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.

Pattern matching

By Gordon Rugg

Note: This article is a slightly edited version of an article originally posted on our Search Visualiser blog on May 17, 2012. I’ve updated it to address recent claims about how Artificial Intelligence might revolutionise research.

So what is pattern matching, and why should anyone care about it?

First picture: Two individuals who don’t care about pattern matching (Pom’s the mainly white one, and Tiddles is the mainly black one (names have been changed to protect the innocent…)

Pattern matching is important because it’s at the heart of the digital revolution. Google made its fortune largely from the simplest form of pattern matching. Computers can’t manage the more complex forms of pattern matching yet, but humans can handle them easily. A major goal in computer science research is finding a way for computers to handle those more complex forms of pattern matching. A major challenge in information management is figuring out how to split a task between what the computer does and what the human does.

So, there are good reasons for knowing about pattern matching, and for trying to get a better understanding of it.

As for what pattern matching is: The phrase is used to refer to several concepts which look similar enough to cause confusion, but which are actually very different from each other, and which have very different implications.

Creativity and idea generation

By Gordon Rugg

So what is creativity, and how can you generate more and better ideas?

There’s pretty general agreement that:

• Creativity is a Good Thing
• Thinking outside the box is a Good Thing
• Thinking laterally is a Good Thing

That’s a good start.

However, when you start asking about how creativity works, or just how you’re supposed to think outside the box, or think laterally, an element of vagueness starts to roll in, like a dense bank of fog off the Atlantic at the start of a horror movie…

You start hearing stories of people and organisations that thought successfully and laterally outside the box, in a way that solved their problems with designing better elevators. You encounter puzzles involving people and items being found in improbable situations, such as stabbed to death with no weapon visible, in the middle of a field of unsullied snow. It’s all very edifying and interesting, but it doesn’t get to grips with what creativity really is, or how to do anything systematic about creating new ideas.

This article gives a brief overview of a systematic framework for making sense of creativity, and for choosing appropriate methods for generating new ideas.

Things people think

By Gordon Rugg

There’s a wryly humorous summary of models of humanity that floats around in academia. It appears in various forms; the one below has an astute punch line that highlights the amount of implicit assumption in the early models.

Models of humankind:

• Man the fallen creation (the Bible)
• Man the thinker (the Enlightenment)
• Heroic man (Nietzsche)
• Economic man (Marx)
• Man the rat (Skinner)
• Man the woman (feminism)

It’s humorous, but it cuts to the heart of the matter. The models that shape our lives – political models, religious models, economic models – are based on underlying assumptions about how people think and what people want. As is often the case with models, these assumptions are often demonstrably wrong.

In this article, I’ll examine some common assumptions, and I’ll discuss some other ways of thinking about what people are really like.

Images from Wikipedia and Wikimedia; details at the end of this article

Connectionism and neural networks

By Gordon Rugg

There have been a lot of major changes in cognitive psychology over the last thirty-odd years. One of the biggest involves the growth of connectionist approaches, which occur at the overlap between neurophysiology and Artificial Intelligence (AI), particularly Artificial Neural Networks (ANNs).

Research in these areas has brought about a much clearer understanding of the mechanisms by which the brain operates. Many of those mechanisms are profoundly counter-intuitive, and tend to be either misunderstood or completely ignored by novices, which is why I’m writing about them now, in an attempt to clarify some key points.

There are plenty of readily available texts describing how connectionist approaches work, usually involving graph theory diagrams showing weighted connections. In my experience, novices tend to find these explanations hard to follow, so in this article, I’ll use a simple but fairly solid analogy to show the underlying principles of connectionism, and of how the brain can handle tasks without that handling being located at any single point in the brain.

Original images from Pinterest and from Wikipedia; details at the end of the article.

Representing argumentation via systematic diagrams, part 1

By Gordon Rugg

This article is a short introduction to some basic principles involved in representing argumentation, evidence and/or chains of reasoning using systematic diagrams.

This approach can be very useful for clarifying chains of reasoning, and for identifying gaps in the evidence or in the literature.

As usual, there’s an approach that looks very similar, but that is actually subtly and profoundly different, namely mind maps. That’s where we’ll begin.

A mind map

Parallel processing and “natural” learning: Inside the black box

By Gordon Rugg

There’s a widespread idea that before entering formal education, people learn via “natural” learning.

It’s a warm, cosy concept; “natural” evokes thoughts of wildflowers and meadows and beauty and fluffy kittens. There’s even a certain amount of truth in it; formal education does generally involve something different from non-formal education. However, when you start looking for clear, practical, explanations of how “natural” learning actually works, you encounter a sudden silence.

There are plenty of descriptions of what “natural learning” looks like, but there’s very little discussion of how it might work, in terms of plausible cognitive or neurophysiological mechanisms. This absence makes a sceptical reader start to wonder whether there actually is such a thing as “natural learning” and whether this strand of education theory is chasing something that doesn’t exist.

In fact, there is a well-understood mechanism that accounts for the phenomena being lumped together as “natural learning” and “formal learning” (or whatever term is being used in juxtaposition to “natural learning”). However, when you look in detail at this mechanism, it soon becomes apparent that using a two-way distinction between “natural” and “non-natural” is simplistic and misleading. This is one reason that the “natural/non-natural” debate in education theory is still rumbling on, after more than two thousand years of fruitless and inconclusive argument.

In this article, I’ll discuss the mechanisms of parallel processing and serial processing, and I’ll outline some implications for education theory and practice.

The joys of nature and of fluffy kittens – not always quite the same thing…

Original images from Wikimedia

 

Tweet-sized thought for the day: Pattern matching, serial processing, politicians and word salad

Pattern matching is an easy way to check if a thing looks right. Serial processing is a hard way to check if it is right. A big difference. hydeandrugg.wordpress.com

There are two computational mechanisms for solving a problem, regardless of whether you’re a human or a computer. One of these mechanisms is parallel processing, where you carry out lots of tasks at the same time; this mechanism is very good for pattern matching, where you identify patterns (whether physical patterns, or underlying regularities in events, etc). The other mechanism is serial processing, where you do one task at a time; slow, but steady, and much better for catching errors in reasoning.

Humans are very good at pattern matching, which we find swift and easy, and very bad at serial processing, which most of us find slow and painful. So what? So this is why we appear to be an illogical species, and why demagogue politicians can get so far despite having policies that are little more than word salad.