[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

Re: Autocorrelation

Dear Peter,

> If the proponents of a theory believe in ellipses, one does not make a
> model with circles, falsify it, and expect that they will agree that
> their model has been falsified.

I did not mean to attack you personally. None of my comments was meant
to degrade the importance of your work. I am sorry I could not convince
you that the results of K&D should be considered seriously by anyone who
is integrating an autocorrelation stage in his/her model. I will
nevertheless uphold this point.

You are right: there is not ONE autocorrelation theory, there are plenty
of them. It would not be very meaningful to falsify only one of them,
and a tremendous work to falsify all of them. Please note that
"Psychophysical evidence against ..." is not "Falsifying ...". It is a
weaker formulation, and I think it can be sustained in this form,
because any of those realistic autocorrelation models will nonetheless
inherently treat first- and higher-order intervals alike (at least to my
intuition). If this is not so, please demonstrate it.

> While autocorrelation alone does not account for the masking
> (you are right, how could it?), I think cochlear filtering + neural
> processing + central all-order interval analysis does.

I would be happy to see the proof of this statement.

> The specific adjustments that we need to make in our assumptions
> involve taking into account the kinds of temporal precedence effects
> that seem to be operant in high-CF fibers when one has unresolved
> harmonics (higher frequency components & higher harmonic numbers).

I can easily imagine that with adaptation processes etc. one could tune
a model such that it would find KXX sequences (K=5 ms, X random from
[0,10]ms, the triple being repeated over and over again) but not find
ABX (A random [0,10], A+B = 10, X random [0,10]). This would be so
because for KXX the model would have to look for K=5ms, and for ABX it
would have to look for A+B=10ms, i.e. at a different temporal "region".
Or one could tune a model such that it would detect KXX (K=5, X random
[0,10]) but fail to discover ABX (A+B=5, X random [0,5]) because of the
higher overall click density. Both approaches would, however have
difficulty to explain why it is possible to detect KXX for K=2.5, 5, 10,
and 20 ms, and why one fails to detect ABX for A+B=5, 10, and 15 ms.