Ergonomics Perceptual training for visual search

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Perceptual training for visual search a

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David Schuster , Javier Rivera , Brittany C. Sellers , Stephen M. Fiore & Florian Jentsch

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Institute for Simulation and Training, University of Central Florida , Orlando , FL , USA Published online: 08 May 2013.

To cite this article: David Schuster , Javier Rivera , Brittany C. Sellers , Stephen M. Fiore & Florian Jentsch (2013) Perceptual training for visual search, Ergonomics, 56:7, 1101-1115, DOI: 10.1080/00140139.2013.790481 To link to this article: http://dx.doi.org/10.1080/00140139.2013.790481

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Ergonomics, 2013 Vol. 56, No. 7, 1101–1115, http://dx.doi.org/10.1080/00140139.2013.790481

Perceptual training for visual search David Schuster, Javier Rivera, Brittany C. Sellers, Stephen M. Fiore* and Florian Jentsch Institute for Simulation and Training, University of Central Florida, Orlando, FL, USA

Downloaded by [University of Central Florida] at 09:47 28 January 2015

(Received 14 December 2011; final version received 24 March 2013) People are better at visual search than the best fully automated methods. Despite this, visual search remains a difficult perceptual task. The goal of this investigation was to experimentally test the ways in which visual search performance could be improved through two categories of training interventions: perceptual training and conceptual training. To determine the effects of each training on a later performance task, the two types of trainings were manipulated using a between-subjects design (conceptual vs. perceptual £ training present vs. training absent). Perceptual training led to speed and accuracy improvements in visual search. Issues with the design and administration of the conceptual training limited conclusions on its effectiveness but provided useful lessons for conceptual training design. The results suggest that when the visual search task involves detecting heterogeneous or otherwise unpredictable stimuli, perceptual training can improve visual search performance. Similarly, careful consideration of the performance task and training design is required to evaluate the effectiveness of conceptual training. Practitioner Summary: Visual search is a difficult, yet critical, task in industries such as baggage screening and radiology. This study investigated the effectiveness of perceptual training for visual search. The results suggest that when visual search involves detecting heterogeneous or otherwise unpredictable stimuli, perceptual training may improve the speed and accuracy of visual search. Keywords: visual search; signal detection; perception; conceptual training; perceptual training

1.

Introduction

People are better at visual search than the best fully automated methods (Cheng et al. 2003). Humans particularly excel at rapid processing of complex images and, in many cases, can make object classification decisions in 150 ms without prior training (Thorpe, Fize, and Marlot 1996). Despite this, visual search remains a difficult perceptual task. Maximising the effectiveness of visual search is the primary goal in a number of industries including radiology (Cheng 2003; Gurney 1996) and airport baggage screening (McCarley et al. 2004). During visual search, an observer must determine if a predetermined area of the visual field contains a target. The target is an object that differs from distracter objects based on one or more visual characteristics. In the case of radiologists, the target object may be a cancerous tumour, while in baggage screening it could be a weapon. Usually, these differences are due to only a few specific features, such as the shape of a tumour or the trigger of a gun. In these and other real-world tasks, the distinctions between targets and distracters are subtle and not detected with complete accuracy; targets may be missed, or distracter objects may be falsely identified as targets. Both of these undesirable outcomes must be minimised as much as possible by training observers to perform visual search accurately and efficiently. In this research, we aimed to inform training for visual search by investigating manipulations that may lead observers to perform faster and more accurate visual searches. Researchers have investigated the ability of observers to visually search for simple targets that differ from distracters according to features such as brightness (Farmer and Taylor 1980), shape (Golcu and Gilbert 2009) and colour (Nagy and Sanchez 1990). However, there are considerable differences that make real-world visual search both perceptually and cognitively more demanding than the tasks in these studies. For instance, in the real-world visual search, the target is unlikely to be present, and therefore, observers spend most of their time examining images without a target (Pisano et al. 2005; Skaane et al. 2007). Low frequency of targets can lead to the ‘prevalence effect’, where observers are more likely to miss targets when they are present (Wolfe et al. 2007). Additionally, a target is often difficult to find due to its visual similarity to the surrounding search space (Duncan and Humphreys 1989; Harris 2002). The task is further complicated by the presence of distracter objects that are potentially similar to the target, but differing by one or more characteristics, as described previously. Essentially, any object that does not meet the criteria for being a target is a distracter. In baggage screening, for example, distracters are non-threat objects packed in baggage such as clothing or consumer electronics. Electronics, in particular, are difficult distracters because their appearance is similar to that of some threats. Finally, unlike

*Corresponding author. Email: [email protected] q 2013 Taylor & Francis

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the laboratory, the real-world visual search adds time pressure on the observer, which has been shown to affect performance (McCarley 2009). Therefore, research that is specific to real-world, complex visual search is needed. The goal of this investigation was to experimentally test ways in which visual search performance could be improved through training. Specifically, the literature supports the use of two categories of training interventions: perceptual training and conceptual training. Because these two types of trainings have different intermediate goals (perceptual performance vs. conceptual understanding), we believe that they may be complementary in improving visual search performance in a real-world task. We first discuss the theoretical basis for visual search before examining prior investigations of these two training paradigms.


1.1.

