Lee, M., Kim, H., & Kim, C-Y. (in prep)

Trust Game & Binocular Rivalry

All participants (N = 36)

Figure 2

2a. Trust Game

(Shades: between subject errors)

dat <- tg %>%
  filter(Trial==1 & (cond=="Generous"|cond=="Intermediate")) %>%
  droplevels()
t.test(invest ~ cond, paired=TRUE, dat)
## 
##  Paired t-test
## 
## data:  invest by cond
## t = -0.29, df = 35, p-value = 0.8
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -449.5  338.4
## sample estimates:
## mean of the differences 
##                  -55.56
dat <- tg %>%
  filter(Trial==1 & (cond=="Generous"|cond=="Selfish")) %>%
  droplevels()
t.test(invest ~ cond, paired=TRUE, dat)
## 
##  Paired t-test
## 
## data:  invest by cond
## t = 0.15, df = 35, p-value = 0.9
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -448.1  522.2
## sample estimates:
## mean of the differences 
##                   37.04
dat <- tg %>%
  filter(Trial==2 & (cond=="Generous"|cond=="Intermediate")) %>%
  droplevels()
t.test(invest ~ cond, paired=TRUE, dat)
## 
##  Paired t-test
## 
## data:  invest by cond
## t = 1, df = 35, p-value = 0.3
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -286.1  841.7
## sample estimates:
## mean of the differences 
##                   277.8
dat <- tg %>%
  filter(Trial==2 & (cond=="Generous"|cond=="Selfish")) %>%
  droplevels()
t.test(invest ~ cond, paired=TRUE, dat)
## 
##  Paired t-test
## 
## data:  invest by cond
## t = 4.6, df = 35, p-value = 0.00005
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##   823.1 2102.8
## sample estimates:
## mean of the differences 
##                    1463
cohensD(invest ~ cond, method="paired", dat)
## [1] 0.7736
dat <- tg %>%
  filter(Trial==2 & (cond=="Intermediate"|cond=="Selfish")) %>%
  droplevels()
t.test(invest ~ cond, paired=TRUE, dat)
## 
##  Paired t-test
## 
## data:  invest by cond
## t = 3.8, df = 35, p-value = 0.0005
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##   556.1 1814.3
## sample estimates:
## mean of the differences 
##                    1185
cohensD(invest ~ cond, method="paired", dat)
## [1] 0.6375
dat <- tg %>%
  filter(Trial==3 & (cond=="Generous"|cond=="Intermediate")) %>%
  droplevels()
t.test(invest ~ cond, paired=TRUE, dat)
## 
##  Paired t-test
## 
## data:  invest by cond
## t = 2.8, df = 35, p-value = 0.008
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##   198.4 1246.0
## sample estimates:
## mean of the differences 
##                   722.2
cohensD(invest ~ cond, method="paired", dat)
## [1] 0.4665

Binocular rivalry: Pre- and Post-learning

2b. Predominance

(Error bars: within subjects errors)

Repeated Measures ANOVA: Predominance ~ Learning * Condition (2 within sbj)

## $ANOVA
##        Effect DFn DFd       SSn     SSd        F
## 1 (Intercept)   1  35 776279.95 18219.5 1491.245
## 2       phase   1  35     37.51   894.4    1.468
## 3        cond   3 105    121.41  1719.3    2.471
## 4  phase:cond   3 105     48.99  1062.6    1.614
##                                    p p<.05      ges
## 1 0.00000000000000000000000000000275     * 0.972568
## 2 0.23376904956924760004000063418061       0.001710
## 3 0.06584195244533741497861001334968       0.005514
## 4 0.19065514840963126985684539249633       0.002232
## 
## $`Mauchly's Test for Sphericity`
##       Effect      W       p p<.05
## 3       cond 0.6784 0.02267     *
## 4 phase:cond 0.8226 0.25343      
## 
## $`Sphericity Corrections`
##       Effect    GGe   p[GG] p[GG]<.05    HFe   p[HF] p[HF]<.05
## 3       cond 0.8205 0.07876           0.8873 0.07368          
## 4 phase:cond 0.8946 0.19590           0.9760 0.19185

Predominance

Condition: F(2.4616, 86.157) = 2.4715, p = 0.0788, \(\eta^2\) = 0.0055

Learning: F(1, 35) = 1.4681, p = 0.2338, \(\eta^2\) = 0.0017

Learning:Condition: F(3, 105) = 1.6136, p = 0.1907, \(\eta^2\) = 0.0022

Pairwise t tests

tpred <- paired.t(pred.tidy)
tpred$t$postgi
## 
##  Paired t-test
## 
## data:  dep by cond
## t = 2.2, df = 35, p-value = 0.03
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.1585 3.7031
## sample estimates:
## mean of the differences 
##                   1.931
tpred$d$postgi
## [1] 0.3686
tpred$t$postgs
## 
##  Paired t-test
## 
## data:  dep by cond
## t = 2.8, df = 35, p-value = 0.007
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.6396 3.8079
## sample estimates:
## mean of the differences 
##                   2.224
tpred$d$postgs
## [1] 0.475
tpred$t$postgn
## 
##  Paired t-test
## 
## data:  dep by cond
## t = 3, df = 35, p-value = 0.005
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.6698 3.4986
## sample estimates:
## mean of the differences 
##                   2.084
tpred$d$postgn
## [1] 0.4986
tpred$pval
## $pre
## [1] 0.88746 0.78982 0.11768 0.87477 0.15847 0.08749
## 
## $post
## [1] 0.033612 0.007283 0.005061 0.715779 0.883380 0.851424
## 
## $pp
## [1] 0.08684 0.99642 0.86133 0.12776
tpred$fdr$pre$p.value
##              Generous Intermediate Selfish
## Intermediate   0.8875           NA      NA
## Selfish        0.8875       0.8875      NA
## New            0.3169       0.3169  0.3169
tpred$fdr$post$p.value
##              Generous Intermediate Selfish
## Intermediate  0.06722           NA      NA
## Selfish       0.02185       0.8834      NA
## New           0.02185       0.8834  0.8834
tpred$fdr$pp
## [1] 0.2555 0.9964 0.9964 0.2555

