Army Research Laboratory 

Aberdeen Proving Ground, MD 2 1005-5425 

ARL-TR-24 12 March 2001 

A Framework of Automation Use 

Mary T. Dzindolet 

Lloyd A. Dawe 

Cameron University 

Hall P. Beck 

Appalachian State University 

Linda G. Pierce 

Human Research & Engineering Directorate, ARL 

Approved for public release; distribution is unlimited. 

i 
Abstract 

Although dramatic increases in the use of automation in recent years 

have occurred across society, research has found that human 

operators often under-use (disuse) and overly rely on (misuse) 

automated aids (Parasuraman & Riley, 1997). A general framework of 

automation use, which proposes that cognitive, social, and 

motivational processes may lead to productivity loss of humancomputer teams, is developed, described, and defended with 

anecdotal and experimental findings. More specifically, automation 

use is predicted to be affected by the difference between the reliability 

of the automated aid and the reliability of manual operation, task 

difficulty, the number of tasks, interest in the task, fatigue, cognitive 

overhead, the rewards for successful performance, the penalties for 

unsuccessful performance, and several cognitive biases. Suggestions 

for future research are briefly discussed. 
Contents 

. 

1. Disuse ........................................... 

2. Misuse ........................................... 

3. Framework of Automation Use. ....................... 

3-l Cognitive Processes: Cognitive Miser Hypothesis - 

Automation Bias .................................... 

3.2 Social Processes: Authority Hypothesis - Trust in Automation . 

3.3 Motivational Processes: Diffusion of Responsibility ......... 

References.. ........................................... 

DistributionList ........................................ 

Report Documentation Page ............................... 

Figures 

1. Automation Bias ................................ 

2. Sample Slide Showing Soldier ........................ 

3. Relative Trust .................................. 

4. SocialProcesses ................................ 

5. Expectancy ................................... 

6. Costs ....................................... 

7. Importance of Outcome ............................ 

8. Framework of Automation Use ....................... 

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A FlLiMEWORK OF AUTOMATION USE 

In December 1995, the crew of an American Airlines Boeing 757, descending through a 

mountain valtey toward Cali, Columbia, attempted to route the aircraft toward their 

destination by entering into thefhght management system (FMS) a substring of the code 

for a CaZi navigational beacon. The computer’s stored database of navigational beacons 

contained two very similar codes. One code denoted the beacon near Cali, which was 

several dozen miles ahead of the airplane. The other code corresponded to a beacon at the 

Bogota airport, several dozen miles behind the airplane. Presented by the FMS with a list 

of nearby beacons matching the entered substring, the crew initiated an overlearned 

behavior and selected the computer’s first presented alternative; unfortunately, the FMS 

had presented the Bogota beacon first. Theflight management computer dutifully began 

turning the aircraft toward Bogota. Shortly after this, the aircraft crashed into the side of 

a mountain, killing all on board (Phillips, 1999). 

Information technology is changing the nature of work. Access to more relevant, 

accurate, and timely information than was previously possible has drastically 

increased the use of automation across society, including the military (Cesar, 

1995). The requirement for automation is highlighted by the increasing 

complexity of the military mission, which is expanding to include the full 

spectrum of conflict, from humanitarian assistance and peace keeping to smallscale contingencies and major wars. To enhance situational awareness and a 

myriad of other tasks, the military decision maker is often provided with 

automated aids. The underlying assumption in providing these automated aids 

to military personnel is that the human-computer “team” will be more 

productive than either the human or the automated aid would be working alone. 

Some researchers have found support for this underlying assumption (Corcoran, 

Dennett, & Carpenter, 1972; Dalal & Kasper, 1994; Parasuraman, 1987; Thackray 

& Touchstone, 1989a). However, as the previous vignette illustrates, humancomputer teams do not always function optimally. 

One strategy used to optimize human-computer performance has been to ask 

system designers to create automated aids that are increasingly more reliable. 

Increasing the automated aid’s reliability is assumed to lead to increased humancomputer “team” performance. As with human teams, however, increasing the 

reliability of one team member’s performance will not necessarily affect the 

team’s performance. Sorkin and Woods’ (1985) signal detection analysis revealed 

that optimizing an automated aid’s performance would not always optimize the 

human-computer team’s performance of a monitoring task. Specifically, they 

found that using a response criterion that yielded the best performance for the 

automated aid (i.e., highest detection rates and fewest false alarms) did not yield 

the best performance for the human-automated team. Although “synergy” can 

be found in human-computer teams (e.g., D&l & Kasper, 1994), it is not likely to 

be gamed by optimizing human-alone or computer-alone performances. The 

interaction between the automated aid and human operator must be considered. 

1 
By understanding the processes that human operators use when deciding to rely 

on their decisions or on an automated aid’s decisions, one may be able to better 

design systems and train military decision makers about the appropriate use of 

automated aids. The purpose of this report is to present a general framework we 

believe will be useful m guiding research to understand human operators’ task 

allocation decisions. 

1. Disuse 

A rational decision maker will rely on an automated aid when doing so will 

maximize gains and/or minimize losses. Failure to rely on an aid in this situation 

constitutes disuse (Parasuraman & Riley, 1997). Disuse is defined as 

“underutilization of automation” (p. 233). Anecdotal evidence supports disuse; 

Parasuraman and Riley (1997) described many real-world incidences in which 

disastrous results occurred because people ignored automated warning signals. 

