organism has been accompanied by growing attention to organismenvironment interaction. Given the importance of self-control to the explanation of criminal behavior (Tooby and Cosmides, 2007), research on self-regulation is particularly instructive, revealing self-control to be less of a fixed "trait" than a complex situational mechanism. How much of this resource individuals may draw on in any given circumstance is influenced by situational features, as well as individual factors. Self-control can be depleted by the prior exercise of self-control (Baumeister et al., 2007) and by the exercise of choice between alternatives (Vohs et al., 2008), with implications for the subsequent ability to self-monitor, cope with stress, control aggression, think logically, and so on. It can be depleted vicariously by watching others exercise restraint (Ackerman et al., 2009), but can also be restored vicariously by taking on the perspective of others engaged in self-control replenishing activities (Egan et al., 2012). Relevantly, self-regulatory depletion is associated with unethical behavior in well-intentioned individuals, though much less so in individuals with highly internalized moral standards, plausibly because they do not need to engage in higher cognitive processes, but automatically disregard the opportunity to behave unethically (Gino et al., 2011). This observation would seem to support situational action models of moral rule-breaking (Svensson et al., 2010).

More generally, self-regulation is sensitive to cognitive load. Decisions-making in environments which impose a high cognitive burden on individuals can lead to greater reliance on (more economical) automated decision-making, which in turn can lead to cognitive shortcuts, such as racial stereotyping (Burgess, 2010). Research into the causes of self-defeating decision-making among the poor suggests that the very conditions that define poverty, such as scarcity, impact decision-making through biosocial mechanisms which produce attentional shifts, self-control depletion, and reduce cognitive capacity generally (Spears, 2010; Shah et al., 2012; Mani et al., 2013). Self-regulation depletion also appears affected by self-belief, whereby individuals' implicit theories of willpower moderate self-control depletion (Job et al., 2010). Overall, modern research offers an increasingly sophisticated picture of self-control as a fluctuating resource subject to the interaction of an array of individual and socio-contextual factors (see Inzlicht and Schmeichel, 2012). It also suggests avenues to integrate mechanistically so-called "root causes" (e.g., poverty) and situational choice perspectives, traditionally at odds in the context of crime studies.

Interaction is, naturally, a chief concern of those disciplines working within an adaptationist framework. In the context of evolutionary psychology, "rationality" is not portrayed as a universal construct; rather, processes are understood as domain-specific and may produce "faulty" choices when considered from another behavioral domain's point-of-view. In this sense, rationality is not so much bounded as *ecological* (Tooby and Cosmides, 2007). This perspective suggests a framework for the continued development of still-rare ecological studies of criminal decision-making (Snook et al., 2011). It might be worthwhile in that context to explore how domain-specific processes relate (or not) to domain-general processes (Chiappe and MacDonald, 2005), as well as to niche construction (Laland and Brown, 2006).

Beyond functional explanations, evolutionary perspectives of human development have yielded constructs such as "differential susceptibility to the environment" and "biological sensitivity to context", which add to an understanding of the role of individual differences in the outcome of person-environment interactions (Ellis et al., 2011). They suggest that heightened vulnerability to context runs both ways—some individuals are more susceptible to *both* negative *and* positive influences—and raise intriguing questions as to the persistent effect, if any, of this susceptibility into adulthood. Even these exceedingly brief examples suggest significant potential to progress CS's take on person-situation interaction beyond its (relatively) primitive state.

#### **SO WHAT?**

The preceding should not be taken as an entreaty for crime scientists to give up their preferred methods and reach for the fMRI—though, as with previous successful imports from epidemiology (e.g., Bowers and Johnson, 2004), greater integration will likely result in substantial methodological gains. Nor is it a demand to adopt any given approach wholesale. Indeed, the most onerous part of the conceptual shift advocated here will be to keep up with fundamental debates internal to other disciplines (e.g., Bolhuis et al., 2011). It should, however, be taken as a plea for scientific realism, for the development of theories of human behavior which go beyond axiomatic, "as-if " theoretical frameworks to specify the constellation of biosocial mechanisms which account for the phenomenon (Bunge et al., 2006). As it stands, CS's standard model, RCP, isolates it from a wealth of knowledge in contemporary disciplines. This is a major obstacle to the development of a modern science of crime prevention.