2c. Face Dominance (Normalized dominance durations)

Repeated Measures ANOVA: Dominance ~ Learning * Condition (2 within sbj)

Pairwise t tests

aov_dom <- ezANOVA(data=dom.tidy, dv=faceDom, wid=sbj, within=.(phase,cond), detailed=TRUE)
aov_dom
## $ANOVA
##        Effect DFn DFd           SSn      SSd             F
## 1 (Intercept)   1  35 290.489072679 0.005055 2011134.78269
## 2       phase   1  35   0.000003005 0.003029       0.03473
## 3        cond   3 105   0.024547981 1.459541       0.58866
## 4  phase:cond   3 105   0.015645949 1.064109       0.51462
##                                                                                            p
## 1 0.0000000000000000000000000000000000000000000000000000000000000000000000000000000000006881
## 2 0.8532377914002912122626298696559388190507888793945312500000000000000000000000000000000000
## 3 0.6237655418184229194622503200662322342395782470703125000000000000000000000000000000000000
## 4 0.6730993644635286932143003468809183686971664428710937500000000000000000000000000000000000
##   p<.05         ges
## 1     * 0.991359882
## 2       0.000001187
## 3       0.009603000
## 4       0.006141976
## 
## $`Mauchly's Test for Sphericity`
##       Effect      W       p p<.05
## 3       cond 0.9375 0.82434      
## 4 phase:cond 0.7306 0.06042      
## 
## $`Sphericity Corrections`
##       Effect    GGe  p[GG] p[GG]<.05    HFe  p[HF] p[HF]<.05
## 3       cond 0.9580 0.6167           1.0528 0.6238          
## 4 phase:cond 0.8209 0.6374           0.8878 0.6516
tdom <- paired.t(dom.tidy)

tdom$pval
## $pre
## [1] 0.5003 0.9903 0.3708 0.5377 0.1599 0.2814
## 
## $post
## [1] 0.6707 0.6479 0.9227 0.3642 0.7761 0.5934
## 
## $pp
## [1] 0.9031 0.6745 0.5056 0.2700
tdom$fdr$pre$p.value
##              Generous Intermediate Selfish
## Intermediate   0.6453           NA      NA
## Selfish        0.9903       0.6453      NA
## New            0.6453       0.6453  0.6453
tdom$fdr$post$p.value
##              Generous Intermediate Selfish
## Intermediate   0.9227           NA      NA
## Selfish        0.9227       0.9227      NA
## New            0.9227       0.9227  0.9227
tdom$fdr$pp
## [1] 0.9031 0.8993 0.8993 0.8993

2d. Face Suppression (Normalized suppression durations)

Repeated Measures ANOVA: Suppression ~ Learning * Condition (2 within sbj)

## $ANOVA
##        Effect DFn DFd           SSn      SSd             F
## 1 (Intercept)   1  35 289.974226255 0.003674 2762427.63001
## 2       phase   1  35   0.000003183 0.003614       0.03083
## 3        cond   3 105   0.153215977 1.047019       5.12174
## 4  phase:cond   3 105   0.026860772 1.059164       0.88761
##                                                                                              p
## 1 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000002663
## 2 0.861641483628234339242624173493823036551475524902343750000000000000000000000000000000000000
## 3 0.002400256835285908933730336656253712135367095470428466796875000000000000000000000000000000
## 4 0.450160314604930111848091200954513624310493469238281250000000000000000000000000000000000000
##   p<.05         ges
## 1     * 0.992764258
## 2       0.000001506
## 3     * 0.067594668
## 4       0.012549815
## 
## $`Mauchly's Test for Sphericity`
##       Effect      W      p p<.05
## 3       cond 0.9552 0.9076      
## 4 phase:cond 0.9054 0.6461      
## 
## $`Sphericity Corrections`
##       Effect    GGe    p[GG] p[GG]<.05   HFe  p[HF] p[HF]<.05
## 3       cond 0.9698 0.002686         * 1.067 0.0024         *
## 4 phase:cond 0.9341 0.444593           1.024 0.4502

