detectability of uneven rhythms
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
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Detectability of uneven rhythms
H.H. Schulze
Philipps Universität Marburg
Fachbereich Psychologie
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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).
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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?
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Conditions
• Number of Periods (1,2,3,4)
• Ear (left,right)
• Period (200ms,300ms,400ms,500ms)
• Session(1,2)
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subjects
• 29 Subjects
• Psychology students
• 26 play an instrument
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Stimuli
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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
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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
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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.
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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
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Mean data
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Mean data nbeats
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Period Effect
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Session effect
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Ear effect
left right
1.0
1.5
2.0
2.5
3.0
3.5
4.0
ear
nbeats
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Summary of statistical analysis
• Significant effects of period, number of beats and session
• No effect of ear
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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: 1. the internal observations in each event are
independent random variables
2. The detectability index is proportional to the relative shift of the uneven beat.
• Predictions for mean data are shown in
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Mean data and multiple look prediction
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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.