This proposal for a more modern approach to conceptual development should not be interpreted, either, as a request to relinquish the problem-solving side of the business. Tackling practical problems generates hypotheses and throws up invaluable challenges to theoretical assumptions. Furthermore, embracing the CBN knowledge-base is bound to open up short-term avenues for crime prevention engineering. Research on the deleterious effects of cognitive load on healthcare decision-making already suggests that environmental changes, learned routines and "reflective practice" could improve the performance of crime controllers working in stressful settings (Burgess, 2010). Understanding the rewards associated with automated brain processes hints at strategies to tackle resistance to change in law enforcement organizations (Becker and Cropanzano, 2010). Experiments which elicit moral emotions such as disgust, combined with eye-tracking studies of anti-smoking warnings, could inform the design and evaluation of crime prevention publicity campaigns (see Oullier and Sauneron, 2010). Likewise, neuroimaging studies of the Ultimatum Game—which investigate why participants "irrationally" turn down money when faced with offers perceived as unfair might help crime controllers understand why "rational" crime prevention advice is sometimes spiritedly rejected by potential victims (such as advice which suggests women should alter their behavior to prevent sexual assault).

More ambitiously, the convergence of cognitive neuroscience, social psychology, architecture (e.g., Sternberg and Wilson,

#### **REFERENCES**

2006), consumer studies (e.g., Mick et al., 2004), and crime prevention might inspire interdisciplinary research into the design of "neurocognitively sustainable" environments, which would aim to minimize deleterious interaction (in terms of cognitive overload, depletion of self-control, and so on), with the prospect of benefit diffusion across multiple categories of social problems. The perspective of a wide-ranging contribution from evolutionary psychology has already captured the imagination of crime scientists (Roach and Pease, 2013), though reminders that adaption is an onerous explanatory concept, and that accounts of ultimate (evolutionary) causes must be accompanied by an understanding of proximal (e.g., neuropsychological) mechanisms, should be heeded (de Waal, 2002). In criminology, embryonic comparative research into the executive functioning of white collar criminals (Raine et al., 2012) hints at the possibility of tailoring prevention technologies by offending type. Executive functioning—self-regulation, but also the functions which underpin cognitive adaptability and flexibility is likely to be a fruitful area of research for CS should it seek to account more deeply for the failure of many criminals to displace. When explaining human behavior, evaluating causal factors in isolation makes poor sense. A science of crime prevention should become comfortable with multilevel theorizing.

This paper proceeded from a simple premise: that a scientific discipline which aims to capture the imagination of future generations of researchers cannot exist only to solve practical problems; it must also set out to answer fundamental questions. While technology must be simple enough for end-users to implement, the science which is the bedrock of these technologies should be as complex as it needs to be. "Good enough" theory surrenders too much of experience to be worth the short-term benefits to any scientific discipline.

and M. Rabin (New York: Princeton University Press), 3–51.

in *Crime and Justice: An Annual Review of Research Vol. 6*, eds M. Tonry and N. Morris (Chicago: University of Chicago Press), 147–185.

delay of gratification," in *Handbook of Self-Regulation: Research, Theory, and Applications*, eds R. F. Baumeister and K. D. Vohs (New York: Guilford), 99–129.

316–326. doi: 10.1007/s10979-010- 9238-0

Wortley, R. (2013). "Rational choice and offender decision making: lessons from the cognitive sciences," in *Cognition and Crime: Offender Decision Making and Script Analysis*, eds B. Leclerc and R. Wortley (London: Routledge), 237–251.

**Conflict of Interest Statement**: The 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.

*Received: 18 August 2013; accepted: 28 September 2013; published online: 23 October 2013.*

*Citation: Bouhana N (2013) The reasoning criminal vs. Homer Simpson:* *conceptual challenges for crime science. Front. Hum. Neurosci. 7:682. doi: 10.3389/fnhum.2013.00682*

*This article was submitted to the journal Frontiers in Human Neuroscience.*

*Copyright © 2013 Bouhana. 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) or licensor 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.*

