[INTERACTION] Human Interaction With Technology

The Accidental User Marsden and Hollnagel

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HUMAN INTERACTION WITH TECHNOLOGY: THE

ACCIDENTAL USER

Phil Marsden, Ph. D.

Human Reliability Associates Ltd., School House, Higher Lane

Dalton, Lancs., WN8 7RP, UK

Erik Hollnagel, Ph.D.

OECD Halden Reactor Project

P. O. Box 173, 1751 Halden, Norway

ABSTRACT

Information technology is part of a growing number of applications in work and

everyday life. It seems inevitable that the average person soon will have to interact

with information technology in many ways, even when there is no desire to do so.

Examples include finding a book in a library, personal financial transactions, the

health sector, traffic and transportation, process control, etc. People who in this

way are forced to interact with information technology shall be called accidental

users. The accidental user poses a particular challenge to the design of

technological artefacts because the disciplines of dealing with human-machine

interaction are predicated on the assumption that users are motivated and have a

minimum level of knowledge and skills. In particular, models of “human error”

and human reliability implicitly assume that users are benign and only fail as

anticipated by designers. In this paper we investigate the extent to which current

models of human erroneous actions and cognitive reliability can be used to

account for interactions between accidental users and technology.

Keywords: Human reliability, system design, human error model

Classification code: 4010 Human Factors Engineering

1. INTRODUCTION

It is a fundamental premise of classical ergonomics that interfaces and functionality must be

designed specifically to optimise performance in a given task. The ability to describe and

model predictable erroneous actions is therefore crucial for good system design.

Unfortunately, the provision of an adequate model to explain these cases has proved difficult

because psychological processes are both hidden and highly adaptive. Thus, preferred styles of

responding and acting appear to undergo radical change as a result of learning (Hoc et al.

1995), while the quality of performance of particular individuals frequently varies widely as a

function of factors such as stress and fatigue (Swain, 1982).


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Whereas erroneous actions play a prominent role in applied disciplines such as human

reliability analysis and cognitive engineering, the problem of modelling erroneous actions has

rarely been adequately addressed by the HCI research community. Here investigators have

tended to adopt general frameworks which portray the average user as someone who knows

how to deal with information technology and who willingly participates in the interaction.

Clearly, there are many situations where such a characterisation is appropriate. Increasingly,

however, information technology is finding its way into application areas where the user is

unfamiliar with it or may even be ill motivated.

1.1 The Accidental User

When the various categories of users are discussed, it is common to make a distinction

between novice and expert users (Dreyfus and Dreyfus, 1980). This distinction refers to the

degree of knowledge and skill that a user has with regard to a specific system and makes the

implicit assumption that the user has a basic motivation, i.e., that the user is motivated to use

the system. The spread of information technology, however, means that there are many

situations where users interact with information technology systems because they have to do it

rather than because they want to do it. The possibility of doing it in another way has simply

disappeared. Examples include finding a book in a library, personal financial transactions, the

health sector, traffic and transportation, process control, etc. A trivial example is the typing of

a letter, since many offices no longer have a type-writer. Another is recording something from

the TV. Here there was never any other choice, but the problems in using a VCR appropriately

has led to the development of many interesting strategies, many of which aim at minimising

the interaction. A more complex example is driving a train, flying an aircraft, or monitoring

anaesthetics in the operating room (Woods et al. 1994). In the near future there will be even

more cases because the conventional modes of interaction disappear, often in the name of

efficiency!

People who in this way are forced to interact with information technology shall be called

accidental users. An accidental user is not necessarily an infrequent or occasional user; the

use (e.g. of the VCR) can occur daily or weekly, but the use is still accidental because there is

no better alternative. An accidental use is not necessarily a novice or an inexperienced user.

For instance, most people are adept at getting money from an automated teller machine but the

use is still accidental because the alternatives are rapidly disappearing. An accidental user is

not necessarily unmotivated, although it is frequently the case. The motivation is, however,

aimed at the results of the interaction rather than the interaction itself. An accidental user is a

person who is forced to use a specific system or artefact to achieve an end, but who would

prefer to do it in a different way if the alternatives existed. From the point of view of the

accidental user, the system is therefore a barrier that is blocking access to the goal - or which

at least makes it more difficult to reach the goal (Lewin, 1951).