Further, laboratory experiments have found disuse in paradigms in which the 

aid’s decisions are provided only after the human operators have indicated their 

decision (Dzindolet, Pierce, Beck, & Dawe, 1999; Moes, Knox, Pierce, & Beck, 

1999). For example, Moes and others (1999) found that most college students 

(67%) chose to ignore the decisions of an automated aid even after being 

provided with feedback that their automated aid made half as many errors as 

they did during 200 prior trials. In addition, Riley (1996) found that students 

favored manual operation over automated control, even when doing so was 

clearly not an optimal strategy. 

2. Misuse ~ 

When ignoring an automated aid will maximize gains and/or minimize losses, 

the rational decision maker will ignore the aid and rely on his or her decisions. 

Relying on the automated aid in this circumstance would constitute misuse 

(Parasuraman & Riley, 1997). Misuse is defined as “over-reliance on automation” 

(p. 233). Parasuraman, Molloy and Singh (1993) and Singh, Molloy, and 

Parasuraman (1997) found misuse among operators performing monitoring 

functions. They labeled this behavior complacency, “a psychological state 

characterized by a low index of suspicion” (Wiener, 1981, p. 809). Misuse has also 

been found with automated decision aids (Dzindolet, Pierce, Beck, Dawe, & 

Anderson, to be published; Layton, Smith, & McCoy, 1994; Mosier & Skitka, 

1996). 

2 
3. Framework of Automation Use 

What are the processes leading to appropriate use, misuse, and disuse of an 

automated aid? Mosier and Skitka (1996) outlined three possible reasons 

(hypotheses) why people inappropriately use automation: (1) cognitive miser, 

(2) authority, and (3) diffusion of responsibility. These three hypotheses parallel 

three processes: cognitive, social, and motivational, which have been implicated 

as the causes of productivity loss found in groups (e-g., Mullen, Johnson, & Salas, 

1991). Since many researchers have considered a human-computer “team” to be 

a group in which one member happens not to be human (e.g., Bowers, Oser, 

Salas, & Cannon-Bowers, 1996), we hypothesize that these same three processes 

can lead to sub-optimal performance in human-computer teams. The framework 

of automation use hypothesizes that cognitive, social, and motivational processes 

of the human operator combine to predict automation use. A discussion of each 

process as it relates to inappropriate use is presented in the next three sections. 

3.1 Cognitive Processes: Cognitive Miser Hypothesis - Automation Bias 

Mosier and Skitka (1996) hypothesized that people may overly rely on 

automated systems when making decisions because of faulty cognitive 

processing. In addition to the large body of literature examining errors 

attributable to flawed cognitive processing in individual decision making 

(Tversky & Kahneman, 1973), the social cognition literature is replete with 

examples of less-than-ideal cognitive processing while people work in teams or 

groups. Errors and b’ rases have been identified in various domains of social 

psychology (e.g., illusory correlation, “in-group” differentiation, and “outgroup” homogeneity in stereotype formation [Hamilton & Sherman, 1989; 

Mullen & Hu, 19891, the halo effect and the negativity bias in impression 

formation [Skowronski & Carlston, 19891, the confirmation bias and selfhandicapping in attribution [Arkin & Baumgardner, 1985; Leyens & Yzerbyt, 

19921). People often adopt effort-saving strategies called “heuristics” instead of 

logically processing relevant pieces of information. Mosier and Skitka (1996, 

p. 205) coined the term “automation bias” to refer to “the tendency to use 

automated cues as a heuristic replacement for vigilant information seeking and 

processing.” 

The fact that the automated system provides a decision may lead the decision 

maker to rely on this information in a heuristic manner (see Figure 1). Rather 

than going through the cognitive effort of gathering and processing information, 

the decision maker uses information supplied by the automated systems (Mosier 

& Skitka, 1996). Conceivably, this may occur in various degrees. In its most 

extreme form, the decision reached by the automated aid is immediately 

adopted. In a less extreme form, the decision reached by the aid may be given an 

inappropriately large role in the human’ s decision-making process. For example, 

3 
Layton, Smith, and McCoy (1994) found that many pilots provided with an 

automated aid’s poor en-route flight plan did not explore other solutions (e.g., 

they did not generate actual flight plans on screen) as much as pilots who were 

not provided with the automated aid’s decision. 

Comitive processes 

Provision of Aid’s 

Evaluation Before 

Decision Making 

(automation bias) 

1 Automation Use ( 

Figure 1. Automation Bias. 

The automation bias will lead participants to rely on the automated aid. Often, 

this strategy will be appropriate. However, during certain conditions, this 

reliance may be inappropriate, leading to misuse. 

Although the automation bias plays a role in predicting automation use, it 

cannot account for the full range of potential findings. Specifically, automation 

bias would not be able to account for the anecdotal evidence for disuse when an 

automated aid’s evaluation is provided to humans before they report a final 

decision. The information received from the automated aid is predicted to 

influence the human’s response. The human operator is not predicted to ignore 

information provided by the automated aid. 

Indirect evidence to support the existence of automation bias can be found by 

examining task allocation decisions across two studies with similar procedures 

(Dzindolet et al., to be published; Dzindolet et al., 1999). 