Theories of visual search

Schneider and Shiffrin (1977) first identified two types of visual search, which they called automatic search and controlled search. Automatic searching is parallel and does not lengthen as the number of distracters increases. Meanwhile, controlled searching is a serial process; for each object in the search area, a systematic comparison is made. Similarly, Treisman and Gelade’s feature-integration theory suggests that complex objects are recognised in two stages: recognition of features in the first stage and recognition of objects in the second stage (1980). The early stage is characterised by fast, automatic and parallel processing of features and the segmentation of the visual field into logical units. Simple visual search tasks, where targets differ from distracters by a single feature, are processed by this mechanism alone. In more complex search tasks, typical of real-world visual search, the targets differ from the distracters only by a combination of multiple features. To detect these types of targets, serial processing is needed to examine each logical unit. This serial process characterises the second stage of feature integration theory. Green (1991) and others (Egeth, Virzi, and Garbart 1984; Mordkoff, Yantis, and Egeth 1990; Wolfe, Cave, and Franzel 1989; Zohary and Hochstein 1989) offered additional support for the two-stage model, but were critical of the early –late dichotomy of feature integration theory; specifically, they argued that observers might utilise strategies that result in a more complex mixture of parallel and serial searching. For example, observers may ignore distracter objects based on a feature (Treisman and Sato 1990) when the early parallel search informs the later serial search, a process known as guided search (Hoffman 1978). Empirical data suggest that there is no discrete distinction between serial and parallel searches (Wolfe 1998); rather than a completely parallel search followed by exclusively serial scanning, variables such as high target – distracter similarity can lead observers to use serial rather than parallel processing (Nagy and Sanchez 1990). Not only does the content of the search area affect performance but individual differences exist as well. Nodine and Kundel’s visual search and detection model (1987) integrates cognitive and applied theories of visual search into a model specific to the visual search task. Their model features a distinction between early global searching and later scanning similar to feature integration theory. The Nodine – Kundel model has three major stages: glancing, scanning and decisionmaking. In the glancing stage, individual differences and training influence a global impression of the visual field and recognition of objects at a holistic level. In the scanning stage, attention is focused on details. Glancing guides the scanning stage, and this leads to a decision. This model includes individual differences in visual search performance and ultimately provides a framework for investigating training interventions. Expert observers are faster and more accurate than novices (Nodine et al. 2002), probably as a result of their deliberate practise at the screening task (Ericcson and Towne 2010). However, the mechanisms by which novice observers become expert observers are not known (Krupinski et al. 1998). The increase in speed appears to be due, at least in part, to improvements in the efficiency of the glancing search stage. Mammography radiologists, for example, are quick to recognise lesions and spend more of their time in the decision-making stage (Kundel et al. 2007; Nodine et al. 2002). However, this effect does not transfer to visual searches outside of the observer’s area of training (Nodine and Krupinski 1998). In a study of radiologists, observers who viewed X-rays within their specialty took less time to fixate on targets (Leong et al. 2007). Moreover, the probability of hits drastically drops approximately 23 ms after the first viewing of the image, and this effect is evident across levels of expertise. Eyetracker data add that successful experts fixate on better sources of information within the image (McRobert et al. 2009; Savelsbergh et al. 2005). These findings suggest that speed and accuracy gains affect the glancing stage of search. Thus, training interventions aimed at improving the speed and accuracy of perception are likely to improve performance. However, it is not known if these improvements are due to perceptual heightening or cognitive strategies (Nodine et al. 1999). An explanation for expert performance is that experts have more knowledge on the features of the targets (Schyns and Rodet 1997). Object recognition improves with the ability to more finely classify objects (Tanaka and Taylor 1991), experience (Gauthier and Tarr 1997) and even the act of providing distinctive yet arbitrary names to object categories (Gauthier et al. 2003). As observers gain more knowledge about the domain, they make more subtle distinctions within categories (Dixon et al. 2002). This occurs because expert observers are able to encode patterns relevant to the domain

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(Ericcson and Staszewski 1989). Better object recognition may be a means by which experts can accomplish a quicker and more accurate glancing stage. In addition, this suggests that knowledge of the targets and distracters impacts visual search performance. This explanation adds to perceptual and attentional models of visual search by suggesting that expert observers may adopt deliberate search strategies based on prior knowledge of the domain. In other words, they may selectively attend to cues based on the conceptual organisation of the objects in the visual field. Nodine and Kundel’s model referred to this as cognitive schema and expectations. Therefore, training interventions that lead the observer to adopt better strategies are likely to be of use in visual search. Next, we will discuss training interventions that have been developed within the context of this model.


1.2.

Perceptual and conceptual trainings

Nodine and Kundel’s model suggests that training would be effective if it could enable more efficient glancing and scanning, resulting in a faster and more accurate examination of the search area. The model also suggests that observers with a cognitive schema that is congruent with the task would also be faster and more accurate. These skills may be developed through two forms of learning: perceptual and conceptual. Perceptual training, which facilitates perceptual learning, should lead to an improvement in the efficiency of the glancing and scanning stages. In the same vein, conceptual training, which facilitates conceptual learning, should increase performance by improving the observer’s cognitive schema and expectations. Although real-world training would likely mix the two, these types of trainings operate using different mechanisms. Both conceptual and perceptual trainings affect strategy development, but they do so in different ways. Perceptual training leads to strategies that enable observers to make perceptual discriminations by recognising the features that differ between targets and distracters (Doane, Sohn, and Schreiber 1999). These features have been shown to generalise to novel targets that share the feature (Golcu and Gilbert 2009). Conceptual training leads to strategies that are verbalisable and enable observers to understand organisational relationships among the features (Bibby and Payne 1993). In prior research, we illustrated differing rates of expertise development in perceptual and conceptual knowledge (e.g. Fiore et al. 2000). This additionally suggests that training should support the different developing trajectories of these types of expertise. 1.2.1.

