|
|
|
|
|
|
by brainmapper
1572 days ago
|
|
|
As a computational neuroscientist who has used ANNs to model brain data for 20 years, I want to suggest another way to think about these sorts of brain-ML/ANN comparisons: successful language comprehension requires representing the underlying statistical structure of language. Humans who learn to comprehend language must learn about the underlying statistical structure of language. ANNs that learn to comprehend language must learn the underlying statistical structure of language. Comparing human data to ANNs only really makes sense in the context of that statistical structure. What aspects of this statistical structure are learned by both machines (i.e., brains and ANNs)? What aspects of this structure are learned differently by the two machines? When the two machines learn different aspects of statistical structure, is this due to differences in architecture, training, or something else? A good understanding of the stimulus statistics is essential for interpretation. |
|
|
This comes up a lot, but is the evidence really there for it? Granted, we (humans) have mental models for "this is most likely what this person means", but I don't think that says a lot about the "underlying statistical structure of language". It seems to me like by the time such statistical probing takes place, for instance trying to decipher what someone means, the language learning already took place. Or do you mean something else?
Note: I am not an expert in this field. I studied some papers back in college addressing these topics and the evidence back then (out of the generative grammar school) was fairly convincing in refuting this statistical learning assertion. I wonder what papers or research you had in mind here.