msbernst — Sep 12, 2013, 11:59 PM
library(plyr)
library(ggplot2)
#library(MASS)
library(car)
Loading required package: MASS Loading required package: nnet
# Analyze the cognitive load study results
results <- read.csv("~/Dropbox/Twitch Crowdsourcing/working-memory/cogLoad.csv")
# results <- rbind(results, read.csv("~/Dropbox/Twitch Crowdsourcing/working-memory/extracted_small.csv"))
results$session <- factor( paste( results$phoneID, results$timestamp, sep = "@" ) ) # uniquely identify each person who did the study
results$task <- factor( ifelse((results$task == "2back" | results$task == "3back"), "back", as.character(results$task)))
results$previousTask <- factor( ifelse((results$previousTask == "2back" | results$previousTask == "3back"), "back", as.character(results$previousTask)))
results$isCorrect = (results$correctAnswer==results$userGuess)
backtasks = subset(results, task=='back' & !is.na(correctAnswer) & !is.na(userGuess) & !is.null(correctAnswer) & !is.null(userGuess) & correctAnswer != 'null' & userGuess != 'null')
head(backtasks)
X_id phoneID number condition task previousTask duration userGuess
5 5 3ad3559c 4 3back back back 3970 0
7 7 3ad3559c 6 3back back Census/Energy 3154 0
8 8 3ad3559c 7 3back back back 1998 1
9 9 3ad3559c 8 3back back back 1363 0
11 11 3ad3559c 10 3back back Census/People 2653 0
12 12 3ad3559c 11 3back back back 891 0
correctAnswer timestamp session isCorrect
5 0 1.379e+12 3ad3559c@1379025691476 TRUE
7 0 1.379e+12 3ad3559c@1379025691476 TRUE
8 1 1.379e+12 3ad3559c@1379025691476 TRUE
9 0 1.379e+12 3ad3559c@1379025691476 TRUE
11 0 1.379e+12 3ad3559c@1379025691476 TRUE
12 0 1.379e+12 3ad3559c@1379025691476 TRUE
# double check that things look right --- look at participants
countsPerSession <- ddply(backtasks,
c('session'), summarise,
count=length(duration)
)
countsPerSession
session count
1 26742641@1379035244203 337
2 3acecffb@1379010624637 39
3 3ad3559c@1379025691476 13
4 3ad3559c@1379026295046 447
5 46b48894@1378844106838 346
6 46b48894@1378845864401 337
7 46b48894@1378890269202 3
8 46b48894@1378890315953 2
9 5d9ab4f5@1378410039867 76
10 5d9ab4f5@1378412329855 1001
11 7f20b1c7@1378712963763 276
12 7f20b1c7@1378713851854 371
13 8ae2fe42@1378827268681 335
14 986c8378@1378826177581 127
15 986c8378@1378826842544 361
16 db95fbf3@1376916217350 15
17 db95fbf3@1377977536970 42
18 db95fbf3@1378285306843 41
19 db95fbf3@1378285383048 3
20 e71d2d62@1378737168452 269
21 fec9b094@1378936198432 375
includedSessions <- subset(countsPerSession, count >= 100)[,'session']
backtasks <- subset(backtasks, backtasks$session %in% includedSessions)
# look at the overall distribution
# Analysis of reaction time data: http://opensiuc.lib.siu.edu/cgi/viewcontent.cgi?article=1077&context=tpr
# Test for normality --- this should be a straight line and not significant
# if it's normally distributed
qqnorm(y=backtasks$duration)
qqline(y=backtasks$duration)
shapiro.test(backtasks$duration)
Shapiro-Wilk normality test
data: backtasks$duration
W = 0.5199, p-value < 2.2e-16
# Box-Cox transformation to make data normal (varsity maneuver!)
boxcox <- powerTransform(backtasks$duration)
backtasks$transformed <- bcPower(backtasks$duration, boxcox$lambda)
qqnorm(y=backtasks$transformed)
qqline(y=backtasks$transformed)
shapiro.test(backtasks$transformed)
Shapiro-Wilk normality test
data: backtasks$transformed
W = 0.9839, p-value < 2.2e-16
# Alternate: drop outliers >= 2 s.d. from mean
filtered <- subset(backtasks, duration <= mean(duration) + 2*sd(duration))
qqnorm(y=filtered$duration)
qqline(y=filtered$duration)
shapiro.test(filtered$duration)
