|©1990, 1995||section list||2: Literature||overview||General Contents|
|Section 1.4||2.1 Outline subsections||Section 2.2|
Within the study of Human-Computer Interaction (HCI) there is a substantial body of literature which uses the phrases `mental models', `user models', `conceptual models', etc. Confusion starts because there is no generally agreed single term which covers these phrases, and other similar ones. For brevity, we will exploit the ambiguity between models of the mind and models in the mind, and here use the term `mental models' as a general term standing for all of the models referred to, and the general models implicit in modelling methods, techniques and theories, as well as having a slightly more circumscribed meaning than it tends to have in the literature.
In this literature there is no universal agreement about exactly which aspects of the users or their actions should be modelled, or how they should be modelled, and as a result writers have tended to define their area of interest implicitly by reference to others' work, or by concentrating on a particular point of view, or a particular practical problem, or particular aspects of modelling. The categories arising from this process of self-definition are not necessarily the best from the point of view of defining independent sub-literatures, or separating out different logical aspects of the subject. Nevertheless, these categories provide a starting point for a sketch of how the literature appears in its own terms: a kind of natural history of the literature. The objective in this first section about the literature is to give that natural history, before starting to discuss individual papers or theories: that will come after substantial discussion of the structure of the whole field.
The lack of definition in the subject also means that the boundaries between it and a number of neighbouring fields are ill-defined. Those fields which are left out here, or only mentioned in passing, include much of cognitive psychology, ergonomics, control theory, general systems theory, cybernetics, information theory, decision theory, management science, and planning. Intelligent tutoring systems and computer-aided instruction are only mentioned briefly. Similarly brief is the discussion of optimal control modelling, since it is less concerned with cognition. A guide to the literatures in control theory, communication theory, statistical decision theory, and information processing is given by Woods & Roth .
The language chosen by authors tends to indicate their background and informal affiliation. The term ``mental model'' emphasises that the object of study is a model which does, or could, belong to a person. It often goes along with the idea that a person has some kind of imagery in their head, which can be `run', or otherwise directly used, to imagine and predict what will happen to something. Authors using this term favourably are concerned with finding out, or modelling, what the person actually `has in their mind'.
``User models'', on the other hand, often represent a view of the user and their attributes to be used equally well by a designer when designing a system, or by a computer system when interacting with a user, the latter model being termed an ``embedded user model''. A ``user'' is generally seen as someone interacting with a computer-based device or system, which provides a service in the context of either work or leisure. Thus a ``user'' might use a word processor, a photocopier, or a library system.
If, in contrast, the person interacts all day long with machinery, the function of which is to serve some purpose external to the person, she or (usually) he tends to be called an ``operator''. Someone may ``operate'' a ship, an aircraft, or a piece of industrial plant. ``Operator models'' tend to model this kind of operator from the perspective of more traditional ergonomics and cognitive psychology. The term is also often used by those writing from the engineering tradition of optimal control modelling.
Various uses of the term ``conceptual models'' have been noted. The most obvious usage follows the idea that a `concept' is an explicitly expressible idea, thus ``conceptual models'' are models communicated to, or used by, people; usually relevant to the early stages of using or operating a system, where it is the general concepts that are the most important features of a model. A conceptual model would be expressible in words or figures, and probably communicated during the course of learning to use or operate a system.
There are other terms used in the literature, of which some (e.g. ``student models'') have an obviously specialised use. More cautious authors say explicitly what aspect of what they are modelling, and do not use any of the general terms unqualified. All these terms are used in the literature, but each of the groupings in the literature tend to have their favourite.
Making a model of task performance can naturally involve task analysis: the breaking down of a task into component sub-tasks and elementary actions. In general, there are many possible ways of doing this, and, unlike the case in some branches of the longer-established sciences, there is no established canonical method for analysis of tasks, nor any widely agreed set of primitive elements which the analysis would give as end-points. The more complex the task, the more potential choice there is of different analyses or styles of analysis, just as there is more potential choice of methods of performing that task.
Traditionally, task analysis has been seen as fulfilling many purposes, including personnel selection, training, and manpower planning, as well as workload assessment and system design, and various analytic procedures have been proposed to meet these purposes. Many of these purposes are given by Phillips et al. , who also note that ``systems which are stimulus-intensive, non-sequential and involve many cognitive responses, such as computer-based systems'' have been less amenable to traditional methods.
There has recently been a growing body of opinion, that styles of task analysis that do not take into account relevant facts about human cognition are less than fully adequate for the analysis of complex tasks. This has led to attempts to describe those principles of cognition which are most relevant for task performance, or to devise task analysis methods that are in harmony with the human appreciation of the tasks. However, the lack of firmly established theoretical principles for such an analysis has meant that various authors have taken, or suggested, different approaches to this goal of a cognitive task analysis.