# A biological security motivation system for potential threats: are there implications for policy-making?

#### *Erik Z. Woody1 \* and Henry Szechtman2*

*1 Department of Psychology, University of Waterloo, Waterloo, ON, Canada*

*2 Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada*

#### *Edited by:*

*Andrea Szymkowiak, University of Abertay Dundee, UK*

#### *Reviewed by:*

*Jacqueline M. Archibald, University of Abertay Dundee, UK Aaron Elkins, Imperial College London, UK*

#### *\*Correspondence:*

*Erik Z. Woody, Department of Psychology, University of Waterloo, 200 University Ave West, Waterloo, ON N2L 3G1, Canada e-mail<EMAIL_ADDRESS>
Research indicates that there is a specially adapted, hard-wired brain circuit, the security motivation system, which evolved to manage potential threats, such as the possibility of contamination or predation. The existence of this system may have important implications for policy-making related to security. The system is sensitive to partial, uncertain cues of potential danger, detection of which activates a persistent, potent motivational state of wariness or anxiety. This state motivates behaviors to probe the potential danger, such as checking, and to correct for it, such as washing. Engagement in these behaviors serves as the terminating feedback for the activation of the system. Because security motivation theory makes predictions about what kinds of stimuli activate security motivation and what conditions terminate it, the theory may have applications both in understanding how policy-makers can best influence others, such as the public, and also in understanding the behavior of policy-makers themselves.

**Keywords: potential danger, precautionary behavior, security motivation, risk, decision making, obsessive-compulsive disorder (OCD)**

#### **INTRODUCTION**

The world in which we currently live confronts people responsible for making decisions about security with very challenging issues. These issues call for sophisticated logical and statistical analysis, detection and forecasting systems, cost-benefit analysis, and the like. However, the crux of security is the necessity of dealing with the prospect of potential danger. Because potential dangers have had very substantial consequences for reproductive fitness for many thousands of years, evolution has shaped brain systems specially adapted for managing them. Thus, in addition to the logical armamentarium that present-day decision-makers bring to issues of security, they inevitably bring the intuitions and motivations that are generated by a biologically ancient, "hard-wired" system.

This potential-threat system in the brain has been termed the defense system (Trower et al., 1990) and the hazard-precaution system (Boyer and Lienard, 2006). In our own work, we have called it the security motivation system (Szechtman and Woody, 2004). Our research investigating this system has focused on its role in everyday circumstances, such as behavior to manage threats of contagion due to dirt and germs, and in pathological variants of these behaviors, such as the compulsive hand-washing seen in obsessive-compulsive disorder (OCD). However, it is likely that the influence of the security motivation system extends well beyond such relatively mundane circumstances. The purpose of this perspective article is to explain briefly what we know about the security motivation system and to advance the following question: Does this biological system affect policy-making about security in important ways? We hope to stimulate the thinking of researchers who investigate security-related decision-making, in particular by sketching some of the kinds of hypotheses that could be examined in such research.

#### **PROPERTIES OF THE SECURITY MOTIVATION SYSTEM**

The security motivation system is hypothesized to be a reasonably distinct module in the brain, which evolved to be specially adapted for handling potential threats (Tooby and Cosmides, 1990, 1992, 2006; Trower et al., 1990; Pinker, 1997). Such a module has several key characteristics. First, it is dedicated to the detection of particular types of stimuli as input, rapidly processing a special class of information of particular relevance for survival. Second, when activated, it functions as a motivational system, driving relevant responses (Kavaliers and Choleris, 2001). Third, its output consists of a characteristic set of species-typical behaviors, and engagement in these behaviors plays a crucial role in terminating the activation of the module.

#### **TYPE OF STIMULI THAT ACTIVATE THE SYSTEM**

Research on how animals manage the threat of predation illuminates the kinds of stimuli that activate the security motivation system. Animals use subtle, indirect cues of uncertain significance as indicators of potential danger (Blanchard and Blanchard, 1988; Lima and Bednekoff, 1999). Evaluating these indirect cues of potential danger is quite different from recognizing imminent danger, such as the actual presence of a predator, and has been characterized in terms of "labile perturbation factors" (Wingfield et al., 1998) and "hidden-risk mechanisms" (Curio, 1993). In short, the security motivation system is tuned to partial, uncertain cues of potential threat, rather than the recognition of imminent danger.