Participants viewed about 200 slides displaying pictures of Fort Sill terrain on a 

computer screen (see Figure 2 for a sample- slide). About half of the slides 

contained one soldier (“target”) in various levels of camouflage; the remaining 

slides were of terrain only. 

Sometimes, the target was easy to detect (as in Figure 2); other times, it was more 

difficult to find. Each slide was presented on the computer screen for about 3/4 

of a second. In the experiment by Dzindolet .and others (to be published), 

participants were provided with the absent or present evaluation of an 

automated aid known to be accurate 60%, 75%, or 90% of the time (based on the 

condition to ~which the participant was randomly assigned). Specifically, 

participants were told that a computer routine had been written to assist them in 

performing their task. They were told that the routine performed a rapid scan of 

4 
the photograph and looked for contrasts that suggested the presence of a human 

being. If the contrast detector routine determined the soldier was probably 

present, the word “present” and a red circle appeared. If the contrast detector 

routine determined the soldier was probably absent, the word “absent” and a 

green circle appeared. 

Figure 2. Sample Slide Showing Soldier. 

The next screen asked the students to indicate whether they believed the soldier 

was in the slide. They were told that they had as much time as they needed to 

make their decision. Participants were informed that there were two types of 

errors that could be made. One error was made when they indicated that the 

soldier was present when he was not. The other error was made when they 

indicated that the soldier was not present when he actually was. Participants 

were told that both errors were equally serious and that they should attempt to 

avoid them. 

Finally, the participants were asked to indicate the extent to which they were 

certain their decision was correct. A five-point Likert scale ranging from “highly 

confident” to “not at all confident” was provided. 

Analyzed with principles from signal detection theory, the error rate was 

determined to be the complement of the area under the receiver operator curve. 

To examine misuse and disuse, the probability of error was determined for two 

subsets of the data. Misuse, or over-reliance on the contrast detector, was 

operationally defined as the e(error I aid error). Disuse, or underutilization of 

5 
the contrast detector, was operationally defined as the p(error I aid correct). 

Analyses indicated that regardless of the reliability of the automated aid, each 

participant was more likely to err by relying on his decision aid than by ignoring 

it, R(error I aid error) = .27; E(error I aid correct) = .13. 

Therefore, when provided with the aid’s decisions first, thereby allowing the 

automation bias to occur, participants were more likely to misuse than disuse 

their automated aids. 

However, in the studies by Dzindolet and others (1999), the automation bias was 

eliminated. Participants were nof provided with the decisions reached by the 

contrast detector until offer they had indicated their decision and their level of 

confidence in their decision. Without the automation bias, would participants 

still be more likely to misuse than disuse their automated aids? 

Participants in these studies viewed the slide of Fort Sill terrain for about 3/d of a 

second, indicated their decision, rated their confidence, and only then were 

provided with the contrast detector’s decision. When they completed all 200 

slides, some participants were told the number of errors they and their aid made. 

Finally, students were told they could earn $0.50 in coupons to be used at the 

cafeteria in the student union for every correct decision made in ten randomly 

chosen trials. Participants had to choose whether the performance would be based 

on their decisions or on the decisions of their aid. After making their choice, 

students were asked to justify their choice in writing. 

Rather than misusing the contrast detector, participants in these studies disused 

the automated aid. Even among participants provided with feedback that their 

aid’s performance was far superior to their own, the majority (81%) chose to rely 

on their own decisions rather than on the decisions of the automated aid! 

Therefore, when the automation bias could play a role in the decision to rely on 

automation, misuse occurred more than disuse (Dzindolet et al., to be published). 

However, when the automation bias was eliminated by the provision of the 

automated aid’s decisions only after participants recorded their decision, disuse 

(not misuse) was found in a subsequent task allocation decision (Dzindolet et al., 

1999). 

Future research should be directed at understanding the automation bias and 

identifying variables that affect it. The results from Dzindolet and others (to be 

published) imply that the reliability of the automated system does not affect the 

automation bias. However, in the study, the reliability information was provided 

to participants, early in their interaction with the system. If the reliability 

information had been provided continually throughout the task (e.g., feedback 

after every trial), perhaps automation reliability would have affected the 

automation bias. Future research should examine this and other variables that 

6 I 
affect the automation bias in an effort to reduce the bias and increase the 

likelihood that human operators will appropriately rely on automated systems. 

Although we hypothesize that automation bias plays a role in automation use 

and misuse, we do not believe it is the only important variable. At the very least, 

processes predicting the bias toward disuse, which are described in the studies 

by Dzindolet and others (1999), are needed. Analyses of the justifications of the 

task allocation decisions provided by participants in one of the experiments by 

Dzindolet and others (1999) indicated that other variables might play a role. For 

example, nearly l/4 (23%) of the participants justified their disuse by stating that 

they did not trust the automated aid as much as they trusted themselves. 

3.2 Social Processes: Authority Hypothesis - Trust in Automation 

A second explanation of the inappropriate use of automated aids has to do with 

the role of the computer as the expert. According to Mosier and Skitka’s (1994) 

authority hypothesis, people rely on the automated system’s decision because 

they believe it to be more reliable and thus place greater trust in it. Many other 

researchers have indicated that trust is one important variable in predicting 

automation use (Cohen, Parasuraman, & Freeman, 1998; Lee & Moray, 1992, 

1994; Moray, Inagaki, & Itoh, 2000; Muir, 1987, 1994; Singh, Molloy, & 

Parasuraman, 1993; Tan & Lewandowsky, 1996). Overly trusting an automated 

aid will lead human operators to misuse; lack of trust in a superior aid will lead 

to disuse. 

