Stat4Ci A sensitive statistical test for smooth classification images.

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1 Stat4Ci A sensitive statistical test for smooth classification images

2 Aspects théorique Image de classification Multiple comparaisons –Aucune correction –Bonferroni Random field theory –Pixel –Cluster –Scale Space

3 .* = Original imageMask of bubblesStimulus Stimuli creation Bubbles stimulus

4 Bubble task: gender discrimination Men or women?

5 Creation of the Ci +…+++++ Expected proportion =.75. ++++…, …,,,,, ==./ =

6 (Gibson, Gosselin, Schyns & Wasserman, Psychonomics, 2002) (Gosselin & Schyns, Vision Research, 2001) Bubble classification images

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8 Visage à lendroit Visage à lenvers

9 Image de classification Régression multiple i=1..n, n nombre dessais u est une variable despace Pour n suffisamment grand

10 Test t p = [ 96,127,127,137,114,119,109,109,143,109,116,114,143,109,117,127,112,112,98, 37,112,109,119,106,109 ] Moyenne = 117.2, 2 = 160 v = [ 114,88,102,127,104,104,91,96,104,106,91,102,104,100,114,109,109,119, 91,81,114,119,102,111,80,119,119,123,119,114,132 ] Moyenne = 106.71, 2 = 167 t=1.97

11 Test t~Z score T(12)=3.56 t=1.97 Z=1.64, p=.05Z=2.35, p=.01

12 Comparaison multiple Aucune correction –p = % de chance de faux positif Bonferroni –p = p/N N est le nombre de comparaison –Soit 5 faux positif pour 100 images 1 test 2 tests 16*16 tests

13 Comparaison multiple p=.05, z=1.65 p=.05, z=1.92 p=.05, z=3.54 p=.05, z=3.89 P=0.00005

14 ? p=.05, z=4.80 P=8.10 -8

15 Solutions Bonf t = 4.5228 Bonf t = 3.5463RFT t = 4.06

16 Random Field theory

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19 Expected Euler Characteristic

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21 Gaussian Random field

22 Seuil non corrigé

23 Bonferroni Correction

24 Pixel test

25 t z = 3.30

26 tsize resels Zmax x y ----------------------------------------- C[2.70] 970 0.44 4.17 122 129 [2.70] 917 0.41 3.95 162 129 ----------------------------------------- P3.30- p-value = [0.05] FWHM = [47.1] Minimum cluster size = 861.7 Seulement 2 paramètres –FWHM = taille du filtre de lissage –p = seuil de confiance Comment choisir FWHM ? –Pour détecter un signal donné, le meilleur filtre est un filtre de taille comparable Problèmes –Si le signal est diffus, le pic est faible Solution –Prendre en compte la taille et le Z score. Cluster test

27 tsize resels Zmax x y ----------------------------------------- C[2.70] 970 0.44 4.17 122 129 [2.70] 917 0.41 3.95 162 129 ----------------------------------------- P3.30- p-value = [0.05] FWHM = [47.1] Minimum cluster size = 861.7 t size resels Zmax x y ----------------------------------------- C[2.70] 1787 0.81 5.2 133208 ----------------------------------------- P3.30- p-value = [0.05] FWHM = [47.1] Minimum cluster size = 861.7

28 Cluster test t z = 2.5 k = 350 pixels

29 Scale space r=w 1 /w 2

30 Scale space t z = 4

31 Scale space t z = 2.5 k = 350 pixels