A Review and Meta Analysis of Vibrotactile and Visual 

Information Displays 

by Linda R. Elliott, Michael D. Coovert, Matthew Prewett, 

Ashley G. Walvord, Kristin Saboe, and Ryan Johnson 

ARL-TR-4955 September 2009 

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ARL-TR-4955 September 2009 

A Review and Meta Analysis of Vibrotactile and Visual 

Information Displays 

Linda R. Elliott 

Human Research and Engineering Directorate, ARL 

and 

Michael D. Coovert, Matthew Prewett, Ashley G. Walvord, 

Kristin Saboe, and Ryan Johnson 

University of South Florida 

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14. ABSTRACT 

Many studies have investigated the impact of vibrotactile cues on task performance, but the wide range of cue and task types 

have made findings difficult to interpret without a quantitative synthesis. We provide a systematic review of studies on 

vibrotactile cue effectiveness with regard to task performance, organized by types of comparisons and cue complexity. Fortyfive studies met the criteria for meta-analytic comparisons. Three types of comparisons were made: (1) the addition of a new 

tactile cue to a “baseline” condition, (2) the comparison of tactile cues to visual cues representing the same information, and

(3) the comparison of visual cues compared to a multimodal combination of tactile and visual cues representing the same 

information. The level of cue information complexity was also examined as a moderator. When added to a baseline task or 

existing visual cues, tactile cues enhanced task performance. When tactile cues replaced visual cues, however, effects are 

attenuated and moderated by cue information complexity. Tactile alerts are effective when replacing visual alerts, but tactile 

direction cues do not improve performance when replacing visual direction cues. This meta-analysis of tactile applications 

underscores the benefits of vibrotactile and multimodal displays, highlights conditions in which tactile cues are particularly 

effective, and identifies areas in need of further investigation. 

15. SUBJECT TERMS

Tactile, Visual, Display, Interface, Design, Military, Army, Attention management, Spatial orientation, Navigation, 

Communication 

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iii 

Contents 

Acknowledgments vi

1. Introduction 1

1.1 Purpose ............................................................................................................................1 

1.2 Background .....................................................................................................................1 

1.3 Attention and Cognition Under High Workload .............................................................2 

1.4 Study Framework: Vibrotactile Cues and Informational Complexity ............................3 

1.5 Tactile Cues for Alerts ....................................................................................................4 

1.6 Tactile Cues for Direction and Spatial Orientation .........................................................5 

1.7 Tactile Cues for Communications ...................................................................................6 

1.8 Hypotheses ......................................................................................................................6 

1.9 Need for Meta-analysis ...................................................................................................7

2. Method 8

2.1 Literature Search .............................................................................................................8 

2.2 Procedure .........................................................................................................................9 

3. Results 11

3.1 Baseline versus Tactile ..................................................................................................13 

3.2 Visual versus Tactile .....................................................................................................13 

3.3 Visual versus Visual-Tactile .........................................................................................14 

3.4 Overall Summary of Results .........................................................................................14 

4. Discussion of the Effectiveness of Tactile Cues 15

4.1 Adding Tactile Cues to Support Performance ...............................................................15 

4.2 Use of Multisensory Cues .............................................................................................15 

4.3 Replacing Visual Cues with Tactile Cues .....................................................................15 

5. Conclusions 17

5.1 Strength of Findings ......................................................................................................17 

5.2 Future Research .............................................................................................................17 
iv 

6. References 19

List of Symbols, Abbreviations, and Acronyms 26

Distribution List 27 
v 

List of Tables 

Table 1. Organization of meta-analyses. .........................................................................................4

Table 2. Meta-analytic results for vibrotactile cue effectiveness. ................................................12
vi 

Acknowledgments 

This project was funded by two Army Technology Objectives: (1) the U.S. Army’s Robotics 

Collaboration Army Technology Objective and (2) the U.S. Army’s Enhanced Situation 

Understanding for Maneuver Teams. We wish to thank Elizabeth Redden and Michael Barnes of 

the U.S. Army Research Laboratory’s (ARL) Human Research and Engineering Directorate for 

their support throughout the process of this effort. We are particularly indebted to the many 

graduate and undergraduate students at the University of South Florida for their dedication and 

long hours worked to identify and code several hundred articles for the literature review and 

analyses. 
1 

1. Introduction 

1.1 Purpose 

Developments in current and future information, decision making, and control systems will 

enable information creation and distribution to achieve extraordinary feats and challenge 

operators to attain new levels of performance. While research and technology address issues of 

information requirements, distribution, and decision support, the problem of information 

overload and the challenge of optimal information display remain enduring obstacles. How can 

future information displays be designed to optimize information perception, interpretation, and 

decision making? Decision making rests on more than effective distribution and fusion of 

information—human interpretation will always be critical. 

With the advance of new technologies, the amount of information that can be distributed and 

displayed has grown exponentially. Under these circumstances, poor decision making, slower 

response times, and generally poor performance can result because the individual is too focused 

on processing information rather than performing tasks (Wickens, 2008, 2002). Consequently, a 

major challenge in the field of Human Factors is to convey critical information as efficiently as 

possible (to maximize recipient comprehension and decision making), while simultaneously 

minimizing negative factors such as overload and distraction. 

One prominent method for improving task information delivery is the use of multisensory 

devices, or devices which convey task information through multiple or alternative sensory 

channels instead of the visual channel. Such devices may aid performance by simplifying 

information, maximizing user comprehension, and minimizing user workload. In this report, we 

address the use of tactile cues as a method for improving task feedback, as observed in task 

performance indices. Specifically, we provide a review of studies and a meta-analysis of 

experiments that compared the effectiveness of tactile and visual cues, organized by 

informational complexity. 

1.2 Background 

Although a wealth of experimental research has examined the impact of tactile cues across a 

variety of situations, there is no comprehensive review available to summarize the effect of 

tactile cues on task performance. Recent reviews (Jones and Sarter, 2008; van Erp, 2007) provide 

information with regard to several different tactile and haptic devices, but these reviews limit 

their focus to perception and localization as the primary criteria of variables such as stimulus 

duration, frequency, and intensity. In contrast, our review addresses the effectiveness of tactile 

cues with task performance as the primary criterion. This review also compares the effectiveness 

of tactile cues with that of visual cues (instead of comparing features of the tactors themselves). 

In addition, our work organizes the review of vibrotactile cues around the type of task 
2 

information being provided (alert, direction, spatial orientation, communication) as a potential 

moderator. What follows is a brief summary of the theoretical background for this review, an 

overview of the study framework, and a discussion highlighting its importance. 

1.3 Attention and Cognition Under High Workload 

There is no doubt that visual displays enable easy and powerful perception, awareness, and 

comprehension. At the same time, researchers have predicted performance gains from the 

presentation of certain types of information using other sensory modalities such as speech, audio, 

haptic, tactile, and three-dimensional (3-D) (e.g., virtual reality, 3-D audio). 