Parasuraman and Riley (1997) described many real-world incidences in which 

disastrous results occurred because people ignored automated warning signals 

they saw as untrustworthy. Ignoring automated alarm systems that have 

previously signaled erroneously has been dubbed the “cry wolf” effect (Bliss, 

1997; Bliss, Dunn, & Fuller, 1995). Publicizing the alarm’s trustworthiness was 

one strategy that proved effective in reducing the cry wolf phenomenon (Bliss et 

al., 1995). 

Muir (19&7,1994), one of the first researchers to focus on automation trust, relied 

on the literature of human trust to understand the human operator’s trust of 

automation. She hypothesized that automation will be seen as more trustworthy 

if it is predictable, dependable, and inspires faith that the automated system will 

behave as expected in unknown situations. Trust is gained in the areas of 

persistence, technical competence, and fiduciary responsibility. 

To test some of Muir’s hypotheses, Lee and Moray (1992, 1994) examined the 

effect of trust in automation on task allocation decisions. Participants controlled a 

simulated orange juice pasteurization plant for 2 hours each day for three days. 

The simulation included three subsystems, each of which could be operated 

manually or automatically. Participants could allocate tasks any way they 

7 
wished and could change their allocations easily. As part of the experiment, 

whether controlled automatically or manually, one of the subsystems failed 

periodically. Lee and Moray (1992, 1994) were especially interested in task 

allocation changes after these failure events, since Muir predicted that after 

failure events, trust would decline rapidly and slowly increase as the system 

performed without making errors. At the end of each session, participants 

completed a questionnaire about their trust in the automation and selfconfidence in performing the tasks manually. 

Results indicated strong individual differences in automation use. Some 

participants were prone to use manual control; others were prone to use 

automation. Singh and others (1993) created a scale to determine individual 

differences in the propensity to misuse automated aids, which may have been 

able to predict which individuals would tend to rely on automation and which 

would tend to rely on manual operation. 

Inconsistent with Muir’s hypotheses, the Lee and Moray (1992,1994) participants 

rarely changed their allocation task decisions. Once the human operator assigned 

a subsystem to automated or manual control, he or she was unlikely to change 

the decision during that sessio-ven after failure events. 

To predict trust in automation dynamically, Cohen, Parasuraman, and Freeman 

(1998) extended Muir’s (and others) work and created an especially promising 

model, Argument-based Probabilistic Trust (APT). This model defines trust as 

the perceived probability of the system’s reliability, given certain situations. 

Instead of trust being correlated with the predicted reliability of the system (e.g., 

Muir’s 1994 theory), trust is the predicted reliability of the system. 

Whether predicted reliability is related to tist (e.g., Muir, 1994) or is trust (e.g., 

APT model), we do not believe that predicted reliability will directly predict 

automation use. The predicted reliability of an automated aid is meaningful only 

in the context of some standard of performance. What standard did Lee and 

Moray’s (1992, 1994) subjects use? Automation use was found to be related to 

subjects’ estimates of the ability of the automated aid relative to esfimates of their 

own ability. Thus, we hypothesize that automation use is determined from the 

outcome of a comparison process between the perceived reliability of the 

automated aid (trust in aid) and the perceived reliability of manual control (trust 

in self). We call the outcome of the decision process the perceived utility of the 

automated aid (see Figure 3). If one perceives the ability of the aid to be greater 

than one’s own, perceived utility of the aid will be high. If one perceives the 

ability of the aid to be inferior to one’s own, perceived utility of the aid will be 

low. People may misuse an automated aid because the perceived utility of the 

aid is overestimated; they may disuse an aid when the perceived utility of the aid 

is underestimated. 
Cotitive 

fiocesses 

Perceived 

Reliability of A a 

Automated Aid 

Perceived ,* 

Reliability of 

Manual Operation 

Perceived 

Utility 

Social 

Processes 

I, Relative 

Trust 

Automation 

El 

Use 

Figure 3. Relative Trust. 

Since accurately perceiving the utility of the aid will lead to appropriate 

automation use, it is very important that we gain an understanding of how this 

perception is formed. The perceived utility of the aid will be most accurate when 

the actual ability of the aid and actual ability of the manual operator are 

compared. However, the “actual” ability is never known. Perceived ability is 

determined through a function of actual ability and error. The larger the error, 

the more likely misuse and disuse is to occur. We suspect that at least two types 

of errors occur. 

One type of error occurs when human operators estimate their own performance. 

Human operators tend to overestimate their own ability. Social psychological 

literature is fraught with examples of self-serving biases, Humans exaggerate 

their contribution to a group product (appropriation of ideas, Wicklund, 1989), 

overestimate the number of tasks they can complete in a given period of time 

(planning fallacy, Buehler, Griffin, & Ross, 1994), are overconfident in 

negotiations (Neale & Bazerman, 1985), and inflate their role in positive 

outcomes (Whitley & Frieze, 1985). Thus, we hypothesize that human operators 

will be likely to overestimate their manual ability. 