Perceptual training

As defined by Goldstone, ‘Perceptual learning involves relatively long-lasting changes to an organism’s perceptual system that improve its ability to respond to its environment and are caused by this environment’ (1998). Perceptual learning is the acquisition of perceptual knowledge and may be learned verbally or nonverbally. Perceptual information is likely to be encoded nonverbally when the acquired information is complex, non-verbalisable or learned under high workload (Melcher and Schooler 2004). Melcher and Schooler described perceptual learning as a bottom-up process based on exposure to a stimulus. Perceptual learning occurs when experience creates long-lasting changes in sensory or motor representations needed in the performance of a task (Karni and Bertini 1997). In prior research, we found differences in perceptual learning dependent upon the complexity of the training environment manipulated via the addition of clutter (cf. Kass, Herschler, and Companion 1991). This work showed improved performance dependent upon spatial abilities and test item difficulty (Fiore, Scielzo, and Jentsch 2004). Later research focused on training for targets using a perceptual discrimination task. In this way, we emphasised perceptual training and the difficulty of target discrimination decisions during learning (Fiore et al. 2006). By combining and building upon training research in perceptual learning (Kass, Herschler, and Companion 1991) and strategic processing (Doane, Sohn, and Schreiber 1999), we were able to show an enhanced and more efficient target detection (Fiore et al. 2006). Perceptual training leads to strategy development by improving the efficiency of task-relevant information encoding (Gold, Bennett, and Sekuler 1999). Many authors have addressed the question regarding which qualities of perceptual training lead to a change in behaviour. Authors do not agree on the specifics, probably due to differences in the definitions of transfer, generalisation and training across studies. What is apparent is that mere, passive exposure to a stimulus can affect performance (Doane et al. 1996; Sowden, Davies, and Roling 2000); however, the impact of exposure on learning a complex perceptual task is limited (Karni and Bertini 1997; Wong, Palmeri, and Gauthier 2009). This led Wong and colleagues to suggest that perceptual training leads to better strategies early in the training that guide the formation of perceptual knowledge as the training progresses. An important question about the nature of perceptual training is the degree to which perceptual training is stimulus specific. If the targets are complex, it would be time consuming, or even impossible, to provide a training that included every potential target. Fortunately, observers demonstrate a present but limited ability to generalise novel stimuli as a result of perceptual training. Observers exhibit higher performance in basic perceptual tasks (e.g. odd element detection, Ahissar 1997), returning tennis serves (Farrow and Abernathy 2002) and tending hockey goals (Williams, Ward, and Chapman

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2003) after perceptual training. Because of the limitless number of potential stimuli in these studies, the research supports the assertion that perceptual training does not lead to memorisation of templates, but rather knowledge of the perceptual features of the target stimuli. However, there are certain limits in the ability to generalise perceptual training to novel presentations. Observers in an X-ray baggage imaging study were less sensitive to novel objects than the observers present in the training (McCarley et al. 2004). This suggests an important limitation for training that exclusively relies on perceptual learning; there are limits on the ability of observers to generalise knowledge of perceptual features. One way to enhance the generalisability of perceptual training may be to utilise a complementary mechanism, such as conceptual training. For instance, Rivera et al. (2011) investigated the impact of both perceptual and conceptual trainings on the performance in a perceptual task. In this study, pilots received one of three types of trainings: (a) perceptual training, (b) conceptual training or (c) a combination of perceptual and conceptual trainings. The results indicated that pilots in the combined training were superior to pilots in the other training conditions on a distance estimation task. Their findings support the notion that, when combined, perceptual and conceptual trainings can be complementary to augment perceptual task performance.


1.2.2.