Face Suppression

Condition: F(3, 105) = 5.1217, p = 0.0024, \(\eta^2\) = 0.0676

Learning: F(1, 35) = 0.0308, p = 0.8616, \(\eta^2\) = 0

Learning:Condition: F(3, 105) = 0.8876, p = 0.4502, \(\eta^2\) = 0.0125

Pairwise t tests

tsup <- paired.t(sup.tidy)
tsup$t$postgi
## 
##  Paired t-test
## 
## data:  dep by cond
## t = -3, df = 35, p-value = 0.005
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.11337 -0.02144
## sample estimates:
## mean of the differences 
##                -0.06741
tsup$d$postgi
## [1] 0.4961
tsup$t$postgs
## 
##  Paired t-test
## 
## data:  dep by cond
## t = -2.5, df = 35, p-value = 0.02
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.11424 -0.01275
## sample estimates:
## mean of the differences 
##                 -0.0635
tsup$d$postgs
## [1] 0.4234
tsup$t$postgn
## 
##  Paired t-test
## 
## data:  dep by cond
## t = -3.3, df = 35, p-value = 0.002
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.13078 -0.03127
## sample estimates:
## mean of the differences 
##                -0.08102
tsup$d$postgn
## [1] 0.551
tsup$pval
## $pre
## [1] 0.25003 0.58225 0.08006 0.64994 0.39942 0.18945
## 
## $post
## [1] 0.005255 0.015665 0.002195 0.875344 0.570810 0.379323
## 
## $pp
## [1] 0.1252 0.6052 0.3755 0.8509
tsup$fdr$pre$p.value
##              Generous Intermediate Selfish
## Intermediate   0.5001           NA      NA
## Selfish        0.6499       0.6499      NA
## New            0.4804       0.5991  0.5001
tsup$fdr$post$p.value
##              Generous Intermediate Selfish
## Intermediate  0.01577           NA      NA
## Selfish       0.03133       0.8753      NA
## New           0.01317       0.6850   0.569
tsup$fdr$pp
## [1] 0.5007 0.8070 0.7510 0.8509

Frequency and First Percepts

freq <- prepData(freq_face, parList, skip=1)
tfreq <- paired.t(freq)

first <- prepData(first_face, parList, skip=1)
tfirst <- paired.t(first)

tfirst$t$postgs
## 
##  Paired t-test
## 
## data:  dep by cond
## t = 2.3, df = 35, p-value = 0.03
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.06089 1.16133
## sample estimates:
## mean of the differences 
##                  0.6111
tfirst$d$postgs
## [1] 0.3758
tfirst$t$pregs
## 
##  Paired t-test
## 
## data:  dep by cond
## t = -0.086, df = 35, p-value = 0.9
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.6823  0.6267
## sample estimates:
## mean of the differences 
##                -0.02778
tfirst$d$pregs
## [1] 0.01436
tfirst$pval
## $pre
## [1] 0.7182 0.9318 0.8032 0.7347 0.9413 0.6891
## 
## $post
## [1] 0.22742 0.03051 0.17316 0.59410 0.83991 0.48026
## 
## $pp
## [1] 0.1985 0.8020 0.5216 0.7046
tfirst$fdr$post$p.value
##              Generous Intermediate Selfish
## Intermediate   0.4548           NA      NA
## Selfish        0.1830       0.7129      NA
## New            0.4548       0.8399  0.7129

2e-f. Histograms and Gamma Function Fit (Post-learning)

K-S test: post-learning distributions
ks.test(allDom_pre$s_gen[[1]], allDom$s_gen[[1]])
## 
##  Two-sample Kolmogorov-Smirnov test
## 
## data:  allDom_pre$s_gen[[1]] and allDom$s_gen[[1]]
## D = 0.058, p-value = 0.002
## alternative hypothesis: two-sided
ks.test(allDom_pre$s_int[[1]], allDom$s_int[[1]])
## 
##  Two-sample Kolmogorov-Smirnov test
## 
## data:  allDom_pre$s_int[[1]] and allDom$s_int[[1]]
## D = 0.021, p-value = 0.7
## alternative hypothesis: two-sided
ks.test(allDom_pre$s_self[[1]], allDom$s_self[[1]])
## 
##  Two-sample Kolmogorov-Smirnov test
## 
## data:  allDom_pre$s_self[[1]] and allDom$s_self[[1]]
## D = 0.022, p-value = 0.7
## alternative hypothesis: two-sided
ks.test(allDom_pre$s_new[[1]], allDom$s_new[[1]])
## 
##  Two-sample Kolmogorov-Smirnov test
## 
## data:  allDom_pre$s_new[[1]] and allDom$s_new[[1]]
## D = 0.026, p-value = 0.5
## alternative hypothesis: two-sided
ks.test(allDom$s_gen[[1]],allDom$s_int[[1]])
## 
##  Two-sample Kolmogorov-Smirnov test
## 
## data:  allDom$s_gen[[1]] and allDom$s_int[[1]]
## D = 0.052, p-value = 0.006
## alternative hypothesis: two-sided
ks.test(allDom$s_gen[[1]],allDom$s_self[[1]])
## 
##  Two-sample Kolmogorov-Smirnov test
## 
## data:  allDom$s_gen[[1]] and allDom$s_self[[1]]
## D = 0.037, p-value = 0.1
## alternative hypothesis: two-sided
ks.test(allDom$s_gen[[1]],allDom$s_new[[1]])
## 
##  Two-sample Kolmogorov-Smirnov test
## 
## data:  allDom$s_gen[[1]] and allDom$s_new[[1]]
## D = 0.052, p-value = 0.007
## alternative hypothesis: two-sided
Figure 2

Figure 3

Correlation between Learning Index (LI) and Rivalry Measures

3a. LI and Face Predominance (Pre-learning)

## 
##  Pearson's product-moment correlation
## 
## data:  indices$pre_pred and indices$LI
## t = -0.37, df = 34, p-value = 0.7
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.3841  0.2706
## sample estimates:
##     cor 
## -0.0636

3b. LI and Face Predominance (Post-learning)

## 
##  Pearson's product-moment correlation
## 
## data:  indices$post_pred and indices$LI
## t = 2.4, df = 34, p-value = 0.02
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.06527 0.63380
## sample estimates:
##    cor 
## 0.3855

3c. LI and Face Dominance (Post-learning)

## 
##  Pearson's product-moment correlation
## 
## data:  indices$post_dom and indices$LI
## t = 1.9, df = 34, p-value = 0.07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.02433  0.57705
## sample estimates:
##    cor 
## 0.3067

3d. LI and Face Suppression (Post-learning)

## 
##  Pearson's product-moment correlation
## 
## data:  indices$post_sup and indices$LI
## t = -2, df = 34, p-value = 0.06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.588255  0.007378
## sample estimates:
##     cor 
## -0.3219
Figure 3

