Introduction

The detection of improvised explosive devices, IEDs, has become increasingly important for law enforcement (LE) and for civilian personnel. The application of relevant cognitive principles to training for IED detection is of similar importance and is gradually being developed. The SMOKE system of IED-detection training (Sharps et al. 2010, 2014) has been experimentally shown to be promising in this regard. The acronym SMOKE is based on the five identified types of errors observed in bomb detection, errors of search, movement, observation, failures to keep looking, and errors of evaluation (see Appendix 1 for descriptions of these error types). This acronym is cognitively useful, in that combining an alphabetic letter associated with each of the five identified error types forms a single rehearsal group (Mandler 2011), a basic unit on which working memory relies; this rehearsal group forms a good mnemonic on which memory of the system and its elements can be organized in the minds of trainees. SMOKE training is based on the installation of a prior framework for the understanding of IED-rich environments (Bransford and Johnson 1973) which is explicit in nature (Haviland and Clark 1974), and which is based initially in feature-intensive comprehension (Sharps and Nunes 2002; Sharps 2017). These three factors have been shown to be of critical importance in IED-detection training, and in other areas of law enforcement training (Sharps 2017). In the SMOKE system, these factors have been configured to defeat each of the error types described in Appendix 1.

The specific methods of SMOKE have been presented in extended detail elsewhere (Sharps 2017; Sharps et al. 2010, 2014). In summary, however, trainees are first provided with a prior-framework discussion of the importance of IEDs and of training to detect them. Second, they see a series of standardized Powerpoint photos, showing real-world scenes of different types of IEDs, presented at the perceptual periphery or nearer to the center of their field of view. Third, there is a review of a variety of types of IED, presented in a real-world context which provides field-valid levels of visual and cognitive competition between the IED and the surroundings. Fourth, trainees observe standardized scenes depicting different types of mock IEDs, placed in different positions. The scenes include an armed or unarmed “perpetrator” confronting a “victim.” This teaches trainees to search for IED’s of different types, both toward the center and periphery of scenes, even in the presence of distracting stimuli (the “assailant,” with or without a handgun, in relation to the “victim” and the mock-IED’s). In the final version of SMOKE (Sharps 2017; Sharps et al. 2014), there is also an exercise in which trainees model the behavior of an instructor who demonstrates three-dimensional search for mock IED’s in a moderately cluttered office setting, followed by a final, repetitive summary of the SMOKE errors and principles. Extended discussion of the specific theoretical points driving each of these evolutions is presented elsewhere (Sharps 2017).

In experimental trials, the SMOKE system was extremely effective, significantly enhancing the mock-IED-detection performance of randomly selected young adult respondents, typical in age and in other relevant characteristics of law enforcement trainees. In several experiments, SMOKE training literally doubled the speed of detection of poorly concealed mock-IEDs and doubled the probability of finding well-hidden ones (Sharps 2017; Sharps et al. 2010, 2014). The reason for this effectiveness is the explicit and deliberate construction of the system with regard to established principles of cognitive psychology, demonstrating the importance of explicitly psychological approaches to law enforcement training (see Sharps 2017).

Further research on both the basic and applied aspects of IED detection is of critical importance. One very important question is the degree to which cognitively based IED-detection training interacts with other aspects of law enforcement (LE). A crucial question lies in the area of tactical cognition: does IED-detection training, which requires trainees to divide their attention within a given search area, potentially interfere with the processes involved in shoot/no-shoot decisions, or does it provide salutary influences outside the immediate realm of IED detection? Interference would pose a major complication for IED-detection training for law enforcement personnel, who must master both sets of skills, both of which rely on vigilance, directed attention, and rapid cognitive processing of perceived stimuli, specifically the given IED and the given potentially-armed assailant. This was a primary question addressed in this research, which dealt specifically with SMOKE and shoot/no-shoot decisions.