#### **NATURE OF ACTIVATION OF THE SYSTEM**

Studies of the threat of predation show that relatively weak cues readily activate vigilance and wariness (Brown et al., 1999). In addition, this activation ebbs only slowly (Wingfield et al., 1998), even if no further, confirming cues follow (Masterson and Crawford, 1982; Curio, 1993; Marks and Nesse, 1994). This protracted activation motivates security-related behaviors. In short, weak cues can readily activate the security motivation system, and once activated, it has a protracted half-life and drives behavior.

#### **OUTPUT BEHAVIORS AND TERMINATION OF ACTIVATION OF THE SYSTEM**

The resulting acts consist of precautionary behaviors, which include probing the environment, checking, and surveillance to gather further information about any potential risks (Blanchard and Blanchard, 1988; Curio, 1993). They also include corrective or prophylactic behaviors, such as washing, that would lessen the effects of the danger if it were to eventuate. Of particular importance, we have characterized security-related behavior as "open-ended," meaning that the environment does not normally provide a clear terminating stimulus to signal goal attainment (Szechtman and Woody, 2004). For example, if checking does not reveal the presence of a predator, this is not a clear indication of reduced risk (Curio, 1993); that is, the success of precautionary behavior is a non-event. Consequently, we proposed that it is the engagement in security-related behavior in itself that terminates security motivation. In short, activation of the security motivation system elicits precautionary behavior, and the system uses these actions themselves as the terminator of the motivation.

#### **NEURAL AND PHYSIOLOGICAL BASIS AND EMPIRICAL EVIDENCE FOR THE SECURITY MOTIVATION SYSTEM**

We have proposed a fairly detailed neuroanatomical-circuit model for the security motivation system, which is based on functional loops consisting of cascades of cortico-striato-pallidothalamo-cortical connections (Alexander et al., 1986; Brown and Pluck, 2000), with feedback connections from the brainstem to terminate activity in these loops (Szechtman and Woody, 2004; Woody and Szechtman, 2011). We have also described the proposed physiological mechanisms of the security motivation system, which involve regulation of the parasympathetic nervous system and activation of the hypothalamic-pituitaryadrenocortical (HPA) axis (Woody and Szechtman, 2011).

We have demonstrated that activation and subsequent deactivation of the security motivation system can be tracked both with subjective ratings (e.g., anxiety and urge to engage in precautionary behavior) and also physiological changes, especially respiratory sinus arrhythmia (RSA; Porges, 2001, 2007a,b), based on heart-rate variability (Hinds et al., 2010). Using these measures, we have conducted a series of experiments that support the hypotheses that the security motivation system has the aforementioned characteristic properties. First, we have shown that the system is responsive to relatively weak, uncertain cues for potential danger (Hinds et al., 2010, Experiment 1). Second, we have shown that activation of the system, in the absence of subsequent precautionary behavior, is persistent over time, dissipating only very slowly (Hinds et al., 2010, Experiment 2). Third, we have shown that corrective behavior, such as hand washing in response to uncertain cues for contamination, deactivates the system (Hinds et al., 2010, Experiment 1). In contrast to the deactivating effect of corrective behavior, the security motivation system, once it has been activated by uncertain cues, is relatively unresponsive to clear cognitive information that disconfirms the potential threat (Hinds et al., 2010, Experiment 3). This finding supports the hypothesis that the system is action-oriented, and engagement in some kind of precautionary behavior plays a crucial role in turning it off.

In a somewhat parallel series of experiments, we have tested our hypothesis that OCD represents a dysfunction of the security motivation system (Szechtman and Woody, 2004; Woody and Szechtman, 2005). It is well known that the content of OCD revolves around issues of potential danger, such as the threat of contamination or physical harm to oneself or close others (e.g., Reed, 1985; Wise and Rapoport, 1989). We hypothesized that in OCD patients, security motivation is activated in a manner that is reasonably similar to how it is activated in non-patients; however, in OCD patients, subsequent precautionary behaviors fail to turn this activation off in the usual fashion. Thus, once activated, OCD patients remain preoccupied with issues of potential danger for a protracted period of time and repeat the precautionary behaviors over and over again, in an attempt to deactivate the concerns. Our experimental data support this hypothesis that OCD is a stopping, rather than a starting, problem (Hinds et al., 2012). In particular, exposure to uncertain cues for contamination activates the security motivation system similarly in OCD patients and control non-patients, as indexed by both subjective measures and RSA. However, a subsequent fixed period of hand-washing, which returns the non-patients to baseline, has no significant effect on the activation levels of the OCD patients.

