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*To*: AUDITORY@xxxxxxxxxxxxxxx*Subject*: Re: Autocorrelation*From*: Christian Kaernbach <chris@xxxxxxxxxxxxxxxxxxxxxxxxxx>*Date*: Wed, 19 Jul 2000 20:38:52 +0200*Comments*: To: peter@epl.meei.harvard.edu*Organization*: Institut fuer Allgemeine Psychologie, Universitaet Leipzig*References*: <200007190404.GAA10891@server1.rz.uni-leipzig.de> <39753A47.DAE4F119@psychologie.uni-leipzig.de> <3975EA57.E2EE102B@epl.meei.harvard.edu>*Reply-to*: Christian Kaernbach <chris@xxxxxxxxxxxxxxxxxxxxxxxxxx>*Sender*: AUDITORY Research in Auditory Perception <AUDITORY@xxxxxxxxxxxxxxx>

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. Best, Christian

**Follow-Ups**:**Re: Autocorrelation***From:*Peter Cariani

**References**:**Re: Autocorrelation***From:*Christian Kaernbach

**Re: Autocorrelation***From:*Peter Cariani

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