Providing accurate feedback will assuage the self-serving biases. Using the 

paradigm of Dzindolet and others (1999) described earlier, Dzindolet, Beck, and 

Pierce (2000) found that providing feedback of the contrast detector’s superior 

performance after each trial and/or at the end of the 200 trials reduced disuse. 

The other type of error occurs when human operators estimate the performance 

of their automated aid. Before working with the aid, the human must rely on 

stereotypes formed about the performance of automated aids. Although 

individual differences exist, a bias toward automation leads many people to 

predict nearly perfect performances from automated aids. Dzindolet and others 

(1999) told half the participants that they would be provided with the decisions 

reached by the contrast detector before they made their soldier absent or present 

9 
decision for each of 200 slides. Other participants were told that they would be 

provided with the decisions reached by the previous participant before making 

their soldier absent or present decision for each of the 200 trials. The instructions 

informed the participants that their aid (human or automated) was not perfect. 

When asked to estimate the number of errors that the automated aid would 

make in the imminent 200 trials, participants predicted that the aid would make 

an average of only 14.19 errors (i.e., correct nearly 93% of the time). When asked 

to estimate the number of errors that the human aid would make, participants 

predicted an average of 46.17 errors (i.e., correct only 77% of the tim-nly 27% 

better than chance!). Although previous researchers have reported that people 

tend to have negative initial expectations of automation (e.g., Halpm, Johnson, & 

Thornberry, 1973), our studies reveal a strong bias toward automation. 

In summary, the perceived reliability of the automated aid is determined by the 

actual reliability of the automated aid and by the bias toward automation. The 

perceived reliability of manual operation is determined by the actual reliability 

of the human operator and by self-serving biases. 

We hypothesize that what is important in dete rmining automation use, however, 

is not the perceived reliability of the aid or the perceived reliability of manual 

control but the result of a comparison process between the two: perceived 

utility. Increasing the reliability of the aid will not increase automation use 

unless the aid’s perceived reliability surpasses that of manual operations. 

This can explain the inconsistent findings concerning automation reliability and 

human-computer performance. For example, Riley (1994) and Moray, Inagaki, 

and Itoh (2000) found that people were more likely to overly rely on a more 

reliable automated system than on a less reliable one. Yet, Dzindolet and others 

(to be published) did not find that participants rely on more reliable decision aids 

than on less reliable ones. 

Similarly, Parasuraman and others (1993) and Singh and others (1997) did not 

find reliability to affect automation use with a monitoring task. Participants were 

required to simultaneously perform three flight simulation tasks. Two of the 

tasks required manual operation, but a third task (monitoring) was automated. 

The automation varied in its reliability and in its variability. Some participants 

worked with an automated system that was consistently correct 87.5% of the 

time; others worked with a system that was consistently correct 56.25% of the 

‘Confusion could arise from the similarity of the term, bias toward automation, to a term discussed 

earlier, automation bias. Bias toward automation refers to the finding that participants initially 

expect automated aids to perform better than human operators (Dzmdolet et al., 1999). The 

automation bias is a term coined by Mosier and Skitka (1996) to refer to “the tendency to use 

automated cues as a heuristic replacement for vigilant information seeking and processing” (p. 

205). In fact, the cues (i.e., the decisions) would not have to come from an automated aid. We 

would expect the decisions of both human and automated aids to be relied on in a heuristic 

manner. 

10 
time. Others worked with machines that varied in reliability. Some students 

started with automated systems that were accurate 87.5% of the time for the first 

10 minutes of the session of the experiment but then alternated m reliability to 

56.25% and 87.5% during lo-minute intervals for the rest of the experimental 

session. Other participants started working with a less reliable automated system 

that alternated to a more reliable every 10 minutes during the experimental 

session. 

Over-reliance on automation was found by participants in both of the constant 

conditions (Parasuraman et al., 1993), and the saliency of the automation failure 

signals did not affect the results (Singh et al., 1997). Thus, when the reliability of 

the automation varied, participants appropriately relied on the automated 

monitoring system. However, when the system was consistent in its reliability 

(either high or low), participants tended to overly rely on the system’s 

monitoring capabilities. Singh and others (1997) hypothesize that complacency 

was attributable to the participants being too trusting of the automation. 

“Participants may have begun the automated sessions with an equal amount of 

trust in the automation. However, as the reliability of the automation fluctuated 

for the variable group, their trust may have declined. Therefore, the variability 

group may have been more skeptical of the automation and thus been more 

vigilant for automation failures” (p. 28). 

Why did reliability not affect automation use in the studies of Parasuraman and 

others (1993) and Singh and others (1997)? This finding is unexpected according 

to the APT model and Muir’s model of trust. Our model predicts that 

automation’s reliability will affect automation use only if it leads the human 

operator to perceive the aid’s reliability as greater than his or her own. 

In summary, we hypothesize that people may misuse an automated aid when the 

perceived utility of the aid is overestimated and disuse an aid when the 

perceived utility of the aid is underestimated. The perceived utility of the system 

results from a comparison between the automated system’s perceived ability and 

one’s own perceived ability. Perceived ability is hypothesized to be affected by 

actual ability and various biases (self-serving and bias toward automation). 