Group Division

Figure 4

Median Split: Learning Index

## [1] 0.9327
## 
##  Two Sample t-test
## 
## data:  li by group
## t = 7, df = 34, p-value = 0.00000002
## alternative hypothesis: true difference in means is greater than 0
## 95 percent confidence interval:
##  0.3865    Inf
## sample estimates:
## mean in group Better mean in group Poorer 
##                1.245                0.735
## [1] 2.329

One-tailed

4a. Better-learners (N = 18)

ANOVA: Predominance

## $ANOVA
##        Effect DFn DFd       SSn    SSd       F                    p p<.05
## 1 (Intercept)   1  17 390196.33 8772.9 756.117 0.000000000000001561     *
## 2       phase   1  17     66.72  339.8   3.338 0.085318587505402846      
## 3        cond   3  51     98.80  630.1   2.666 0.057543188142706835      
## 4  phase:cond   3  51    113.93  438.2   4.420 0.007734973080774151     *
##        ges
## 1 0.974572
## 2 0.006510
## 3 0.009612
## 4 0.011066
## 
## $`Mauchly's Test for Sphericity`
##       Effect      W      p p<.05
## 3       cond 0.7825 0.5711      
## 4 phase:cond 0.7037 0.3561      
## 
## $`Sphericity Corrections`
##       Effect    GGe   p[GG] p[GG]<.05    HFe    p[HF] p[HF]<.05
## 3       cond 0.8470 0.06844           1.0084 0.057543          
## 4 phase:cond 0.8352 0.01224         * 0.9911 0.007927         *

Predominance: Better-learner

Condition: F(3, 51) = 2.6659, p = 0.0575, \(\eta^2\) = 0.0096

Learning: F(1, 17) = 3.3377, p = 0.0853, \(\eta^2\) = 0.0065

Learning:Condition: F(3, 51) = 4.4202, p = 0.0077, \(\eta^2\) = 0.0111

tpred_be <- paired.t(pred_better_t)
tpred_be$t$gen
## 
##  Paired t-test
## 
## data:  dep by phase
## t = -3.1, df = 17, p-value = 0.007
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -7.26 -1.35
## sample estimates:
## mean of the differences 
##                  -4.305
tpred_be$d$gen
## [1] 0.7244
tpred_be$t$postgi
## 
##  Paired t-test
## 
## data:  dep by cond
## t = 2.3, df = 17, p-value = 0.03
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.2641 5.8953
## sample estimates:
## mean of the differences 
##                    3.08
tpred_be$d$postgi
## [1] 0.5439
tpred_be$t$postgs
## 
##  Paired t-test
## 
## data:  dep by cond
## t = 3.9, df = 17, p-value = 0.001
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  1.877 6.254
## sample estimates:
## mean of the differences 
##                   4.065
tpred_be$d$postgs
## [1] 0.9237
tpred_be$t$postgn
## 
##  Paired t-test
## 
## data:  dep by cond
## t = 4.2, df = 17, p-value = 0.0005
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  1.988 5.911
## sample estimates:
## mean of the differences 
##                    3.95
tpred_be$d$postgn
## [1] 1.001
tpred_be$pval
## $pre
## [1] 0.6065 0.5062 0.5926 0.8203 0.2282 0.1648
## 
## $post
## [1] 0.0338616 0.0011058 0.0005418 0.3560390 0.5780487 0.9266408
## 
## $pp
## [1] 0.006887 0.529767 0.604361 0.406847
tpred_be$fdr$pre$p.value
##              Generous Intermediate Selfish
## Intermediate   0.7278           NA      NA
## Selfish        0.7278       0.8203      NA
## New            0.7278       0.6847  0.6847
tpred_be$fdr$post$p.value
##              Generous Intermediate Selfish
## Intermediate 0.067723           NA      NA
## Selfish      0.003317       0.5341      NA
## New          0.003251       0.6937  0.9266
tpred_be$fdr$pp
## [1] 0.02755 0.60436 0.60436 0.60436