The accidental user poses a particular challenge to the design of technological artefacts

because the disciplines of Human-Computer Interaction (HCI) and Man-Machine Interaction

(MMI) are predicated on the assumption that users are motivated and have a minimum level

of knowledge and skills. In particular, models of “human error” and human reliability

implicitly assume that users are benign and only fail as anticipated by designers. In this paper


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we investigate the extent to which current models of human erroneous actions and cognitive

reliability can be used to account for interactions between accidental users and technology.

1.2 User Models And Accidental Users

In HCI / MMI design the notion of a user model looms large. The user model helps the

designer to predict what the likely user reactions will be, hence to develop an interface and a

dialogue flow that is as good as possible for the tasks. Newell (1993) has eloquently argued

for the need to consider users that are temporarily or permanently impaired in their perceptual

and motor functions. In addition to that, the notion of the accidental user argues that designers

should consider how people who have little or no motivation will perform. In particular,

designers should consider the following possibilities:

� That the user will misinterpret the system output, e.g. instructions, indicators and

information. The misinterpretation need not be due to maliciousness, though that might

sometimes be the case, but simply that users do not know the same things that designers

know, and that users do not see the world in the same way. A simple example is that the

user has a different culture or native language and therefore is unfamiliar with symbols

and linguistic expressions.

� That the user’s response will not be one of the set of allowed events, hence not be

recognisable by the system. System design can go some way towards preventing this by

limiting the possibilities for interaction, but the variability of user responses may easily

exceed the built-in flexibility of the system.

� That the user will respond inappropriately if the system behaves in an unexpected way.

This means that the user may get stuck in an operation, break and/or restart a sequence

any number of times, loose orientation in the task and respond to the wrong prompts,

leave the system, use inappropriate modes of interaction (hitting it, for instance), etc.

In some sense, the accidental user should be considered as if governed by a version of

Murphy’s Law, such as: “Everything that can be done wrongly, will be done wrongly”. More

seriously, it is necessary to be able to model how actions are determined by the context rather

than by internal information processing mechanisms, and how the context may be partially or

totally inappropriate. In order to design a system it is necessary to consider all the situations

that can possibly occur. This means that the designer must be able to account for how users,

whether accidental or not, understand and control the situation.

The purpose of this paper is to consider the issue of modelling erroneous actions for situations

which involve the accidental user. Specifically, we explore whether which current framework

models of human performance and erroneous actions can account for interactions involving

information technology and accidental users. It is argued that a cognitive systems framework

is best suited to providing design guidance in this vital growth area because of the special

emphasis the approach places on contextual, as opposed to experiential, determination of

behaviour. The discussion of user models begins by considering what is meant by the term

erroneous actions.


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2. THE CONCEPT OF ERRONEOUS ACTIONS

It has long been recognised that the occurrence of erroneous actions constitutes a major source

of vulnerability to the efficiency and integrity of HCI. Although reliable figures are difficult to

obtain there is general agreement that somewhere in the range 30-90% of all system failures

involve a human contribution of some type (Hollnagel, 1993a). Despite the apparent level of

agreement among HCI researchers regarding the scale of the problem, however, there is much

less agreement concerning the issue of what is meant by the term erroneous actions (Embrey,

1994; Reason, 1984; Senders and Moray, 1991; Singleton, 1973; Woods et al. 1994). Thus, an

engineer might prefer to view the human person as a system component subject to the same

kind of successes and failures as equipment. Psychologists, on the other hand, often begin

with the assumption that human behaviour is essentially purposive and can only be fully

understood with reference to subjective goals. Finally, sociologists have traditionally ascribed

the primary forms of erroneous actions to features of the prevailing socio-technical system.