Conceptual training

Conceptual learning, also referred to as concept attainment, is defined as ‘the search for and listing of attributes that can be used to distinguish exemplars from non exemplars of various categories’ (Bruner, Goodnow, and Austin. 1967). Essentially, conceptual learning involves the ability to categorise and differentiate things according to their features and characteristics. In addition, conceptual learning can be described as ‘the acquisition and application of new knowledge to result in concepts and symbolic representations not previously in the individual’s knowledge network’ (Maclellan 2005). In contrast to perceptual learning, conceptual learning is a top-down, largely verbal process. Recent research has shown that conceptual knowledge can influence perceptual processes, suggesting that perception and conceptual understandings may not be independent from each other (Dixon et al. 2002; Goldstone and Barsalou 1998; Goldstone et al. 2000; James and Cree 2010; Morita et al. 2008; Schyns et al. 1998). James and Cree (2010) stated that experts use conceptual and perceptual information when classifying objects. Conceptual training provides basic descriptions and knowledge of the components of a system (Santhanam and Sein 1994). That is, it describes why a system and its components behave and interact in a given way. As a result, conceptual training produces an in-depth understanding and leads to a more complete mental model of the system. Sein and Bostrom (1989) explained that mental models are ‘conceptual representations of the system that provide predictive and explanatory powers to the user in understanding the system’. They found that conceptual model training lead to the formation of mental models and that mental model quality predicted the performance in a complex transfer task. Support for the efficacy of conceptual training comes from the finding that conceptual knowledge can improve performance on perceptual tasks. Shiffrin and Schneider (1977) found that learning categories improved controlled search. Biederman and Shiffrar (1987) found that a short instruction sheet for categorisation could effectively train chicken sexers, a task requiring visual discrimination between features. Conversely, Peron and Allen (1988) found that people were able to detect identical beer flavours after training involving tasting them, but not after learning terminology about beer flavour. The difference in these two studies was the degree to which the conceptual training could lead to the development of strategies. In the case of the Biederman and Shiffrar study, the conceptual training explicitly gave strategies for distinguishing between sexes. However, the beer terminology training used in the Peron and Allen study likely did not lead to strategy development because of the fundamental differences between taste adjectives and gustation. If participants could not associate tastes with the proper adjectives, then it may have been impossible for them to use the information in the training. An example of a conceptual training affecting a perceptual task comes from Manning et al. (2006). Radiology students working towards a degree in medical image interpretation completed 30 h of formal lectures on anatomy and decisionmaking. Despite being faster and more accurate, post-training students made larger saccades and fixated longer. Similar to the experts mentioned earlier, these students may have spent more time fixating in the decision-making stage. In another study, Gauthier et al. (2003) trained individuals to associate objects in a category with semantic information. After training, conceptual knowledge was found to influence responses to a visual discrimination task. This suggests that training that leads to increased conceptual understanding of objects or a domain can also lead to more efficient performance on a related perceptual task. In the case of the radiology students, a largely verbal presentation of concepts led to the acquisition of strategies for the visual search, facilitating performance gains. 1.3.

Hypotheses

The purpose of this study was to determine the singular and combined effects of perceptual and conceptual trainings on visual search performance in a real-world task. Given that past research has found that expert observers are faster and

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better at the glancing and scanning stages, and that glancing and scanning are tasks that can improve after perceptual training, our first hypothesis was that perceptual training would be associated with visual search such that perceptual training would improve visual search speed and accuracy. Similarly, conceptual training improves schema acquisition, suggested by Nodine and Kundel, to contribute to visual search speed and accuracy. Therefore, our second hypothesis was that conceptual training would be associated with visual search such that conceptual training would improve visual search speed and accuracy. Finally, our third hypothesis was that the inclusion of both perceptual and conceptual trainings would lead to the highest levels of speed and accuracy during visual search. If supported, these hypotheses would suggest that training interventions should target the development of effective strategies perceptually and conceptually. Furthermore, it would add support for Nodine and Kundel’s model, which describes visual search in the real-world tasks.

2. Method Our investigation of visual search used airport baggage screening as the domain, specifically baggage screening for improvised explosive devices (IEDs). Baggage screening was selected for two reasons. First, the task, even in the real world, is well described as a visual search task (Koller, Drury, and Schwaninger 2009). Realistic performance measures used in the laboratory can be reasonably expected to generalise to the real-world screening task. Second, baggage screening is a complex visual search; a nearly unlimited number of target and distracter objects exist. In the real world, exposure to every possible size, shape and orientation of a target is possible. Therefore, training is not useful unless it enables the observer to generalise beyond the exact images presented. Because we aimed to investigate conceptual and perceptual learning, the generalisability of training is of real world and theoretical interest. IEDs were used because they are not only a continued concern (Burgoon and Varadan 2006) but also heterogeneous and difficult to detect (Koller, Drury, and Schwaninger 2009). Many people are familiar with the typical shape of a gun or knife, but IEDs have less salient features. Consequently, individuals are less likely to have feature maps necessary for identifying IEDs in the first stage of visual search prior to training (Koller, Drury, and Schwaninger 2009). IEDs were included to prevent familiarity effects on the dependent measure (see Fincannon, Curtis, and Jentsch 2006) and also to minimise the participants’ prior familiarity with the targets. The perceptual training used in our experiment was based on a discrimination paradigm. Our basis for using this type of training was prior research demonstrating improvements in reaction time and accuracy on an operationally relevant detection test (see Fiore et al. 2006; Schuster et al. 2010; Sellers et al. 2010). In discrimination training, observers are asked to examine two threat items for differences in the threats’ shape and contour. This training is almost exclusively perceptual because nearly all of each training trial is spent examining the features of the threat items. As a result, the deep focus on features helps with perceptual strategy development and improvement of perceptual skills (Doane et al. 1996). Other perceptual training methods, such as X-Ray Tutor (Schwaninger 2003), have also been found to improve IED detection rates (Schwaninger and Hofer 2004) using an exposure training paradigm in which learners perform trials largely equivalent to their job task (cf. Fiore, Scielzo, and Jentsch 2004). The conceptual training was based on a concept map method in which structural knowledge about IEDs was provided. A concept map was used because it provides with visual representation and semantic information of the relationship between concepts (see Curtis et al. 2007; Hoeft et al. 2003). To create the IED concept map, four major IED components and their functions were identified, as delineated in Pickett (2009). Each major component was structurally organised in a map which depicted connections and relationships between components. In addition, semantic information was provided to illustrate the propositions between the components. As mentioned above, conceptual training has been shown to improve performance through an increased categorical understanding of the relevant target features (Gauthier et al. 2003) and also to supplement perceptual training in order to improve performance on a perceptual task (Rivera et al. 2011). This training was aimed at providing observers with a thorough conceptual understanding of the role each of the IED components plays individually, how they are connected and how they function together. Ultimately, with this conceptual understanding, participants may be better able to identify these components when presented individually and also as part of an entire IED in the visual search task.