Reducing the biases should decrease inappropriate automation use. Dzindolet 

and others (to be published) found that disuse could be reduced by providing 

participants multiple forms of feedback of the aid’s superior performance. 

In addition to trust (through perceived utility) affecting automation use, we 

conjecture that two other social processes may affect automation use: feelings of 

control and moral obligation to rely on self. Figure 4 illustrates the social 

processes. 

11 
Task Difficult 

I 

Qgr& .i ye Processes 

I Bias Toward _ 1 

Self-Serving Biases I 

Figure 4. Social Processes. 

Analyses of the justifications of the task allocation decisions provided by 

participants in one of the experiments by Dzindolet and others (1999) revealed 

that 71% of the students (who were provided cumulative feedback that indicated 

that the aid made about an equal number of errors as the participant) justified 

self-reliance with statements indicating they would not earn more rewards if 

they relied on the aid. Since the task allocation decision would not affect the size 

of their reward, why did participants choose self-reliance? We hypothesize that 

self-reliance provides participants with an illusion of control. Langer (1983) has 

found that people often behave illogically in order to have an illusion of control. 

In addition, many participants (although more of whom worked with human 

aids [n=24, 43.64%] than automated aids [n=9, 16.67%], x2 = 8.58, p c .Ol) 

justified self- reliance with statements concerning a moral obligation to rely on 

self. One student wrote, “I would rather the amount of coupons I receive be 

based on my performance; it seems more ‘fair’ to me.” Another wrote, “I feel 

anything earned should be based on how well I did or didn’t do.” 

Much research remains to be performed to explore the social processes and their 

effect on automation use. Only with a more clear understanding of these 

processes will we be able to suggest ways that misuse and disuse can be reduced. 

The variables that affect perceived utility are of special interest to us because 

perceived utility is not only predicted to affect trust but also to affect the last of 

the processes, the motivational procesjthat is, the variables that affect 

automation use by increasing or decreasing the amount of effort expended on the 

task. ~ 

12 
3.3 Motivational Processes: Diffusion of Responsibility 

A third explanation of the over-reliance on automation discussed by Mosier and 

Skitka (1996) involves the idea that when people work in a group, the 

responsibility for the group’s product is diffused among the group members. 

Several researchers have thought of the human-computer system as a dyad or 

team in which one member is not human (Bowers et al., 1996; Scerbo, 1996; 

Woods, 1996). Thus, the human may feel less responsible for the outcome when 

working with an automated system than when working without one. The person 

may not be as motivated to extend as much effort when paired with an 

automated system as when working alone. In the social psychological literature, 

this phenomenon has been dubbed “social loafing” (Latane, Williams, & Harkins, 

1979) or “free riding” (Kerr & Bruun, 1983). 

One theory that has been successful in accounting for much of the findings in the 

social loafing literature is Shepperd’s Expectancy Value Theory (1993, 1998). 

According to this theory, motivation is predicted from a function of three factors: 

expectancy, instrumentali~, and outcome value. 

3.3.1 Expectancy 

The first factor, expectancy, is the extent to which members feel that their efforts 

are necessary for the group to succeed (see Figure 5). When members feel their 

contributions are dispensable or when one’s individual contribution is 

unidentifiable or not evaluated, one is likely to free ride or work less (Kerr & 

Bruun, 1983; Williams & Karau, 1991). 

Task Difficulty 

Dispenmbility * 

I 

Figure 5. Expectancy. 

1 -+?I 

With a human-computer system, individual contributions tend to be identifiable 

and evaluated; thus, these variables are not thought to affect motivational 

processes. However, when the perceived utility of a system is high, one is likely 

to feel that his or her efforts are more dispensable than when he or she is when 

working with a system low in perceived utility. Thus, we would expect human 

operators to be likely to misuse an automated system deemed more reliable than 

themselves in the same way people free ride on group members deemed more 

reliable than themselves. 

13 
Task difficulty, which is predicted to affect perceived utility (see Figure 5), has 

also been found to directly affect dispensability. In fact, one of the methods 

researchers have used to make group members feel that their efforts are 

indispensable has been to imply that the difficulty of the task makes the 

demands on each group member particularly high (Shepperd, 1993). For 

example, Harkins and Petty (1982) asked participants to generate as many uses 

as they could for either a knife (easy task) or for a detached door knob (difficult 

task) either alone or in nine-member groups. Although they found social loafing 

with the easy task (group members did not generate as many ideas as those 

working alone), they did not find social loafing with the difficult task. 

In summary, the more dispensable the human operator is made to feel, the lower 

expectancy will be; effort will probably be low and the likelihood that the 

automated aid will be relied upon will be high. In some instances, this will lead 

to automation misuse. 

3.3.2 Instrumentality 

Instrumentality, the extent to which members feel that the group’s successful 

performance will lead to a positive overall outcome, is also predicted to affect 

effort. Members who feel the outcome is not contingent on the group’s 

performance are less likely to work hard, Thus, inappropriate use should be high 

among members who feel their group’s performance is irrelevant. In one study, 

Shepperd (1998) varied instrumentality. Half the participants were told that the 

seven groups (of ten groups) with the highest number of ideas generated would 

earn a reward. Other participants were told that the members of the four groups 

(of 40 groups) with the highest number of ideas generated would have their 

names entered into a lottery. One name would be drawn and that person would 

earn a reward. Thus, in the former condition, members had 7 in 10 chances of 

attaining the reward; in the latter condition, there was only 1 in 200 chances of 

attaining the reward. In addition to the optimistic bias (participants estimated 

their chance of winning to be about 1 in 4), Shepperd found that performance 

suffered when instrumentality was lowered. 