#### **IMPLICATIONS OF THE SECURITY MOTIVATION SYSTEM**

The security motivation system would be expected to have some important characteristics that are common to evolved, specialpurpose modules. One important characteristic of such modules is that they tend to be encapsulated, operating relatively automatically and autonomously, and their internal computations are not accessible to introspection (Fodor, 1983). That is, they operate largely in the background, apart from the realm of volitionally directed formal logic, and their outputs become evident to the individual intuitively as feelings.

This distinction between a feeling-based system and rational analysis may not always be readily evident in everyday circumstances, because normally the two kinds of output are reasonably well aligned. However, the distinction becomes extremely striking in OCD. OCD patients feel driven to continue their obsessive concerns about potential danger and to repeat precautionary behaviors, such as checking or washing, even though at a rational level they find these concerns and behaviors to be excessive, illogical, and even absurd (Hollander et al., 1996). Indeed, OCD demonstrates that an intuitive, feeling-based module like the security motivation system is very powerful and can override the rational control of behavior.

The relatively automatic, intuitive, feeling-based operation of the security motivation system corresponds with what Kahneman (2011) has termed System 1, in contrast to the formal logic of System 2. What is important to appreciate is that even though the intuitive feelings generated by the security motivation system are vivid, immediate, and phenomenologically compelling to the individual, they are not the same as objective reality, nor are they necessarily closely aligned to conclusions derivable from formal logic. They are, in essence, intuitions that worked well in our remote past but may have limited applicability to any specific, current set of circumstances.

#### **DOES THIS BIOLOGICAL SYSTEM INFLUENCE POLICY-MAKING ABOUT SECURITY IN IMPORTANT WAYS?**

The nature of the security motivation system may have important implications for policy makers wishing to involve others, such as the public, in the detection and appraisal of potential threat, as well as to shape their perceptions and get their support for policy initiatives. Even though the security motivation system is sensitive to the detection of slight, partial, uncertain cues, it evolved in such a way that it is tuned more to certain types of stimuli, but not others. It seems clear that the security motivation system is particularly sensitive to concrete and surprising or novel changes in the environment, and relatively insensitive to relatively abstract and gradual changes (which can become familiar and therefore lack novelty). Thus, for example, hearing some details of the latest terrorist attack, even if it occurred at a distant location, is likely to much more readily elicit activation of the security motivation system than is information about global warming, which is relatively abstract and involves very slow change. In addition, because activation of the security motivation system leads to probing for further information, there is a positive feedback cycle in which further concrete details are added, magnifying the initial difference.

Let's examine the case in which it seems relatively difficult influence others to take potential threats seriously, such as global warming. We would advance the hypothesis that for stimuli to be regarded as possible indicators of potential threat, they must elicit the feeling of a potential threat—that is, anxiety, and wariness, which is the indication that the security motivation system is activated. In other words, if the indicators of a putative potential threat fail to evoke the emotional resonance of potential threats, then the potential threat in question will not be perceived as credible. Because the cues for the potential threat of global warming are abstract, distant, and involve very gradual change, they do not resemble the types of cues the security motivation system is designed to respond to. We would suggest that this is why the issue strikes many people as "academic" or merely political—the relevant cues lack the feeling of potential threats, because they do not readily activate the security motivation system. One solution may be to use the arts to help supply the missing emotion. This is a possibility that is currently being explored in many ways by artists—film-makers, painters, writers, and so on—and directors of art museums; the idea, in the words of a director of New York's Museum of Modern Art, is to "touch and disturb" people and get them engaged (Economist, 2013, July 20–26).