Two theories predict gains from a multisensory display of information. First, Wickens’ Multiple 

Resource Theory (MRT) (2002, 2008) suggests that (1) people have several semi-independent 

cognitive resources; (2) some resources can be used near-simultaneously without detriment to 

performance, while others cannot; (3) tasks requiring the use of different resources can often be 

effectively performed together; (4) competition for the same resource can produce interference; 

and (5) dissimilar cognitive resources exist to process information from different sensory 

modalities (e.g., visual, audio, or tactile information). MRT defines these capacities and 

contingencies while predicting the degree to which information from a particular sensory channel 

can be effectively offloaded to another channel. In demonstrating the theory, particular attention 

has been paid to pilot performance (Wickens, Goh, Helleberg, Horrey, and Talleur, 2003) 

although there is nothing unique in that environment that would lead the theory to make 

differential predictions. 

The effectiveness of offloading depends on many factors including: (1) overall workload (e.g., 

offloading is more likely to ease high workload), (2) visual attention requirements (e.g., 

offloading is more likely effective when there are high requirements for both focal and ambient 

vision), (3) cue overlap on subtasks (e.g., visual scanning and tactile/audio direction cues 

effectively combine to support a common task such as target detection or navigation), and (4) the 

degree to which separate tasks demand attention. Wickens (2002) provides extensive discussion 

and examples of these factors in MRT. 

Whereas MRT is focused on aspects of workload and conflict in information processing, the 

Prenav model proposed by van Erp (2007) has a strong emphasis on performance that can be 

automated, such as steering, navigation, and control systems. Prenav explains how tactile cues 

can affect attention, cognition, and performance, with particular regard for the effects of practice, 

automaticity, and intuitive response. Prenav consists of two loops: the information processing 

loop and the workload loop. The information processing loop has the usual links from sensation 

to perception, perception to decision, and decision to action. However, shortcuts from sensation 

and perception to action are hypothesized in order to better account for skill-based performance. 

These shortcuts are related to concepts of automaticity (Shiffrin and Schneider, 1984), skillbased learning (Rasmussen, 1983), and naturalistic decision-making (e.g., Klein, 2008; Zsambok 
3 

and Klein, 1997). These shortcuts are proposed to reduce cognitive overload, thus preserving 

performance quality. 

Within the Prenav model the tactile sense is described as highly intuitive and associated with fast 

reaction times. In fact, studies have found that tactile cues can even be faster than other sensory 

channels, when used for alarms or direction cues. For example, faster braking responses occurred 

in reaction to tactile rear-end collision warnings, compared to when visual cues were used (Scott 

and Gray, 2008). In another context, operators fired more quickly in outdoor target practice using 

targets to the left, right, and center when using torso-mounted tactile direction cues than with 

visual cues (Gilson, Redden, and Elliott, 2007). Tactile cues added to visual cues also yielded 

faster reaction times in simulation-based studies of operator decision-making and performance 

(e.g., Calhoun, Fontejon, Draper, Ruff, and Guilfoos, 2004; Calhoun, Ruff, Draper, and Guilfoos, 

2005; Forster, Cavina-Pratesi, Aglioti, and Berlucchi, 2002). 

1.4 Study Framework: Vibrotactile Cues and Informational Complexity 

MRT and Prenav theories provide the background for the hypotheses and analyses in the current 

review. However, whereas MRT focused on workload and Prenav focused on tasks that are more 

easily automated (e.g., steering and control), we examined the literature by comparing task 

performance of users receiving visual and tactile information cues, organized by informational 

complexity. The framework we employed and associated studies are presented in table 1. 

Preliminary meta-analyses on broad cue comparisons indicated performance benefits from tactile 

cues in general (Prewett et al., 2006). This review organized experimental studies to further 

distinguish the different types of comparisons made between visual and tactile cues to specify 

tactile cue effectiveness. 

The first analysis includes experiments with a baseline condition and a condition where tactile 

cues were added to the baseline (e.g., baseline visual [B] vs. tactile [T]). In this comparison, the 

tactile cues neither replace nor repeat information provided by any visual cues inherent in the 

task. The second analysis examines studies that compared visual and tactile cue conditions, 

where the visual and tactile cues represented the same information, but only one cue or the other 

is used in experimental conditions (e.g., visual [V] vs. tactile [T]). Finally, the third analysis 

compares conditions with visual cues against conditions with redundant tactile cues are added to 

reinforce the visual information (e.g., visual [V] vs. visual-tactile [VT]). 

Each type of comparison is further distinguished with regard to the level of information 

complexity within cues. A review of the literature reveals numerous vibrotactile research studies 

addressing four primary purposes: tactile cues for alerts, tactile cues for direction, tactile cues for 

orientation, and tactile cues for communication (see table 1). Across these groupings many 

experiments demonstrated that tactile displays were associated with good localization or 

performance but did not have a baseline or visual condition. The studies included in our analyses 

were restricted to those that met criteria necessary for the meta-analysis. 
4 

Table 1. Organization of meta-analyses. 

Studies included in Baseline Versus Vibrotactile Comparisons by Information Type 

Alerts Hammeed et al., 2006; Ho et al., 2007; Hopp et al., 2005; Moorhead et al., 2004; 

Scott and Gray, 2008 

Direction Bloomfield and Badler, 2007; Chen and Terrence, 2008; Davis, 2007; 

Dorneich et al., 2006; Elliott et al., 2006; Glumm et al., 2006; Hameed et al., 2007; 

Lindemann et al., 2003; Lindeman et al., 2005; Murray et al., 2003; Tan et al., 2003 

Spatial orientation Aretz et al., 2006; Chiasson et al., 2002; Diamond et al., 2002; 

McGrath et al., 2004; Raj et al., 2000; Small et al., 2006; Tripp et al., 2007; 

van Erp et al., 2002; van Erp et al., 2003; van Erp et al., 2006 

Studies included in Visual Versus Visual and Vibrotactile Comparisons by Information Type 

Alerts Calhoun et al., 2002; Calhoun et al., 2004; Calhoun et al., 2005; Forster et al., 2002; 

Hopp et al., 2005; Krausman et al., 2007; Moorhead et al., 2004; 

Sklar and Sarter, 1999 

Direction Bloomfield and Balder, 2007; Elliott et al., 2007; Latham and Tracy, 2002; 

Tan et al., 2003; van Erp et al., 2002 

Spatial orientation Chiasson et al., 2002; Eriksson et al., 2006; McKinley and Tripp, 2007; 

van Erp et al., 2007 

Studies included in Visual Versus Vibrotactile Comparisons by Information Type 

Alerts Calhoun et al., 2002; Forster et al., 2002; Hammeed et al., 2007; 