Shapiro-Wilk normality test
data: filtered$duration
W = 0.8217, p-value < 2.2e-16
# Conclusion: dropping outliers doesn't help; let's use Box-Cox
# --------------------------------
# What's the mean/median delay?
delay <- ddply(backtasks,
c('previousTask','condition'), summarise,
p.05=quantile(duration, prob=0.05),
p.25=quantile(duration, prob=0.25),
p.50=quantile(duration, prob=0.50),
p.75=quantile(duration, prob=0.75),
p.95=quantile(duration, prob=0.95),
meanDelay=mean(duration),
meanAccuracy=mean((correctAnswer == userGuess))
)
delay
previousTask condition p.05 p.25 p.50 p.75 p.95 meanDelay
1 back 2back 488.8 689.0 1155 2182 4933 1812
2 back 3back 507.0 767.0 1160 2226 5424 1889
3 Census/Activity 2back 524.0 1216.0 1681 2761 8661 2629
4 Census/Activity 3back 526.8 1082.0 1609 2501 11574 3644
5 Census/Dress 2back 536.9 995.5 1778 3288 5559 2535
6 Census/Dress 3back 496.2 1060.0 2249 3897 9091 3278
7 Census/Energy 2back 548.4 861.0 1596 3308 6497 2332
8 Census/Energy 3back 555.6 1024.0 1887 2857 5844 2693
9 Census/People 2back 531.0 960.2 1576 3142 12502 2772
10 Census/People 3back 539.8 1264.2 1675 3158 6608 2535
11 PhotoRanking 2back 591.6 1084.0 1657 2594 8509 2630
12 PhotoRanking 3back 589.2 997.0 1588 2933 8940 2479
13 SlideToUnlock 2back 663.2 933.5 1405 2098 6004 1926
14 SlideToUnlock 3back 649.7 943.8 1728 2915 7075 2418
meanAccuracy
1 0.9237
2 0.8873
3 0.9459
4 0.9167
5 0.9474
6 0.9730
7 0.9459
8 0.9189
9 0.9211
10 0.8333
11 0.8378
12 0.8611
13 0.9107
14 0.8545
# Plot the delays
qplot(duration, data=backtasks, geom="histogram")
stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust
this.
boxplot <- ggplot(backtasks, aes(x=condition, y=duration)) + geom_boxplot() + scale_y_continuous(limits = c(0, 2500))
boxplot + facet_grid(. ~ previousTask)
Warning: Removed 843 rows containing non-finite values (stat_boxplot).
Warning: Removed 21 rows containing non-finite values (stat_boxplot).
Warning: Removed 32 rows containing non-finite values (stat_boxplot).
Warning: Removed 26 rows containing non-finite values (stat_boxplot).
Warning: Removed 21 rows containing non-finite values (stat_boxplot).
Warning: Removed 22 rows containing non-finite values (stat_boxplot).
Warning: Removed 60 rows containing non-finite values (stat_boxplot).
transformedDelay <- ddply(backtasks,
c('previousTask', 'condition'), summarise,
p.05=quantile(transformed, prob=0.05),
p.25=quantile(transformed, prob=0.25),
p.50=quantile(transformed, prob=0.50),
p.75=quantile(transformed, prob=0.75),
p.95=quantile(transformed, prob=0.95),
meanDelay=mean(transformed),
meanAccuracy=mean((correctAnswer == userGuess))
)
transformedDelay
previousTask condition p.05 p.25 p.50 p.75 p.95 meanDelay
1 back 2back 2.603 2.643 2.696 2.749 2.803 2.698
2 back 3back 2.607 2.655 2.696 2.751 2.808 2.702
3 Census/Activity 2back 2.611 2.701 2.729 2.766 2.832 2.728
4 Census/Activity 3back 2.612 2.690 2.725 2.759 2.845 2.725
5 Census/Dress 2back 2.614 2.681 2.733 2.778 2.810 2.727
6 Census/Dress 3back 2.605 2.688 2.751 2.789 2.835 2.739
7 Census/Energy 2back 2.617 2.667 2.724 2.778 2.818 2.720
8 Census/Energy 3back 2.618 2.684 2.738 2.769 2.811 2.728
9 Census/People 2back 2.613 2.678 2.723 2.774 2.849 2.723
10 Census/People 3back 2.615 2.704 2.728 2.775 2.819 2.728
11 PhotoRanking 2back 2.626 2.690 2.727 2.762 2.831 2.730
12 PhotoRanking 3back 2.625 2.682 2.724 2.770 2.834 2.726
13 SlideToUnlock 2back 2.639 2.675 2.713 2.746 2.814 2.714
14 SlideToUnlock 3back 2.637 2.676 2.731 2.770 2.822 2.728
meanAccuracy
1 0.9237
2 0.8873
3 0.9459
4 0.9167
5 0.9474
6 0.9730
7 0.9459
8 0.9189
9 0.9211
10 0.8333
11 0.8378
12 0.8611
13 0.9107
14 0.8545
qplot(transformed, data=backtasks, geom="histogram")
stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust
this.
boxplot <- ggplot(backtasks, aes(x=condition, y=transformed)) + geom_boxplot() #+ scale_y_continuous(limits = c(0, 10000))
boxplot + facet_grid(. ~ previousTask)
###########
# Tests
#TODO: check homogeneity of variance (levene's test)
# ANOVA predicting transformed time using the previous task
model <- aov(transformed ~ previousTask * condition + session, data=backtasks)
untransformedModel <- aov(duration ~ previousTask * condition + session, data=backtasks)
# Original model is significant
anova(untransformedModel)
Analysis of Variance Table
Response: duration
Df Sum Sq Mean Sq F value Pr(>F)
previousTask 6 3.08e+08 5.14e+07 11.66 5.3e-13 ***
condition 1 1.61e+07 1.61e+07 3.65 0.056 .