The opposite type of case is one in which stimuli too readily activate the security motivation system, as with some terrorist incidents, in which the attention-grabbing qualities of some potential dangers may have little relation to and even interfere with objective analysis of their severity or likelihood. To inject these more abstract considerations into the operation of the security motivation system requires connecting System 2, which handles abstract ideas, to System 1, which is based on concrete stimuli. We would hypothesize that to be effective, information putting potential threats into a broader critical perspective needs to come early, prior to exposure to the potential-threat stimuli. According to our model of the functional components of the security motivation system (Szechtman and Woody, 2004), such information can come into play at the stage of appraisal of potential danger, which integrates internal factors, such as plans, with external factors, such as concrete stimuli. In contrast, our work suggests that once the security motivation system is activated, it is not affected much by further cognitive information, but instead becomes highly action-oriented, driving, for example, checking and corrective behaviors rather than reappraisal (Hinds et al., 2010).

Of course, the security motivation system theory may have implications for policy-makers themselves, rather than simply those they hope to influence. For everyone, this system is intuitive and feeling-based, operating at least somewhat independently of rational analysis. Because the emotions that the system generates evolved to address crucial survival issues, they are powerful and strongly motivating. Thus, it is natural for decision-makers engaged with an issue of potential danger to be guided by their "gut feelings," which are more vivid and pressing than the details of rational analysis. Unfortunately, feelings of potential threat (wariness and anxiety) are likely to map imperfectly onto the reality of potential threat. In a related vein, Schneier (2008) pointed out: "Security is both a feeling and a reality. And they are not the same." A rational analysis of potential danger would need to take account of probabilities and other statistical information, but the intuitive operations of the security motivation system do not work this way. According to Suskind (2006, p. 62), as Vice President, Dick Cheney took the position that potential threats should not be evaluated according to "our analysis, or finding a preponderance of evidence," but instead by a "one-percent doctrine": if there is any chance of the reality of the threat, "we have to treat it as a certainty in terms of our response." This position has a gut-intuitive appeal, in that fragmentary cues suggesting any potential of threat activate security motivation, which in turn naturally drives action. However, this is unlikely to be an adequate basis for making very difficult decisions about how to allocate resources to security-related behavior vs. other important goals.

We would also hypothesize that work circumstances that divide up the tasks involved in managing potential threats may tend to disrupt the stopping mechanism of the security motivation system—because, for example, the policy makers do not get to carry out any of the protective actions themselves. We would propose that this problem can lead some agencies working on security issues to function in a way that is analogous to our characterization of OCD—namely, too much can seem like too little (Hinds et al., 2012). Consider, for instance, that between 2001 and 2013, the Foreign Intelligence Surveillance Act (FISA) court of 14 judges in the USA approved 20,909 requests to monitor individuals or search properties, and turned down only 10. Recently, they apparently ruled that all American phone calls should be considered "relevant" to the investigation of terrorist threats (Economist, 2013, July 13–19). The reason why everything may come to seem relevant may be that the stopping function of the security motivation system is based not on cognitive closure, but instead on concrete action, and those setting policy may not be involved in protective and corrective action at all (e.g., searching and evaluating records).

There are also other implications of the idea that the precautionary behaviors are crucial for turning off security motivation. The security motivation system operates according to what Kahneman (2011) terms System 1 processes. Unfortunately, as Kahneman has very convincingly demonstrated, System 1 is prone to substituting something that has only the form or appearance of a solution in place of a real solution, especially if the better solution would be more difficult. Thus, although turning off the anxiety of security motivation requires action, the details of what is done may not matter as much to the system. Possibly for this reason, policy-making responses to potential threats often seem only to be reactive, rather than proactive. For example, to prevent another shoe-bombing attempt, it is decided that all passengers' shoes must be inspected. Such a prescribed set of actions may be effective in calming security motivation for both policy-makers and the public. However, such a solution seems to ignore the fact

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that biological agents (even germs) change strategies, so that what would have worked against them in the past may not do so in the future.

#### **CONCLUSION**

The foregoing hypotheses illustrate just a few of the ways in which the security motivation system theory could be used to generate interesting hypotheses for research on the psychology of security-related policy-making. Although these hypotheses need to be evaluated in future research, we hope they provide a convincing case that the security motivation system theory offers a novel, generative framework for advancing our understanding of policy-making processes related to security and potential danger.

#### **ACKNOWLEDGMENTS**

The authors' contributions were supported by grants from the Canadian Institutes of Health Research (MOP134450 and MOP-64424) and the Natural Sciences and Engineering Research Council of Canada (RGPIN A0544 and RGPGP 283352-04).

