Can Cognition be Modeled with ANNs?

Posted 21 Aug 2005 at 21:06 UTC by steve Share This

Artificial Neural Networks (ANNs) are useful and popular computational models but are they a suitable way to demonstrate the plausibility of neural structures responsible for high-level human cognition? A new paper (PDF format) by Peter R. Krebs argues that ANNs are a bottom-up methodology and, as such, may not be the best way up understanding the top-down concepts studied in cognitive science. "When the tools (simple artificial neural networks) to solve the problems (explaining aspects of higher cognitive functions) are mismatched, models with little value in terms of explaining functions of the human mind are produced." Though ANNs are useful tools, they represent a universal framework for modeling any cognitive theory. This means that modeling some aspect of human cognition with an ANN merely shows that it's neurlogically possible but cannot demonstrate that it is neurologically plausible.

Depends on why you use them, posted 23 Aug 2005 at 00:55 UTC by marcin » (Journeyer)

I remember learning about ANNs at uni (my thesis actually involved using ANNs in a basic classification task). Possibly tainted by my previous, but limited, experience I have always looked at ANNs and the such as useful tools to achieve an outcome, rather than as a method for explaining the workings of the mind - to me, the ANN is a transfer function you don't have to know - obviously better suited to LTI systems than non-linear ones.

I remember that a friend's thesis involved trying to extract classification algorithms out of a neural net (ie determine the transfer function) - that was a harder job, and I can see how that sort of work would be beneficial to trying to fathom how information is stored in the brain - although Krebs has a point in his top-down vice bottom-up view. After all, ANNs and SRNs are just models, and there's no such thing as a universal model.

Nevertheless, I still would like to put an ANN into a mobile robot and teach it that walls are bad, stairs are bad, low battery is bad, people are good, recharge plate is good, etc. The problem is, that ANNs generally need so, so much pre-processing (or a LOT of learning time and CPU power).

My AUD0.02.

Cheers, Marcin

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