session 11 5.13e+09 4.66e+08 105.85 < 2e-16 ***
previousTask:condition 6 3.79e+07 6.32e+06 1.44 0.197
Residuals 4557 2.01e+10 4.41e+06
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# No differences between factor levels are significant, but there is a significant effect of condition
TukeyHSD(untransformedModel, 'previousTask')
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = duration ~ previousTask * condition + session, data = backtasks)
$previousTask
diff lwr upr p adj
Census/Activity-back 1282.01 550.77 2013.25 0.0000
Census/Dress-back 1053.78 332.17 1775.38 0.0003
Census/Energy-back 664.78 -61.59 1391.16 0.0985
Census/People-back 808.99 82.61 1535.36 0.0178
PhotoRanking-back 707.93 -23.31 1439.17 0.0651
SlideToUnlock-back 322.22 -104.72 749.16 0.2815
Census/Dress-Census/Activity -228.23 -1246.18 789.71 0.9946
Census/Energy-Census/Activity -617.23 -1638.56 404.11 0.5600
Census/People-Census/Activity -473.02 -1494.36 548.31 0.8199
PhotoRanking-Census/Activity -574.08 -1598.89 450.72 0.6480
SlideToUnlock-Census/Activity -959.79 -1795.12 -124.46 0.0125
Census/Energy-Census/Dress -388.99 -1403.45 625.47 0.9187
Census/People-Census/Dress -244.79 -1259.25 769.67 0.9919
PhotoRanking-Census/Dress -345.85 -1363.80 672.10 0.9537
SlideToUnlock-Census/Dress -731.56 -1558.47 95.36 0.1232
Census/People-Census/Energy 144.20 -873.65 1162.06 0.9996
PhotoRanking-Census/Energy 43.14 -978.19 1064.48 1.0000
SlideToUnlock-Census/Energy -342.56 -1173.64 488.51 0.8883
PhotoRanking-Census/People -101.06 -1122.39 920.28 1.0000
SlideToUnlock-Census/People -486.77 -1317.84 344.31 0.5974
SlideToUnlock-PhotoRanking -385.71 -1221.04 449.63 0.8220
# Transformed model is significant
anova(model)
Analysis of Variance Table
Response: transformed
Df Sum Sq Mean Sq F value Pr(>F)
previousTask 6 0.34 0.056 28.3 < 2e-16 ***
condition 1 0.03 0.025 12.7 0.00037 ***
session 11 9.88 0.898 450.0 < 2e-16 ***
previousTask:condition 6 0.01 0.002 0.9 0.49219
Residuals 4557 9.09 0.002
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Which pairs of factor levels are significantly different from each other?
TukeyHSD(model)
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = transformed ~ previousTask * condition + session, data = backtasks)
$previousTask
diff lwr upr p adj
Census/Activity-back 0.0265974 0.011036 0.04216 0.0000
Census/Dress-back 0.0334440 0.018088 0.04880 0.0000
Census/Energy-back 0.0241928 0.008736 0.03965 0.0001
Census/People-back 0.0257237 0.010266 0.04118 0.0000
PhotoRanking-back 0.0284599 0.012899 0.04402 0.0000
SlideToUnlock-back 0.0212574 0.012172 0.03034 0.0000
Census/Dress-Census/Activity 0.0068466 -0.014815 0.02851 0.9674
Census/Energy-Census/Activity -0.0024045 -0.024139 0.01933 0.9999
Census/People-Census/Activity -0.0008737 -0.022608 0.02086 1.0000
PhotoRanking-Census/Activity 0.0018626 -0.019945 0.02367 1.0000
SlideToUnlock-Census/Activity -0.0053400 -0.023116 0.01244 0.9747
Census/Energy-Census/Dress -0.0092512 -0.030839 0.01234 0.8683
Census/People-Census/Dress -0.0077203 -0.029308 0.01387 0.9409
PhotoRanking-Census/Dress -0.0049840 -0.026646 0.01668 0.9938
SlideToUnlock-Census/Dress -0.0121866 -0.029783 0.00541 0.3877
Census/People-Census/Energy 0.0015308 -0.020129 0.02319 1.0000
PhotoRanking-Census/Energy 0.0042671 -0.017467 0.02600 0.9974
SlideToUnlock-Census/Energy -0.0029354 -0.020621 0.01475 0.9990
PhotoRanking-Census/People 0.0027363 -0.018998 0.02447 0.9998
SlideToUnlock-Census/People -0.0044663 -0.022152 0.01322 0.9897
SlideToUnlock-PhotoRanking -0.0072025 -0.024979 0.01057 0.8962
$condition
diff lwr upr p adj
3back-2back 0.00471 0.002118 0.007303 4e-04
$session
diff lwr
3ad3559c@1379026295046-26742641@1379035244203 -0.1023880 -0.1129244
46b48894@1378844106838-26742641@1379035244203 -0.0059490 -0.0171269
46b48894@1378845864401-26742641@1379035244203 0.0050133 -0.0062380
5d9ab4f5@1378412329855-26742641@1379035244203 -0.1094633 -0.1186614
7f20b1c7@1378712963763-26742641@1379035244203 0.0096483 -0.0022083
7f20b1c7@1378713851854-26742641@1379035244203 -0.0422905 -0.0532809
8ae2fe42@1378827268681-26742641@1379035244203 -0.0630171 -0.0742852
986c8378@1378826177581-26742641@1379035244203 -0.0161432 -0.0313502