Krausman et al., 2005; Scott and Gray, 2008; Sklar and Sarter, 1999; 

van Erp et al., 2004 

Communication Brewster and King, 2005; Ho et al., 2001; Pettitt et al., 2006; 

Direction Bloomfield and Badler, 2007; Cholewiak and McGrath, 2006; Davis, 2006; 

Elliott et al., 2006; Elliott et al., 2007; Glumm et al., 2006; Hameed et al., 2006; 

Lindeman et al., 2003; Savick et al., 2008; van Erp et al., 2002; van Erp et al., 2004 

Spatial orientation McKinley and Tripp, 2007; Small et al., 2006 

 

1.5 Tactile Cues for Alerts 

With regard to use as alerts, research participants have successfully perceived and interpreted 

vibrotactile cues in averse, demanding, and distracting situations, such as combat vehicles (e.g., 

Carlander and Errikson, 2006; Krausman and White, 2008), aircrew cockpits (e.g., Diamond, 

Kass, Adrasik, Raj, and Rupert (2002); Rupert, 2000a; 2000b; Rupert, Graithwaite, McGrath, 

Estrada, and Raj, 2004; Sklar and Sarter, 1999), unmanned aerial vehicle (UAV) operation 

(Calhoun, Ruff, Draper, and Guilfoos, 2005; Calhoun, Fontejon, Draper, Ruff, Guilfoos, 2004; 

Calhoun, Draper, Ruff, and Fontejon, 2002), high-speed watercraft (Dobbins and Samways, 

2002); underwater environments (Self, van Erp, Eriksson, and Elliott, 2007), driving (Ho, Reed 

and Spence, 2007; Scott and Gray, 2008), and during strenuous movements (Pettitt, Redden, and 

Carstens, 2006; Krausman and White, 2008). Tactile alerts have been demonstrated effective for 

managing attention during high workload and task switching (Hameed, Ferris, Jayaraman, and 

Sarter, 2006; Ho, Nikolic, and Sarter, 2001; Hopp, Smith, Clegg, and Heggestad, 2005). 

Users have successfully perceived and responded to tactile cues in various body locations. The 

placement of the tactors should correspond with task demands to provide information that easily 

and rapidly recognized. For example, torso-mounted tactors are particularly effective for alerts, 

direction and orientation cues (e.g., Chiasson, McGrath, and Rupert, 2002; Cholewiak and 
5 

McGrath, 2006; Elliott, Duistermaat, Redden, and van Erp, 2007; van Erp, 2007). Other 

locations that have been successfully used include the wrist (e.g., Dobbins and Samways, 2002; 

Brewster and King, 2005; Calhoun and Draper, 2006; Ho, Nikolic, and Sarter, 2001), fingers

(Hameed, Ferris, Jayaraman, and Sarter, 2007; Lathan and Tracey, 2002), palm of hand (Tang, 

Beebe, and Kramer, 1997), arm (Bloomfield and Badler, 2007), abdomen (Ho, Reed, and 

Spence, 2007; Moorhead, Holmes, and Furnell, 2004), shoulder (Hopp, Smith, Clegg, and 

Heggestad, 2005), and back (e.g., Hameed, Jayaraman, Ballard, and Sarter, 2006; Jones, 

Lockyer, and Piateski, 2006; Lindeman, Yanagida, Sibert, and Lavine, 2003). 

1.6 Tactile Cues for Direction and Spatial Orientation 

Tactile direction and spatial orientation cues have demonstrated faster reaction times, better 

awareness of the task situation, and stable spatial orientation (Gilson, Redden, and Elliott, 2007; 

van Erp, 2007). Direction cues can be simple, composed of two tactors (e.g., Dobbins and 

Samways (2002); Moorhead, Holmes, and Furnell, 2004) or tactors situated within a glove 

(Latham and Tracey 2002), but are more often composed of 8–12-tactor torso belts or linear 

arrangements of tactors in a vest-type garment. They have been demonstrated for navigation in 

cars (Fitch, Kiefer, Kleiner, and Hankey, 2007, van Erp and van Veen, 2004) and simulations 

(Davis, 2006, 2007; Ferris, Penfold, Hammed, and Sarter, 2006; Ferris, Hammed, Penfold, and 

Rao ,2007; Lindeman, Sibert, Mendez-Mendez, Patil, and Phifer, 2005), and for personal land 

navigation (Elliott, Duistermaat, Redden, and van Erp, 2007; Duistermaat, 2005; Duistermaat, 

Elliott, van Erp, and Redden, 2007; Van Erp, Spapé, and Van Veen, 2003). Tactile direction cues 

have also been demonstrated for targeting tasks in computer-based simulations (Aretz, Andre, 

Self, and Brenaman (2006; Chen and Terrence, 2007, 2008; Davis, 2006; Glumm, Kehring, and 

White, 2006; McKinley, Gallimore, Lanning, and Simmons, 2005), high-fidelity simulations 

(Eriksson, van Erp, Carlander, Levin, van Veen, and Veltman, 2006; Van Erp, Eriksson, Levin, 

Carlander, Veltman, and Vos, 2007) and vehicles (Carlander and Eriksson, 2006). They have 

also been used in visual search tasks. A common approach to aid visual search uses a vibrotactile 

array that stimulates quadrants on the back that correspond to quadrants on the visual search 

area. Tactors used in this manner have been found to significantly reduce visual search time 

(Brill, Terrence, Downs, Gilson, Hancock, and Mouloua, 2004; Lindeman, Yanagida, Sibert, and 

Lavine, 2003; Tan, Gray, Young, and Traylor, 2003; Young, Hong, and Gray, 2003) and increase 

detection rate (Hameed, Jayaraman, Ballard, and Sarter, 2007). 

Spatial orientation cues, while similar to direction cues, are distinguished based on somewhat 

greater complexity and purpose. Here, the goal is to provide the user with feedback on his or her 

position and location in disorienting environments. There has been particular investment and 

investigation of tactile applications for aircraft and helicopter pilot performance, resulting in 

many demonstrations of effectiveness for aircraft landing and hover tasks. (Chiasson, McGrath, 

and Rupert, 2002; Jansen, Wennemers, Vos, and Groen, 2008; McGrath, Estrada, Graithwaite, 

Raj, and Rupert, 2004; Raj, Kass, and Perry, 2000; Raj, McGrath, Rochlis, Newman, and Rupert, 

1998; Rupert, Graithwaite, McGrath, Estrada, Raj, 2004; Small, Keller, Wickens, Socash, 
6 

Ronan, and Fisher, 2006; Rupert, 2000a, 200b; Van Erp, Veltman, Van Veen, and Oving, 2003; 

Van Erp, 2005; Van Erp, Groen, Bos, and van Veen, 2006; Van Erp, Veltman, and Van Veen, 

2003). 