Irrespective of the above differences there seem to be at least three intuitive parts to any

definition of erroneous action:

� First, there must be a clearly specified performance standard or criterion against

which a deviant response can to be measured. Human reliability analysis has

traditionally dealt with the criterion problem by using objective measures such as system

parameters as the standard for acceptable behaviour. Thus Miller and Swain (1987)

argued that erroneous actions should be defined as “any member of a set of responses

that exceeds some limit of acceptability. It is an out-of-tolerance action where the limits

of performance are defined by the system”.

In contrast to the above, cognitive psychology has tended to define erroneous action

relative to subjective criteria such as the momentary intentions, purposes and goal

structures of the acting individual. Defined thus there are two basic ways that an action

can go wrong. In one the intention to act is adequate but a subsequent act is incorrectly

performed; in the other actions proceed according to plan but the plan is inadequate

(Reason and Mycielska, 1982). In the former case the erroneous action is conventionally

defined as a slip, in the latter it is usually classed as a mistake (Norman, 1981).

� Second, there must be an event - either cognitive or physical - which results in a

measurable performance shortfall such that the expected level of system performance

is not met by the acting agent. Irrespective of how one chooses to define erroneous

actions most analysts agree that erroneous actions are largely linked with events in

which there is some kind of failure to meet a pre-defined performance standard.

Despite this level of agreement in the literature there has been much less of a consensus

between investigators regarding how best to conceptualise the psychological

mechanisms that lead to erroneous actions. Some investigators have adopted a

pessimistic interpretation of human performance capabilities (Vaughan and Maver,

1972), whilst others have attempted to account for the occurrence of erroneous actions

from within a framework of competent human performance (Reason, 1979, 1990;

Woods et al. 1994). According to this erroneous actions have their origins in processes


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that perform a useful and adaptive function. Such approaches take a much more

beneficial view of erroneous actions and relate their occurrence to processes that

underpin the human ability to deal with complex, ambiguous, and uncertain data.

� Third, there must be a degree of volition such that the actor had the opportunity to act in

a way that would be considered appropriate. Thus as Zapf et al. (1990) have observed, if

something was not avoidable by some action of the person, it is not acceptable to speak

of erroneous action. According to Norman (1983) factors that occur outside the control

of the individual, for example, “acts-of-God”, are better defined as accidents.

In this paper we propose that the concept of erroneous action for the accidental user is best

defined with reference to the observable characteristics of behaviour. Specifically, we suggest

that erroneous actions among this group are simply actions with undesirable system

consequences. Such a definition is in keeping with the treatment of erroneous action in human

reliability analysis and avoids the potential confusion which can arise when discussing the

causes and consequences of erroneous action (e.g., Hollnagel, 1993b). Moreover, a

behavioural definition of the concept of erroneous action is neutral with regard to the issue of

what causes failures in human performance. In relation to this debate we take an optimistic

viewpoint of human performance capabilities and propose that the erroneous actions made by

accidental - and other - users have their origins in processes that perform a useful and adaptive

function in relation to most everyday activities. This suggests that the search for causes in the

accidental user requires an analysis of the complex interactions that occur between human

cognition and the situation or context in which behaviour occurs (e.g., Hollnagel, 1993a;

Woods et al. 1994).

3. FRAMEWORK MODELS OF ERRONEOUS ACTIONS

Modelling user behaviour requires a framework model of human performance that can be

used to characterise the likely forms of erroneous actions that people will exhibit. A review of

the psychological literature indicates that there are three classes of models of human

performance failures that are candidates to explain erroneous actions. One comes from

traditional human factors where focus is on the overt behaviour of the human component of a

man-machine system. The second comes from a line of work which views the human as an

information processing system. The third arises in work carried out in a cognitive engineering

tradition and views the human-machine combination as a joint cognitive system. In this

section, the major features of each class of model are reviewed in turn.

3.1 Traditional Human Factors Models

Several attempts have been made over the years to develop models of user behaviour that

characterise man-machine interaction failures in terms of their basic behavioural (e.g.,

Embrey, 1992; Gagné, 1965; McCormick and Tiffin, 1974). In one early scheme proposed by

Altman (1964) the observable characteristics of erroneous behaviour were differentiated

according to three general types of work activity: (a) activities involving discrete acts, (b)

tasks that involve a continuous process, (c) and tasks which involve a monitoring function.