986c8378@1378826842544-26742641@1379035244203 -0.0638570 -0.0749197
e71d2d62@1378737168452-26742641@1379035244203 -0.1072739 -0.1192150
fec9b094@1378936198432-26742641@1379035244203 -0.0138291 -0.0247917
46b48894@1378844106838-3ad3559c@1379026295046 0.0964391 0.0859811
46b48894@1378845864401-3ad3559c@1379026295046 0.1074013 0.0968650
5d9ab4f5@1378412329855-3ad3559c@1379026295046 -0.0070753 -0.0153836
7f20b1c7@1378712963763-3ad3559c@1379026295046 0.1120364 0.1008558
7f20b1c7@1378713851854-3ad3559c@1379026295046 0.0600976 0.0498402
8ae2fe42@1378827268681-3ad3559c@1379026295046 0.0393709 0.0288166
986c8378@1378826177581-3ad3559c@1379026295046 0.0862448 0.0715589
986c8378@1378826842544-3ad3559c@1379026295046 0.0385310 0.0281963
e71d2d62@1378737168452-3ad3559c@1379026295046 -0.0048858 -0.0161559
fec9b094@1378936198432-3ad3559c@1379026295046 0.0885589 0.0783315
46b48894@1378845864401-46b48894@1378844106838 0.0109623 -0.0002156
5d9ab4f5@1378412329855-46b48894@1378844106838 -0.1035143 -0.1126225
7f20b1c7@1378712963763-46b48894@1378844106838 0.0155973 0.0038103
7f20b1c7@1378713851854-46b48894@1378844106838 -0.0363415 -0.0472568
8ae2fe42@1378827268681-46b48894@1378844106838 -0.0570682 -0.0682629
986c8378@1378826177581-46b48894@1378844106838 -0.0101942 -0.0253470
986c8378@1378826842544-46b48894@1378844106838 -0.0579080 -0.0688961
e71d2d62@1378737168452-46b48894@1378844106838 -0.1013249 -0.1131969
fec9b094@1378936198432-46b48894@1378844106838 -0.0078801 -0.0187673
5d9ab4f5@1378412329855-46b48894@1378845864401 -0.1144766 -0.1236747
7f20b1c7@1378712963763-46b48894@1378845864401 0.0046350 -0.0072216
7f20b1c7@1378713851854-46b48894@1378845864401 -0.0473038 -0.0582942
8ae2fe42@1378827268681-46b48894@1378845864401 -0.0680304 -0.0792985
986c8378@1378826177581-46b48894@1378845864401 -0.0211565 -0.0363635
986c8378@1378826842544-46b48894@1378845864401 -0.0688703 -0.0799330
e71d2d62@1378737168452-46b48894@1378845864401 -0.1122872 -0.1242283
fec9b094@1378936198432-46b48894@1378845864401 -0.0188424 -0.0298050
7f20b1c7@1378712963763-5d9ab4f5@1378412329855 0.1191117 0.1091822
7f20b1c7@1378713851854-5d9ab4f5@1378412329855 0.0671729 0.0582957
8ae2fe42@1378827268681-5d9ab4f5@1378412329855 0.0464462 0.0372276
986c8378@1378826177581-5d9ab4f5@1378412329855 0.0933201 0.0795627
986c8378@1378826842544-5d9ab4f5@1378412329855 0.0456063 0.0366399
e71d2d62@1378737168452-5d9ab4f5@1378412329855 0.0021895 -0.0078408
fec9b094@1378936198432-5d9ab4f5@1378412329855 0.0956342 0.0867916
7f20b1c7@1378713851854-7f20b1c7@1378712963763 -0.0519388 -0.0635483
8ae2fe42@1378827268681-7f20b1c7@1378712963763 -0.0726655 -0.0845381
986c8378@1378826177581-7f20b1c7@1378712963763 -0.0257916 -0.0414518
986c8378@1378826842544-7f20b1c7@1378712963763 -0.0735054 -0.0851832
e71d2d62@1378737168452-7f20b1c7@1378712963763 -0.1169222 -0.1294354
fec9b094@1378936198432-7f20b1c7@1378712963763 -0.0234775 -0.0350605
8ae2fe42@1378827268681-7f20b1c7@1378713851854 -0.0207267 -0.0317343
986c8378@1378826177581-7f20b1c7@1378713851854 0.0261473 0.0111322
986c8378@1378826842544-7f20b1c7@1378713851854 -0.0215666 -0.0323639
e71d2d62@1378737168452-7f20b1c7@1378713851854 -0.0649834 -0.0766792
fec9b094@1378936198432-7f20b1c7@1378713851854 0.0284614 0.0177667
986c8378@1378826177581-8ae2fe42@1378827268681 0.0468739 0.0316545
986c8378@1378826842544-8ae2fe42@1378827268681 -0.0008399 -0.0119196
e71d2d62@1378737168452-8ae2fe42@1378827268681 -0.0442567 -0.0562137
fec9b094@1378936198432-8ae2fe42@1378827268681 0.0491880 0.0382083
986c8378@1378826842544-986c8378@1378826177581 -0.0477138 -0.0627818
e71d2d62@1378737168452-986c8378@1378826177581 -0.0911307 -0.1068549
fec9b094@1378936198432-986c8378@1378826177581 0.0023141 -0.0126805
e71d2d62@1378737168452-986c8378@1378826842544 -0.0434168 -0.0551805
fec9b094@1378936198432-986c8378@1378826842544 0.0500279 0.0392590
fec9b094@1378936198432-e71d2d62@1378737168452 0.0934447 0.0817752
upr p adj
3ad3559c@1379026295046-26742641@1379035244203 -0.0918517 0.0000
46b48894@1378844106838-26742641@1379035244203 0.0052289 0.8495
46b48894@1378845864401-26742641@1379035244203 0.0162646 0.9518
5d9ab4f5@1378412329855-26742641@1379035244203 -0.1002652 0.0000