1.7 Tactile Cues for Communications 

Tactile patterns (e.g., tactons, tactile melodies) have been shown effective for communications 

(Brewster and King, 2005; Brill and Gilson, 2006; van Veen and van Erp, 2003). This is an 

emerging area of research, so that studies are limited and often focused on localization and 

correct interpretation; there are few studies that compare tactile communications with visual 

communication cues. 

1.8 Hypotheses 

Regarding vibrotactile cue effectiveness and information complexity, MRT and Prenav theories 

yield predictions that guide this review’s hypotheses. MRT emphasizes that multisensory 

displays are more effective when workload is high with multiple non-conflicting tasks. Based on 

this perspective, we expect that the addition of unique or complementary vibrotactile cues will 

facilitate effective task performance across a range of task demands. On the other hand, tactile 

cues that replace visual cues will not provide multisensory cues, but rather replace one modality 

cue for another. Thus, we expect tactile cues to improve performance when added to a baseline 

task or when complementing existing visual cues. We predict that replacement cues, however, 

will not significantly improve performance beyond the level achieved with visual cues. 

H1: Conditions which use complementary vibrotactile cues will have stronger performance 

scores than baseline conditions or visual-only cues (baseline versus tactile [BvT], visual versus 

visual-tactile [VvVT]). 

H2: Conditions which use vibrotactile cues to replace visual cues will not lead to higher levels of 

performance than conditions that use visual cues only (visual versus tactile [VvT]). 

Prenav predicts that tactile cue effectiveness depends on the type of information provided by 

tactile cues, primarily because certain tactile cues are more intuitive. In particular, this approach 

anticipates benefits in tactile alert, direction, and spatial orientation cues, because they are more 

easily processed by device users. Using the Prenav model, we expect that tactile cue efficacy will 

depend upon the complexity of information being presented. Specifically, we hypothesize that 

conditions using tactile cues for alerts, direction, or spatial orientation will exhibit a stronger 

effect than the more complex communication cues. 

H3: The advantage of tactile cues over baseline visual cues will be moderated by the 

informational complexity of the cue, in which alerts, direction, and spatial orientation cues will 

exhibit a stronger mean difference statistic than communication cues. 
7 

1.9 Need for Meta-analysis 

Although existing research has generally indicated that tactile cues benefit performance, much of 

the available evidence is weak when assessed individually. Many studies use small samples in 

which the influence from a single individual could disproportionally impact the outcome. Study 

conclusions are often based exclusively on the p-value associated with an obtained point 

estimate. It is well known that an obtained p-value poorly predicts any future obtained p-value, 

even when the p is obtained from large samples. Cumming (2008), for example, demonstrated 

that if an initial experiment results in two-tailed p = 0.05, the p – interval (e.g., the range of 

p-values that is likely to occur upon experiment replication) is very large. Using p-values is also 

a problem for the small sample studies observed in this area, primarily because there is a greater 

chance to obtain a strong effect with a small sample size. With small samples, a single outlier has 

a larger influence on mean differences. This is not the case in larger samples where any 

individual case has a lesser influence on the overall computed statistic (e.g., the F ratio). 

Furthermore, a focus exclusively on statistical significance ignores the value of the effect size. 

For example, two statistically significant (or insignificant) effects may differ by a magnitude that 

yields notable differences in task performance. 

Because of the strong requirement to replicate and validate small study findings with a larger 

sample size, there is a need for a meta-analysis of tactile displays and task performance. Such an 

endeavor addresses the weaknesses of individual experiments by computing an average statistic 

derived from many studies and samples. A meta-analytic review not only serves to validate 

results of individual experimental studies, it also highlights the differences between cue 

complexities and experimental focus that exists between studies (e.g., different independent 

variables). Recent reviews (Jones and Sarter, 2008; van Erp, 2007) provide comprehensive 

information with regard to several kinds of tactile and haptic devices and discuss the 

characteristics that affect perception and localization, such as stimulus type, duration, frequency, 

and intensity. However, they did not address the effectiveness of tactile arrays for task 

performance, nor did they test the impact that informational complexity has on these 

relationships. We argue that the perception of tactor arrays and patterns is not sufficient if the 

cue does not improve task performance. 

The current study addresses the need for a review of the relationships between cue modality, cue 

complexity, and task performance. Our work fulfills this need via meta-analysis of experimentbased comparisons of vibrotactile and visual cues in relation to task performance. To further 

clarify the context, these studies are further organized by cue information complexity. 
8 

2. Method 

2.1 Literature Search 

Multimodal research is being performed in disparate disciplines. As such, key terms (e.g., 

multimodal, audio, tactile, haptic, interface, display) were searched using the following 

databases: Association for Computing Machinery (ACM), Institute of Electrical and Electronics 

Engineers (IEEE), IngentaConnect, PsycInfo, Web of Science, Defense Technical Information 

Center (DTIC), and Cambridge Scientific Abstracts (CSA). Next, a seven-year retrograde handsearch was conducted on the table of contents for the following journals and conference 

proceedings: Human Factors, Human Computer Interaction, Military Psychology, Eurohaptics, 

Society of Industrial and Organizational Psychology, and Institute of Electrical and Electronics 

Engineers. Moreover, the references cited in relevant articles were back-checked to identify 

additional studies. Finally, a number of articles were deemed eligible for inclusion, but lacked 

the statistics (e.g., standard deviations) necessary for meta-analysis. In such cases, the primary 

and/or secondary authors were contacted in an attempt to obtain the requisite statistics for 

inclusion. Study information was entered into a Web-based bibliographic reference database 

(Coovert, Walvoord, Elliott, and Redden, 2008) and is available upon request. 

Inclusion Criteria 

To be included in the meta-analysis, studies had to belong to one of three categories of 

comparisons. In the BvT analysis, tactile cues adding additional information are evaluated 

against a baseline task situation. Examples include adding tactile alerts to a command and 

control decision task or adding tactile direction cues to a simulated driving task. In the VvT 

comparison, tactile cues are evaluated against a condition where visual cues portray the same 

information. This included studies comparing visual versus tactile cues for alerting or navigation 

functions. In this comparison, the tactile cue condition replaces a visual cue with a tactile cue. In 

the VvVT comparison, visual cues are evaluated against the combination of visual and tactile 

cues representing the same information. This is a comparison of single versus multisensory 

display conditions. Only studies with experimentally controlled manipulations (e.g., in which cue 

conditions are assigned) were included. This eliminates the more naturalistic studies in which 

subjects were able to choose which cues to use. We also screened studies for the quality of visual 

and tactile cues. Comparisons were limited to studies that compared cues that were accurate and 

reliable. Thus we excluded some interesting studies that manipulated cue reliability since our 

focus is not on the trust associated with a cue. We also did not include studies of cross-modal 

interactions using cues that were counterintuitive (e.g., where a tactor or visual cue on the left 

was used to cue an event to occur on the right). Although those studies have reason and purpose, 

they are not appropriate for these analyses as our interest is on the effectiveness of the cue itself, 

not on boundary conditions associated with the cue’s use.
9 

In addition, eligible studies had to compare modalities in relation to task performance indices 

(e.g., user errors, completion time, and/or reaction time). We did not include perception as an 

outcome nor subjective ratings of workload, preference, or evaluations of device characteristics. 