There have been a number of approaches to the empirical evaluation of shoot/no-shoot decisions, ranging from the computer generation of images of armed and unarmed assailants to the use of videogame formats (e.g., Correll et al. 2007). In the present study, we used an established shoot/no-shoot paradigm (Sharps and Hess 2008; Herrera et al. 2015), in which respondents chose either to fire or not to fire, by means of a push-button apparatus, firing or failing to do so on a Powerpoint image of an armed assailant aiming a handgun at a “victim.” This paradigm has allowed us to assess shoot/no-shoot decisions (see Sharps and Hess 2008; Correll et al. 2007). We were therefore able to assess the effectiveness of shoot/no-shoot decisions, in the presence of a shooter threat (the only type of threat evaluated here), with reference to SMOKE training or its absence. As will be detailed below, we were also able to evaluate the degree to which SMOKE training influenced another critically important aspect of forensic cognition, the eyewitness identification of a given assailant.

A civilian population was chosen for this initial enquiry, to avoid potential confounding effects from other elements of LE training or experience in cadet or officer populations. Therefore, this study addressed only fundamental processual dynamics, restricted to the procedure and training types employed here, without reference to other factors derived from training or experience, or to the complex and important issues of race and cross-racial factors (see Correll et al. 2007) in shoot/no-shoot decisions or in eyewitness processes. Future research will of course be required to address these critical applied questions.

Method

Participants

Fifty-one respondents were recruited from the lower-division student population at a central California university, receiving course credit for their participation. Thirty-six were female (mean age = 19.79 years, SD = 2.01 years), and fifteen were male (mean age = 21.80 years, SD = 8.02 years). Gender ratio reflected the proportions of the classes. All respondents were demonstrated, by means of a modified Snellen vision test, to possess visual acuity in excess of 20/40, sufficient to distinguish the smallest relevant features of the scenes to be presented (see Sharps and Hess 2008). The university at which this study was conducted is located in a highly multicultural area and has a high attrition rate. The research population was therefore relatively representative of the current American population, with the obvious exceptions that this relatively young population tended to be possessed of relatively good health and strong visual acuity. It should also be noted that this population, in terms of age, is similar to the multicultural young-adult American population from which law enforcement cadets and trainees are typically drawn, and that the number of respondents in this research is similar to that of many law enforcement academy classes, which, however, show considerable variation in this regard (e.g., Moore 2006).

Materials and Procedure

Twenty-six respondents were provided with the latest version of SMOKE training, detailed in Sharps et al. (2014). Twenty-five control respondents received the standard control treatment for SMOKE studies, a brief discussion of the importance of IED training (Sharps et al. 2010, 2014; Sharps 2017).

Following this evolution, respondents participated in an established shoot/no-shoot procedure (Sharps and Hess 2008; Herrera et al. 2015; Sharps 2017). Respondents participated individually. They were instructed that they would see situations which might involve hazard to others, and that they could choose to shoot or not to shoot, depending on their judgment of the given hazard. They were further instructed that a decision to shoot involved the pressing of a hand-held button. This button was a switch interfaced to a Lafayette Instruments 4101A projection tachistoscope apparatus, which was modified, for this procedure, to time responses on an interfaced Lafayette clock/counter.

Respondents were presented with a high-quality digital image of a male “perpetrator,” taken with a tripod-mounted Sony Alpha digital camera. The perpetrator was depicted aiming a weapon, a Beretta M 92 semiautomatic handgun, at a male “victim,” in a suburban driveway setting used in previous research (e.g., Sharps and Hess 2008; Herrera et al. 2015). Safety protocols in these studies have been developed and tested repeatedly, with expert law enforcement advice and assistance; the authors would strongly discourage any attempt to replicate or extend these results, or these types of studies, without similarly stringent safety protocols, developed with expert law enforcement advice and supervision from highly experienced tactical officers and commanders, which was the case in these procedures. The physical safety of all participants in this type of research is obviously paramount.

Lighting in the image was strong overhead sunlight; no shadows obscured any features of the assailant, victim, or weapon. The exposure time was limited to a period of 10 s, a considerably longer period than the fraction of a single second typical of a shoot/no-shoot decision under field conditions (e.g., Grossman and Christensen 2004; Moore 2006; Montejano 2004); those who did not fire within this extended time frame were classified as having made “no-shoot” decisions, as would typically be the case in real-world tactical operations. The scene was presented on a white screen, located approximately 15 ft from the subject.

This design made it possible to compare shoot/no-shoot responses, and time to respond, between those respondents who had received SMOKE training and those who had not, each actively requiring a shooting response (see Sharps and Hess 2008; Sharps 2017). As stated above, respondents indicated their decision to “shoot,” if they did so, by pressing the hand-held button, which in turn stopped the timer.