7f20b1c7@1378712963763-26742641@1379035244203 0.0215050 0.2465
7f20b1c7@1378713851854-26742641@1379035244203 -0.0313000 0.0000
8ae2fe42@1378827268681-26742641@1379035244203 -0.0517491 0.0000
986c8378@1378826177581-26742641@1379035244203 -0.0009362 0.0262
986c8378@1378826842544-26742641@1379035244203 -0.0527943 0.0000
e71d2d62@1378737168452-26742641@1379035244203 -0.0953327 0.0000
fec9b094@1378936198432-26742641@1379035244203 -0.0028666 0.0022
46b48894@1378844106838-3ad3559c@1379026295046 0.1068970 0.0000
46b48894@1378845864401-3ad3559c@1379026295046 0.1179377 0.0000
5d9ab4f5@1378412329855-3ad3559c@1379026295046 0.0012331 0.1868
7f20b1c7@1378712963763-3ad3559c@1379026295046 0.1232169 0.0000
7f20b1c7@1378713851854-3ad3559c@1379026295046 0.0703550 0.0000
8ae2fe42@1378827268681-3ad3559c@1379026295046 0.0499252 0.0000
986c8378@1378826177581-3ad3559c@1379026295046 0.1009308 0.0000
986c8378@1378826842544-3ad3559c@1379026295046 0.0488658 0.0000
e71d2d62@1378737168452-3ad3559c@1379026295046 0.0063843 0.9604
fec9b094@1378936198432-3ad3559c@1379026295046 0.0987864 0.0000
46b48894@1378845864401-46b48894@1378844106838 0.0221402 0.0605
5d9ab4f5@1378412329855-46b48894@1378844106838 -0.0944062 0.0000
7f20b1c7@1378712963763-46b48894@1378844106838 0.0273843 0.0009
7f20b1c7@1378713851854-46b48894@1378844106838 -0.0254262 0.0000
8ae2fe42@1378827268681-46b48894@1378844106838 -0.0458734 0.0000
986c8378@1378826177581-46b48894@1378844106838 0.0049585 0.5499
986c8378@1378826842544-46b48894@1378844106838 -0.0469200 0.0000
e71d2d62@1378737168452-46b48894@1378844106838 -0.0894529 0.0000
fec9b094@1378936198432-46b48894@1378844106838 0.0030070 0.4292
5d9ab4f5@1378412329855-46b48894@1378845864401 -0.1052785 0.0000
7f20b1c7@1378712963763-46b48894@1378845864401 0.0164917 0.9818
7f20b1c7@1378713851854-46b48894@1378845864401 -0.0363133 0.0000
8ae2fe42@1378827268681-46b48894@1378845864401 -0.0567624 0.0000
986c8378@1378826177581-46b48894@1378845864401 -0.0059495 0.0003
986c8378@1378826842544-46b48894@1378845864401 -0.0578076 0.0000
e71d2d62@1378737168452-46b48894@1378845864401 -0.1003460 0.0000
fec9b094@1378936198432-46b48894@1378845864401 -0.0078799 0.0000
7f20b1c7@1378712963763-5d9ab4f5@1378412329855 0.1290411 0.0000
7f20b1c7@1378713851854-5d9ab4f5@1378412329855 0.0760500 0.0000
8ae2fe42@1378827268681-5d9ab4f5@1378412329855 0.0556648 0.0000
986c8378@1378826177581-5d9ab4f5@1378412329855 0.1070775 0.0000
986c8378@1378826842544-5d9ab4f5@1378412329855 0.0545727 0.0000
e71d2d62@1378737168452-5d9ab4f5@1378412329855 0.0122197 0.9999
fec9b094@1378936198432-5d9ab4f5@1378412329855 0.1044768 0.0000
7f20b1c7@1378713851854-7f20b1c7@1378712963763 -0.0403293 0.0000
8ae2fe42@1378827268681-7f20b1c7@1378712963763 -0.0607929 0.0000
986c8378@1378826177581-7f20b1c7@1378712963763 -0.0101313 0.0000
986c8378@1378826842544-7f20b1c7@1378712963763 -0.0618275 0.0000
e71d2d62@1378737168452-7f20b1c7@1378712963763 -0.1044090 0.0000
fec9b094@1378936198432-7f20b1c7@1378712963763 -0.0118944 0.0000
8ae2fe42@1378827268681-7f20b1c7@1378713851854 -0.0097190 0.0000
986c8378@1378826177581-7f20b1c7@1378713851854 0.0411623 0.0000
986c8378@1378826842544-7f20b1c7@1378713851854 -0.0107692 0.0000
e71d2d62@1378737168452-7f20b1c7@1378713851854 -0.0532876 0.0000
fec9b094@1378936198432-7f20b1c7@1378713851854 0.0391560 0.0000
986c8378@1378826177581-8ae2fe42@1378827268681 0.0620933 0.0000
986c8378@1378826842544-8ae2fe42@1378827268681 0.0102399 1.0000
e71d2d62@1378737168452-8ae2fe42@1378827268681 -0.0322997 0.0000
fec9b094@1378936198432-8ae2fe42@1378827268681 0.0601678 0.0000
986c8378@1378826842544-986c8378@1378826177581 -0.0326458 0.0000
e71d2d62@1378737168452-986c8378@1378826177581 -0.0754064 0.0000
fec9b094@1378936198432-986c8378@1378826177581 0.0173087 1.0000
e71d2d62@1378737168452-986c8378@1378826842544 -0.0316532 0.0000
fec9b094@1378936198432-986c8378@1378826842544 0.0607968 0.0000
fec9b094@1378936198432-e71d2d62@1378737168452 0.1051143 0.0000
$`previousTask:condition`
diff lwr
Census/Activity:2back-back:2back 0.0304351 0.0055827
Census/Dress:2back-back:2back 0.0284498 0.0039209
Census/Energy:2back-back:2back 0.0214074 -0.0034450
Census/People:2back-back:2back 0.0247679 0.0002390
PhotoRanking:2back-back:2back 0.0328535 0.0080010