Studies that used computer mice or joysticks with haptic or tactile features were also not 

included in these comparisons, as they are the focus of a separate meta-analysis. Studies with 

pathological participant populations (e.g., deaf, blind) or with sample sizes of less than four were 

not included. Lastly, studies with the required modalities and outcome variables were excluded if 

we were unable to obtain enough information to calculate an effect size (i.e., information was not 

reported in an article and the authors failed to respond to requests for statistics). 

2.2 Procedure 

Study Coding 

Studies meeting the selection criteria were evaluated on the following dimensions: (1) article 

characteristics (source used to identify the article, type of publication), (2) sample characteristics 

(age, gender, participant population), (3) research design (setting, within vs. between 

comparisons, random assignment, counter-balancing), (4) modality comparisons (BvT, VvT, and 

VvVT), (5) outcome variable (e.g., error rate, completion time, reaction time), and (6) cue 

information complexity (alert, direction, spatial awareness, communication). Ratings were 

recorded for reliability analyses, and initial differences in ratings were resolved through 

consensus meetings. 

A few studies reported multiple outcome measures; for example, a study may have included 

more than one measure of task error. Using multiple effect sizes (due to multiple criteria) in the 

same analysis would violate the assumption of independence. For those cases, the most 

representative single measure was selected for inclusion. If the criterion measures did not differ 

in quality and also represented similar outcomes (e.g., two types of error), the two effect sizes 

were averaged. When studies reported different types of outcomes (e.g., errors and navigation 

time), they were averaged if they were both expected to be influenced by the focal cue of the 

study (visual or tactile). For example, if the visual or tactile cue is providing direction 

information for navigation during target acquisition, the direction information from the tactile 

cue was expected to enable navigation and also allow more visual attention to look for targets. 

Thus, relevant outcome measures could include waypoint completion, navigation time, and target 

detection rate. In this way, multiple measures comprised the combined effect size estimate when 

appropriate. 

Reliability Analyses 

For categorical variables (modality comparison, outcome variable, task level, task type, and 

workload), a kappa coefficient was computed to assess reliability. Coefficients ranged from 0.84 

(outcome) to 0.90 (modality comparison), indicating acceptable reliability levels for the ratings. 

Initial rater consistency on effect sizes was modest, due to the many ways an effect size could be 
10 

obtained from the study (e.g., due to numerous outcomes). Thus, an intraclass correlation 

coefficient using two of five random raters (ICC2, 5) (Shrout and Fleiss, 1979) was calculated 

after the consensus meeting. Inter-rater reliability is 0.92 for effect size ratings. 

Calculation of Statistics 

A random effects model (Hunter and Schmidt, 2004) was used for the analyses because variance 

was assumed to stem from study characteristics as well as sampling error, whereas the fixed 

effects model only assumes sampling error. As a result, random effects models attribute less 

variance to the treatment effect than fixed models, resulting in wider confidence intervals and a 

more conservative test. Analyses were conducted using the procedures of Hedges and Olkin 

(1985) as applied in Comprehensive Meta-analysis (CMA) 2.0 (Borenstein, Hedges, Higgins, 

and Rothstein, 2005). This procedure weights studies by the inverse of their error variance, such 

that studies with higher standard errors for their test statistic receive lower weights. Analyses 

yielded Hedge’s g, which is preferred in that it corrects for a small-sample bias where the 

standard error (SE) is overestimated (Hedges, 1981). For within-groups studies in which paired 

correlation data were not available, we estimated a correlation of 0.3 to 0.5, using the more 

conservative estimate if there was a difference in overall effect size. Since they are not relevant 

here, we did not correct for measurement artifacts (e.g., for restriction of range, unreliability), 

which would be done if the focus of our study were an issue such as test validation for personnel 

selection (Hunter and Schmidt, 2004). 

The Hedges and Olkin (1985) procedure also estimates the amount of variance among the 

included studies that exceeds what one would expect from sampling error alone (the random 

effects variance component [REVC]). Whereas the REVC is a technical number used in metaanalytic calculations, the percentage of total variance that cannot be accounted for by sampling 

error is more intuitively presented as I2

. We also report the SE associated with Hedge’s g, as well 

as 95% confidence and credibility intervals. Whereas confidence intervals present the precision 

and possible range of the meta-analytic statistic, credibility intervals reflect the expected 

distribution of effect sizes within the population of studies. Confidence intervals are calculated 

from Hedge’s g and the SE; credibility intervals are calculated from g and the REVC. 

Moderator analyses were conducted using the analog to analysis of variance (ANOVA) 

procedure (Hedges and Olkin, 1985). This procedure treats studies in different coding categories 

as independent groups and determines if their effects are statistically different. Significant study 

heterogeneity (Qtotal) in overall analyses indicates a possibility that the overall effect is impacted 

by a moderator variable. Chi square tests then compare within-group variance to betweensubgroup variance (Qb), yielding a test of moderation. 

Finally, in any meta-analysis there is the threat of publication bias, whereby the sample of 

studies used in the analysis is skewed due to the publication process. Statistically insignificant 

studies are less likely to be pursued or published and tend to be under-represented in metaanalytic samples. To detect publication bias, we created funnel plots to graphically observe the 
11 

distribution of studies by sample and effect size. The presence of publication bias is detected by 

an asymmetric graph, in which small-sample studies only co-occur with large effect sizes, but 

large-sample studies contain both small and large effects (for a review, see Light and Pillemer, 

1984; Hunter and Schmidt, 2004, pp. 501). In addition to funnel plots, a classic fail-safe N can be 

calculated. This statistic estimates the number of missing null studies (g = 0) needed to turn the 

significant results found in a meta-analysis into an insignificant one (Rosenthal, 1984). 

In sum, the funnel plots provides a visual depiction of the relationship between sample size and 

reported effect size in order to reveal if a bias exists in reported studies. The fail-safe N tells how 

many studies with non-significant results would have to be added to a significant meta-analytic 

finding to turn it into a non-significant one. Combined, the information provided by the funnel 

plots and the fail-safe N allows us to gauge the confidence of our findings. Due to space 

constraints we do not provide the funnel plots as they collaborate the reported fail-safe N. 

3. Results 

Table 2 presents the meta-analytic statistics (described previously) for our work. In general, 

tactile cues lead to increased task performance across all three comparisons. We now present 

results for meta-analyses associated with each hypothesis. 
12 

Table 2. Meta-analytic results for vibrotactile cue effectiveness. 

