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Detectability of uneven rhythms. H.H. Schulze Philipps Universität Marburg Fachbereich Psychologie. Uneven rhythms. The metrum is not divided into equal temporal intervals Example: 3:4,4:5,6:7 In turkish music these rhythms are called limping rhythms (aslak). Questions.
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Detectability of uneven rhythms H.H. Schulze Philipps Universität Marburg Fachbereich Psychologie
Uneven rhythms • The metrum is not divided into equal temporal intervals • Example: 3:4,4:5,6:7 • In turkish music these rhythms are called limping rhythms (aslak).
Questions • What is the threshold for detecting unevenness? • How does it depend upon the period of the pulses and the length of the sequence? • Does it depend upon the ear to which the sound is presented? • Does it improve with training?
Conditions • Number of Periods (1,2,3,4) • Ear (left,right) • Period (200ms,300ms,400ms,500ms) • Session(1,2)
subjects • 29 Subjects • Psychology students • 26 play an instrument
Method • Two alternative forced-choice uneven vs even • The five different periods were randomized from trial to trial • The adaptive method of Kaernbach was used with five parallel staircases with random switching
Kaernbachs adaptive Method • Rule: After a correct response decrease level by 1 step • after an incorrect response increase the level by 3 steps • the procedure converges to a level with p-correct of .75
Examples of individual data • The following figures show the threshold of the detectability as a function of the number of periods for three subjects. • Lines with the triangular symbol are for the first session. • Lines with a circle symbol are for the second session. • The color codes the ear condition.
Individual Parameters • Fitting a linerar model for the threshold function with period as a factor and nbeats as a covariate. • The following figure shows the individual parameters and confidence intervals for all subjects. • The intercept reflects the threshold for nperiod = 1 • The coefficients of nbeats reflect the decrease of the threshold with the number of periods presented. • The coefficients of period are for the dummy coded period variable
Summary of statistical analysis • Significant effects of period, number of beats and session • No effect of ear
Multiple look prediction for improvement • The multiple look prediction of SDT is that the threshold is inverse proportional to the square root of the number of periods. • Assumptions: • the internal observations in each event are independent random variables • The detectability index is proportional to the relative shift of the uneven beat. • Predictions for mean data are shown in
Conclusions • There is large interindividual variability for the thresholds of detectability. • Webers law does not hold. The thresholds are lowest for the 500ms conditions. • The ear to which the rhythms are presented does not have any effect on the discriminability of the stimuli. • With training the sensitivity to unevenness can be improved • The improvement with the number of periods presented is less than expected by a simple multiple look model of SDT in the mean data, but the estimation of individual parameters of the threshold function still has to be done.