Group items matching

in title, tags, annotations or url

The first way to think about probability is as a measure of the frequency of an event: if I say that the probability of a coin to land heads up is 50% I may mean that, if I flip the coin say 100 times, on average I will get heads 50 times. This is not going to get us out of Hume’s problem, because probabilities interpreted as frequencies of events are, again, a form of induction

Secondly, we can think of probabilities as reflecting subjective judgment. If I say that it is probable that the coin will land heads up, I might simply be trying to express my feeling that this will be the case. You might have a different feeling, and respond that you don’t think it's probable that the coin will lend heads up. This is certainly not a viable solution to the problem of induction, because subjective probabilities are, well, subjective, and hence reflect opinions, not degrees of truth.

Lastly, one can adopt what Okasha calls the logical interpretation of probabilities, according to which there is a probability X that an event will occur means that we have objective reasons to believe (or not) that X will occur (for instance, because we understand the physics of the solar system, the mechanics of cars, or the physics of coin flipping). This doesn’t mean that we will always be correct, but it does offer a promising way out of Hume’s dilemma, since it seems to ground our judgments on a more solid foundation. Indeed, this is the option adopted by many philosophers, and would be the one probably preferred by scientists, if they ever gave this sort of thing a moment’s thought.

The challenge of personal computing became manifest at an opportune time. The broad project of cognitive science, which incorporated cognitive psychology, artificial intelligence, linguistics, cognitive anthropology, and the philosophy of mind, had formed at the end of the 1970s. Part of the programme of cognitive science was to articulate systematic and scientifically-informed applications to be known as "cognitive engineering". Thus, at just the point when personal computing presented the practical need for HCI, cognitive science presented people, concepts, skills, and a vision for addressing such needs. HCI was one of the first examples of cognitive engineering. Other historically fortuitous developments contributed to establishment of HCI. Software engineering, mired in unmanageable software complexity in the 1970s, was starting to focus on nonfunctional requirements, including usability and maintainability, and on non-linear software development processes that relied heavily on testing. Computer graphics and information retrieval had emerged in the 1970s, and rapidly came to recognize that interactive systems were the key to progressing beyond early achievements. All these threads of development in computer science pointed to the same conclusion: The way forward for computing entailed understanding and better empowering users.

One of the most significant achievements of HCI is its evolving model of the integration of science and practice. Initially this model was articulated as a reciprocal relation between cognitive science and cognitive engineering. Later, it ambitiously incorporated a diverse science foundation, notably Activity Theory, distributed cognition, and ethnomethodology, and a culturally embedded conception of human activity, including the activities of design and technology development. Currently, the model is incorporating design practices and research across a broad spectrum. In these developments, HCI provides a blueprint for a mutual relation between science and practice that is unprecedented.

In the latter 1980s and early 1990s, HCI assimilated ideas from Activity Theory, distributed cognition, and ethnomethodology. This comprised a fundamental epistemological realignment. For example, the representational theory of mind, a cornerstone of cognitive science, is no longer axiomatic for HCI science. Information processing psychology and laboratory user studies, once the kernel of HCI research, became important, but niche areas. The most canonical theory-base in HCI now is socio-cultural, Activity Theory. Field studies became typical, and eventually dominant as an empirical paradigm. Collaborative interactions, that is, groups of people working together through and around computer systems (in contrast to the early 1980s user-at-PC situation) have become the default unit of analysis. It is remarkable that such fundamental realignments were so easily assimilated by the HCI community.