As in all previous research in this series (summarized in Sharps 2017), a 10-min retention interval was imposed, during which respondents provided demographic information similar to that which would be requested by a police dispatcher on receiving a report of a violent crime. (The 10-minute interval was standardized for this research as it provides a reasonable average of police response; e.g., Moore 2006; Montejano 2004], yielding a reasonably typical period between the time of a crime and the first eyewitness statements to law enforcement).

At the conclusion of the retention interval, respondents were presented with a simultaneous “six pack” lineup, incorporating a full-face photograph of the “assailant,” the shooter, they had observed in the shoot/no-shoot decision scene. This photographic lineup was constructed and administered according to standard Department of Justice (1999) guidelines; respondents were asked to identify the individual they had seen wielding the firearm, if that individual was present in the lineup.

Results

No significant differences in shoot/no-shoot decisions (crosstabulative chi-square analysis), or in the time required to come to a shooting decision (two-way analysis of variance), resulted either from SMOKE training or from the gender of the respondent. However, those receiving SMOKE training, confined to the realm of IED detection, were significantly more accurate in their lineup identification of the perpetrator, χ2 (1) = 4.173, p = .041.

Discussion

All LE officers know that human attentional resources are limited, resulting in such phenomena as the “tunnel vision” frequently observed in deadly force encounters (see Grossman and Christensen 2004; Klinger 2004; Sharps 2017). If we train people in SMOKE or similar cognitively based IED-detection programs, is it possible that they might pay so much attention to potential IEDs that they might ignore other hazards, such as armed combatants, and might therefore make incorrect shoot/no-shoot decisions?

It is not likely that IED-detection training would interact negatively with law enforcement training per se. Although further research is important in this area, law enforcement officers have to employ hazard detection skills in a vast spectrum of other duties (for example, the arrest of suspects in complex visual environments). They do this, literally, every day. They are usually successful.

From the standpoint of cognitive science, this is generally what we would expect. As human beings become expert in any field, including law enforcement, the knowledge networks in their brains become increasingly integrated, resulting in more and better cognitive pathways among different elements of the relevant knowledge. These principles have been known in one form or another (e.g., Collins and Loftus 1975) for over a century (Boring 1957), and certainly apply to the realm of tactically relevant training as well (see Grossman and Christensen 2004). Again, further research is needed on the best ways to integrate cognitively based training with other aspects of LE training and field experience; but based on what we already know about the cognitive integration of officer skills, we would expect trained officers to assimilate SMOKE and related IED-detection training effectively. In fact, cognitively based training such as SMOKE appears to enhance related pathways in terms of vigilance, and of improved memory as a result; respondents with SMOKE training were superior at identifying the perpetrator from a standard lineup (DOJ 1999), indicating a salutary crossover from cognitively-based training in IED detection to identification of significant elements (in this case, the perpetrator) of a given crime-related scene. In summary, in the present research, there was no evidence of negative crossover between SMOKE and shoot/no-shoot decisions, but there was enhanced eyewitness memory for individuals.

Law enforcement training aside, this question of SMOKE integration with the skills involved in shoot/no-shoot decisions, and with eyewitness memory, remains relevant for the basic operation of the human nervous system. To begin to address this question, we needed to examine the performance of people without prior law enforcement training or experience, people with essentially naïve, pristine nervous systems in the convoluted worlds of IED detection and shoot/no-shoot decisions. In the presence of SMOKE training, these individuals did not alter their shoot/no-shoot judgments, but their ability to identify a perpetrator was enhanced. Obviously, the negative finding here says nothing about tactical or other forms of LE training with reference to interactions with other types of IED-training, or even about the effect of SMOKE in other contexts; much more research on this point is needed in many more contexts with relevant research populations, with reference to all reasonable variations of the training scenarios administered (see Grossman and Christensen 2004; Sharps 2017). However, at this point, we can say that under the precise conditions observed here, cognitively based training, at least in the case of SMOKE, had no identifiable influence on shoot/no-shoot decisions, but that it did in fact enhance other aspects of vigilance and memory in relevant related areas of forensic cognitive performance. Future applied research will be needed to address the specific parameters and boundaries of these effects.