SlideToUnlock:2back-back:2back 0.0159819 0.0014533
back:3back-back:2back 0.0042052 -0.0005517
Census/Activity:3back-back:2back 0.0266142 0.0014248
Census/Dress:3back-back:2back 0.0425329 0.0176805
Census/Energy:3back-back:2back 0.0308852 0.0060328
Census/People:3back-back:2back 0.0307481 0.0055586
PhotoRanking:3back-back:2back 0.0279057 0.0027162
SlideToUnlock:3back-back:2back 0.0305713 0.0159177
Census/Dress:2back-Census/Activity:2back -0.0019853 -0.0366016
Census/Energy:2back-Census/Activity:2back -0.0090277 -0.0438740
Census/People:2back-Census/Activity:2back -0.0056672 -0.0402835
PhotoRanking:2back-Census/Activity:2back 0.0024183 -0.0324279
SlideToUnlock:2back-Census/Activity:2back -0.0144532 -0.0428733
back:3back-Census/Activity:2back -0.0262299 -0.0511146
Census/Activity:3back-Census/Activity:2back -0.0038209 -0.0389083
Census/Dress:3back-Census/Activity:2back 0.0120978 -0.0227485
Census/Energy:3back-Census/Activity:2back 0.0004501 -0.0343961
Census/People:3back-Census/Activity:2back 0.0003129 -0.0347745
PhotoRanking:3back-Census/Activity:2back -0.0025295 -0.0376169
SlideToUnlock:3back-Census/Activity:2back 0.0001362 -0.0283480
Census/Energy:2back-Census/Dress:2back -0.0070424 -0.0416587
Census/People:2back-Census/Dress:2back -0.0036819 -0.0380666
PhotoRanking:2back-Census/Dress:2back 0.0044036 -0.0302126
SlideToUnlock:2back-Census/Dress:2back -0.0124679 -0.0406055
back:3back-Census/Dress:2back -0.0242446 -0.0488062
Census/Activity:3back-Census/Dress:2back -0.0018356 -0.0366946
Census/Dress:3back-Census/Dress:2back 0.0140831 -0.0205331
Census/Energy:3back-Census/Dress:2back 0.0024354 -0.0321808
Census/People:3back-Census/Dress:2back 0.0022982 -0.0325608
PhotoRanking:3back-Census/Dress:2back -0.0005441 -0.0354031
SlideToUnlock:3back-Census/Dress:2back 0.0021215 -0.0260808
Census/People:2back-Census/Energy:2back 0.0033605 -0.0312557
PhotoRanking:2back-Census/Energy:2back 0.0114460 -0.0234002
SlideToUnlock:2back-Census/Energy:2back -0.0054255 -0.0338456
back:3back-Census/Energy:2back -0.0172022 -0.0420869
Census/Activity:3back-Census/Energy:2back 0.0052068 -0.0298806
Census/Dress:3back-Census/Energy:2back 0.0211255 -0.0137208
Census/Energy:3back-Census/Energy:2back 0.0094778 -0.0253684
Census/People:3back-Census/Energy:2back 0.0093406 -0.0257468
PhotoRanking:3back-Census/Energy:2back 0.0064983 -0.0285892
SlideToUnlock:3back-Census/Energy:2back 0.0091639 -0.0193203
PhotoRanking:2back-Census/People:2back 0.0080855 -0.0265307
SlideToUnlock:2back-Census/People:2back -0.0087860 -0.0369236
back:3back-Census/People:2back -0.0205627 -0.0451243
Census/Activity:3back-Census/People:2back 0.0018463 -0.0330127
Census/Dress:3back-Census/People:2back 0.0177650 -0.0168512
Census/Energy:3back-Census/People:2back 0.0061173 -0.0284989
Census/People:3back-Census/People:2back 0.0059801 -0.0288788
PhotoRanking:3back-Census/People:2back 0.0031378 -0.0317212
SlideToUnlock:3back-Census/People:2back 0.0058034 -0.0223989
SlideToUnlock:2back-PhotoRanking:2back -0.0168715 -0.0452916
back:3back-PhotoRanking:2back -0.0286483 -0.0535329
Census/Activity:3back-PhotoRanking:2back -0.0062392 -0.0413266
Census/Dress:3back-PhotoRanking:2back 0.0096795 -0.0251668
Census/Energy:3back-PhotoRanking:2back -0.0019682 -0.0368145
Census/People:3back-PhotoRanking:2back -0.0021054 -0.0371928
PhotoRanking:3back-PhotoRanking:2back -0.0049478 -0.0400352
SlideToUnlock:3back-PhotoRanking:2back -0.0022821 -0.0307663
back:3back-SlideToUnlock:2back -0.0117767 -0.0263605
Census/Activity:3back-SlideToUnlock:2back 0.0106323 -0.0180829
Census/Dress:3back-SlideToUnlock:2back 0.0265510 -0.0018691
Census/Energy:3back-SlideToUnlock:2back 0.0149033 -0.0135167
Census/People:3back-SlideToUnlock:2back 0.0147661 -0.0139491
PhotoRanking:3back-SlideToUnlock:2back 0.0119238 -0.0167915
SlideToUnlock:3back-SlideToUnlock:2back 0.0145894 -0.0055299
Census/Activity:3back-back:3back 0.0224090 -0.0028122
Census/Dress:3back-back:3back 0.0383277 0.0134430
Census/Energy:3back-back:3back 0.0266800 0.0017954
Census/People:3back-back:3back 0.0265429 0.0013216
PhotoRanking:3back-back:3back 0.0237005 -0.0015208
SlideToUnlock:3back-back:3back 0.0263661 0.0116579