Dominance / Suppression: Better-learner

## $ANOVA
##        Effect DFn DFd         SSn      SSd            F
## 1 (Intercept)   1  17 145.1516830 0.002261 1091160.3481
## 2       phase   1  17   0.0001007 0.002316       0.7395
## 3        cond   3  51   0.0184041 0.654806       0.4778
## 4  phase:cond   3  51   0.0296923 0.403458       1.2511
##                                                 p p<.05        ges
## 1 0.000000000000000000000000000000000000000002612     * 0.99273095
## 2 0.401771547809225415548439741542097181081771851       0.00009477
## 3 0.699134381636512647339998238749103620648384094       0.01702124
## 4 0.301007185219620831961861995296203531324863434       0.02717749
## 
## $`Mauchly's Test for Sphericity`
##       Effect      W      p p<.05
## 3       cond 0.8341 0.7232      
## 4 phase:cond 0.8286 0.7072      
## 
## $`Sphericity Corrections`
##       Effect    GGe  p[GG] p[GG]<.05   HFe  p[HF] p[HF]<.05
## 3       cond 0.8995 0.6796           1.086 0.6991          
## 4 phase:cond 0.9060 0.3012           1.095 0.3010
## 
##  Paired t-test
## 
## data:  dep by phase
## t = -1.9, df = 17, p-value = 0.07
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.096549  0.004086
## sample estimates:
## mean of the differences 
##                -0.04623
## [1] 0.4569
## 
##  Paired t-test
## 
## data:  dep by cond
## t = 0.48, df = 17, p-value = 0.6
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.06015  0.09590
## sample estimates:
## mean of the differences 
##                 0.01788
## [1] 0.1139
## 
##  Paired t-test
## 
## data:  dep by cond
## t = 1.3, df = 17, p-value = 0.2
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.02934  0.12740
## sample estimates:
## mean of the differences 
##                 0.04903
## [1] 0.3111
## 
##  Paired t-test
## 
## data:  dep by cond
## t = 2, df = 17, p-value = 0.06
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.002668  0.107463
## sample estimates:
## mean of the differences 
##                  0.0524
## [1] 0.4732
## $pre
## [1] 0.3170 0.4764 0.7280 0.8121 0.4983 0.6094
## 
## $post
## [1] 0.63496 0.20432 0.06085 0.29030 0.31820 0.92779
## 
## $pp
## [1] 0.06935 0.79595 0.28146 0.51882
##              Generous Intermediate Selfish
## Intermediate   0.8121           NA      NA
## Selfish        0.8121       0.8121      NA
## New            0.8121       0.8121  0.8121
##              Generous Intermediate Selfish
## Intermediate   0.7620           NA      NA
## Selfish        0.4773       0.4773      NA
## New            0.3651       0.4773  0.9278
## [1] 0.2774 0.7960 0.5629 0.6918
## $ANOVA
##        Effect DFn DFd           SSn      SSd             F
## 1 (Intercept)   1  17 145.002960029 0.001894 1301189.84347
## 2       phase   1  17   0.000002525 0.001872       0.02293
## 3        cond   3  51   0.116073431 0.443255       4.45173
## 4  phase:cond   3  51   0.073909212 0.497943       2.52329
##                                                  p p<.05         ges
## 1 0.0000000000000000000000000000000000000000005851     * 0.993525330
## 2 0.8814339082038329387103203771403059363365173340       0.000002672
## 3 0.0074683187070066761256703635751819092547520995     * 0.109396107
## 4 0.0679888073824788663479523620480904355645179749       0.072540103
## 
## $`Mauchly's Test for Sphericity`
##       Effect      W      p p<.05
## 3       cond 0.8431 0.7492      
## 4 phase:cond 0.7519 0.4829      
## 
## $`Sphericity Corrections`
##       Effect    GGe    p[GG] p[GG]<.05   HFe    p[HF] p[HF]<.05
## 3       cond 0.9080 0.009668         * 1.098 0.007468         *
## 4 phase:cond 0.8546 0.078703           1.019 0.067989
## 
##  Paired t-test
## 
## data:  dep by phase
## t = 2.4, df = 17, p-value = 0.03
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.01048 0.14272
## sample estimates:
## mean of the differences 
##                  0.0766
## [1] 0.5761
## 
##  Paired t-test
## 
## data:  dep by cond
## t = -3.1, df = 17, p-value = 0.007
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.18563 -0.03471
## sample estimates:
## mean of the differences 
##                 -0.1102
## [1] 0.7261
## 
##  Paired t-test
## 
## data:  dep by cond
## t = -2.9, df = 17, p-value = 0.009
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.19100 -0.03165
## sample estimates:
## mean of the differences 
##                 -0.1113
## [1] 0.6949
## 
##  Paired t-test
## 
## data:  dep by cond
## t = -3.4, df = 17, p-value = 0.003
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.20571 -0.04901
## sample estimates:
## mean of the differences 
##                 -0.1274
## [1] 0.8083
## $pre
## [1] 0.4181 0.6751 0.7698 0.8139 0.6465 0.8957
## 
## $post
## [1] 0.006783 0.008996 0.003197 0.972897 0.619652 0.550757
## 
## $pp
## [1] 0.02571 0.65483 0.44708 0.15023
##              Generous Intermediate Selfish
## Intermediate   0.8957           NA      NA
## Selfish        0.8957       0.8957      NA
## New            0.8957       0.8957  0.8957
##              Generous Intermediate Selfish
## Intermediate  0.01799           NA      NA
## Selfish       0.01799       0.9729      NA
## New           0.01799       0.7436  0.7436
## [1] 0.1028 0.6548 0.5961 0.3005
## $ANOVA
##        Effect DFn DFd       SSn    SSd        F                    p p<.05
## 1 (Intercept)   1  17 10920.250 262.75 706.5433 0.000000000000002744     *
## 2       phase   1  17     2.250  60.25   0.6349 0.436567178177456383      
## 3        cond   3  51     5.639  99.36   0.9648 0.416563284870548944      
## 4  phase:cond   3  51     8.750  98.75   1.5063 0.224025935027569068      
##        ges
## 1 0.954454
## 2 0.004299
## 3 0.010705
## 4 0.016514
## 
## $`Mauchly's Test for Sphericity`
##       Effect      W      p p<.05
## 3       cond 0.7977 0.6158      
## 4 phase:cond 0.6549 0.2485      
## 
## $`Sphericity Corrections`
##       Effect    GGe  p[GG] p[GG]<.05    HFe  p[HF] p[HF]<.05
## 3       cond 0.8925 0.4098           1.0751 0.4166          
## 4 phase:cond 0.7789 0.2326           0.9107 0.2276
## 
##  Paired t-test
## 
## data:  dep by cond
## t = 2.4, df = 17, p-value = 0.03
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.1137 1.8863
## sample estimates:
## mean of the differences 
##                       1
## [1] 0.5611
## $pre
## [1] 0.8076 1.0000 0.4038 0.8381 0.3722 0.1631
## 
## $post
## [1] 0.61361 0.02926 0.79032 0.10496 0.80453 0.11146
## 
## $pp
## [1] 0.3054 0.7789 0.1974 0.1681
##              Generous Intermediate Selfish
## Intermediate   1.0000           NA      NA
## Selfish        1.0000       1.0000      NA
## New            0.8075       0.8075  0.8075
##              Generous Intermediate Selfish
## Intermediate   0.8045           NA      NA
## Selfish        0.1755       0.2229      NA
## New            0.8045       0.8045  0.2229
## [1] 0.4072 0.7789 0.3947 0.3947

