Psychology as a Science of Design in Engineering

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1 Proceedings of the British Psychological Society (vol. 7, No. 2) and the Bulletin of the Scottish Branch of the British Psychological Society in Psychology as a Science of Design in Engineering Patrik O Brian Holt and George T. Russell Image Systems Engineering Laboratory Department of Computing and Electrical Engineering Heriot-Watt University Edinburgh BACKGROUND Psychology is a science, which possesses potential for contributing to other disciplines and can frequently be seen as a component in multidisciplinary projects. While such contributions are important, psychology also shows the potential for becoming a science of design in engineering in a way, which integrates psychology into other disciplines. This is a form of interdisciplinary work, which may result in new sub-disciplines or hybrids as opposed to multidisciplinary approaches where the parent disciplines collaborate while retaining all the original features, e.g. models, methods, tools and techniques. The work reported here originates in an interdisciplinary approach taken by the Image Systems Engineering Laboratory at the Department of Computing and Electrical Engineering at Heriot-Watt University. The laboratory was founded in 1996 to promote and foster multidisciplinary research in image processing, image interpretation, and scientific visualisation through the investigation of complex human and machine systems and their interaction with a special emphasis on cognition. The core scientific approach is generic systems analysis, cybernetic modelling, and visualisation of visual sensor data, both human and artificial. An important characteristic of the work is the application of human perceptual and cognitive computational models to the design of sensors systems and subsequent analysis and interpretation. THE PROBLEMS OF MULTIDISCIPLINARITY APPROACHES Multidisciplinary approaches are seen as a fruitful avenue for enhancing research and such approaches are frequently looked upon favourably by funding bodies. However, it should be emphasised that multidisciplinary work is not without problems. In a study of user involvement in information systems development, Gasson (1995) shows that conflict can be observed between psychologists and software experts when attempting to design a system co-operatively. Rather than working in complementary ways, the two groups attempt to gain control over project components in order to gain overall control. Communication between the two groups may be very poor, both may be suspicious of each other, and there is limited evidence of either understanding or appreciation of the professional skills or roles each group might have.

2 PSYCHOLOGY AND HUMAN-COMPUTER INTERACTION (HCI) Contrary to the negative tone expressed above the contribution that psychology can make to other cognate areas has been recognised for decades. Psychology has, for example, made substantial and important contributions to ergonomics, human factors engineering, artificial intelligence, cognitive science, cognitive engineering, and human-computer interaction (HCI) (see for example Norman and Draper 1986, Norman 1988 and 1998). In HCI, psychology, in particular cognitive psychology, has been a dominant influence for the past twenty years and according to Carroll (1997) is showing the potential for becoming a science of design: The emergence of HCI in the last two decades illustrates the possibility of psychological inquiry in the context of system development, of progress with fundamental issues joined with engineering design. Psychology s increasing contribution to engineering as a whole can also be witnessed in the currently emerging specialism of engineering psychology for which there is now a BPS SIG. HCI is regarded as a sub-discipline of computer science or more specifically software engineering, yet the discipline is dominated by cognitive psychology. One of the key issues behind the development is that psychological theory and practice has been integrated in to the emerging discipline of HCI, in other words, the approach is interdisciplinary not multidisciplinary. MEETING FUTURE NEEDS: THE INTEGRATED MODEL (IM) APPROACH While psychology continues to make important contributions to engineering directly or as part of emerging disciplines new developments in engineering and humanmachine interaction will require new responses. Engineering design is moving towards what can be termed VERY complex systems and of particular importance here are control systems, e.g. control of complex systems such as aeroplanes, plants, Power Stations, remotely operated vehicles etc. The word "complex" here refers to both the system itself (hardware and software) and the interaction or control of the system. In very generic terms, we define that any complex system obtains, stores and processes information; makes decisions and takes action. This generic definition implies that both human operators and artefacts can be described within the same framework and this is the approach taken in the work described in this paper. To meet emerging needs an integrated modelling approach, with the following characteristics, is proposed: 1. The human operator and the artefacts (e.g. control systems) are regarded as components in a single system. This can be described as a systemic approach, which involves an integrated design. 2. There is a need for principled task allocation, i.e. the identification of core tasks and the allocation of the tasks to either the human operator or the artefact. 3. The design of the artefact is based on models of human information processing.