Census/Dress:3back-Census/Activity:3back 0.0159187 -0.0191687
Census/Energy:3back-Census/Activity:3back 0.0042710 -0.0308164
Census/People:3back-Census/Activity:3back 0.0041338 -0.0311931
PhotoRanking:3back-Census/Activity:3back 0.0012914 -0.0340355
SlideToUnlock:3back-Census/Activity:3back 0.0039571 -0.0248216
Census/Energy:3back-Census/Dress:3back -0.0116477 -0.0464939
Census/People:3back-Census/Dress:3back -0.0117849 -0.0468723
PhotoRanking:3back-Census/Dress:3back -0.0146272 -0.0497147
SlideToUnlock:3back-Census/Dress:3back -0.0119616 -0.0404458
Census/People:3back-Census/Energy:3back -0.0001372 -0.0352246
PhotoRanking:3back-Census/Energy:3back -0.0029796 -0.0380670
SlideToUnlock:3back-Census/Energy:3back -0.0003139 -0.0287981
PhotoRanking:3back-Census/People:3back -0.0028424 -0.0381693
SlideToUnlock:3back-Census/People:3back -0.0001767 -0.0289554
SlideToUnlock:3back-PhotoRanking:3back 0.0026657 -0.0261130
upr p adj
Census/Activity:2back-back:2back 0.0552875 0.0032
Census/Dress:2back-back:2back 0.0529787 0.0076
Census/Energy:2back-back:2back 0.0462598 0.1820
Census/People:2back-back:2back 0.0492968 0.0451
PhotoRanking:2back-back:2back 0.0577059 0.0008
SlideToUnlock:2back-back:2back 0.0305106 0.0162
back:3back-back:2back 0.0089621 0.1509
Census/Activity:3back-back:2back 0.0518037 0.0269
Census/Dress:3back-back:2back 0.0673853 0.0000
Census/Energy:3back-back:2back 0.0557377 0.0025
Census/People:3back-back:2back 0.0559375 0.0034
PhotoRanking:3back-back:2back 0.0530951 0.0147
SlideToUnlock:3back-back:2back 0.0452249 0.0000
Census/Dress:2back-Census/Activity:2back 0.0326309 1.0000
Census/Energy:2back-Census/Activity:2back 0.0258185 0.9999
Census/People:2back-Census/Activity:2back 0.0289490 1.0000
PhotoRanking:2back-Census/Activity:2back 0.0372646 1.0000
SlideToUnlock:2back-Census/Activity:2back 0.0139669 0.9141
back:3back-Census/Activity:2back -0.0013452 0.0277
Census/Activity:3back-Census/Activity:2back 0.0312665 1.0000
Census/Dress:3back-Census/Activity:2back 0.0469441 0.9968
Census/Energy:3back-Census/Activity:2back 0.0352964 1.0000
Census/People:3back-Census/Activity:2back 0.0354003 1.0000
PhotoRanking:3back-Census/Activity:2back 0.0325580 1.0000
SlideToUnlock:3back-Census/Activity:2back 0.0286204 1.0000
Census/Energy:2back-Census/Dress:2back 0.0275738 1.0000
Census/People:2back-Census/Dress:2back 0.0307028 1.0000
PhotoRanking:2back-Census/Dress:2back 0.0390199 1.0000
SlideToUnlock:2back-Census/Dress:2back 0.0156697 0.9702
back:3back-Census/Dress:2back 0.0003169 0.0572
Census/Activity:3back-Census/Dress:2back 0.0330234 1.0000
Census/Dress:3back-Census/Dress:2back 0.0486993 0.9857
Census/Energy:3back-Census/Dress:2back 0.0370517 1.0000
Census/People:3back-Census/Dress:2back 0.0371572 1.0000
PhotoRanking:3back-Census/Dress:2back 0.0343148 1.0000
SlideToUnlock:3back-Census/Dress:2back 0.0303238 1.0000
Census/People:2back-Census/Energy:2back 0.0379767 1.0000
PhotoRanking:2back-Census/Energy:2back 0.0462923 0.9982
SlideToUnlock:2back-Census/Energy:2back 0.0229946 1.0000
back:3back-Census/Energy:2back 0.0076825 0.5403
Census/Activity:3back-Census/Energy:2back 0.0402943 1.0000
Census/Dress:3back-Census/Energy:2back 0.0559718 0.7460
Census/Energy:3back-Census/Energy:2back 0.0443241 0.9998
Census/People:3back-Census/Energy:2back 0.0444281 0.9998
PhotoRanking:3back-Census/Energy:2back 0.0415857 1.0000
SlideToUnlock:3back-Census/Energy:2back 0.0376481 0.9985
PhotoRanking:2back-Census/People:2back 0.0427018 1.0000
SlideToUnlock:2back-Census/People:2back 0.0193516 0.9989
back:3back-Census/People:2back 0.0039988 0.2200
Census/Activity:3back-Census/People:2back 0.0367053 1.0000
Census/Dress:3back-Census/People:2back 0.0523813 0.9085
Census/Energy:3back-Census/People:2back 0.0407336 1.0000
Census/People:3back-Census/People:2back 0.0408391 1.0000
PhotoRanking:3back-Census/People:2back 0.0379968 1.0000
SlideToUnlock:3back-Census/People:2back 0.0340057 1.0000
SlideToUnlock:2back-PhotoRanking:2back 0.0115485 0.7731
back:3back-PhotoRanking:2back -0.0037636 0.0085
Census/Activity:3back-PhotoRanking:2back 0.0288482 1.0000
Census/Dress:3back-PhotoRanking:2back 0.0445257 0.9997
Census/Energy:3back-PhotoRanking:2back 0.0328781 1.0000