On the battlefield, there will be many soldier-computer teams. If the human 

determines that the overall outcome is not contingent on his or her humancomputer team (either because he estimates other teams to be more able to do the 

task or that his human-computer team is dispensable), then the human will put 

little effort into the task. 

3.3.3 Outcome Value 

Finally, the value of the outcome is predicted to affect motivation. Outcome 

value is the difference between the importance of the outcome and the costs 

associated with working hard. Increasing the costs or mmirnizing the importance 

of the reward will lead members to put forth less effort. More effort will be 

extended toward tasks that lead to valuable outcomes without requiring much 

14 
cost. Costs vary with the number of other tasks one must perform, fatigue, 

intrinsic interest in the task, and cognitive overhead (see Figure 6). 

Number 

Motivational Processes 

Overhead 

Figure 6. Costs. 

3.3.3.1 Number of tasks 

As workload increases, the cost of performing a specific task increases, thereby 

increasing automation use (and the potential for misuse). In Study 2 of 

Parasuraman and others (1993), misuse did not occur among participants who 

were required only to perform the automated task. Over-reliance on automation 

was found only among participants who had to manually perform two 

additional tasks. Similarly, Thackray and Touchstone (1989b) did not find 

differences in detection of failures between aided and unaided participants 

performing a single task. Thus, consistent with the framework, increasing the 

number of tasks increased the likelihood for misuse. 

3.3.3.2 Fatigue 

The more fatigued the human operator is, the higher the cost of performing an 

additional task. Therefore, we would expect automation use to be greater with 

more fatigued than less fatigued human operators. In some instances, this will 

lead to appropriate automation use, but in other instances, this will lead to 

misuse of the automated aid. 

3.3.3.3 Interest in the task 

The costs of performing a task that is intrinsically interesting are less than the 

costs of performing a less interesting task. For example, the cost of performing 

boring, redundant tasks is higher than the cost of performing interesting tasks. If 

the task is boring enough, the costs might outweigh the importance; outcome 

15 
value will be decreased, effort will be lower, and automation use and the 

potential for misuse will rise. 

3.3.3.4 Cognitive overhead 

If the cost to change from manual operation to automation use is great, one is less 

likely to put forth the effort to make the change. Parasuraman and Riley (1997) 

indicate that high cognitive overhead can induce complacency or misuse if 

participants initially allocate the task to the automated system. Similarly, high 

cognitive overhead can induce disuse if participants initially allocate the task to 

manual operation. 

In summary, if the costs of manual operation exceed importance because of a 

great number of tasks, fatigue of the human operator, boredom of the task, 

and/or cognitive overhead, then outcome value will be low. The amount of 

effort expended on the task is predicted to be low. Thus, the likelihood for 

automation use and the potential for misuse will be great. 

Costs will only affect automation use if they are deemed greater than the 

importance of the outcome. If the importance of the outcome is deemed greater 

than the cost, outcome value will be high, thereby increasing effort. The 

likelihood for automation use is low in this situation, making the possibility for 

disuse potentially large. 

Importance is predicted to be affected by the rewards of successful task 

completion and the penalties of task failure (see Figure 7) 

wational Processes 

Figure 7. Importance of Outcome. 

3.3.3.5 Rewards 

When successful completion of the task leads to highly valued resources (e.g., 

money, prestige), and if the rewards are not outweighed by the costs of 

succeeding, then outcome value will be high and participants are predicted to 

work hard. Human operators will not rely on the automated aid; rather, they will 

put forth the effort to complete the task. Often, this strategy will be appropriate; 

sometimes, disuse will occur, however. 

16 
3.3.3.6 Penalties 

Similarly, when grave penalties are a consequence of unsuccessful completion of 

the task, specifically when the penalties of failure outweigh the costs of 

succeeding, then outcome value will be high and participants are predicted to 

work hard. In some situations, this will lead to an optimal task allocation 

decision; sometimes, it will lead to disuse of automated aids. 

On the battlefield, sub-optimal human-computer performance can be lethal; 

outcome value is extremely high. In combat, disuse may be more of a problem 

than misuse. Among such highly motivated people, misuse may not exist at all! 

This is consistent with findings from some interviews with Gulf War soldiers 

who indicated that they turned off their automated systems. 

For this reason, it is imperative that some of the research testing of the model be 

performed in more combat-like environments. At the very least, researchers 

should examine automation reliance while varying the consequences for 

successful task performance. Although it is intuitively appealing, albeit 

somewhat obvious, to predict that instituting positive consequences for 

successful performance would lead to improved automation reliance, this may 

not necessarily occur. In Studies 2 and 3 of Dzindolet and others (1999) and 

experiments by Moes and others (1999), participants earned coupons or extra 

credit for correct decisions. Yet, in all three studies, disuse prevailed. Of course, 

in these studies, the consequences were not varied. We predict that increasing 

the rewards and/or the penalties for correct decisions would have decreased 

disuse. Future research needs to determine the validity of such predictions. 

