Chapter 1 Theoretical Analysis and Theory Creation

ion – here one tries to find common features from past experience and use these to create more abstract concepts. When new situations arise they are expressed in terms of the abstract concepts. We do both these things in day-to-day life and are usually not aware that we are doing them. In particular, our brains do a lot of analogical 'reasoning' in the form of associative pattern matching at subconscious levels including interpreting raw perceptions. In any analogical reasoning there is already an implicit theoretical model that determines what is considered important in comparing two instances. However, it is when we give names to the attributes and classes that we move from analogical to theoretical thinking. If we say "drunk drivers are dangerous because they take risks and have slower responses", then we have taken experience (our own, national statistics, medical facts) and put it into concepts ("drunk driver", "risk") with relations between them – a theory. This language of generalisation makes it possible to reason more explicitly about the subject of theory, as well as communicate general observations. Theory is not just the language of generalisation; it is the language of technical culture. The main difference between work-a-day theories (like the above) and academic theories is that in the latter we take an additional step of identifying and naming the collection of concepts and relations that make the theory. When the theory is given a named identity it can then become the subject of more explicit critique and analysis. It is discussable, refutable and sometimes even provable. Theory and analogy interweave. We often derive our theories after previous more analogical thinking, so analogy helps abstraction. But also theory helps analogy: if we have ideas of abstract concepts and properties, we can use these to explicitly retrieve previous examples ("I recall a case of a drunk driver last year .") and then use the retrieved examples to draw conclusions that perhaps the theory does not (yet) support. Furthermore, having seen a past example that is similar to a new one, we need to adapt the past case, and to do that, we need to understand sufficiently the implications of the differences – which is precisely what theoretical understanding helps us to do. The power of abstraction is not just about communication, but also that it gives us the means to reason about things. As we shall see later this has enormous practical benefits in HCI research and practice. 1.1. 2 What is theory? Theory is a hard word to define. As noted, in common language a 'theory' is a hunch or bright idea. But scientific theories are very different. The Oxford English Dictionary has a number of definitions of different uses of the term (including the common use one! ), but the one most pertinent to scientific theory is: "A scheme or system of ideas or statements held as an explanation or account of a group of facts or phenomena; a hypothesis that has been confirmed or established by observations or experiment, and is propounded or accepted as accounting for the known facts: a statement of what are held to be the general laws, principles, or causes of something known or observed. " (OED, 1973) If we were describing a theory in the social sciences, we might use slightly different words, but the essentials are the same: structure – A theory is not just an isolated set of statements, laws or facts, but has some level of structure or inter-relation. explanation – Because of this, theories can be used to give an explanation of how or why things are true, not just what is true. abstraction – Theories account for more than single observations, but offer more general or abstract accounts.ion – Theories account for more than single observations, but offer more general or abstract accounts. verity – However, theory is not divorced from particulars; it is usually based on experience or observation (induction) and can be used to explain or predict future observations. The last of these is the critical difference from the common use of the word. While theories differ in the extent to which they have been justified or verified, they must all have some relation to reality, not mere hunches. However, theories may be wrong. For example, I might have a theory that gravity is caused by the pressure of air holding you down and that as you go higher up the air pressure reduces and hence so does gravity. This happens to be a false explanation of gravity, but is not without some justification from experience (air pressure is indeed powerful, although caused by gravity rather than the other way round). Critically, the theory has enough explanatory power to make predictions, for example, if we make a vacuum where there is zero air pressure, then things will be weightless in it. Because we can make predictions we can then test this theory, and in this case find it was wrong. Karl Popper, a philosopher of science, regarded this ability to test scientific theory, falsification, as being a key element that distinguishes true science (Popper, 1959). By this criterion, if it is inconceivable that a theory could be disproved, then it is not a scientific theory. While this captures some aspects of science, in fact science often works by holding onto theories and modifying them rather discarding them 'just' because the evidence does not fit. This is partly a human tendency towards conservatism and partly a sensible use of the intellectual investment of a community. Theories may be built upon other theories or laws; for example, the Model Human Processor (Card et al., 1983) is a theoretical model of human information processing built upon older psychological theories and results. I might then construct a theoretical account of text entry on mobile devices based on MHP and other knowledge of mobile use. 1.1. 3 Things like theories Theories differ from laws, which are also abstractions over observed phenomena, but do not offer systematic explanations for the phenomena. For example, Boyle's Law says that if you double the pressure on a gas and maintain the temperature it halves in volume, but does not say why this is true. Similarly Fitts' Law says that the time to hit a target varies logarithmically with the distance to the target, but again, as a formula, does not say why this is true. Theories do relate to laws. Theories may explain laws: for example a molecular theory of gases where the gas molecules cause pressure by bouncing off the walls of a container can be used to explain and derive Boyle's law. Similarly, you can offer theories of Fitts' Law in terms of information theory (as in Fitts’ original paper (Fitts, 1954)) or in terms of the feedback loop between perception and action (Keele, 1968; Meyer et al., 1988). Theories may also build on laws: for example, we may use a combination of Boyle's laws and observed data on the calorific value of coal to develop a theory of steam engines. Similarly, we may use Fitts' Law as part of a theory of pen-based mobile interaction. Theories also differ from paradigms, which are more 'world views', ways of looking at things. Theories are framed within a paradigm, as it is the paradigm that gives the theory a way of looking at phenomena, and so for someone outside that paradigm a theory may be hard to comprehend (imagine explaining quantum mechanics in the 14 th century). There may be many theories operating within a single paradigm, and theories may develop and change while still operating within the same paradigm. We have already noted that real science tends to modify rather than discard theories, but sometimes as an area develops it gets harder and harder to 'fix' the theories until eventually a whole new way of thinking about things is required. Thomas Kuhn, another philosopher of science, identified this phenomenon; he describes these times of critical change as 'scientific revolutions' with associated 'paradigm shifts' in the way people think (Kuhn, 1962). Perhaps the most well-known example of paradigm shift is the change from a Newtonian worldview in the early years of the 20 th century. Until this point, the very small and very large are seen as operating very much like the 'normal' world we experience (a form of reasoning by analogy). However experiments were increasingly at variance with the associated theoretical models (problems of verity) until the point at which the Newtonian worldview was challenged and superseded by Einstein's General Relativity (for the large) and Quantum Mechanics (for the small). In HCI a similar (although less dramatic) shift happened in the late 1980s when the more cognitively based theories of early HCI were challenged by more situated accounts in Winograd and Flores’ 'Understanding Cognition' (Winograd and Flores, 1985) and Suchman's ‘Plans and Situated Actions' (Suchman, 1987). Because theories in HCI are by their nature partial and approximate, the disparity with the 'facts' of observation is less clear and so, rather than replacing an 'older' view, this is more an additional way of looking at things. Naively, one might imagine that when the 'truth' of theories and paradigms is partial, or not easily verified against the world, academics would tend to use them more pragmatically. However, the opposite tends to be the case and in HCI (as in the social sciences) one tends to get partisan adherents of one viewpoint or other! Finally, another related idea is that of a model. Minsky (1965) defined a model using the following “To an observer B, an object A* is a model of an object A to the extent that B can use A* to answer questions that interest him about A. ” This definition is remarkably similar to attempts to define metaphor or analogy, and indeed, writing from a social science perspective, Hawes defines a model precisely as an analogue. Models are similar to theories and operate at a similar 'level'; however, whilst models, like laws, are mainly about telling you what is true, theories