2.1.

Participants

Forty undergraduate students were recruited from the University of Central Florida. Participants received course credit for participation. Participants’ age ranged from 18 to 28, and the mean age was 19.5. Of the 40 participants, 16 were male and 24 were female. No participants reported having any prior experience with the X-ray baggage screening.

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2.2. Apparatus Participants completed the experiment using a personal computer running Empirisofte, MediaLabe and DirectRTe stimuli presentation softwares. With the exception of the informed consent and control condition activity, all measures were collected using this computer system.


2.2.1. IED conceptual training Participants assigned to the IED conceptual training without perceptual training condition completed a self-paced, computer-based training tutorial. During this training, participants were presented with an IED concept map (Figure 1) that displayed the major components of an IED and their relationships to the other components. Participants explored the function of each component by clicking on a hyperlink that would direct them to a page containing additional information on that specific component (Figure 2). IEDs consist of four major components (power source, timer, fuse and explosive), and each component must be connected to the subsequent part for the IED to function (and thus become a threat). The training was developed to provide this information while showing the functional relationships between components.

2.2.2.

IED perceptual training

Participants assigned to the IED perceptual training without conceptual training condition completed 30 min of computerbased discrimination training. During each trial, two suitcases were presented simultaneously, each containing three clutter items and one assembled IED (Figure 3). Participants were asked to identify if the two threat items appearing in the two bags were identical in shape and contour, and to respond using a marked key on the keyboard. The participant had up to 3 s to respond to each stimulus. If the response was correct, the word ‘CORRECT’ was displayed on the screen, in green lettering, for 1 s. If the response was incorrect, the word ‘INCORRECT’ was displayed in red for 1 s.

2.2.3.

IED conceptual and perceptual trainings

When the participant was assigned to both the conceptual training and perceptual training conditions, both trainings were performed. Each aspect of these components was kept consistent with their individual training counterparts (as mentioned before), and the order of presentation was counterbalanced. When neither perceptual nor conceptual training was included, participants completed 30 min of an unrelated verbal assessment containing questions involving analogies, reading comprehension and sentence completion.

2.2.4.

Threat identification test

A 160-item test was developed to measure threat detection performance in an X-ray screening task (see Schuster et al. 2010). The test was the same for all participants. Each test item consisted of an X-rayed suitcase containing either clutter items (distracter bags) or clutter items with a threat item (target bags). Threat items were novel images manipulated to

source of spark for

Power Source

initiates

source of spark for

Timer

delays initiation of

Fuse

leads to detonation of

Explosive

Figure 1.

IED concept map used in conceptual training conditions.


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Figure 2.

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Example of additional information about an IED component.

resemble whole and part IED components. Sixty-three target stimuli were randomly presented to each participant with 97 distracter stimuli. Test stimuli were developed systematically according to two criteria: difficulty and overlap. Difficult stimuli were populated with 8– 10 clutter items, whereas easy stimuli had fewer than eight clutter items. Overlapping bags had clutter that partially occluded other clutter and any threat item. In non-overlapping bags, no item occluded any other item. Participants were given up to 12 s to respond to each stimulus.

2.2.5.

Concept map assessment

A concept map assessment was developed to measure the participant’s understanding of how the IED components are functionally related to one another. To administer the concept map assessment, the TPL-Knowledge Assessment Testing Suite (TPL-KATS) concept mapping tool (Hoeft et al. 2003) was used. The tool allowed participants to drag the provided concepts (the four components mentioned above) into appropriate positions on the concept map board and connect the concepts using words to form propositions (Figure 4).

Figure 3.

Discrimination training trial.

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2.3. Procedure Participants were randomly assigned to one of the four training conditions. After arriving, each participant completed a series of written tests of spatial ability and colour vision. Then, the participants completed a computer-based demographics form followed by the training tutorial slideshow. After a timed 5-min break, a counterbalanced threat identification test and concept map assessment were presented. Participants were then debriefed and dismissed. 3.

Results

3.1. Training 3.1.1. Perceptual training performance


Participants in the perceptual training condition completed a mean of 377 practice trials (SD ¼ 121.15). The mean performance on the practice activity was evaluated using d0 (M ¼ 1.05, SD ¼ 0.28). There was a weak correlation between perceptual training d0 and threat detection test d0 for participants who completed this training, r ¼ 0.24, p , 0.313, N ¼ 20. 3.1.2. Training duration To determine the differences in training time across the four conditions, a between-subjects analysis of variance was conducted with condition as the independent variable and pre-testing duration as the dependent measure. Pre-test duration was calculated as the number of minutes from the start of the computer-based demographics survey until the participant began the threat detection test. Although this measure is confounded by the time spent completing the demographics survey, the survey was short in duration compared with the training time, and should have been constant across all training conditions. Nonetheless, there was a significant difference in training time across the four conditions, F(3, 36) ¼ 24.08, p , 0.05, h2 ¼ 0.667 (Table 1). Most importantly, participants who were presented with conceptual training alone did not spend as much time in training as participants in the other training groups.