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Altman suggested that these three categories of behaviour constituted the basis of an error

model of observable action. An example of a recent and much more detailed error model has

been that provided by Embrey (1992). This model is part of the PHEA (Predictive Human

Error Analysis) technique and is specified at the level of the behaviour of a user of an

automated aid. Described in overview, PHEA breaks down erroneous actions into six major

categories and these are further subdivided to identify the basic error types. PHEA has been

used on several occasions to quantify the risk posed to the integrity of complex man-machine

systems by the actions of the human component.

To a large degree most human factors models, including those developed by Altman and

Embrey, are variants of a scheme first proposed by Alan Swain in the early 1960’s (Swain,

1963) and used in a number of guises since that time (see for example, Swain, 1982). In

essence, Swain’s basic model makes a distinction between: (a) errors of omission, defined as

the failure to perform a required operation, (b) errors of commission, defined as an action

wrongly performed, and (c) extraneous errors, defined as the wrong act performed, cf. Figure

1. Of these three error modes, errors of omission and commission are ubiquitous in the field of

human reliability analysis.

Actioncarried out

Need

to act!

Correctaction

Correctexecution

Action / error type

Erroneous execution

Correctly performed action

CommissionNo

No

Yes

No

Yes

Yes

Omission

Figure 1: A pseudo-event tree for omission-commission error classification.

Human factors models provide very simple descriptions of the causes for erroneous actions,

more in terms of observable characteristics than in terms of mental or cognitive functions. In

relation to these models two important points need to be made.

� Despite minor variations in detail there remains considerable agreement between the

various schemes concerning the issue of what constitutes the basic categories of

erroneous actions when discussed in terms of observable behaviour. In many ways the

model originally proposed by Swain and his colleagues remains the “market standard”

in relation to the topic of human reliability analysis although it is clear from field

investigations that the basic framework often needs to be modified to take account of

special constraints imposed by an actual operating environment.

� The second point relates to an observation made by Reason (1986), who pointed out that

there is typically a large measure of agreement between judges when they are asked to

assign erroneous actions to these relatively limited behavioural categories. This finding


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suggests that behavioural models of erroneous actions such as the ones identified above

possess a high degree of face validity.

Traditional human factors models of human performance and erroneous action are

nevertheless quite weak in their ability to characterise events in terms of their psychological

features and are essentially without an underlying theory. Although the categories of

omission-commission are easy to apply, all the available evidence suggests that erroneous

actions which appear in the same behavioural categories frequently arise from quite different

psychological causes, while, erroneous actions which differ according to their external

manifestations share the same or similar psychological origins (e.g., lack-of-knowledge,

failure of attention). For this reason, in the mid 1970s to mid 1980s a number of people began

a line of research aimed at specifying the psychological bases of predictable erroneous action.

The products of this research effort are considered in more detail in the following section.

3.2 Information Processing Models

Models of human behaviour aimed at providing explanations of erroneous actions in terms of

the malfunction of psychological mechanisms have been influenced by the adoption of the

digital computer as the primary model for human cognition (e.g., Newell and Simon, 1972;

Reason, 1979; Simon, 1979), and the belief that the methods of information theory can be

meaningfully applied to the analysis of mental activities of all types (e.g., Anderson, 1980;

Attneave, 1959; Lindsay and Norman, 1976). An analysis of human performance from an

information-processing standpoint aims to trace the flow of information through a series of

stages that are presumed to mediate between a stimulus and response. Resultant theories are

conventionally expressed in terms of information flow diagrams analogous to those which are

prepared when developing a specification for a computer program.

Information processing analyses use either quantitative or qualitative methods. Quantitative

methods involve assessing a person’s performance under the controlled conditions of the

psychological laboratory. Qualitative models, on the other hand, are usually developed on the

basis of observations of human performance under real or simulated conditions. The relative

advantages of these two approaches are of central importance in the behavioural sciences and

have been discussed at length elsewhere (e.g., Bruce, 1985; Neisser, 1982). In relation to the

explanation of erroneous actions the two approaches have produced models of human

performance and error that are quite distinct.