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**Conflict of Interest Statement:** The 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.

*Received: 24 April 2013; paper pending published: 27 June 2013; accepted: 22 August 2013; published online: 09 September 2013.*

*Citation: Woody EZ and Szechtman H (2013) A biological security motivation system for potential threats: are there implications for policy-making? Front. Hum. Neurosci. 7:556. doi: 10.3389/ fnhum.2013.00556*

*This article was submitted to the journal Frontiers in Human Neuroscience.*

*Copyright © 2013 Woody and Szechtman. 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) or licensor 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.*

### Sensing, assessing, and augmenting threat detection: behavioral, neuroimaging, and brain stimulation evidence for the critical role of attention

#### *Raja Parasuraman1 \* and Scott Galster 2*

*1 Center of Excellence in Neuroergonomics, Technology and Cognition, George Mason University, Fairfax, VA, USA 2 Applied Neuroscience Branch, Air Force Research Laboratory, Dayton, OH, USA*

#### *Edited by:*

*Elena Rusconi, University College London, UK*

*Reviewed by: Raymond Nickerson, Tufts University, USA Douglas D. Potter, University of Dundee, UK*

#### *\*Correspondence:*

*Raja Parasuraman, Center of Excellence in Neuroergonomics, Technology and Cognition, George Mason University, 4400 University Drive, MS 3F5, Fairfax, VA 22030-4444, USA e-mail<EMAIL_ADDRESS>
Rapidly identifying the potentially threatening movements of other people and objects—biological motion perception and action understanding—is critical to maintaining security in many civilian and military settings. A key approach to improving threat detection in these environments is to sense when less than ideal conditions exist for the human observer, assess that condition relative to an expected standard, and if necessary use tools to augment human performance. Action perception is typically viewed as a relatively "primitive," automatic function immune to top-down effects. However, recent research shows that attention is a top-down factor that has a critical influence on the identification of threat-related targets. In this paper we show that detection of motion-based threats is attention sensitive when surveillance images are obscured by other movements, when they are visually degraded, when other stimuli or tasks compete for attention, or when low-probability threats must be watched for over long periods of time—all features typical of operational security settings. Neuroimaging studies reveal that action understanding recruits a distributed network of brain regions, including the superior temporal cortex, intraparietal cortex, and inferior frontal cortex. Within this network, attention modulates activation of the superior temporal sulcus (STS) and middle temporal gyrus. The dorsal frontoparietal network may provide the source of attention-modulation signals to action representation areas. Stimulation of this attention network should therefore enhance threat detection. We show that transcranial Direct Current Stimulation (tDCS) at 2 mA accelerates perceptual learning of participants performing a challenging threat-detection task. Together, cognitive, neuroimaging, and brain stimulation studies provide converging evidence for the critical role of attention in the detection and understanding of threat-related intentional actions.

**Keywords: action understanding, attention, biological motion, brain stimulation, human performance augmentation, neuroimaging, security, threat detection**

#### **INTRODUCTION**

Rapidly detecting and identifying the movements and actions of other people—biological motion perception—is an important function of many civilian and military operational settings involving surveillance and other security related tasks. For example, cameras mounted in prisons (Tickner and Poulton, 1975) and other sensitive locations (Stedmon et al., 2011), or on unmanned air (Cummings et al., 2007) and ground vehicles (Chen and Barnes, 2012), are increasingly used to provide video or infrared images to remotely located operators. Surveillance images typically show people or vehicles in motion and engaged in various activities. Such information can be used to identify individuals who pose potential threats or to determine the potential for danger in gatherings of large groups of people. The images are examined for possible threats by skilled human observers (Blake and Shiffrar, 2007), by automated systems (Cohen et al., 2008), or by a combination of the two.