3.2. Threat detection test Because we wanted to investigate the singular and combined effects of conceptual and perceptual trainings, two 2 £ 2 £ (2 £ 2) mixed between –within subjects analyses of variance were conducted to assess the impact of training on participants’ detection accuracy and reaction time across threat detection test item difficulty and overlap. In line with other visual detection literature, we used d0 as the measure of overall performance (Pashler and Yantis 2004), accounting for both hits and false alarms. Response time was the mean number of milliseconds participants took to respond to each stimulus.

Figure 4.

TPL KATS concept map.

Ergonomics Table 1.

Pre-test duration across training conditions.

Both Perceptual training only Neither conceptual nor perceptual (control) Conceptual training only

Table 2.

d Reaction time (s)

M

Median

SD

9 11 10 10

45.48 47.52 36.99 8.12

44.95 49.33 38.19 8.76

8.59 15.32 14.82 2.49

N

M

Median

SD

40 40

0.95 3179.34

1.00 3091.50

0.55 1137.35

Descriptive statistics

Descriptive statistics for d0 and response time on the threat detection test are presented in Table 2. Neither d0 nor response time was significantly skewed or kurtotic. Threat detection test trials were examined to see if any participants went over the time limit. Of 6400 total test trials in the study, 55 were allowed to reach the 12 second mark, affecting 0.86% of the trials total. Participants ranged from zero to six trials over the time limit (M ¼ 1.35, SD ¼ 1.44). These items were scored as incorrect. 3.2.2.

Accuracy

A 2 perceptual training (present vs. absent) £ 2 conceptual training (present vs. absent) £ 2 overlap (overlap vs. no overlap) £ 2 difficulty (easy vs. difficult) mixed between – within subjects analysis of variance was conducted to assess the impact of each training on participants’ d0 across threat detection test item difficulty and overlap. There was a significant main effect for perceptual training, F(1, 36) ¼ 17.04, p , 0.001, h2 ¼ 0.321. Participants with perceptual training had higher accuracy (M ¼ 1.24, SD ¼ 0.61) than participants without perceptual training (M ¼ 0.64, SD ¼ 0.67). The effect of conceptual training, however, was not significant, F(1, 36) ¼ 0.01, p , 0.907, h2 ¼ 0.001. Furthermore, there was no significant interaction with perceptual and conceptual training, F(1, 36) ¼ 0.29, p , 0.593, h2 ¼ 0.008 (Figure 5). A main effect of difficulty approached, but did not reach significance, F(1, 36) ¼ 3.85, p , 0.058, h2 ¼ 0.097. The manipulation of overlap failed to reach significance F(1, 36) ¼ 0.78, p , 0.384, h2 ¼ 0.021. No significant interaction effects were observed amongst the within-subjects variables on accuracy. To determine whether participants who received conceptual training were better at finding targets that were whole IEDs versus IED components relative to those who received perceptual training, a 2 perceptual training (present vs. absent) £ 2 conceptual training (present vs. absent) £ 2 item type (whole IED vs. IED component) mixed between– within subjects analysis of variance was conducted using hit rate as the dependent variable. Hit rate was used in the place of d0 to avoid 1.4 Easy Items Difficult Items

1.2 Accuracy (d')


N

Descriptive statistics for d0 and response time.

0

3.2.1.

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1 0.8 0.6 0.4 0.2 0 No Overlap

Overlap Overlap

Figure 5.

Interaction of within-subjects factors on accuracy.

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3.2.3. Reaction time A 2 £ 2 £ (2 £ 2) mixed between –within subjects analysis of variance was conducted to assess the impact of training on participants’ reaction time across threat detection test item difficulty and overlap. There was a significant main effect for perceptual training, F(1, 36) ¼ 11.30, p , 0.002, h2 ¼ 0.239. Participants with perceptual training had lower reaction times (M ¼ 2630.29, SD ¼ 929.41) than participants without perceptual training (M ¼ 3729.32, SD ¼ 1224.94). The effect of conceptual training, however, was not significant, F(1, 36) ¼ 0.033, p , 0.856, h2 ¼ 0.001. The manipulation of overlap significantly affected reaction time, F(1, 36) ¼ 24.689, p , 0.001, h2 ¼ 0.407. Participants took longer to respond to overlapping items (M ¼ 3362.56, SD ¼ 1290.52) than to non-overlapping items (M ¼ 2997.05, SD ¼ 1115.00). In addition, item difficulty significantly affected reaction time, F(1, 36) ¼ 36.039, p , 0.001, h2 ¼ 0.500. Participants took longer to respond to difficult items (M ¼ 3399.32, SD ¼ 1298.12) than to easy items (M ¼ 2960.28, SD ¼ 1093.12). 3.2.4.

