Measuring sensorimotor synchronization abilities

Transcription

1 TIMELY School on «Timing and Time Perception: Procedures, Measures, & Applications» Corfu, February 4-8, 2013 Measuring sensorimotor synchronization abilities Simone Dalla Bella EuroMov, M2H Laboratory, University Montpellier-1 Montpellier, France Institut Universitaire de France (IUF) BRAMS (Montreal, Canada)

2 Lecture & Lab session plan Lecture Some theory on SMS (in a nutshell) Measuring SMS Synchronized tapping task The data file easy! Measures of accuracy and variability Linear statistics vs. Circular statistics Some examples Battery of SMS tasks and perceptual timing (BAASTA) Lab session MLP (Maximum Likelihood Procedure) 2 tasks from BAASTA

3 Some theory on SMS (in a nutshell)

4 Rhythm & movement Link very tight between auditory rhythm and movement Universal (e.g., case of dance) Very ancient from an evolutionary standpoint (Mithen, 2006) Precocious (first year of life) (e.g. Phillips (e.g. Phillips-Silver & Trainor, 2005; Winkler et al., 2009) Sustained by neural circuitry including cortical areas (e.g., dorsal premotor cortex) and subcortical structures (basal ganglia and cerebellum) (e.g. Wing, 2002; Zatorre, Chen, & Penhune, 2007)

5 What is rhythm? Temporal pattern resulting from the organization of sound durations and intensity Periodicity Regularity Temporal predictability Accents (strong beats) Orienting/shifting attention to the expected times, driven by the stimulus (Large & Jones, 1999; Jones 2009; Large, 2010)

6 Dynamic Attending Theory (DAT) (e.g., Large & Jones, 1999)

7 Mechanisms underlying synchronization (e.g., Mates, 1994; Vorberg & Schulze, 2002) Period correction INTERNAL TIME- KEEPER Period T Phase correction interval t(k) interval t(k+1) Perceptual delay MOTOR IMPLEMENTATION asynchrony time (from Repp, 2006)

8 Measuring SMS

9 How do we study sensorimotor synchronization? Finger tapping task ( Metronome Tapping Synchronization Continuation

10 How do we study sensorimotor synchronization? Finger tapping task Metronome Unpaced tapping Tapping Synchronization Continuation

11 How do we study sensorimotor synchronization? Collecting tapping data Via a MIDI tapping pad or motion capture (if interested in kinematics) Stimulus presentation and response recording with MAX MSP (for MIDI devices), Presentation, etc. Technical «challenges» synchronization of sound with response/movement hardware-related delays (e.g., with MIDI devices) sensitivity of the device (e.g., force threshold)

12 The data file easy! Subject Trial Event Type Time S1 3 Sound S1 3 Response S1 4 Sound S1 4 Response S1 5 Sound S1 5 Response S1 6 Sound S1 6 Response S1 7 Sound S1 7 Response S1 8 Sound S1 8 Response S1 8 Response S1 9 Sound S1 9 Response S1 10 Sound METRONOME SOUNDS TAP TIMES Assign taps to metronome sounds (or music beats, speech accents)

13 Analyzing SMS data preliminary steps Data cleaning before measuring SMS Eliminate artefacts due to the tapping device (e.g., rebounds) Eliminate outlier taps (e.g., when the Inter-tap-interval is > 3SD of the mean ITI) Assign taps to the pacing stimuli (metronome sounds, musical beats, etc.) Pacing stimuli Taps But life is not always that easy...

14 Analyzing SMS data preliminary steps How to cope with situations with missing data or excess of data? Pacing stimuli Taps? X When it is hard to assign taps to pacing stimuli data are discarded How to treat sequences of missing taps? (quite long sequences stationarity - are needed for modelling) Assignment becomes trickier with complex stimuli, such as music or speech (metrical or accentual levels) Strict and consistent criteria are needed to choose the correct metrical level (e.g., quarter note/ eighth note with music)

15 Analyzing SMS data preliminary steps Metrical pattern Ravel s Bolero (Dutoit,1997) Taps (metrical levels)...

16 Analyzing SMS data preliminary steps

17 Analyzing SMS data preliminary steps

18 Analyzing SMS data accuracy and variability Two ways for analyzing tapping data 1) Consider the motor performance irrespective of the pacing stimulus Accuracy -> Mean Inter-tap-interval (ITI) for each time series Variability -> SD of the ITI Better: Coefficient of Variation (CV) of the ITI (SD of the ITI / Mean ITI) Pacing stimuli Taps ITI... same ITI same CV...

19 Analyzing SMS data accuracy and variability 2) Consider the taps with respect to the pacing stimulus Accuracy -> Asynchrony between the taps and the pacing stimuli Pacing stimuli Taps time Asynchrony Based on asynchronies you can compute: signed asynchrony absolute asynchrony (synchronization error)

20 Analyzing SMS data accuracy and variability Variability -> SD or SE of asynchrony Synchronization accuracy and variability are typically based on linear statistics Sufficient with performances by «master tappers» Less adequate for children, patients, beat-deaf individuals Pacing stimuli Taps time Asynchrony??

21 Why circular statistics? Need to reject lots of data in a trial sometimes lack of data to analyze even if the participant produced a lot of taps Risk of underestimating residual synchronization abilities Alternative method circular statistics (they do not require a 1-to-1 correspondence between taps and pacing stimuli)

22 Vector length How can we decide if somebody synchronizes or not? Reyleigh s test Direction = accuracy Length = variability (or consistency) Vector direction (theta) (From Sowinski & Dalla Bella, under revision)

23 Accurate & not-variable (consistent) Inaccurate but not variable (consistent) Inaccurate & variable (inconsistent)