3 The IM approach bears some resemblance to artificial intelligence (AI) work but should not be regarded as traditional AI as the aims, tools, and techniques used differ. If AI were to be defined simply as making artefacts "smarter" then the proposed IM approach falls in to that category. Figure 1 below shows the basic concepts behind the IM approach. Human IP Control Engineering Tools Design Rules ARTEFACT Design Figure 1: An IM approach to engineering design The basis of the IM approach lies in human information processing models as described generally in the psychological and cognitive science literature. The models are scientific representations, which have been tested empirically with human participants and with computer simulation and modelling (e.g. Collins and Loftus 1975, Collins and Quillian 1969, Marr 1982). Such models tell us much about human perception and cognition and can be used to inform engineering design. There are however, known problems with this approach as the models tend to be too vague and general to be applied directly in the design of engineering artefacts (Landauer 1995). This has resulted for example, in the emphasis on prototyping and empirical evaluation (for refining designs) in HCI as a means of overcoming the general problem. Arguably the problem is also exacerbated by the fact that cognitive models are not expressed or represented in a way which allows artefact builders to convert the model into other representations which result in specifications for a system, e.g. software and there have been suggestions that it might be possible to express cognitive models, e.g. of the writing process, using some forms of task grammars which might aid the route to engineering design (Holt 1992). The IM approach goes to the roots of human information processing models in cybernetics and analyses these in terms of modern controls systems engineering. While both human information processing and control systems engineering models share a common root in traditional cybernetics, the latter has developed more powerful mathematical and computational tools for expressing control or cybernetic models. These tools may provide the means necessary for expressing human information processing models with formalisms, which allow artefacts to be implemented. What is also needed is a framework or a set of rules for applying the tools in the context of design. PSYCHOLOGY AS DESIGN The IM approach outlined above is not new and a good example can be seen in work done by Marr and Hildreth (1980) on edge detection. Here, perceptual visual processing is investigated through embedding a model based on psychophysics and physiology in software. This allows the edge detection model to be specified very precisely and tested computationally. The approach also results in an "intelligent" artefact, i.e. a better edge detector. Many modern software packages aimed at the

image analysis market (e.g. Matlab) and the graphic design market (e.g. PhotoShop) contain special filters, which include the Marr-Hildreth edge detector.

It is interesting to note that users of the filter may not be aware that that originally it represents a computational model.

4 image analysis market (e.g. Matlab) and the graphic design market (e.g. PhotoShop) contain special filters, which include the Marr-Hildreth edge detector. This software can be applied to an image and edges detected and emphasised. An example is shown in figure 2 below. It is interesting to note that users of the filter may not be aware that that originally it represents a computational model. Figure 2: Example of the Marr-Hildreth edge detector The Marr-Hildreth edge detector provides a good example of the approach we wish to adopt for engineering design using human information processing models and control engineering. A GENERAL INTEGRATION MODEL The main focus of our work is image processing, a large area of interest in science and engineering where there is considerable overlap between a number of research areas such as human perception and cognition, signal detection and processing, control engineering, machine or computer vision, mathematics and statistics. Image processing and interpretation has the characteristic that some form of sensor data (from organic or artificial sensors) is visualised and manipulated or enhanced to improve human perception or interpretation of the image. Conversely, in machine vision research, data is manipulated and enhanced to improve artificial or machine interpretation. Figure 3 shows a general model for image processing and interpretation. In the model data in input to the system, conditioned (prepared for further processing) and processed, i.e. enhanced for visualisation and display. Typically, compression and decompression of data is used for practical reasons (data-sets can be very large). These processes are guided by knowledge (a knowledge base, a world model and image interpretation). Overall, control of the system rests with some form of operator control. This type of model is generated and represented through control engineering modelling using cybernetic principles.

5 MACHINE INFORMATION PROCESSING INPUT Data Acquisition & Storage Compression Decompression Data Conditioning Image Processing Scene Visualisation Display & Report Knowledge Base World Model Image Interpretation Operator Control HUMAN INFORMATION PROCESSING Figure 3: An Integrated Information Processing Model The model would be recognised by most workers in image processing but if the model is examined in the context of the IM approach and task allocation, there are two categories of processes, machine information processing tasks, and human information processing tasks. The components that can be regarded as machine information processes are the focus of research in engineering while the human information processing components are the focus of psychologists and other cognitive scientists. The long-term goal of IM is to represent the "human information processing components", i.e. the underlying models, in the same manner as the machine based models and embed these in an integrated system. This also aims to create an "intelligent artefact" but will also allow testing of human information processing in a working system, an approach similar to that used by Marr and Hildreth (1980). A prototype system is currently being designed in a specific application area and is outlined below. A WORKING PROTOTYPE: AN IM FOR X-RAY IMAGING The model outlined in figure 3 above represents a longer-term goal for our work. However, we are currently designing and implementing an IM system for enhancing and displaying dental X-rays for diagnosis. The medical problem being addressed is that X-rays do not generally image soft tissue. In dentistry, the diagnosis of temporomandibular joint (TMJ) problems is greatly enhanced through medical imaging. Standard dental X-ray equipment usually does not image the soft tissue of the meniscus or the meniscal disc, which can be crucial as problems frequently arise from the meniscus being displaced. Computed tomography and invasive methods involving dyes can give good results (Wilson 1990), while magnetic resonance imaging (MRI) is frequently used with excellent results but this can be expensive and result in delays due to referral times. The applied research question is therefore whether standard dental X-rays can be enhanced to allow diagnosis of TMJ problems. A more basic research question asks whether the IM approach can be used, 1) to design and implement an imaging system, b) whether medically useful results van be obtained and c) whether models of human information processing can be tested through this approach?

