Are you saying that a probabilistic analysis of communication that treats communication as evidence for hidden variables/information cannot deal with the meaning of words? If so, why can’t the meaning itself be such a hidden information revealed by the message?
What does “the meaning itself” mean, in order for us to do probabilistic inference on it? In order to do reliable probabilistic inference, we need to have a sufficient starting theory of the thing (orwe need gold-standard data so that it’s not inaccessible information; or we need a working theory of inaccessible information which allows us to extract it from learning systems).
That is to say: humans succeed at doing probabilistic inference about this, but in order to construct a machine that would do it, we need more information than just “do probabilistic inference”.
One such theory is that the meaning of an utterance is just the probabilistic inferences you can make from it; but, I’m rejecting that theory.
So your criticism is that you are looking for the underlying assumption/theory that help humans do this probabilistic inference, and the signalling analysis of meaning tells you there is no such theory?
No, my criticism is that the signalling theory isn’t very good. It doesn’t allow for our intuitive concept of lying, and it doesn’t have an account of literal meaning vs implicature.
Are you saying that a probabilistic analysis of communication that treats communication as evidence for hidden variables/information cannot deal with the meaning of words? If so, why can’t the meaning itself be such a hidden information revealed by the message?
What does “the meaning itself” mean, in order for us to do probabilistic inference on it? In order to do reliable probabilistic inference, we need to have a sufficient starting theory of the thing (or we need gold-standard data so that it’s not inaccessible information; or we need a working theory of inaccessible information which allows us to extract it from learning systems).
That is to say: humans succeed at doing probabilistic inference about this, but in order to construct a machine that would do it, we need more information than just “do probabilistic inference”.
One such theory is that the meaning of an utterance is just the probabilistic inferences you can make from it; but, I’m rejecting that theory.
So your criticism is that you are looking for the underlying assumption/theory that help humans do this probabilistic inference, and the signalling analysis of meaning tells you there is no such theory?
No, my criticism is that the signalling theory isn’t very good. It doesn’t allow for our intuitive concept of lying, and it doesn’t have an account of literal meaning vs implicature.