Biological motion perception has typically been investigated in psychophysical studies using simple point-light "stick-figure" movements of the type pioneered by Johansson (1973). More recent studies have examined more complex, naturalistic scenes of people moving or handling objects (Blake and Shiffrar, 2007; Ortigue et al., 2009; Parasuraman et al., 2009; Grafton and Tipper, 2012; Thompson and Parasuraman, 2012). Identifying the mechanisms and neural bases of action observation when people view naturalistic scenes can advance both the theory and practice in threat detection. More broadly, understanding the mechanisms of threat detection can contribute to scientific approaches to security based on human factors/ergonomics (Nickerson, 2010), neuroscience (National Research Council, 2008), and the intersection of these two fields, neuroergonomics (Parasuraman and Rizzo, 2008; Parasuraman, 2011; Parasuraman et al., 2012).

A key approach to improving threat detection is to *sense* when less than ideal conditions exist for the human operator in a particular security environment. The next step is to *assess* the threat detection performance of the human operator with respect to a standard baseline of required capability. Subsequently, and if necessary, methods can be implemented to *augment* the human operator in case the standard is not met. In this paper we first describe this Sense—Assess—Augment framework in the context of security research and practice in the military. We then examine the perceptual and cognitive mechanisms of biological motionbased threat detection, focusing on the critical role of attention. Neuroimaging studies that point to the influence of attention are also discussed. Finally, given that humans have limited attentional capabilities, we discuss how non-invasive brain stimulation can be used to enhance threat detection and mitigate operator performance decrements.

#### **SENSE-ASSESS-AUGMENT FRAMEWORK**

A number of agencies in the military are planning how best to match personnel and emerging advanced technologies that are being rapidly implemented for use in both the civilian and defense sectors. For example, in 2010, the Chief Scientist of the US Air Force released a report outlining the science and technology needs in the 2010–2030 time frame (Dahm, 2010). A key conclusion of that report was that natural human capacities are becoming increasingly mismatched to the enormous data volumes, processing capabilities, and decision speeds that computer technologies either offer or demand. Although humans today remain more capable than machines for many tasks, particularly higher-order decision making and planning, by 2030 machine capabilities may increase to the point that human capabilities will be significantly challenged in a wide array of systems and processes. It is also the case that human operators are being overloaded *today* by data that the new technologies are able to provide at ever increasing speed. Both of these trends mean that humans and machines will need to become far more closely coupled, through improved human-machine interfaces and by direct augmentation of human performance. Focused research efforts over the next decade will permit significant practical instantiations of augmented human performance. These may come from increased use of autonomous systems, from novel human-machine interfaces to couple humans more closely and more intuitively with automated systems, or from direct augmentation of humans themselves. In this paper we focus on the last of these possibilities.

There are two primary questions to ask when deciding how to provide human performance augmentation: *when* to provide the augmentation and *how* to provide it. The answers to these questions will likely determine if the right augmentation technique is being employed at the right time to produce the desired effects. The Sense-Assess-Augment taxonomy provides answers to these questions. **Figure 1** shows a representation of this taxonomy. The objective is to sense individual and team cognitive or functional state (using behavioral or neural measures, or both), assess the state relative to performance, and if necessary augment performance to optimize mission effectiveness. This taxonomy is being applied to improve human performance by leveraging the integration of several neurocognitive sensing technologies coupled with multiple assessment approaches to provide a robust understanding of the causes of operator performance decrements (Galster and Johnson, 2013). Given a better understanding of the causes for sub-optimal performance, targeted augmentation techniques can be employed to improve individual or team performance.

Threat detection has many features in common with other tasks the Air Force undertakes to defend capabilities in its air, space, and cyberspace operations. Many of the problems associated with information overload are exacerbated in threat detection tasks due to the exponential growth in the number of pictures or full motion videos that must be processed for actionable information. The use of the Sense-Assess-Augment taxonomy allows for the identification of specific bottlenecks that may occur during the information processing of the data that is required for accurate threat detection. It also allows for the augmentation of the operator based on the characteristics of the bottleneck, for example, whether due primarily to issues with divided or sustained attention, or a lack of the ability to discriminate threats regardless of the amount of training. The correct identification of the individual's source for sub-optimal performance will drive what augmentation method should be utilized to enhance and optimize human performance.

In this paper we describe the results of a number of studies of threat detection within the Sense-Assess-Augment framework. We begin with a discussion of behavioral studies that have investigated mechanisms of biological motion perception in relation to threat detection.

#### **BEHAVIORAL STUDIES**

#### **AUTOMATICITY OF BIOLOGICAL MOTION PERCEPTION**