If we take a view exemplified by Sutcliffe , ``...task analysis is a description of human activity systems and the knowledge necessary to complete a task'', then task analysis is closely related to mental modelling. Cognitive task analysis could be regarded as task analysis done from a mental models viewpoint. Much of the literature reviewed in §2.3 could be seen equally from the perspective of mental models or cognitive task analysis.
Barnard  (see §188.8.131.52) sees cognitive task analysis as analysis in terms of the cognitive subsystems needed to perform the task. This brings in different considerations from those of other authors, and thus the phrase needs using with caution. For this reason, the discussion here prefers the term `mental models'.
The first strand of the literature that we shall consider here focuses around the issues raised by Card, Moran & Newell's book, ``The Psychology of Human-Computer Interaction'' . This book is exemplary in defining an audience, and giving a clear scenario of the possible use of the end products of the analysis given. It sets the scene well to quote some extracts from their application scenario (pp. 9--10):
A system designer, the head of a small team writing the specifications for a desktop calendar-scheduling system, is choosing between having users type a key for each command and having them point to a menu with a lightpen. ... The key-command system takes less time ... he calculates that the menu system will be faster to learn ... A few more minutes of calculation and he realizes ... training costs for the key-command system will exceed unit manufacturing costs! ... Are there advantages to the key-command system in other areas, which need to be balanced? ...This gives us a picture of the use of formal models, which is in calculating reasonable values for expected human performance on a system. The model is of human abilities and ways of doing tasks. Card, Moran & Newell suggest that systems designers will be able to use their models and principles of user performance in the design process, and also that psychologists, computer scientists, human factors specialists, and engineers will find their work of interest.
Card, Moran & Newell expect designers, before using their methods, to have considered the psychology of the user and the design of the interface, specified the performance requirements, the user population, and the tasks. Thus it can be seen that their methods assume a context of use where the problem has already been specified, rather than being of use in the initial stage of breaking down a real-world problem to create the initial task or interface design. This is a fairly limited objective, but by being limited it becomes achievable, and it is not difficult to see how it may work in various circumstances.
A number of authors have followed this general lead, by taking a simplified tractable model of the user, and analyzing human-computer interaction in terms of a formalised approach to modelling. Some models have a worked-out formalism, and they could be called formal models, whereas other models have not yet reached that stage (though clearly intended to) and could therefore be called formalisable. In another sense, the modelling method only provides the means of producing a formal model, actually produced when the method is applied, and in this sense we could say that the methods given in the literature are able to formalise facts or suppositions into usable models. For these reasons, we call these modelling methods `formalisable models', following Green et al. . Some reviewers here distinguish `performance' models, which make predictions, generally quantitative, about the time or the effort involved in operations [12, 20, 66]; and `competence' models, which tell what someone is, or is not, able to do (e.g. ). There are also papers which discuss this kind of approach in general [16, 21, 45, 127, 140], in particular [122, 123], or take their own specific related approach [9, 83].
This class of model answers the question, ``what models can or could predict aspects of human performance?'' As techniques for implementing models get more sophisticated, the boundary between working and just potential models is likely to shift, with more being included in the class of implemented, predictive models. The application scenarios are likely to widen and diversify from Card, Moran & Newell's vision given above.
Engineering and operator models could also be described as formal or formalisable, since they work with numerical or mathematical formalisms, and control theory, rather than informal ideas; and they offer predictions on issues such as: amount of deviation from some ideal performance; workload; errors; and task allocation. Some examples of literature discussing such issues are given in the present bibliography [46, 115, 132, 136, 139, 153].
Since this kind of model is based on engineering control theory, it is most suited to the simulation of tasks which would be amenable to that discipline, such as the manual control task of track-following, where there is a clear definition of ideal performance, and therefore measurements of deviation from the ideal are possible. The models are usually justified by comparing the characteristics of the performance of the models, with measurements of similar characteristics of human performance. But there is little or no justification in terms of psychological mechanisms for the generation of the human behaviour. Thus, these models are models of output, rather than models of internal processes.
The limitations of this kind of approach are acknowledged by some authors in the field (e.g., Rouse , Zimolong et al. ). Such models cannot easily and accurately deal with multiple tasks. Variation between individuals is clearly not easy to accommodate within an optimal model, and higher-level decisions taken at longer intervals are much less easy to model this way than continuous control tasks with short time constants. As limited-purpose models they are no doubt useful, but they are not well suited to rule-based tasks, and even less to knowledge-based ones, because in these tasks it is more difficult to posit ideals. And since a complex task has initially been defined here as one for which there are many practical strategies, these models, based on uniform strategies, even where parameters are allowed to vary, cannot be a good choice for modelling complex tasks.