In summary, penalties and rewards will affect the importance of successful 

completion of the task. If importance is greater than costs associated with 

performing the task, outcome value will be high, leading effort to be expended, 

and automation use to be less likely. In some situations, this will lead to an 

optimal task allocation decision; sometimes, it will lead to disuse of automated 

aids. 

In conclusion, the framework of automation use predicts that cognitive, 

motivational, and social processes work together to cause misuse, disuse, and 

appropriate automation use. Many factors affect each of the processes and may 

therefore affect automation use. The reliability of the automated aid, the 

reliability of manual operation, and several cognitive biases (including selfserving and the bias toward automation) combine to affect the perceived utility 

of the aid. When the perceived utility of the aid is high, the operator is likely to 

trust the aid and feel dispensable; his or her efforts are not necessary for the task 

to be completed. Automation use is predicted to be high through both social and 

motivational processes. Fatigue, number of tasks, intrinsic interest in the task, 

cognitive overhead, penalties for task failure, and rewards for task completion 

combine to affect the outcome value, which also will affect the effort the human 

17 
will expend on the task and the likelihood he or she will rely on the automated 

aid (see Figure 8). 

Figure 8. Framework of Automation Use. 

Future research needs to determine the effect of each of the cognitive, social, and 

motivational processes on automation use. To examine one such process, the 

other two processes will have to be controlled or held constant. Such a procedure 

was discussed earlier in the report (see pages 4-5) (Studies 2 and 3 by Dzindolet 

et al., 1999). In both experiments, researchers eliminated the automation bias by 

providing participants with the automated aid’s decision only after the human 

operator had indicated his or her decision. Jn addition, motivational processes 

were controlled. The effort necessary to expend for a s&-reliance derision was 

equivalent to the effort necessary to expend for an automation-reliance decision. 

Using this procedure, the researchers were able to examine the social processes. 

Disuse was found to be widespread and difficult to improve. 

In addition, the interaction of the three processes must be examined. Specifically, 

the relative importance of each process needs to be determined. When all three 

processes were free to impact automation use, Dzindolet and others (to be 

published) found misuse to be more prevalent than disuse. Would increasing the 

penalties and rewards for correct decisions reduce the misuse? How high would 

the consequences have to be before the reduction in misuse became disuse? 

Controlling the automation bias and the cognitive processes, Dzindolet and 

others (1999) found disuse rather than misuse to be prevalent. Ostensibly, the 

18 
tendency toward disuse found in these studies was counteracted by the 

automation bias in the study by Dzmdolet and others (to be published) to such 

an extent that misuse became more of a problem than disuse. How much weight 

does the automation bias play in automation use? How strong must the disuse 

due to social processes be to counteract the automation bias? 

Clearly, much research is needed to examine this framework. We believe the 

framework will prove useful to researchers interested in reducing automation 

misuse and disuse. 

19 
20 
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REPORT DOCUMENTATION PAGE Form Approved 

OMB No. 0704-O 188 

Public,reporting burde? for this collection of information is estimated to average I hour per response, including the time for reviewing instructions, searching existing data sources 

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1. AGENCY USE ONLY (Leave blank) 2. REPORT DATE 

March 200 1 

3. REPORT TYPE AND DATES COVERED 

Final 

4. TITLE AND SUBTITLE 5. FUNDING NUMBERS 

I A Framework of Automation Use I AMS: 622716 

PR: AH70 

Dzindolet, M.T. (Cameron University); Beck, H.P. (Appalachian State University); Pierce, L.G. 

(ARL); Dawe, L.A. (Cameron University) 

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS 8. PERFORMING ORGANIZATION 

REPORT NUMBER 

U.S. Army Research Laboratory 

Human Research & Engineering Directorate 

Aberdeen Proving Ground, MD 2 1005-5425 

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS 

U.S. Army Research Laboratory 

Human Research & Engineering Directorate 

Aberdeen Proving Ground, MD 21005-5425 

11. SUPPLEMENTARY NOTES 

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ARL-TR-24 12 

I 12a. DISTRIBUTION/AVAILABILITY STATEMENT 12b. DISTRIBUTION CODE 

Approved for public release; distribution is unlimited. 

I 13. ABSTRACT (Maximum 200 words) 

Although dramatic increases in the use of automation in recent years have occurred across society, research has found that human 

operators often under-use (disuse) and overly rely on (misuse) automated aids (Parasuraman & Riley, 1997). A genera1 

framework of automation use, which proposes that cognitive, social, and motivational processes may lead to productivity loss of 

human-computer teams, is developed, described, and defended with anecdotal and experimental findings. More specifically, 

automation use is predicted to be affected by the difference between the reliability of the automated aid and the reliability of 

manual operation, task difficulty, the number of tasks, interest in the task, fatigue, cognitive overhead, the rewards for successful 

performance, the penalties for unsuccessful performance, and several cognitive biases. Suggestions for future research are briefly 

discussed. 

14. SUBJECT TERMS 15. NUMBER OF PAGES 

automation decision aids misuse 35 

bias disuse 16. PRICE CODE 

I 17. SECURITY CLASSIFICATION 

I 

18. SECURITY CLASSIFICATION 

I 

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OF REPORT OF THIS PAGE OF ABSTRACT 

I Unclassified Unclassified Unclassified 

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298-l 02