Comparison k g SE 95% CI 95% CV REVC I2 Qtotal 

Overall Analyses

BvT 26 1.15** 0.21 (0.74, 1.56) (0.02, 2.28) 0.335 78.10 114.16** 

VvT 23 –0.95 0.22 (–0.41, 2.32) (–1.02, 2.92) 1.011 92.02 288.09** 

VvVT 17 0.89** 0.18 (0.53, 1.24) (–0.01, 1.79) 0.210 65.49 46.36** 

Analyses x Cue info

BvT 114.16** 

Alarms 5 1.60** 0.36 (0.90, 2.29) (–0.60, 3.80) 1.255 90.33 41.38** Direction 11 0.91** 0.21 (0.50, 1.32) (0.25, 1.57) 0.114 58.22 23.93** 

Spatial Orientation 10 1.13** 0.24 (0.66, 1.60) (–0.24, 2.50) 0.490 81.46 48.53** 

VvT 288.09** 

Alarms 7 1.81** 0.45 (0.92, 2.69) (–2.15, 3.05) 1.760 90.58 63.71** 

Direction 11 –0.20 0.29 (–0.78, 0.37) (–1.80, 1.40) 0.665 90.04 110.46** Communication 3 2.29** 0.61 (1.09, 3.49) (–0.74, 5.32) 2.390 95.00 39.98** 

Spatial Orientation 2 -0.02 0.70 (–1.40, 1.36) (–0.02, -0.02) 0.000 0.00 0.31 

VvVT 46.36** 

Alarms 8 1.08** 0.24 (0.62, 1.55) (0.02, 2.14) 0.294 68.47 22.20** 

Direction 5 0.64* 0.26 (0.12, 1.15) (0.19, 1.10) 0.054 41.41 6.83 

Spatial Orientation 4 0.93** 0.40 (0.27, 1.60) (–0.68, 2.54) 0.674 78.28 13.82** 

Note. B=Baseline, V=Visual, T=Tactile. k = number of studies; g = weighted mean difference; SE = Standard error of g; * p<0.05, ** pInterval; CV = Credibility Interval; REVC = random effects variance component; I2 = percentage of between-study variance that is not 

Qb reflect total variance and the between-category variance, respectively; Fail-safe N is the number of studies where g =0 that is needed 

12 
13 

3.1 Baseline versus Tactile 

The comparison between tactile cues and a baseline condition was the most common one found 

in the literature. Studies of this type are relatively homogenous in characteristics. Typical 

examples are provided by Elliott and colleagues (2006) and Dorneich et al. (2006), whose work 

examined actual land navigation (and secondary tasks) in the field. Most other studies were 

based on simulation and virtual reality (VR) based situations. Performance measures varied, but 

most reflected some type of speeded performance (e.g., reaction time, time to navigate, number 

of targets or icons found in speeded situations, and so forth). 

Hypothesis 1 stated that adding tactile cues to a baseline would improve task performance. This 

hypothesis was generally supported as nearly all studies favored the tactile condition. Overall, 

Hedge’s g was 1.15 (SE = 0.21, p < 0.01). Although nearly all studies reported a positive effect 

in tactile cues, there was significant variation in study effect sizes (Qtotal = 114.16, p < 0.01), 

indicating the potential for moderators. 

Moderator analyses by cue complexity, however, were not significant (Qb = 0.31, n.s.). Each 

type of tactile cue provided a significant benefit to tactile performance compared to baseline 

conditions: alerts (g = 1.60; SE = 0.36, p < 0.01), direction cues (g = 0.91, SE = 0.21, p < 0.01), 

and spatial orientation cues (g = 1.13, SE = 0.24, p < 0.01). 

We can have a good deal of confidence in these findings. Although the funnel plot dispersion 

indicated more small-sample studies with positive effect for tactile cues, the fail-safe N reveals 

that it would take 1709 “missing” studies with null findings to change the obtained results. 

3.2 Visual versus Tactile 

Visual and tactile alerts or direction cues were compared for a variety of tasks, including simple 

reaction time or visual search (cf., Forster et al., 2002), simulated driving and/or targeting tasks 

(e.g., Davis, 2006), complex cockpit, UAV, or command simulations (e.g., Sklar and Sarter, 

1999), as well as communicating information (e.g., Redden, Carstens, Turner, and Elliott, 2007), 

localizing from dense multi-tactor displays (Cholewiak and McGrath, 2006), land navigation in 

the field (e.g., Elliott et al., 2007), and orienting in virtual environments (Bloomfield and Badler, 

2007). 

Hypothesis 2 stated that tactile cues would not be effective when replacing visual cues and was 

partially supported. Hedge’s g was not significant for this comparison, primarily due to the 

conservative test of the random effects model. Although the effect of replacing visual cues with 

tactile cues was large (g = 0.95, SE = 0.22), significant variation (Qtotal = 288.89, p < 0.01) 

prevented a statistically significant result. We note that a more liberal, fixed effects model would 

find this mean difference statistically significant. The effectiveness of replacing visual cues with 

tactile appears to be highly dependent upon other experimental factors, as judged by the strong 

variation between individual studies. 
14 

Moderator analyses by cue complexity revealed a significant interaction (Qb = 23.13, p < 0.01). 

Examination by type reveals tactile cues for alerts (g = 1.81, SE = 0.45, p < 0.01) and 

communication (g = 2.29, SE = 0.61, p < 0.01) lead to stronger performance; this is not the case 

for cues relative to direction (g = –0.20, SE = 0.29, n.s.) and spatial orientation (g = –0.02, SE = 

0.70, n.s.). 

Hypotheses 3 specified alert, direction, and spatial orientation cues would aid performance more 

so than communication cues. Results were consistent with predictions for alerts, but not for other 

types of cues. Inspection of the funnel plot is satisfactory and the classic fail-safe N computes to 

198, meaning it would take 198 studies with non-significant findings to change our results. 

3.3 Visual versus Visual-Tactile 

VvVT studies ranged from simple reaction time or tracking tasks (e.g., Moorhead et al., 2004) to 

more complex demands such as communications (e.g., Bloomfield and Badler, 2007), driving 

(e.g., van Erp et al., 2002), cockpit or command, control, and communications (C3) simulations 

(e.g., Calhoun et al., 2004), land navigation (Elliott, 2007), teleoperation in VR environments 

(Lathan and Tracey, 2002), advanced tactile arrays for spatial orientation in flight (e.g., Chiasson 

et al., 2002), or localization of a dense array of tactors (Tan et al., 2003). When comparing visual 

only cues to complementary visual and tactile cues, we predicted a difference between conditions 

in favor of multisensory conditions (Hypothesis 1). This prediction was strongly supported as 

nearly all studies demonstrated a positive effect (g = 0.89, SE = 0.18, p < 0.01). There was also 

significant (though small) variation in the effect magnitude (Qtotal = 46.36, p < 0.01), suggesting 

that cue complexity may yet moderate this comparison. 