There would seem to be little prospect for any contact between engineering models and the psychological and other models considered in this study: the references cited by authors in this field overlap little with those in the other fields. For all these reasons, this kind of model is not reviewed in detail here.
There is a set of models, less closely-defined than the formalisable models above, which can be seen as attempting to model what users ``have in their heads'', at the same time trading off the clear-cut predictive ability of the previous classes of models. Gentner & Stevens' book, ``Mental Models''  is a wide-ranging collection of papers which are fairly representative of this kind of approach to modelling, and many authors cite that collection, or at least some paper from it [18, 37, 56, 70, 84, 85, 133, 150]. Inasmuch as these models attempt to model human cognitive processes from a cognitive psychology standpoint, they have also been called ``cognitive models''.
It is difficult to find a common subject in such a broad-ranging literature, which includes models of memory, of learning, of analogy, of commonsense reasoning, all from some kind of computational or algorithmic point of view. What these papers have in common is more like a general attitude: the authors seem to wish to propose models of various aspects of human action broadly in terms of computational processes, but not at such a simplified level as the previous class of models. This usually means that they do not expect practical implementation of their models now or in the near future. They do not in general provide performance predictions. However, the nature of these models is generally such that we could envisage them being formalised and implemented if there was enough time and interest.
Most of these models attempt to clarify what is going on in one area of human thinking, rather than trying to be all-embracing. This means that they can be taken as complementary to one another. Different methods which look irreconcilable may actually be appropriate for different areas of human action. These approaches could be seen as addressing the question, ``what may we suppose to be the basis of a user's or operator's performance?''
The previous group of models focuses on the theoretical basis, but the application of models to training is to produce a practical result. Kieras & Bovair found (in a well-cited study ) that explicit conceptual models presented to learners made a difference to the efficiency of their learning. This bears on the topics of training and learning, where, as some authors have pointed out (e.g. ), explicit conceptual models, and analogies, have a large role to play. The model delivered to the learner or trainee is simpler than that of the designer or trainer, and although it is intended to form the basis of the learner's model of the system, it could hardly be the same as the learner's final model, for if it were, the learner could not be continuing to refine his or her model (as is clearly the case in practice). There is therefore a sense in which the study of this kind of model is not a study of the actual mental models that are in the subject's mind, but rather a study of how to stimulate a user to develop his or her own model more quickly. The corresponding question is, ``what model can we give to a user to help him or her understand and control a new system, or device, or machine, or program?'' There are other opinions on this question that differ from Kieras & Bovair: for instance, Halasz & Moran  consider analogy harmful in the training of new users.
Conceptual models, as in Kieras & Bovair, are unlikely to be identical with the model in the trainee's head, so there is scope for studying the latter model as part of an attempt to see how much of the presented conceptual model has been assimilated. The present study relates to a possible study of what the trainee has actually learnt, but not directly to studies that omit this.
In decision support, and skilled operator's models of complex dynamic processes, effective HCI relies on knowing what is currently in the operators mind, which may be complex and only partially conscious or able to be verbalised, rather than knowing about the conceptual level of the model, which may have been taught to the operator, or which the operator may teach to someone else. For this reason no review of the training and learning literature is given in this paper, other than in passing. Murray  gives a useful wide-ranging review of the modelling literature with close attention to this part of the field.
Different again are models of human learning, which many or may not be used in the context of training. A model of learning would imply a model of what is learnt, and modelling what has been learnt is a reasonable precursor to modelling (rather than merely emulating) the learning process itself. Again, the present study does not relate centrally to the modelling of learning independently from what is learnt, since in complex tasks it is both difficult and important to model what has been learnt, and one cannot expect to achieve this thoroughly from an a priori approach to modelling learning.
Training focuses more on the performance of the user than on the models they actually develop. So what can we find out about what is in a user's model? There are a number of papers that consider how to derive mental models from the users or operators themselves [2, 6, 95, 124]. This is a tricky problem, since users can be quite idiosyncratic, (as recognised long ago by Newell & Simon [91, p.788]). Knowledge elicitation techniques such as protocol analysis and personal construct theory have been used to this end.
