Syndromes and permutations

By Jennifer Skillen, Gordon Rugg and Sue Gerrard

Some diagnoses are clear and simple, and give you a crisp either/or categorisation; either the case in question has a positive diagnosis, or it doesn’t. Diagnosis involves identification of the cause of a problem; this may occur in medicine, or in mechanical fault finding, or in other fields. This article focuses on medicine, but the concepts involved are relevant to diagnosis in other fields.

The diagram below shows crisp either/or diagnosis visually; cases either go in box A or box B, with no other options.

Not all diagnoses are this simple. Medical syndromes are an example. A syndrome in the medical sense involves a pattern of features that tend to co-occur, but where the cause is unknown. They typically involve multiple signs and symptoms, each of which may or may not be present in a particular case. (Signs are features that can be observed by someone other than the patient; symptoms can only be observed by the patient.) Making sense of syndromes, and of how to diagnose syndromes, is difficult, and often encounters problems with misunderstandings and miscommunications.

This article discusses ways of defining syndromes and related issues. Its main focus is on medical diagnosis, but the same principles apply to problems in other fields that have the same underlying deep structure.

Artificial Intelligence and reality

By Gordon Rugg

There’s a lot of talk about Artificial Intelligence (AI) at the moment, usually framed in either/or terms. For anyone who worked with AI in the 1980s, this is depressingly familiar.

The brief version is that yes, AI will bring massive changes in some areas but not in the ways that most people are claiming, and no, it won’t bring massive changes in others that most people are worried about, and by the way, there are a lot of really useful things that it could be doing, but that have been marginalised or ignored or unknown for over forty years.

So, what’s the reality about AI?

User-centred grammar

By Gordon Rugg

Within linguistics, the scientific study of language, there is a long-established and useful distinction between two approaches to grammar, namely prescriptive grammar and descriptive grammar. This article describes a third approach, namely user-centred grammar. Before describing this third approach, I’ll briefly summarise the other two.

Background

Prescriptive grammar typically takes the view that there is a “correct” form of grammar for a dialect or language that should be followed by everyone, including people for whom that dialect or language is their native tongue. An example from Standard English is the claim that one should say “It is I” as opposed to “It is me”. Similarly, prescriptive grammar often makes normative claims about correctness based on claims that a particular phrasing is in some sense logically correct. An example in English is how to treat two negatives (e.g. “I haven’t got nothing”). Prescriptive grammar typically claims that these should cancel each other out because that is “logical” by analogy with formal logic.

Descriptive grammar describes the grammar used by native speakers of a dialect or language, without any normative claims. For the first example above, the descriptive approach would say that some native speakers of Standard English say “It is I” but that most native speakers of English say “It is me”. For the second example above, descriptive grammar typically states that in various dialects of English, the two negatives are treated as reinforcing each other.

Historically, prescriptive grammar has tended to invoke semi-arbitrary norms based on the grammar of other languages that have high status. In the case of Standard English, for example, prescriptive grammar frequently invoked norms from Latin (“we should use this phrasing in English because that’s what Latin would do”).

The use of descriptive grammar in linguistics has numerous advantages in terms of internal consistency, of avoiding imposition of arbitrary and semi-arbitrary norms on native speakers of a language, and of avoiding the social stigmatisation of dialects and languages other than the most prestigious ones. However, there are situations where phrasings can make a significant difference in how well a person can communicate their intended message to others. This causes problems for anyone attempting to teach ways of avoiding unintended consequences from particular phrasings, because this can look like an attempt to impose subjective norms.

The approach described in this article, namely user-centred grammar, provides a possible way of resolving this apparent paradox.

It’s logic, Jim, but not as we know it: Associative networks and parallel processing

By Gordon Rugg

A recurrent theme in our blog articles is the distinction between explicit knowledge, semi-tacit knowledge and tacit knowledge. Another recurrent theme is human error, in various forms. In this article, we’ll look at how these two themes interact with each other, and at the implications for assessing whether or not someone is actually making an error. We’ll also re-examine traditional logic, and judgement and decision-making, and see how they make a different kind of sense in light of types of knowledge and mental processing. We’ll start with the different types of knowledge.

Explicit knowledge is fairly straightforward; it involves topics such as what today’s date is, or what the capital of France is, or what Batman’s sidekick is called. Semi-tacit knowledge is knowledge that you can access, but that doesn’t always come to mind when needed, for various reasons; for instance, when a name is on the tip of your tongue, and you can’t quite recall it, and then suddenly it pops into your head days later when you’re thinking about something else. Tacit knowledge in the strict sense is knowledge that you have in your head, but that you can’t access regardless of how hard you try; for instance, knowledge about most of the grammatical rules of your own language, where you can clearly use those rules at native-speaker proficiency level, but you can’t explicitly say what those rules are. Within each of these three types, there are several sub-types, which we’ve discussed elsewhere.

