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Results
Methods
MoCvaCon
Hypothesis:
CogniAve loads affect Somatosensory Motor States (SMS) at different levels of control from deliberate to automaAc to autonomic
Goals of the study: • To develop outcome measures that capture change in
physiological states as a funcAon of different mindsets
• To develop a simple experimental paradigm that characterizes mindsets and SMS in neurological disorders
Characterization of sensory-motor behavior under different mindsets Jihye Ryu1,2, Elizabeth Torres1,2,3
1Rutgers University, Psychology Department 2Rutgers Center for CogniAve Science 3Rutgers University , Computer Science Department
Acknowledgements This research is funded in part by The New Jersey Governor’s Council for Medical Research and Treatment of AuAsm and the New Jersey Department of Health
References • Torres, E. B. (2011). Two classes of movements in motor control. Experimental brain research, 215(3-‐4), 269-‐283.
Conclusion & Future DirecCon • Frequency distribuAon of the speed profile do not differ between forward and backward movement, implying that in this task cogniAve loads influence both deliberate and automaAc poinAng movement segments.
• Individuals show varying degrees of physiological changes under low and high cogniAve load mindsets. Hence, examining the frequency distribuAon of the acceleraAon, temperature, and heart beat across may allow characterizing cogniAve load.
• StaAsAcal parameter ranges were determined for this typical cohort.
• Data will be further collected for different populaAon with neurological disorders.
TASK STRUCTURE
Ø Control condiAon (Control): Performed 60 trials right aCer becoming familiar with the procedure.
Ø Low cogniAve load condiAon (Low): Performed 60 trials while repeatedly counAng forward from 1 to 5.
Ø High cogniAve load condiAon (High): Performed 60 trials while counAng backwards from 400 by 3.
1. Speed profile analysis A. Extract deliberate (forward) and automaAc (retracAon) hand movement
segments and obtain corresponding angular speed profiles B. Normalize peak speed (NpV = pV / (pV + Avrg(local V)) between local mins C. Empirically esAmate the probability distribuAon parameters fiung
histogram in C) using the conAnuous Gamma family of probability distribuAons. Obtain summary staAsAcs (mu, sigma, skewness, kurtosis).
2. AcceleraCon data analysis A. Extract median acceleraAon data (128Hz) at each second B. Empirically esAmate the probability distribuAon parameters fiung
histogram in B) as in (1)
3. Temperature data analysis A. Extract temperature data (128Hz) B. Empirically esAmate the probability distribuAon parameters fiung
histogram in B) as in (1)
4. Heart rate data analysis A. Extract heart rate data (256Hz) and compute inter-‐beat interval (IBI) B. Empirically esAmate the probability distribuAon parameters fiung
histogram in B) as in (1)
Red: Forward move for poinCng (P) Blue: RetracCon move for poinCng (P)
Red: Forward move to indicate Cme (T) Blue: RetracCon move to indicate Cme(T)
Forward trajectory
Num
ber o
f tria
ls
RetracAon trajectory
Analyses
Sample parsing
Expe
rimen
t Paradigm
1. A)
X-‐pos
Z-‐po
s
Z-‐po
s
1. B)
1. C) 2. B)
4. B) 3. B)
Time Interval PercepCon (ms)
ReacCon Time Analysis (ms)
Time from signal to touch
Time from signal to move
Time from move to touch
Low-‐Cntl High-‐Cntl Low-‐Cntl High-‐Cntl Low-‐Cntl High-‐Cntl
skew
ness
Angular AcceleraCon gamma-‐fit distribuCon pdf parameter plot
Temperature gamma-‐fit distribuCon pdf parameter plot IBI gamma-‐fit distribuCon pdf parameter plot
Median AcceleraCon gamma-‐fit distribuCon pdf parameter plot
X-‐pos Time(ms)
Angular A
cceleraA
on (m
/s)
Noramlized AcceleraAon (m/s)
Num
ber o
f tria
ls
Median AcceleraAon (m/s)
Temperature(C) IBI(s)
Num
ber o
f tria
ls
Num
ber o
f tria
ls
0s 4s 8s