4b. Poorer-learners (N = 18)

pred_poor_t <- prepData(poorer$Predominance.Face,poorerPar,skip=1)
dom_poor_t <- prepData(poorer$NormalizedDur.Face,poorerPar,skip=1)
sup_poor_t <- prepData(poorer$NormalizedDur.Scene,poorerPar,skip=1)
first_poor_t <- prepData(poorer$FirstPercept.Face,poorerPar,skip=1)

aov_pred_po<-ezANOVA(data=pred_poor_t,dv=.(keyVar), wid=.(sbj), within=.(phase,cond), detailed = TRUE)
aov_pred_po
## $ANOVA
##        Effect DFn DFd        SSn    SSd          F                    p
## 1 (Intercept)   1  17 386089.060 9441.2 695.198958 0.000000000000003139
## 2       phase   1  17      0.244  525.1   0.007899 0.930217051078758406
## 3        cond   3  51     32.456 1079.4   0.511147 0.676398495777788877
## 4  phase:cond   3  51     29.808  529.7   0.956689 0.420341677317375928
##   p<.05        ges
## 1     * 0.97089144
## 2       0.00002108
## 3       0.00279600
## 4       0.00256846
## 
## $`Mauchly's Test for Sphericity`
##       Effect      W       p p<.05
## 3       cond 0.4887 0.04695     *
## 4 phase:cond 0.6582 0.25506      
## 
## $`Sphericity Corrections`
##       Effect    GGe  p[GG] p[GG]<.05    HFe  p[HF] p[HF]<.05
## 3       cond 0.7543 0.6262           0.8760 0.6528          
## 4 phase:cond 0.7957 0.4060           0.9345 0.4162
tpred_po <- paired.t(pred_poor_t)
tpred_po$t$gen
## 
##  Paired t-test
## 
## data:  dep by phase
## t = 0.53, df = 17, p-value = 0.6
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -2.029  3.397
## sample estimates:
## mean of the differences 
##                  0.6842
tpred_po$d$gen
## [1] 0.1254
tpred_po$t$postgi
## 
##  Paired t-test
## 
## data:  dep by cond
## t = 0.71, df = 17, p-value = 0.5
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -1.531  3.095
## sample estimates:
## mean of the differences 
##                  0.7819
tpred_po$d$postgi
## [1] 0.1681
tpred_po$t$postgs
## 
##  Paired t-test
## 
## data:  dep by cond
## t = 0.38, df = 17, p-value = 0.7
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -1.761  2.526
## sample estimates:
## mean of the differences 
##                  0.3823
tpred_po$d$postgs
## [1] 0.08869
tpred_po$t$postgn
## 
##  Paired t-test
## 
## data:  dep by cond
## t = 0.26, df = 17, p-value = 0.8
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -1.578  2.015
## sample estimates:
## mean of the differences 
##                  0.2185
tpred_po$d$postgn
## [1] 0.06049
aov_dom_po<-ezANOVA(data=dom_poor_t,dv=.(keyVar), wid=.(sbj), within=.(phase,cond), detailed = TRUE)
aov_dom_po
## $ANOVA
##        Effect DFn DFd          SSn       SSd           F
## 1 (Intercept)   1  17 145.33741939 0.0027643 893802.5415
## 2       phase   1  17   0.00005753 0.0005577      1.7538
## 3        cond   3  51   0.00983451 0.8010443      0.2087
## 4  phase:cond   3  51   0.06250791 0.5840970      1.8193
##                                                p p<.05        ges
## 1 0.00000000000000000000000000000000000000001424     * 0.99053703
## 2 0.20293523084876935458353841568168718367815018       0.00004144
## 3 0.88990909647199423204710910795256495475769043       0.00703320
## 4 0.15537080749481393948663310311530949547886848       0.04308005
## 
## $`Mauchly's Test for Sphericity`
##       Effect      W       p p<.05
## 3       cond 0.8369 0.73128      
## 4 phase:cond 0.4473 0.02723     *
## 
## $`Sphericity Corrections`
##       Effect    GGe  p[GG] p[GG]<.05    HFe  p[HF] p[HF]<.05
## 3       cond 0.9107 0.8737           1.1022 0.8899          
## 4 phase:cond 0.6760 0.1770           0.7682 0.1707
tdom_po <- paired.t(dom_poor_t)
tdom_po$t$gen
## 
##  Paired t-test
## 
## data:  dep by phase
## t = 2.2, df = 17, p-value = 0.05
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.0009811 0.1005306
## sample estimates:
## mean of the differences 
##                 0.05076
tdom_po$d$gen
## [1] 0.5071
tdom_po$pval
## $pre
## [1] 0.9803 0.5713 0.1988 0.5413 0.2224 0.3378
## 
## $post
## [1] 0.2356 0.3752 0.1385 0.7548 0.6955 0.3950
## 
## $pp
## [1] 0.04611 0.74948 0.96124 0.06708
tdom_po$fdr$pre$p.value
##              Generous Intermediate Selfish
## Intermediate   0.9803           NA      NA
## Selfish        0.6855       0.6855      NA
## New            0.6671       0.6671  0.6757
tdom_po$fdr$post$p.value
##              Generous Intermediate Selfish
## Intermediate   0.5925           NA      NA
## Selfish        0.5925       0.7548      NA
## New            0.5925       0.7548  0.5925
tdom_po$fdr$pp