So why is it that we don’t know what’s going on in our own heads, and does it relate to the problems that human beings have when they try to make logical, rational decisions? This takes us into the mechanisms that the brain uses to tackle different types of task, and into the implications for how people do or should behave, and the implications for assessing human rationality.

Tacit knowledge: Can’t and won’t

By Gordon Rugg and Sue Gerrard

This is the third post in a short series on semi-tacit and tacit knowledge. The first article gave an overview of the topic, structured round a framework of what people do, don’t, can’t or won’t tell you. The second focused on the various types of do (explicit) and don’t (semi-tacit) knowledge. Here, we look at can’t (strictly tacit) and won’t knowledge.

The issues involved are summed up in the diagram below.

Explicit and semi-tacit knowledge

By Gordon Rugg and Sue Gerrard

This is the second in a series of posts about explicit, semi-tacit and tacit knowledge.

It’s structured around a four way model of whether people do, don’t, can’t or won’t state the knowledge. If they do state it, it is explicit knowledge, and can be accessed via any method. If people don’t, can’t or won’t state the knowledge, then it is some form of semi-tacit or strictly tacit knowledge, which can only be accessed via a limited set of methods such as observation, laddering or think-aloud.

This is summed up in the image below.

The previous article in this series gave an overview. In the present article, we focus on do and don’t knowledge, i.e. explicit and semi-tacit knowledge.

Tacit and semi tacit knowledge: Overview

By Gordon Rugg and Sue Gerrard

Tacit knowledge is knowledge which, for whatever reason, is not explicitly stated. The concept of tacit knowledge is widely used, and has been applied to several very different types of knowledge, leading to potential confusion.

In this article, we describe various forms of knowledge that may be described as tacit in the broadest sense; we then discuss the underlying mechanisms involved, and the implications for handling knowledge. The approach we use derives from Gordon’s work with Neil Maiden on software requirements (Maiden & Rugg, 1996; reference at the end of this article).

In brief, the core issue can be summed up as whether people do, don’t, can’t or won’t state the knowledge. If they do state it, it is explicit knowledge, and can be accessed via any method. If people don’t, can’t or won’t state the knowledge, then it is some form of semi-tacit or strictly tacit knowledge, which can only be accessed via a limited set of methods such as observation, laddering or think-aloud. Because of the neurophysiological issues involved, interviews, questionnaires and focus groups are usually unable to access semi-tacit and tacit knowledge.

The image below shows the key issues in a nutshell; the rest of this article unpacks the issues and their implications. There are links at the end of the article to other articles on the methods mentioned in the table. The image below is copyleft; you’re welcome to use it for any non-commercial purpose, including lectures, as long as you retain the coplyleft statement as part of the image.

The gulf of instantiation

By Gordon Rugg

Two of the most common complaints about managers and about teachers/lecturers are:

• They’re too vague
• They’re too specific

Although these complaints are opposites, the underlying problem for both of them is the same. It involves the gulf of instantiation, which is the topic of this article.

What’s so great about live lectures anyway?

By Gordon Rugg

So what’s so great about live lectures anyway, and why do people get so worked up about whether to put lectures online?

Live lectures have some significant advantages over other media; however, these advantages can be difficult to put into words unless you’ve encountered the relevant bodies of research and practice. This can be very frustrating if your employer wants to put everything online for whatever reason, and if they think that anyone who disagrees is simply a lazy Luddite unwilling and unable to change with the times.

There are very real reasons for including face to face lectures, tutorials etc in education and training. However, some important reasons aren’t as widely known as they should be. In this article, I’ll look at these reasons, and then consider the implications for choice of delivery methods in education.

Games people play, and their implications

By Gordon Rugg

There are regularities in how people behave. There are numerous ways of categorising these regularities, each with assorted advantages and disadvantages.

The approach to categorisation of these regularities that I’ll discuss in this article is Transactional Analysis (TA), developed by Eric Berne and his colleagues. TA is designed to be easily understood by ordinary people, and it prefers to use everyday terms for the regularities it describes.

I find TA fascinating and tantalising. On the plus side, it contains a lot of powerful insights into human behaviour; it contains a lot of clear, rigorous analysis; it has very practical implications. On the negative side, it doesn’t make use of a lot of well-established methods and concepts from other fields that would give it a lot more power. It’s never really taken off, although it has a strong popular following.

An illustrative example of why it’s fallen short of its potential is the name of Berne’s classic book on the topic, Games People Play. When you read the book, the explanation of the name makes perfect sense, and the deadly seriousness of games becomes very apparent. However, if you don’t read the book, and you only look at the title, then it’s easy to assume that the book and the approach it describes are about trivial passtimes, rather than core features of human behaviour.

In this article, I’ll look at some core concepts of Transactional Analysis, and how they give powerful insights into profoundly serious issues in entertainment, in politics, and in science.

The image below shows games at their most deadly serious; Roman gladiatorial combat, where losing could mean death.

By Unknown author – Livius.org, Public Domain, https://commons.wikimedia.org/w/index.php?curid=3479030