eli_sennesh comments on An overview of the mental model theory - LessWrong
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Indeed.
In fact, when I got to this part, I actually skipped the rest of the article, thinking, "What sort of halfway competent cognitive scientist actually proposes that we represent the world using first-order symbolic logic? Where's the statistical content?"
Hopefully I'm not being too harsh there, but I think we know enough about learning in the abstract and its relation to actually-existing human cognition to ditch purely formal theories in favor of expecting that any good theory of cognition should be able to show statistical behavior.
The mental model theory was initially created to describe the comprehension of discourse and deductive reasoning. But, it can also describe naive probabilistic reasoning. I might write something about this later, but it will probably be a while. I think that I should read this book first if I did. I didn't go into any of the details of this in the above post. There are few other principles and it gets a bit more complicated. You can see this paper if you are interested.
A mental model is defined as a representation of a possibility that has a structure and content that captures what is common to the different ways in which the possibility might occur. It is a theory about how people naively reason, not neccesarily how they have been trained to reason which will probably be more explicit.
Here is an example:
The model theory allows that probabilities can be represented in models and that the assertion can be represented by either a model of equiprobable possibilities or a model with numerical tags on the possibilities. This tag might be frequencies or probabilities. This means that people might make the follow models.
or an equivalent one if they are thinking in probabilities is