3.2.1 Models Based Upon Quantitative Analysis

A good example of a human performance model based upon a quantitative analysis is the

general model of human cognition developed by Christopher Wickens and his associates

(Wickens, 1984; 1987). In this model, shown in Figure 2, information is described as passing

through three basic stages of transformation: (a) a perceptual process involving the detection

of an input signal and a recognition of the stimulus material, (b) a judgmental phase in which

a decision must be made on the basis of that information, relying where necessary on the

availability of working memory, and (c) a response stage in which a response must be selected

and executed. Each stage has optimal performance limits (as determined by limited attention


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resources) and when these limits are exceeded each is subject to error. The determination of

optimal performance limits and the various error forms that emerge when these limits are

exceeded were estimated for the model using data obtained from laboratory-based

psychological experiments.

(c)(b)(a)

Feedback

Controls

Displays

Plant state

Decision and

response

selection

Control

actions

Working

memory

Long term

memory

Sensory store

Attentional resources

Human operatorProcess Interface

Figure 2: Wickens’ (1984) model of human information processing applied to the human-machine interface.

Although this type of model has not been universally accepted it is nevertheless representative

of a wide variety of theoretical models which have been developed on the basis of error data

elicited from experiments conducted in the psychological laboratory. Parasuraman’s (1979)

attempt to explain vigilance decrement in terms of working memory defects is an example of

a typical “error experiment”. Similarly, Signal Detection Theory (SDT), response latency and

the speed-accuracy trade-off paradigms have provided methodological tools appropriate to

this type of research.

3.2.2 Models Based Upon Qualitative Analysis

Arguably the most influential qualitative information processing model is the skill-based,

rule-based, knowledge-based framework proposed by Rasmussen and his associates (e.g.,

Rasmussen, 1981; Rasmussen and Jensen, 1974). The SRK model is based upon the

proposition that there is a normal and expected sequence of processing activities that a person

will engage in when performing a problem-solving or decision-making task, but that there are

many situations where people do not perform according to the ideal case. Borrowing a phrase

first used by Gagné (1965), Rasmussen talked of people ‘shunting’ certain mental operations

where varying amounts of information processing can be avoided depending on the person’s

familiarity with the task.

The error component of the SRK framework has been discussed at length by Reason (1986)

and Reason and Embrey (1985). The results of this effort were incorporated into an error

model called GEMS (Generic Error Modelling System), which is based on the distinction


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between slips and mistakes superimposed onto the SRK framework. The resultant model

gives rise to some basic error types conceptualised in terms of information processing failures.

At the lowest level of the system are skill-based slips and lapses. Reason (1990) defines these

as errors where action deviates from current intention due either to an error of execution (e.g.,

a slip) or memory storage failure (e.g., a lapse). In contrast mistakes are defined as

deficiencies or failures in the judgmental and/or inferential processes involved in the selection

of an objective. Like slips and lapses, mistakes are viewed as dividing into two broad types.

Rule-based mistakes occur from the inappropriate application of diagnostic rules of the form:

IF <CONDITION>, THEN <ACTION>. Knowledge-based mistakes occur whenever people

have no ready knowledge to apply to the situation which they face. Thus in contrast to errors

at the skill-based and rule-based levels, knowledge-base failures reflect qualities typical of the

novice.

The major features of the GEMS model are summarised in below. Each error type is

distinguished according to five factors: (a) the type of activity being performed at the time the

error is made, (e.g., routine or non-routine); (b) the primary mode of cognitive control,

(attention or unconscious processing); (c) focus of attention (on a task or activity); (d) the

dominant error form (strong habit intrusions or variable); and (e) the ease with which the error

can be detected and corrected (easy or difficult).