## [1] 0.1342 0.9612 0.9612 0.1342
aov_sup_po<-ezANOVA(data=sup_poor_t,dv=.(keyVar), wid=.(sbj), within=.(phase,cond), detailed = TRUE)
aov_sup_po
## $ANOVA
##        Effect DFn DFd          SSn      SSd            F
## 1 (Intercept)   1  17 144.97126709 0.001779 1385605.3971
## 2       phase   1  17   0.00001691 0.001726       0.1666
## 3        cond   3  51   0.06539570 0.575511       1.9317
## 4  phase:cond   3  51   0.01442928 0.499743       0.4908
##                                                  p p<.05        ges
## 1 0.0000000000000000000000000000000000000000003429     * 0.99261377
## 2 0.6882446091353839889848131861072033643722534180       0.00001568
## 3 0.1361600979606792105602863784952205605804920197       0.05715635
## 4 0.6901932254278441147832268143247347325086593628       0.01319927
## 
## $`Mauchly's Test for Sphericity`
##       Effect      W      p p<.05
## 3       cond 0.8267 0.7018      
## 4 phase:cond 0.8028 0.6311      
## 
## $`Sphericity Corrections`
##       Effect    GGe  p[GG] p[GG]<.05   HFe  p[HF] p[HF]<.05
## 3       cond 0.8779 0.1448           1.054 0.1362          
## 4 phase:cond 0.8852 0.6680           1.064 0.6902
tsup_po <- paired.t(sup_poor_t)
tsup_po$t$gen
## 
##  Paired t-test
## 
## data:  dep by phase
## t = -0.56, df = 17, p-value = 0.6
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.05970  0.03483
## sample estimates:
## mean of the differences 
##                -0.01244
tsup_po$d$gen
## [1] 0.1309
tsup_po$pval
## $pre
## [1] 0.42257 0.72745 0.05032 0.70449 0.20720 0.05382
## 
## $post
## [1] 0.3351 0.6037 0.2483 0.8133 0.7729 0.5348
## 
## $pp
## [1] 0.5860 0.7805 0.6281 0.2612
tsup_po$fdr$pre$p.value
##              Generous Intermediate Selfish
## Intermediate   0.6339           NA      NA
## Selfish        0.7275       0.7275      NA
## New            0.1615       0.4144  0.1615
tsup_po$fdr$post$p.value
##              Generous Intermediate Selfish
## Intermediate   0.8133           NA      NA
## Selfish        0.8133       0.8133      NA
## New            0.8133       0.8133  0.8133
tsup_po$fdr$pp
## [1] 0.7805 0.7805 0.7805 0.7805
aov_first_po<-ezANOVA(data=first_poor_t,dv=.(keyVar), wid=.(sbj), within=.(phase,cond), detailed = TRUE)
aov_first_po
## $ANOVA
##        Effect DFn DFd                                   SSn    SSd
## 1 (Intercept)   1  17 9735.11111111111858917865902185440063 478.89
## 2       phase   1  17    0.00000000000000000000000000008697  80.50
## 3        cond   3  51    5.16666666666670515439818700542673  91.83
## 4  phase:cond   3  51    4.27777777777774570466817749547772  84.22
##                                      F                  p p<.05
## 1 345.58515081206513741562957875430584 0.0000000000009851     *
## 2   0.00000000000000000000000000001837 0.9999999999999967      
## 3   0.95644283121597795105373052138020 0.4204571255272189      
## 4   0.86345646437994061361820286037982 0.4661090890204816      
##                                    ges
## 1 0.9297607046214251269589112780522555
## 2 0.0000000000000000000000000000001183
## 3 0.0069762208386468134435620669364653
## 4 0.0057829515583927458208246541460085
## 
## $`Mauchly's Test for Sphericity`
##       Effect      W       p p<.05
## 3       cond 0.5358 0.08141      
## 4 phase:cond 0.7079 0.36650      
## 
## $`Sphericity Corrections`
##       Effect    GGe  p[GG] p[GG]<.05    HFe  p[HF] p[HF]<.05
## 3       cond 0.7929 0.4059           0.9305 0.4161          
## 4 phase:cond 0.8299 0.4500           0.9836 0.4647
tfirst_po <- paired.t(first_poor_t)
tfirst_po$t$postgs
## 
##  Paired t-test
## 
## data:  dep by cond
## t = 0.68, df = 17, p-value = 0.5
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.4718  0.9162
## sample estimates:
## mean of the differences 
##                  0.2222
tfirst_po$d$postgs
## [1] 0.1592
tfirst_po$pval
## $pre
## [1] 0.4384 0.9163 0.5079 0.5399 0.1576 0.6309
## 
## $post
## [1] 0.27306 0.50841 0.09672 0.25964 1.00000 0.10368
## 
## $pp
## [1] 0.4467 0.9353 0.8162 0.1720
tfirst_po$fdr$pre$p.value
##              Generous Intermediate Selfish
## Intermediate   0.7570           NA      NA
## Selfish        0.9163        0.757      NA
## New            0.7570        0.757   0.757
tfirst_po$fdr$post$p.value
##              Generous Intermediate Selfish
## Intermediate   0.4096           NA      NA
## Selfish        0.6101       0.4096      NA
## New            0.3111       1.0000  0.3111
tfirst_po$fdr$pp
## [1] 0.8935 0.9353 0.9353 0.6879

