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Question/comparisonValuesStatistical test and n numbersstat results
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Distance measures [onset period]Mean (SEM)LME, n=5subs x 6 speeds
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f = 'vals ~ state + (1|speed) + (1|sesh)';
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Distance travelled Stat: 26.2406 (1.0390)p = 0.22119
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Run: 25.1319 ( 1.0030)
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Direct distanceStat: 3.9880 ( 0.2903)p = 9.3017e-19
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Run: 8.1150 (0.4322)
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Distance Ratio (stats on log2 transform)Stat: 7.8033 (0.8106)p = 3.7498e-19
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Run: 3.2266 (0.1308)
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Distance measures [offset period]Mean (SEM)LME, n=5subs x 6 speeds
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f = 'vals ~ state + (1|speed) + (1|sesh)';
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Distance travelled 3.4389 0.2655p = 2.9479e-10
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5.2039 0.3384
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Direct distance1.8828 0.1703p = 1.6257E-17
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3.7855 0.2412
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Distance Ratio2.0462 0.1440p = 2.7651e-07
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1.3763 0.0309
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Speed and AccelerationMean (SEM)
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Max speed (onset)Stat: 8.5380 (0.4404)p =2.7519e-07
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Run: 10.3263 ( 0.3659)
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Offsetstat: 4.1932 (0.3619)p = 1.1402e-10
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run: 6.3788 ( 0.4273)
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Relative spectral power of accelerationsMean (SEM)
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Low band (0-6 Hz)Stat: 0.9829 ( 0.0842)
adtest for normality, p = 0.4543
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power spectrum (0-20Hz) normalised by mean of spectrum
Run: 1.6003 (0.0505)paired t-testp = 7.4565e-07
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High band (6-20 Hz)Stat: 1.0068 (0.0401)
same p-values since using relative power
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Run: 0.7153 (0.0240)paired t-test
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Angle of approachMean (SEM)LME
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Cumulative change in angle (onset)Stat: 516.9536 (18.1672)
Run: 318.8556 (9.4587)
f = 'vals ~ state + (1|speed) + (1|sesh)';p = 2.5761e-17
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OffsetStat: 363.2068 (15.9508)p = 9.4107e-13;
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Run: 220.9804 (11.8478)
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Trajectory tanglingRM-ANOVA (n=5subs x 6 speeds)
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[p,tbl,stats,terms] = anovan(allVals,{timeVec,stateVec,subjVec},'model',2,'random',3,'varnames',{'Time','State','Subj'});
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Source Sum Sq. d.f. Mean Sq. F Prob>F
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Time 0.00622 179 0.00003 3.35 0
State 0.00698 1 0.00698 18.91 0.0025
Subj 0.00295 4 0.00074 1.7 0.3063
Time*State 0.00537 179 0.00003 8.36 0
Time*Subj 0.00743 716 0.00001 2.9 0
State*Subj 0.00171 4 0.00043 119.21 0
Error 0.03482 9716 0
Total 0.06548 10799
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Distance between trajectoriesMean (SEM)
paired t-test on mean range over stimulus perirod
[h,p] = adtest(meanStat-meanRun), p = 0.1725
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Stat: 2.8697 (0.4900)n = 5 subs
ttest, p = 1.5966e-04
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Run: 5.9521 (0.5448)
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LDA decodingRM ANOVA
[p,tbl,stats,terms] = anovan(decVec,{timeVec,stateVec,subjVec},'model',2,'random',3,'varnames',{'Time','State','Subj'});
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Source Sum Sq. d.f. Mean Sq. F Prob>F
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Time 56.0652 189 0.2966 85.95 0
State 11.0198 1 11.0198 37.86 0.0035
Subj 4.3994 4 1.0999 3.77 0.1129
Time*State 1.9574 189 0.0104 3.61 0
Time*Subj 2.6091 756 0.0035 1.2 0.0056
State*Subj 1.1642 4 0.291 101.45 0
Error 2.1689 756 0.0029
Total 79.3841 1899
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Cross-condiiton tanglingRM ANOVAa
[p,tbl,stats,terms] = anovan(allVals,{timeVec,stateVec,subjVec},'model',2,'random',3,'varnames',{'Time','State','Subj'});
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Source Sum Sq. d.f. Mean Sq. F Prob>F
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Time 0.0133 179 0.00007 80.56 0
State 0.00398 1 0.00398 121.06 0
Subj 0.00015 4 0.00004 2.24 0.305
Time*State 0.00274 179 0.00002 28.74 0
Time*Subj 0.00066 716 0 1.72 0
State*Subj 0.00009 4 0.00002 4.3 0.3286
Error 0.02822 52916 0
Total 0.04915 53999
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Single trial analyses
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Distance ratiosMean (SEM)LME
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OnsetStat: 5.9616 (0.2457)f = 'vals ~ state + (1|speed) + (1|sesh)';p = 3.7556e-17
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Run: 3.5406 (0.1175)
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OffsetStat 3.3985 (0.2760)p = 0.0026599
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Run: 2.6915 (0.0603)
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Trajectory tangling
RM-ANOVA, n =5 subs x 6 speeds (single-trial tanngling averaged to give conditon mean)
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[p,tbl,stats,terms] = anovan(allVals,{timeVec,stateVec,subjVec,speedVec},'model','full','varnames',{'Time','State','Subj','Speed'});
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Max traj speedLME on top speed
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OnsetMean (SEM)f = 'vals ~ state + (1|speed) + (1|sesh)';p = 2.2739e-91
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Stat: 5.5875 (0.1275)
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Run: 5.0372 (0.1479)
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OffsetStat: 3.0462 (0.1466)p = 6.5038E-64
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Run: 3.6087 (0.0873)
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Power spectrum of accelerations -> can't do on single trials (too noisy)
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Angle of approachMean (SEM)
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OnsetStat: 570.2476 (24.5051)f = 'vals ~ state + (1|speed) + (1|sesh)';p = 1.0438e-96
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Run: 524.2078 (26.0895)
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OffsetStat: 495.3645 (28.6612)p = 1.4504e-152
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Run: 415.1111 (19.0039)