Re-analyses based on cue complexity revealed a non-significant moderation effect (Qb = 1.63, 

n.s.). Direction cues produced a marginally weaker effect size (g = 0.64, SE = .26, p < 0.05) than 

alert (g = 1.08, SE = .24, p < 0.01) or spatial orientation cues (g = .93, SE = .40, p < 0.01). 

However, these differences were not strong enough to confirm a significant interaction by cue 

complexity. 

Inspection of the funnel plot suggested some heteroscedastic dispersion. However, the classic 

fail-safe N is 431, signaling a large number of “missing” studies with non-significant results 

would be needed to reverse the conclusion that adding tactile information to visual will increase 

performance significantly beyond that found in a visual-only system. 

3.4 Overall Summary of Results 

When added to a baseline task to existing visual cues, the addition of tactile cues enhanced task 

performance. Using tactile to replace visual cues produced mixed results that were moderated by 

cue information complexity. Tactile alerts were effective when replacing visual alerts, but tactile 

direction cues did not improve performance when replacing visual direction cues. 
15 

4. Discussion of the Effectiveness of Tactile Cues 

This review addressed a need to understand when, why, and how tactile cues enhance task 

performance. We first discussed two theoretical approaches (Wickens’ MRT, van Erp’s Prenav) 

to tactile cues and then provided a quantitative review of the literature to determine empirical 

performance benefits. To conduct our review, we developed a framework that organized studies 

by the type of comparison and by the complexity of information conveyed in the cue. We 

predicted that tactile cues would be more effective when adding unique task information (e.g., to 

a baseline) or when adding redundant task cues (e.g., added to a visual cue). Conversely, we 

expected a smaller influence when tactile cues were used to replace visual ones. Study results 

generally confirmed these predictions. In addition, we expected that tactile cues would be more 

effective in intuitive applications (e.g., cues for alerts, direction, and orientation). Study results 

partially confirmed this expectation, as cue complexity moderated the relationship between 

visual and tactile cues as replacements (VvT), but this effect was not observed in other 

comparisons. Specifically, tactile cues effectively replaced visual cues when providing alerts and 

communications, but they did not provide an advantage when giving information on direction or 

spatial orientation. 

4.1 Adding Tactile Cues to Support Performance 

When tactile cues were added to a baseline task, all subgroup analyses were significant, 

including alert, direction, and spatial orientation cues. There were no studies in this comparison 

category that used tactile cues for communication cues. Tactile cues are found to be very 

effective when used to manage attention and to support tasks such as visual search, navigation, 

driving, target acquisition, and piloting. 

4.2 Use of Multisensory Cues 

Tactile cues are also effective when used in a multisensory presentation. Comparisons from this 

review suggest using multisensory over visual cues to convey task information. Results when 

using tactile information as alerts favor tactile cues, with only one study having a confidence 

interval that included zero (indicating the results are not conclusive). Subgroup analysis for 

directional cues also significantly favors the multisensory condition. Finally, studies comparing 

VvVT cues for orientation also favored a multisensory presentation. 

4.3 Replacing Visual Cues with Tactile Cues 

Results for studies that compared a tactile to a visual presentation of the same information 

indicate considerable variation among studies. Studies using tactile cues as alerts or 

communication tended to support use of tactile cues. However, while tactile communications 

were favored, it must be noted that they were contrasted with hand and arm signals (vs. verbal 
16 

communications). Thus, additional research is needed that compares the effectiveness of tactile 

communication with verbal/audio communication. 

In contrast, studies using tactile direction or spatial orientation cues to replace visual ones did not 

yield a significant effect, though few studies examined spatial cues. It is worth noting that the 

benefit of replacing visual direction cues with tactile direction cues varied significantly between 

studies. 

An examination of studies in which tactile cues did not enhance performance fails to indicate a 

single explanation, but rather suggests a number of situational factors that might influence 

results. Consider the fact that a rich variety of direction cues occur in experiments, yet were 

combined in this meta-analysis. Glumm et al. (2006), for instance, examined effectiveness of a 

torso-based tactile array that provided targeting information in a PC-based single screen 

simulation. The visual icon was within operator view at all times and exactly matched the 

targeting control device in appearance. Thus, the visual cue was easily seen, always in view, and 

intuitively comprehended. Consequently, operators performed very well with the visual cues. In 

another example, Davis (2006) compared a torso tactile belt to visual icons in a PC-based single 

screen simulation. The visual icon was also readily seen, always in view, very easily 

comprehended (i.e., a moving map), and was also associated with better performance. In 

contrast, Savick et al. (2008) and Krausman et al. (2005) used PC-simulations, but required the 

operator to look at multiple screens. This forced operators to divide attention, searching for 

incoming information on yet additional screens. In these situations, the tactile cues were more 

effective as an alert and a directional cue. These contrasting results are entirely consistent with 

MRT theory, in that the tactile cues were more effective when workload was higher and 

comprised multiple tasks needing a high degree of attention management. 

Another set of conflicting results is found when tactile cues support land navigation. Elliott et al. 

(2006) documented a positive relationship between the tactile presentation of navigation 

(direction) information and task performance. Conversely, a follow-up study (Elliott et al., 2007) 

found that visual and multisensory cues were associated with higher performance when the 

visual cue was more easily comprehended. The visual display used in the latter study was very 

simple, using an intuitive arrow display from a commercial handheld global positioning system 

(GPS), compared to the more effortful standard Army alphanumeric display used in the former 

study. Other differences between studies include those commonly found in the field 

environment, such as participants navigating in daylight conditions versus night operations, good 

versus adverse weather conditions, and so forth. Another factor that may have affected 

navigation time with the tactile belt is operator familiarity and trust. Although participants 

rapidly learned how to use the tactile belt and were told they could not “outrun” the signals, they 

reported that they tended to increase speed only after they had used the system for a while. 

Similarly, ratings of the operational effectiveness of the tactile belt increased significantly 

between pre-experiment and post-experiment assessments. In summary, a closer examination of 
17 

studies suggest that tactile cues support waypoint land navigation, particularly when visibility is 

low or attention must be focused on local terrain (e.g., obstacles, dangerous terrain). 

5. Conclusions 

5.1 Strength of Findings 

Use of Hedge’s procedure requires complete data from primary studies, such as the standard 

error of the statistic, the study effect sizes (mean difference), and sample size. One might assume 

this is a limitation to our meta-analysis because many studies that met preliminary experimental 

criteria did not report complete data for calculating Hedge’s g and the SE. Also, indicators of 

variance or confidence intervals were sometimes not reported. In addition, F tests were reported 

for multiple groups, but there is no reference to planned or follow-up paired comparisons, and 

detailed results (means, variance, t or p values) are often not reported for non-significant 

comparisons. We argue, however, that using a conservative set of criteria for article selection in 

meta-analysis will yield a solid foundation of articles from which to conduct the meta-analysis. 