Bias

To determine if participants adopted a different response criterion across conditions, a 2 perceptual training (present vs. absent) £ 2 conceptual training (present vs. absent) £ 2 overlap (overlap vs. no overlap) £ 2 difficulty (easy vs. difficult) mixed between – within subjects analysis of variance was conducted to assess the impact of each training on participants’ bias across threat detection test item difficulty and overlap. Bias was measured as log (bG) as described by Macmillian and Creelman (1991). There was a significant main effect for difficulty, F(1, 36) ¼ 5.15, p , 0.029, h2 ¼ 0.125. Participants adopted a more liberal criterion (M ¼ -.015, SD ¼ 0.75) for difficult items than for easy items (M ¼ 0.153, SD ¼ 0.75). That is, participants were more likely to indicate a threat in difficult images. In addition, an interaction effect was found for overlap and conceptual training, F(1, 36) ¼ 5.42, p , 0.026, h2 ¼ 0.131. Driving this interaction was a more conservative criterion adopted by conceptually trained participants on non-overlapping items than participants without conceptual training (Figure 6). Thus, for overlapping items, participants who had been exposed to conceptual training were more likely to indicate a threat than participants who were not exposed to conceptual training. 3.3. Conceptual assessment All participants completed a concept map assessment. These were scored according to the method described by Hoeft et al. (2003). Concept maps were scored for agreement (M ¼ 0.61, SD ¼ 0.25) and for the number of links shared with the referent map (M ¼ 0.63, SD ¼ 0.28). There was little association between concept map scores and d0 for both agreement, r ¼ 0.05, p , 0.743, N ¼ 40, and number of shared links, r ¼ 0.08, p , 0.645, N ¼ 40. To see if performance on the concept map was affected by the presence of perceptual or conceptual training, a multivariate two-way analysis of variance Conceptual Training No Conceptual Training 0.16 0.14 0.12 0.1 Bias (βG)


counting distractor items twice in this analysis of threat type. Although there was a significant difference in hit rate between whole IEDs and components, F(1, 36) ¼ 27.02, p , 0.001, h2 ¼ 0.429, no significant interaction effect was observed between the trainings and whole IED versus IED components. Overall, participants were more sensitive to whole IEDs (M ¼ 0.72, SD ¼ 19) than IED components (M ¼ 0.55, SD ¼ 0.24).

0.08 0.06 0.04 0.02 0 –0.02 –0.04

Overlapping Items

Non-Overlapping Items

Overlap

Figure 6.

Interaction between conceptual training and item overlap on bias.

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3.3.1. Concept map propositions Concept map propositions, the verbal description of the relationship between each linked IED component, were examined by two raters based on how well a participant’s answer reflected the referent map shown during training. A score of one would be given for each proposition that closely resembled one in the referent map. Reliability between the two raters was measured by the kappa measure of agreement (Cohen’s kappa) to correct for chance agreements. The kappa coefficient obtained a value of 0.70, indicating that there was good agreement between the two raters. To assess how the training affected proposition accuracy, a 2 perceptual training (present vs. absent) £ 2 conceptual training (present vs. absent) £ 5 propositions (power source to timer, power source to fuse, power source to explosive, timer to fuse, fuse to explosive) mixed between– within analysis of variance was conducted. There was a significant main effect for propositions, F(4, 33) ¼ 4.03, p , 0.009, h2 ¼ 0.33 (see Figure 7). There were no significant interactions between the conceptual training and propositions, F(4, 33) ¼ 0.606, p , 0.661, h2 ¼ 0.07, or the perceptual training and propositions, F (4, 33) ¼ 0.460, p , 0.764, h2 ¼ 0.053. However, there was a significant main effect for conceptual training, F(1, 36) ¼ 6.64, p , 0.014, h2 ¼ 0.156, indicating that participants with conceptual training (M ¼ 0.50, SD ¼ 0.31) were better at correctly determining the relationship between components than participants without conceptual training (M ¼ 0.25, SD ¼ 0.31). Lastly, there was a weak association between proposition scores and d0 , r ¼ 0.09, p , 0.599, N ¼ 40. 4.

Discussion

The aim of this study was to determine the singular and combined effects of perceptual and conceptual trainings on visual search performance in a real-world task. Specifically, our first hypothesis was that perceptual training would lead to improvements in visual search speed and accuracy. Second, we hypothesised that conceptual training would have a similar effect. Third, we hypothesised that because these two types of training are distinct and complementary, the presence of both would lead to the highest visual search speed and accuracy. The finding that speed and accuracy were higher when perceptual training was used supported our first hypothesis. This finding suggests that perceptual training does, as suggested by Wong, Palmeri, and Gauthier (2009), lead to strategy development early in learning. Memorisation of the specific images used in the training is not an appropriate explanation because it does not account for performance on novel stimuli. Rather, we argue that perceptual training leads to knowledge 0.6

0.5

0.4 Accuracy


was conducted with conceptual training (present vs. absent) and perceptual training (present vs. absent) as independent variables. Concept map agreement and shared links were the two dependent measures. No significant effects of our manipulations were observed for either agreement, F(3, 36) ¼ 1.99, p , 0.133, h2 ¼ 0.142, or shared links, F(3, 36) ¼ 1.87, p , 0.152, h2 ¼ 0.135.

0.3

0.2

0.1

0 Power Source to Power Source to Power Source to Timer to Fuse Fuse to Explosive Timer Explosive Fuse Propositions

Figure 7.