Characteristics Of GEMS Error Types

Activity Mode of

control

Focus of

attention

Error forms Error detection

Skill-based

slips and

lapses

Routine

actions

Mainly

automatic

processes

(schemata)

On something

other than the

task at hand

Largely predictable

“strong-but-

wrong” error

forms

(schemata)

Usually fairly

rapid

Rule-

based

Problem (rules) Directed at (rules) Hard, and often

mistakes -solving Resource- problem Variable only achieved

Knowledg

e-based

mistakes

limited

conscious

processes

related issues with help from

others

Theoretical models of human information-processing have been used on numerous occasions

to account for systematic erroneous actions in HCI / MMI. Information processing models use

three basic assumptions regarding to provided answers to questions about the relation between

erroneous actions and human cognition (e.g., Kruglanski and Azjen, 1983).

� It is assumed that there are reliable criteria of validity against which it is possible to

measure a deviant response. In some cases the performance standards employed are

derived objectively; in other cases the standard is determined subjectively in accordance

with the person’s intentions at the time the erroneous action was perpetrated.


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� Psychological factors that intervene in processing activities act to bias responses away

from standards considered appropriate. Most common are the various information-

processing limitations which are presumed to make human performance intrinsically

sub-optimal. These limitations are brought into play whenever there is more information

than can be processed within a particular period of time. Other psychological factors are

“emotional charge” (e.g., Reason, 1990) and performance influencing factors such as

fatigue and stress (Embrey, 1980; Swain and Guttman, 1983).

� The information processing system comprises a diverse range of cognitive limitations

which are invoked under particular conditions. Thus, failures of attention are likely to

occur whenever too much - or too little - happens in the environment, decision-making

is likely to fail whenever judgements of a certain type are to be made (e.g., estimation of

quantitative data), and so on. It is sometimes also assumed that there is a one-to-one

mapping of external manifestations of erroneous behaviour onto the categories of

“failure” that are presumed to occur in human information processing.

These assumptions have important implications for the study of erroneous actions. They imply

that the appropriate approach is to identify the basic error mechanisms which shape human

performance and couple that with an analysis of the types of information-processing a person

is likely to engage in. The results may be then be mapped out in a matrix which permits the

prediction of information-processing behaviour from knowledge of the information-

processing domain and basic error tendencies. Several analysts have attempted to define such

a matrix with perhaps the framework proposed by Reason (1987) as the best example.

3.3 The Cognitive Systems Perspective

A third class of models are based on the perspective of cognitive systems engineering

(Hollnagel and Woods, 1983; Woods, 1986). Cognitive systems engineering makes two

important assumptions regarding the analysis of human performance in a work setting.

� The interactions between the human agent and automated control systems are best

viewed in terms of a joint cognitive system. It is no longer reasonable to view the

relative roles of the person and supporting control systems in terms of a Fitts’ list type

of tables which describe the relative merits of men and machines (Fitts, 1951). Instead

the notion of the joint cognitive system implies that machine and user should be

modelled on equal terms. Furthermore, the coupling of the two models is necessary to

appreciate and analyse the details of the interaction. In other words, modelling the user

as an entity in itself is not sufficient. Because of this, classical information processing

models of human cognition are inadequate for analyses of erroneous actions. Although

the context or environment is present in the form of input (signals, messages,

disturbances) the representation is not rich enough to capture the dynamics and

complexity of the interaction. This can only be achieved by providing a coupled model

of the human-machine system, and by making the model of both parts equally rich.

Several projects have used this approach, e.g. Corker et al. (1986), Cacciabue et al.

(1992), Hollnagel et al. (1992), Roth et al. (1992).


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� The person’s behaviour - including possible erroneous actions - is shaped

primarily by the context in which it occurs. The information processing approach

considers activities as essentially reactive, i.e., the person responds to an input event. In

cognitive systems engineering cognition is an active process influenced by the person’s

goals and the prevailing context. The focus of attention should therefore not be confined

to an analysis of malfunctions of presumed cognitive mechanisms but rather be put on

the global characteristics of human performance - both correct and incorrect responses

to specific environmental circumstances. The implications of cognitive systems

engineering have been used to guide the definition of contextual models of behaviour.