As a result, because the data employed for the meta-analysis has been thoroughly examined and 

vetted, our findings and conclusions are veridical and conservative. 

The results of every significant meta-analysis should be examined via the funnel plot and classic 

fail-safe N to ensure the conclusions are warranted. In our case, the funnel plots are all 

reasonable, and the aggregated effect size for each comparison is high. Classic fail-safe N tests 

for each comparison indicate that the results are robust, as the statistic ranged from several 

hundred to nearly two thousand non-significant studies that would have to be added to the metaanalysis in order to change our conclusions. 

5.2 Future Research 

Although our conclusions provide a solid foundation upon which to build, there was still 

significant variation in study comparisons that were not accounted for by cue complexity (as 

indicated by the heterogeneity statistics). Further research in this area should clarify moderating 

factors and refine guiding principles to determine when, where, why, and how to best use tactile 

cues in support of operator performance in demanding or complex environments. For example, 

research should ascertain the effects on levels of performance complexity, from simple reaction 

times to complex decision making under uncertainty. Workload was ultimately excluded from 

our review because very few studies manipulated or measured subjective workload in a 

systematic manner. Future studies also need to further test and refine theory-driven predictions 

with regard to overall workload and demands for attention management. Additional experiments 

should examine individual differences, particularly with regard to attention management skills, 

task experience, and automaticity. Attention management and other cognitive skills can be 

assessed via PC-based cognitive batteries such as the Automated Neuropsychological 
18 

Assessment Metrics (ANAM) (Reeves, Winter, Kane, Elsmore, and Bleiberg, 2001) and the 

Synwin task (Elsmore, 1994). 

To date, experiments have focused on short-term performance, in which tasks are performed by 

novices or are associated with short periods of training. There is reason to believe that “ease of 

comprehension” is a critical factor in the design of any perceptual cue, be it visual, audio, or 

tactile. This begs several questions. To what degree will initial differences among cue displays 

last? Can such differences be overcome or reversed with extended practice? Research comparing 

alternate and multiple modalities of cue information should be performed in task situations 

where these factors can be effectively manipulated and controlled; these designs would further 

predict and model the impact of levels of task demand and cue complexity. Finally, we must 

consider effects across different task demand situations, from stationary control operators to 

expert first responders in an emergency environment. Principles cannot be applied regardless of 

situation. Instead, designers must always consider in detail the specific cognitive and situational 

demands, in order to best determine where bottlenecks occur and how they can be alleviated.

In summary, we provide a comprehensive analysis of the impact of vibrotactile cues on task 

performance. In order to distinguish different effects of tactile cues, we differentiated between 

comparison types and cue complexity in our analyses. Results demonstrate positive evidence for 

the effectiveness of tactile cues, but benefits are not as distinct when tactile cues replace, rather 

than complement, visual cues. Results are also consistently positive for multisensory 

applications. The variation among studies indicates the need for careful consideration of task 

demands and cue characteristics by interface designers. 
19 

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26 

List of Symbols, Abbreviations, and Acronyms 

ACM Association for Computing Machinery 

ANAM Automated Neuropsychological Assessment Metrics 

ANOVA analysis of variance 

B baseline visual 

BvT baseline versus tactile 

C3 command, control, and communications 

CMA Comprehensive Meta-analysis 

CSA Cambridge Scientific Abstracts 

DTIC Defense Technical Information Center 

GPS global positioning system 

IEEE Institute of Electrical and Electronics Engineers 

SE standard error 

T tactile 

UAV unmanned aerial vehicle 

V visual 

VR virtual reality 

VT visual-tactile 

VvT visual versus tactile 

VvVT visual versus visual-tactile comparison 
27 

NO. OF 

COPIES ORGANIZATION 

 1 ADMNSTR 

 ELEC DEFNS TECHL INFO CTR 

 ATTN DTIC OCP 

 8725 JOHN J KINGMAN RD STE 0944 

 FT BELVOIR VA 22060-6218 

 1 DARPA 

 ATTN IXO S WELBY 

 3701 N FAIRFAX DR 

 ARLINGTON VA 22203-1714 

 1 CD OFC OF THE SECY OF DEFNS 

 ATTN ODDRE (R&AT) 

 THE PENTAGON 

 WASHINGTON DC 20301-3080 

 1 US ARMY RSRCH DEV AND 

 ENGRG CMND 

 ARMAMENT RSRCH DEV AND 

 ENGRG CTR 

 ARMAMENT ENGRG AND 

 TECHNLGY CTR 

 ATTN AMSRD AAR AEF T J MATTS 

 BLDG 305 

 ABERDEEN PROVING GROUND MD 

 21005-5001 

 1 ARMY RSRCH LABORATORY-HRED 

 ATTN RDRL HRM W L ELLIOTT 

 BLDG 4 RM 332 

 FT BENNING GA 31905-5400 

 1 PM TIMS, PROFILER (MMS-P) 

 AN/TMQ-52 

 ATTN B GRIFFIES 

 BUILDING 563 

 FT MONMOUTH NJ 07703 

 1 US ARMY INFO SYS ENGRG CMND 

 ATTN AMSEL IE TD A RIVERA 

 FT HUACHUCA AZ 85613-5300 

 1 COMMANDER 

 US ARMY RDECOM 

 ATTN AMSRD AMR 

 W C MCCORKLE 

 5400 FOWLER RD 

 REDSTONE ARSENAL AL 35898-5000 

NO. OF 

COPIES ORGANIZATION 

 1 US GOVERNMENT PRINT OFF 

 DEPOSITORY RECEIVING SECTION 

 ATTN MAIL STOP IDAD J TATE 

 732 NORTH CAPITOL ST NW 

 WASHINGTON DC 20402 

 4 UNIV OF SOUTH FLORIDA 

 ATTN A WALVORD 

 ATTN K SABOE 

 ATTN M PREWETT 

 ATTN R JOHNSON 

 TAMPA FL 33620 

 1 UNIV OF SOUTH FLORIDA 

 DEPT OF PSYCHOLOGY 

 ATTN M D COOVERT 

 TAMPA FL 33620 

 1 US ARMY RSRCH LAB 

 ATTN RDRL CIM G T LANDFRIED 

 BLDG 4600 

 ABERDEEN PROVING GROUND MD 

 21005-5066 

 3 US ARMY RSRCH LAB 

 ATTN IMNE ALC HRR 

 MAIL AND RECORDS MGMT 

 ATTN RDRL CIM L TECHL LIB 

 ATTN RDRL CIM P TECHL PUB 

 ADELPHI MD 20783-1197 

TOTAL: 18 (1 ELEC, 1 CD, 16 HCS) 
28 

INTENTIONALLY LEFT BLANK.