Introduction: This article is one I wrote some time ago about modelling cognition and never released. It's incomplete but might be useful to spur thought and conversation. I suspect that it's more about mental models - my concept of cognitive models were more functional and evidence-based.
But this doesn't take into account the fact that there are individual differences within our target populations. This is obvious for large populations: a site selling air flights will have almost anyone as its target. In this case, it's clear that people will differ not just in the characteristics that will affect their performance in booking a flight, but also in other characteristics.
However, even relatively homogenous populations, say a group of dermatologists using a tool in to diagnose skin diseases, will also differ.
But despite this, there are things in common, the main one being that they have a human mind.
This is useful for practical research because it means that usability professionals can concentrate on certain things about the mind that are stable across target populations to ensure usability.
The problem then is that understanding these things is extremely difficult. One way of capturing how a mind works is by using cognitive modelling.
There are many ways of doing this: some are simple and some are incredibly complex. Here I will discuss mental models and how usability researchers can understand them. I will use these because they are simple to understand and investigate with low-tech methods.
One of the seminal papers in the field was by my PhD supervisor, Professor Stephen Payne. He investigated the mental models that people had of cashpoint, or ATM machines. This work helped designers to understand how to make them simple to use for a wide section of the population.
His investigation method was simply the interview. He discussed ATM operation with users and found that they recruited analogies to explain the machine's operation. Quite often, people would even use several analogies even though they conflicted with others. I haven't read the data to this study, but let's take a hypothetical example.
Someone might view an ATM as a sort of one-armed bandit where they have to do some things and out pops money! Additionally, they may have thought of the ATM as a monster that "eats" cards if something is wrong.
For researchers, take careful note of the words that users provide when describing something. The key words for both of the above analogies would be something like "payout" (for the one-armed bandit) and "eat" for the monster. If you notice interesting terms like these being used, carefully try to question them closer. Be careful because if you make the subject feel stupid or self-conscious, they may clam up. Try to get across that you share the analogy but ask for further explanations.
Once you have the analogies, you can understand their mental models better. From these, you can work out some cognitive modelling by remembering that with experience, explanations of machines change from being parochial to something closer to the actual operation (in general: some people are fond of their analogies and don't like to change them).
What is this and is it useful to practical usability work?
Yes it is useful indeed. Most current work in usability (as in psychology) focuses upon things that apply to entire populations. We look for a site that will appeal to our target users.But this doesn't take into account the fact that there are individual differences within our target populations. This is obvious for large populations: a site selling air flights will have almost anyone as its target. In this case, it's clear that people will differ not just in the characteristics that will affect their performance in booking a flight, but also in other characteristics.
However, even relatively homogenous populations, say a group of dermatologists using a tool in to diagnose skin diseases, will also differ.
But despite this, there are things in common, the main one being that they have a human mind.
This is useful for practical research because it means that usability professionals can concentrate on certain things about the mind that are stable across target populations to ensure usability.
The problem then is that understanding these things is extremely difficult. One way of capturing how a mind works is by using cognitive modelling.
There are many ways of doing this: some are simple and some are incredibly complex. Here I will discuss mental models and how usability researchers can understand them. I will use these because they are simple to understand and investigate with low-tech methods.
One of the seminal papers in the field was by my PhD supervisor, Professor Stephen Payne. He investigated the mental models that people had of cashpoint, or ATM machines. This work helped designers to understand how to make them simple to use for a wide section of the population.
His investigation method was simply the interview. He discussed ATM operation with users and found that they recruited analogies to explain the machine's operation. Quite often, people would even use several analogies even though they conflicted with others. I haven't read the data to this study, but let's take a hypothetical example.
Someone might view an ATM as a sort of one-armed bandit where they have to do some things and out pops money! Additionally, they may have thought of the ATM as a monster that "eats" cards if something is wrong.
For researchers, take careful note of the words that users provide when describing something. The key words for both of the above analogies would be something like "payout" (for the one-armed bandit) and "eat" for the monster. If you notice interesting terms like these being used, carefully try to question them closer. Be careful because if you make the subject feel stupid or self-conscious, they may clam up. Try to get across that you share the analogy but ask for further explanations.
Once you have the analogies, you can understand their mental models better. From these, you can work out some cognitive modelling by remembering that with experience, explanations of machines change from being parochial to something closer to the actual operation (in general: some people are fond of their analogies and don't like to change them).
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