An example of a model of this type is provided by COCOM - for Contextual Control Model -

which integrates a theory of human competence and a model of cognitive control (Hollnagel,

1993a). The competence represents the possible actions that a person may carry out (the

activity set) in order to complete a particular task, given a characteristic way in which

available actions may be grouped. The latter structure is named as the template set, to

emphasise that sets of actions will be grouped together in special ways to be utilised as a

single unit in particular circumstances (e.g., a procedure for responding to a steam generator

tube leak). The separation of competence into the activity set and the template set makes it

possible to model a number of characteristic performance phenomena, such as fixation and

mistakes, using a few relatively simple functional principles.

The purpose of cognitive control is to describe how actions are selected and subsequently

carried out. The model identifies four basic control modes as characteristic performance

regions. These are: (a) scrambled control, where the event horizon is confined to the present

and there is no consideration of preceding events or prediction of the outcome of future

events, (b) opportunistic control, where an action is chosen to match the current context with

minimal consideration given to long-term effects, (c) tactical control, where actions are

governed by longer term considerations and extensive feedback evaluation, and, (d) strategic

control, where the person is fully aware of what is happening and is deliberately making plans

to deal with the situation which requires the selection and execution of particular controlling

actions. The purpose of the model is to describe how performance switches between the

various control modes dependent upon the outcome of previous action as well as

consideration of available time, cf. Figure 3. (The dotted lines indicate a feedforward.) Other

parameters that can influence the control mode are the number of simultaneous goals, the

availability of plans (which are part of the competence), the perceived event horizon, and the

mode of execution.

4. MODEL EVALUATION

Traditional human factors models are reasonably effective where designers are interested in

possible error modes specified in terms of their external manifestations. This is especially the

case where the objective of analysis is to predict the probability of simple errors which may

occur in relatively trivial tasks such as searching for a book in a library. Although more

complex models of human behaviour have been specified at the level of observable behaviour,

the basic limitation is that the approach provides little information regarding the psychological


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causes of erroneous actions. It is therefore unable to distinguish between an accidental and an

intentional user. The analytic capability of human factors models is typically quite low and

resultant analyses of failures in experimental systems tend to yield few general principles to

help designers with the predicament of the accidental user.

Information processing models have a high analytic capability, but are not very good at

converting field data to useful and practical tools for prediction of possible erroneous actions.

The analytic capability derives mainly from the large number of general statements relating to

“error” tendencies that are typically part of such models. The validity of predictions based on

these models is, however, unclear. Experience shows that actions frequently fail when they

should be well within the user’s performance capability. Conversely, it is easy to document

instances where user performance has been quite accurate for tasks where the model would

predict failure (Neisser, 1982). A particular shortcoming in relation to the situation of

accidental users is that information processing models account for failures in terms of the

automation of skill-based behaviour. This means that the predominant explanation of

predictable erroneous action refers to the expertise of the perpetrator. Such a view is not

consistent with the idea of an accidental user where the hallmark of human behaviour often is

lack of specialist task knowledge and familiarity with the system.

Event / Action

feedback

Determination of outcome

Number of goals

Subjectively available time

Control mode Scrambled Opportunistic Tactical Strategic

Competence: Plans, actions, templates

Choice of next action

Next

action

Figure 3: Principles of a contextual control model.

Models from cognitive systems engineering provide a better approach for characterising the

interactions between information technology and accidental users - and intentional users as

well. These models are particularly strong in their technical content because they are based on

viable and well articulated descriptions of human action - rather than of covert mental

processes. Moreover, the emphasis on the contextual determination of human behaviour is

clearly better suited to explanations of a user predominantly driven by environmental signals

(e.g. the interface) as opposed to past experience and prior knowledge. The accidental user

will typically have limited competence and limited control, and the ability of these models to


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describe how the interaction between competence, control and feedback determines

performance is therefore essential. The cognitive systems perspective also affords a

comparatively high level of predictive capability in a model that can be converted into

practical tools for investigation of erroneous actions. In our view the cognitive systems

perspective is therefore the all round approach best suited to model how an accidental user

interacts with information technology.

5. REFERENCES

Altman, J. W. (1964). Improvements needed in a central store of human performance data.

Human Factors, 6, 681-686.

Anderson, J. R. (1980). Cognitive psychology and its implications. San Francisco: Freeman.

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