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It is learned because the brain has specialized circuitry to be able to learn it. The machinery obviously has been evolved and is inherited, while a grammar of
by lngnmn 10y ago
It is learned because the brain has specialized circuitry to be able to learn it.
The machinery obviously has been evolved and is inherited, while a grammar of a particular language has been learned.
There is also a strong hypothesis that the structure of the specialized areas of the brain "reflects" the general notions of the shared environment, such as things, attributes, processes which corresponds to nouns, adjectives and verbs.
Like visual cortex has its own hard-wired heuristics and presuppositions (that use environmental cues such as shadows, distances, illumination) which are used in the process of scene decomposition and interpretation, the language area should have its own hard-wired heuristics, which, presumably, are used in grammar learning.
The knowledge (heuristics) are encoded in the structure, not in some kind of information.
- jjtheblunt 10y agoWhy "obviously" ?
- lngnmn 10y agoIt is obvious after taking a decent psychology course, like the one of Paul Bloom at Yale.
- _dps 10y ago> The knowledge (heuristics) are encoded in the structure, not in some kind of information. When you're talking about neural computation, distinguishing between an evolved network structure/architecture and "information" (or at least biases toward certain kind of information) is not very useful. The nature of these systems as computing substrates is that they encode information in network structure and rules for updating that structure.
- lngnmn 10y ago> they encode information in network structure and rules for updating that structure I think use of the term 'information' is not accurate. There are no bits and the whole information theory, it seems, is synthetic and has nothing to do with how the brain actually works. It is not a digital machine but electro-mechanical, if you wish.
- _dps 10y agoRespectfully, this is begging the question :) You're correct that neural networks don't encode information in the way current digital machines do (i.e. with explicit records/files/etc encoded in bytes). I would disagree that this is the only useful view of information. And certainly, from a Shannon information perspective, the network structure contains information (in the Shannon sense) about the environment or problem for which it was optimized.
- lngnmn 10y ago> the network structure contains information (in the Shannon sense) about the environment ... the way a map represents a territory instead of how a guide book does ;) Or the way a 3D structure made out of the same 20 joined amino-acids could represent either an enzyme (the code) or a protein (the data) and that there is no distinction between code and data (which is the great insight realized in the original Lisp. It is not a coincidence that Lisp was the language or the classic AI. Sorry for the reminiscence - just love this part).