6 The justification for the IM approach is based on the simple initial premise that people view X-rays. This then poses the questions: Given the characteristics (mathematical) of an image (X-ray film) - 1. How should the image be modified to optimise viewing by a human? 2. What should be enhanced to give optimal viewing that allows diagnosis by a human? 3. Which aspects of human visual information processing should be embedded in a model to facilitate viewing and diagnosis? The initial design phase of the project resulted in an outline IM that is shown in figure 4 below. Clinical Requirements Capture Image Architecture Pre-diagnostic Visualisation Diagnostic Visualisation Automated Diagnostics Algorithm Development based on Human Information Processing Models 6 Figure 4: IM for imaging X-rays The model has six components that can be summarised as: 1. Image Architecture: Statistical/mathematical representation of the image (scanned X-ray films). 2. Pre-diagnostic Visualisation: Procedure for modifying an image to optimise human visual perception. 3. Diagnostic Visualisation: Procedure for enhancing an image to optimise and facilitate diagnosis by a dentist. 4. Automated Diagnostic Support: Procedures for automatically carrying out diagnosis. This module represents machine intelligence and will be addressed in the future. 5. Clinical Requirements Capture: This represents knowledge capture and aims to collect and represent the "visual" cues that result in reliable diagnosis. 6. Algorithm Development based on Human Visual Information Processing: Represents the design, development and implementation (embedding) of algorithms based on models of human visual information processing from psychophysics, Gestalt theory, Pattern and Feature Processing, 2D and 3D extraction and visual interpretation. Components 1-4 represent software modules (computational models) while 5 and 6 represent core activities that allow the other components to be created. While components 5 and 6 represent continuous activity, modules 1-3 are in an early prototyping stage and are being tested with clinical X-ray films. Figures 5 and 6 show sample output from initial tests with the IM. Figure 5 shows three images. The image on the left shows a typical scanned X-ray film with an arrow, which indicates the location of the meniscus sitting on the top of condyle.

Figure 5: Sample output The image in the middle shows a modified version of the

out by module 3 Diagnostic Visualisation.

The enhanced image shows a second arrow, which draws attention to the meniscal

Figure 6 Sample output Figure 6 shows an example of alternative visualisation of

Here, a surface plot is produced to aid diagnosis.

In some instances, the modifications and enhancements involve techniques that are

7 Figure 5: Sample output The image in the middle shows a modified version of the original X-ray film, the modifications being carried out by module 2, Pre-Diagnostic Visualisation. The image on the right shows an enhanced version, with enhancements being carried out by module 3 Diagnostic Visualisation. Again, the arrows show the location of the meniscus. The enhanced image shows a second arrow, which draws attention to the meniscal disc, which is visualised in this example. Figure 6 Sample output Figure 6 shows an example of alternative visualisation of an X-ray film, which has been both modified and enhanced. Here, a surface plot is produced to aid diagnosis. The arrow indicates what appears to be a displaced meniscus. In some instances, the modifications and enhancements involve techniques that are standard in image processing. However, the key is the development of procedural algorithms, which map on to human visual needs for viewing and can be embedded in the model. Initial evaluations with dental practitioners indicate that the modifications and enhancements of the kind shown in figures 5 and 6 are value adding the X-ray films.

8 DISCUSSION The work described and discussed in this paper represents an experimental approach to enhance engineering design in the context of imaging systems. The approach is interdisciplinary and represents an attempt to integrate psychological information processing models into an engineering design framework. This is an alternative to using psychology to inform or guide design in engineering. The attempts to apply the model to image processing have been encouraging from the applied perspective and in the next phase of work attempts will be made to test the computational models within a framework of hypotheses about human visual information processing. Additionally the models will be expanded to include higher-level cognitive information processing. REFERENCES 1. Collins, A.M. and Loftus E.F. (1975) A spreading activation theory of semantic processing. Psychological Review, 82, Collins, A.M. and Quillian, M.R. (1969) Retrieval time from semantic memory. Journal of Verbal Learning and Verbal Behaviour, 8, Gasson S. (1995) User involvement in decision-making in information systems development, IRIS 18, Gjern, Denmark. 4. Holt, P.O B. (1992) User-centred design and writing tools: designing with writers, not for writers. Intelligent Tutoring Media, Vol. 3 No. 2/3, May/August. pp Landauer, T.K. (1995) The Trouble with Computers (Usefulness, Usability And Productivity), MIT Press, Cambridge, Mass. 6. Marr, D. (1982) Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. W.H. Freeman, San Francisco. 7. Marr, D. and Hildreth, E. (1980) Theory of Edge Detection. Proceedings of the Royal Society of London, B, Norman, D. (1988) The Psychology of Everyday Things, BasicBooks, New York. 9. Norman, D. (1998) The Design of Everyday Things, MIT Press, London 10. Norman, D. and Draper, S. (1986) User Centred System Design, Lawrence Album Associates, Hillsdale 11. Wilson, D.J. (1990) Imaging. In Norman, J.E.deB. and Bramley, P. (Eds) A Textbook and Colour Atlas of the Temporomandibular Joint (Diseases, Disorders, Surgery), Wolfe Medical Publications, London.

Models of Information Retrieval

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