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BAD MSG: estimates per subject using this method. I wonder what difference it ould make using less reversals per threshold, or a different lag than 20. How did the five accepted tracks inter-correlate? You could probably correct as Kollmeier et al did for the lack of independence, if you know how much dependence there is and then add the (correlation * variance) to the error term. Your approach sounds similar (in principle) to one by Wetherill in the 1960s. He argued that if you took the average signal level in pairs of peaks and valleys, you reduce the serial correlation to the point where the SE estimate gets some validity. The problem with Wetherill's approach is that it is highly sensitive to variability in the psychometric function :-if the threshold or shape of the actual or estimated function varies then an extra source of autocorrelation creeps in which the pairs of up-downs correlate. I did some montecarlos to see how well Wetherill's approach performs. It did poorly and underestimates the variance almost as badly as no correction at all. I also found that the variance within a track is an excellent predictor of the variance across tracks provided the psychometric function is constant and you know what the slope parameter of the PF is (you can also roughly approximate on a 3-up 1-down staircase that the SE is approximately twice the SD of the reversals). But ofcourse, rough approximations aren't good enough, the PF is never constant and there are biases involved in estimating the PF from a single staircase track, so it never went anywhere... Chris -- -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=- Chris Chambers Department of Psychology Monash University Clayton, Victoria 3168 AUSTRALIA Tel. +61 3 9905 3978 Fax. +61 3 9905 3948 EMAIL: chris.chambers(at)sci.monash.edu.au -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-

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Electrical Engineering Dept., Columbia University