Typically, if a user or operator knows a lot about something, they will be able to answer many direct questions about what they do in different situations, but they may not be able to answer questions about their knowledge, such as ``how much do you know about such-and-such?'' If they do not know the extent of their knowledge, and even more so if some of that knowledge is unconscious, they will not be able to give an exhaustive unprompted account of it, and therefore the knowledge that is actually elicited will be restricted by what questions the analyst asks, which will in turn be restricted by the concepts held by the analyst (or in the case of the repertory grid technique , restricted by the elements chosen for the elicitation of constructs). If we have an explicit model of the user's knowledge (and model) of the system, our model will limit what we will be able to find out about the user's model, which will not be unhelpful, if we wish to find out correspondingly limited things. But if we have no explicit model, what we discover about the user's model will be at the mercy of chance, or our intuition. In other words, the way we think of the user's model is intimately bound up with what we are able to discover about it. This means that advances in our model of the user's model will enable more knowledge to be recognised, and conversely, if we know something informally about the user's model that we want to capture, but cannot yet because of the restrictions of our model of the user, that informal knowledge may act as a stimulus to elaborating our model of the user.
Another branch of this literature, where knowledge is gathered from people, is the field of expert systems. The normal area of application of expert systems is, as their name implies, to encode the knowledge of an expert in a way that a non-expert can have access to the expert knowledge and, if possible, reasoning behind that knowledge. Typically, though not exclusively, this has been in such areas as diagnosis.
Diagnosis is generally something that can be discussed and reasoned about, and even if it is difficult to elicit all the knowledge from an expert in diagnosis (in whatever field), it is at least conceivable. In process operation, however, Bainbridge  points out that much of a process operator's knowledge is typically not able to be expressed readily in verbal form. This means that it is difficult to construct a faithful model of operator's decision-making, from the basis of verbal reports or protocols, as would be the norm following the expert system methodology.
Woods & Roth  did not consider the problems of elicitation, but instead considered the cognitive activities performed in nuclear power plants, as their criterion for selecting an expert system as a possible basis for a model of behaviour in the nuclear power plant domain. The system they chose, CADUCEUS, originally from medical diagnosis, fulfilled their criteria of: having a structured knowledge representation; having the ability to simulate problem-solving under multiple-fault, multiple constraint conditions; and allowing data-driven revision of knowledge over time. As with engineering operator models, one question of importance here is, to what extent are such models simply modelling some overall features of human performance, rather than providing a causative model compatible with cognitive psychology? If the object of such a model is merely to provide expert system decision support, then one cannot object to it just on the grounds that it does not model human cognitive processes. However, to provide a sound basis in general for designing operator aids, we do indeed need to model the operators' cognitive processes, at least in terms of their information usage. But Zimolong et al.  did not find any expert systems for process control, whose output matched human output well at a detailed level. For these reasons, it was decided to omit detailed review of papers following this approach.
Whatever the difficulties in elicitation, any good model has to explain the major observed features of human cognition in complex tasks. What needs to be in a good model of a user or operator? A few authors approach the subject of modelling from the standpoint of knowing about the realities of controlling complex dynamic systems [2, 3, 7, 57, 58, 100, 101, 125, 138, 143, 146, 148]. These realities are of such intricacy that there are as yet no proposed fully-fledged models which attempt to account in detail for both normal and emergency operation of complex systems, including errors and recovery from them. The papers of this type therefore tend to tell us (and system designers) about the features which should be taken into account when forming models of process operators and their knowledge and skill. Jens Rasmussen is a central author here, and most of the other authors cite him.
This is the least formalised area in the field of mental models, and this can lead to a sense of vagueness when reading the papers. This is due to the complexity of the questions being approached, compared with the questions dealt with by the cut-down idealisations in the currently formalised models. In this trade-off, informal models gain breadth of applicability at the expense of precision. These authors are more concerned with realism than with ease of formalisation.
Since this division of the field of mental models has not yet reached the stage of common agreement, it is not surprising that there are papers which do not fall neatly into one of these classes, including several review papers covering various parts of the field [13, 51, 88, 106, 140, 142, 150].
Another reason why the literature is not well-defined is because it is possible to see ways in which the divisions will break down in future. It may be naïve to think that one grand mental model could perform all the functions of all the different classes outlined above, but the idea is certainly attractive. If a definitive, theoretically sound model were invented (such as we might expect in the established physical sciences), it would certainly have something to say about all the areas of mental modelling.
More realistically, there are developments which are more easy to envisage, which would move or remove some of the boundaries implied above. Firstly, there is the tendency for implementation techniques to become more intricate and powerful, thus enabling more of the models which have been devised to be used in a practical way. This may mean that some of the less-restricted models pass into being formalisable. Also we may expect models to become more like the humans they are intended to model, which is a conceptual, as well as a technical, advance. The section of the literature on models taken from real life may find greater contact with other sections.
We cannot expect predictive models which accurately reflect all the aspects of human cognition relevant to systems designers until development and integration of the research areas has taken place.
|Next Section 2.2|