Main effect of propositions on accuracy.


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of the perceptual features of the target stimuli and this, in turn, enabled generalisation to novel stimuli. Because the nature of our training prevented us from including the same images in both test and training, we were unable to directly test the findings of McCarley and colleagues (2004) that performance was higher on images present in the training. Mere exposure and learning of templates may be the processes that are always present to some degree, but our findings demonstrate perceptual learning beyond what would have been expected from mere, passive exposure. The limitations associated with our conceptual training prevent us from making conclusions about the effectiveness of conceptual training or any interactive effects with perceptual training. The primary limitation was that we did not control for the amount or duration of exposure; participants spent more time on the perceptual training than on the conceptual training, and the perceptual-only training group spent more time in training than the group that was exposed to both trainings. This is most likely a result of the conceptual training being self-paced, while the perceptual training had more structure by including a specific number of trials. Furthermore, the conceptual training had less content, which leads to it being completed faster. In future work, we hope to better match exposure across the groups. Although the differences in training duration confound the comparison between perceptual and conceptual trainings, our results indicate beneficial effects of the perceptual training on both performance and response time. One lesson resulting from the apparent ineffectiveness of the conceptual training was that tasks may vary in the degree to which conceptual knowledge relates to performance outcomes. Because conceptual learning is thought to be a largely verbal process, the extent to which verbalisation leads to useful strategies depends on the task. It could be that knowledge about the relationships between IED components did not lead to effective strategies for detecting them in visual search. Along these lines, we did not observe a difference in whole IED sensitivity for participants in the conceptual training group. At minimum, we would have expected that the conceptual training would lead to better recognition of whole IEDs, as whole IEDs were displayed in the configural groups that formed the consent of the conceptual training. We did find that the conceptual training leads to a more liberal response criterion on overlapping items, suggesting that conceptually trained participants were more willing to indicate that a threat was present for this type of bag. However, this did not provide evidence of either effectiveness nor detriment of our conceptual training given the issues associated with training time. Further reflection on the apparent ineffectiveness of the conceptual training leads us to consider other aspects of training theory that were not manipulated in our conceptual training. Overall, it is likely that the skills that learners gained from the conceptual training did not translate to the skills necessary for correctly identifying targets in the perceptual task (Graf and Ryan 1990). Although IEDs are complex perceptually due to the heterogeneity in their appearance, IEDs are simple conceptually; they consist of four components that must be connected together in a valid configuration, and each component only has two other valid components to which it could be connected. Framed in terms of the Nodine –Kundel model, the details about these relationships may not have supplied enough relevant information to affect the observer’s cognitive schema or expectations of the targets in order to support strategy development. Without the proper strategy for identifying and classifying target items, the novices demonstrated a more liberal criterion, including many distracters into this category, similar to the results found in other visual search studies (Manning and Leach 2010). Perhaps training that focused more on the characteristics across threats (i.e. the features that determine whether an item meets the criteria for a category of target) rather than characteristics within threats (i.e. emphasising the relationships between components) would have provided more relevant conceptual information to improve strategy development. In addition, there were differences in feedback across perceptual and conceptual trainings. Feedback was available to the perceptually trained participants after each stimulus. Conceptual training was not trial based and did not offer this opportunity for feedback. There is also evidence that the positive effect of feedback can extend to novel stimuli that require transfer (Rohrer, Taylor, and Sholar 2010). On the conceptual map assessment, training conceptually helped participants correctly determine propositions between related IED components. This finding supports Bibby and Payne’s (1993) notion that conceptual training enables observers to understand relationships among features. Nonetheless, understanding the relationship between IED concepts was not enough to influence sensitivity in the visual search task. Conceptual training may be beneficial to visual search only when sufficient conceptual information exists about the target and when the conceptual information can lead to strategy development. Neither of these conditions was satisfied in our study. Given this, we cannot directly address the question of whether these two types of training are indeed complementary. Despite the limitations of our conceptual training, these findings do have implications for many types of visual search. Indeed, training for visual search in real-world tasks must include consideration of perceptual learning that can assist strategy development in novices. Even a relatively short perceptual training is likely to lead to improvements in detection ability as well as faster reaction time. Furthermore, perceptual training is useful for real-world visual search because of its ability to transfer to novel stimuli. Thus, when the visual search task involves detecting heterogeneous or otherwise unpredictable stimuli, perceptual training should be considered.

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This study supports Nodine and Kundel’s model of visual search. We observed differences in reaction time when perceptual training was included. A mean difference of 0.63 s was observed, suggesting that perceptually trained participants were faster in the glancing and scanning stage of search. Of course, the addition of eye tracker data would provide a useful means of seeing the impact of perceptual training on the path of the eye across the search area. Our study further establishes this model as a way to understand X-ray baggage screening. To be maximally effective, developers of training for real-world visual search tasks must consider the perceptual knowledge that best leads to strategy development in the observer while carefully selecting the training content that best represents the knowledge needed for performance. Acknowledgements


This investigation was sponsored by the Human Factors Program of the Transportation Security Laboratory, Science and Technology Directorate, U.S. Department of Homeland Security through DHS grant #02-G-004 and has been approved by DHS for publication. The views herein are those of the authors and do not necessarily reflect those of the organisations with which they are affiliated or their sponsoring agencies.

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