Census/People:3back-PhotoRanking:2back 0.0329820 1.0000
PhotoRanking:3back-PhotoRanking:2back 0.0301396 1.0000
SlideToUnlock:3back-PhotoRanking:2back 0.0262020 1.0000
back:3back-SlideToUnlock:2back 0.0028071 0.2734
Census/Activity:3back-SlideToUnlock:2back 0.0393476 0.9940
Census/Dress:3back-SlideToUnlock:2back 0.0549711 0.0962
Census/Energy:3back-SlideToUnlock:2back 0.0433234 0.8939
Census/People:3back-SlideToUnlock:2back 0.0434814 0.9072
PhotoRanking:3back-SlideToUnlock:2back 0.0406390 0.9828
SlideToUnlock:3back-SlideToUnlock:2back 0.0347087 0.4559
Census/Activity:3back-back:3back 0.0476303 0.1452
Census/Dress:3back-back:3back 0.0632124 0.0000
Census/Energy:3back-back:3back 0.0515647 0.0225
Census/People:3back-back:3back 0.0517641 0.0282
PhotoRanking:3back-back:3back 0.0489217 0.0914
SlideToUnlock:3back-back:3back 0.0410744 0.0000
Census/Dress:3back-Census/Activity:3back 0.0510061 0.9638
Census/Energy:3back-Census/Activity:3back 0.0393584 1.0000
Census/People:3back-Census/Activity:3back 0.0394607 1.0000
PhotoRanking:3back-Census/Activity:3back 0.0366183 1.0000
SlideToUnlock:3back-Census/Activity:3back 0.0327358 1.0000
Census/Energy:3back-Census/Dress:3back 0.0231986 0.9978
Census/People:3back-Census/Dress:3back 0.0233026 0.9977
PhotoRanking:3back-Census/Dress:3back 0.0204602 0.9822
SlideToUnlock:3back-Census/Dress:3back 0.0165226 0.9811
Census/People:3back-Census/Energy:3back 0.0349502 1.0000
PhotoRanking:3back-Census/Energy:3back 0.0321078 1.0000
SlideToUnlock:3back-Census/Energy:3back 0.0281702 1.0000
PhotoRanking:3back-Census/People:3back 0.0324845 1.0000
SlideToUnlock:3back-Census/People:3back 0.0286019 1.0000
SlideToUnlock:3back-PhotoRanking:3back 0.0314443 1.0000
#########
# Accuracy investigation
accuracyLogit <- glm(isCorrect ~ factor(previousTask) + factor(condition) + factor(session), data=backtasks, family='binomial')
summary(accuracyLogit)
Call:
glm(formula = isCorrect ~ factor(previousTask) + factor(condition) +
factor(session), family = "binomial", data = backtasks)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.616 0.332 0.397 0.460 0.926
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.052 0.245 12.48 < 2e-16
factor(previousTask)Census/Activity 0.308 0.471 0.65 0.51325
factor(previousTask)Census/Dress 0.887 0.595 1.49 0.13589
factor(previousTask)Census/Energy 0.332 0.471 0.71 0.48038
factor(previousTask)Census/People -0.347 0.365 -0.95 0.34261
factor(previousTask)PhotoRanking -0.596 0.338 -1.77 0.07745
factor(previousTask)SlideToUnlock -0.292 0.219 -1.33 0.18265
factor(condition)3back -0.429 0.104 -4.13 3.6e-05
factor(session)3ad3559c@1379026295046 -1.402 0.265 -5.29 1.2e-07
factor(session)46b48894@1378844106838 0.226 0.349 0.65 0.51698
factor(session)46b48894@1378845864401 0.197 0.349 0.56 0.57261
factor(session)5d9ab4f5@1378412329855 -0.861 0.256 -3.36 0.00077
factor(session)7f20b1c7@1378712963763 -0.370 0.325 -1.14 0.25457
factor(session)7f20b1c7@1378713851854 -0.179 0.315 -0.57 0.56871
factor(session)8ae2fe42@1378827268681 -0.380 0.311 -1.22 0.22111
factor(session)986c8378@1378826177581 -0.150 0.420 -0.36 0.72129
factor(session)986c8378@1378826842544 -0.318 0.310 -1.02 0.30551
factor(session)e71d2d62@1378737168452 -0.550 0.317 -1.74 0.08246
factor(session)fec9b094@1378936198432 -0.551 0.296 -1.86 0.06271
(Intercept) ***
factor(previousTask)Census/Activity
factor(previousTask)Census/Dress
factor(previousTask)Census/Energy
factor(previousTask)Census/People
factor(previousTask)PhotoRanking .
factor(previousTask)SlideToUnlock
factor(condition)3back ***
factor(session)3ad3559c@1379026295046 ***
factor(session)46b48894@1378844106838
factor(session)46b48894@1378845864401
factor(session)5d9ab4f5@1378412329855 ***
factor(session)7f20b1c7@1378712963763
factor(session)7f20b1c7@1378713851854
factor(session)8ae2fe42@1378827268681
factor(session)986c8378@1378826177581
factor(session)986c8378@1378826842544
factor(session)e71d2d62@1378737168452 .
factor(session)fec9b094@1378936198432 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 2857.7 on 4581 degrees of freedom
Residual deviance: 2742.6 on 4563 degrees of freedom
AIC: 2781
Number of Fisher Scoring iterations: 5
# Census is significantly less accurate than 'back'