Figure 4

Trust Game & Dot Probe Test

N = 24

Figure 6

d_rt_t <- prepData(dpt, parList,skip=1)

aov_rt<-ezANOVA(data=d_rt_t,dv=.(keyVar), wid=.(sbj), within=.(phase,cond), detailed = TRUE)
aov_rt
## $ANOVA
##        Effect DFn DFd         SSn    SSd         F
## 1 (Intercept)   1  23 21025384.68 259895 1860.6908
## 2       phase   1  23     1241.28  10721    2.6630
## 3        cond   3  69       48.69   1714    0.6533
## 4  phase:cond   3  69      166.12   1794    2.1299
##                              p p<.05       ges
## 1 0.00000000000000000000001645     * 0.9871301
## 2 0.11632417023240547437890058       0.0045078
## 3 0.58356435472850498591412816       0.0001776
## 4 0.10432224817373549063947991       0.0006056
## 
## $`Mauchly's Test for Sphericity`
##       Effect      W      p p<.05
## 3       cond 0.8052 0.4532      
## 4 phase:cond 0.8896 0.7706      
## 
## $`Sphericity Corrections`
##       Effect    GGe  p[GG] p[GG]<.05   HFe  p[HF] p[HF]<.05
## 3       cond 0.8780 0.5646           1.002 0.5836          
## 4 phase:cond 0.9252 0.1097           1.065 0.1043
trt <- paired.t(d_rt_t)
trt$t$gen
## 
##  Paired t-test
## 
## data:  dep by phase
## t = 2.9, df = 23, p-value = 0.009
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##   2.314 14.225
## sample estimates:
## mean of the differences 
##                   8.269
trt$d$gen
## [1] 0.5863
trt$t$postgi
## 
##  Paired t-test
## 
## data:  dep by cond
## t = -0.94, df = 23, p-value = 0.4
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -4.963  1.859
## sample estimates:
## mean of the differences 
##                  -1.552
trt$t$postgs
## 
##  Paired t-test
## 
## data:  dep by cond
## t = -1.2, df = 23, p-value = 0.3
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -6.264  1.769
## sample estimates:
## mean of the differences 
##                  -2.247
trt$t$postgn
## 
##  Paired t-test
## 
## data:  dep by cond
## t = -2.5, df = 23, p-value = 0.02
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -6.0619 -0.5909
## sample estimates:
## mean of the differences 
##                  -3.326
trt$d$postgn
## [1] 0.5135
trt$pval
## $pre
## [1] 0.11637 0.05548 0.22969 0.69772 0.53865 0.64451
## 
## $post
## [1] 0.35642 0.25892 0.01932 0.67692 0.19698 0.55375
## 
## $pp
## [1] 0.008605 0.250565 0.241958 0.303004
trt$fdr$pre$p.value
##              Generous Intermediate Selfish
## Intermediate   0.3491           NA      NA
## Selfish        0.3329       0.6977      NA
## New            0.4594       0.6977  0.6977
trt$fdr$post$p.value
##              Generous Intermediate Selfish
## Intermediate   0.5346           NA      NA
## Selfish        0.5178       0.6769      NA
## New            0.1159       0.5178  0.6645
trt$fdr$pp
## [1] 0.03442 0.30300 0.30300 0.30300

aov_ci<-ezANOVA(data=dpt_ci,dv=.(ci), wid=.(sbj), within=.(cond), detailed = TRUE)
aov_ci
## $ANOVA
##        Effect DFn DFd     SSn     SSd     F      p p<.05     ges
## 1 (Intercept)   1  23 0.01995 0.17411 2.636 0.1181       0.08809
## 2        cond   3  69 0.00310 0.03247 2.196 0.0963       0.01479
## 
## $`Mauchly's Test for Sphericity`
##   Effect      W      p p<.05
## 2   cond 0.8933 0.7841      
## 
## $`Sphericity Corrections`
##   Effect    GGe  p[GG] p[GG]<.05   HFe  p[HF] p[HF]<.05
## 2   cond 0.9285 0.1015           1.069 0.0963
cit <- list()

dat <- dpt_ci %>%
  filter(cond=="Generous")
  cit$t$gen <- t.test(dat$ci, mu=0)
  cit$d$gen <- cohensD(dat$ci, mu=0)
cit$pval[1] <- cit$t$gen$p.value
dat <- dpt_ci %>%
  filter(cond=="Intermediate")
  cit$t$int <- t.test(dat$ci, mu=0)
  cit$d$int <- cohensD(dat$ci, mu=0)
cit$pval[2] <- cit$t$int$p.value
dat <- dpt_ci %>%
  filter(cond=="Selfish")
  cit$t$sel <- t.test(dat$ci, mu=0)
  cit$d$sel <- cohensD(dat$ci, mu=0)
cit$pval[3] <- cit$t$sel$p.value
dat <- dpt_ci %>%
  filter(cond=="New")
  cit$t$new <- t.test(dat$ci, mu=0)
  cit$d$new <- cohensD(dat$ci, mu=0)
cit$pval[4] <- cit$t$new$p.value

cit$t$gen
## 
##  One Sample t-test
## 
## data:  dat$ci
## t = -3, df = 23, p-value = 0.006
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.040810 -0.007624
## sample estimates:
## mean of x 
##  -0.02422
cit$d$gen
## [1] 0.6163
cit$t$int
## 
##  One Sample t-test
## 
## data:  dat$ci
## t = -1.1, df = 23, p-value = 0.3
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.03409  0.01034
## sample estimates:
## mean of x 
##  -0.01187
cit$t$sel
## 
##  One Sample t-test
## 
## data:  dat$ci
## t = -1.1, df = 23, p-value = 0.3
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.031903  0.009499
## sample estimates:
## mean of x 
##   -0.0112
cit$t$new
## 
##  One Sample t-test
## 
## data:  dat$ci
## t = -1.1, df = 23, p-value = 0.3
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.030476  0.009724
## sample estimates:
## mean of x 
##  -0.01038
cit$pval
## [1] 0.00611 0.28033 0.27452 0.29664
p.adjust(cit$pval, method='fdr')
## [1] 0.02444 0.29664 0.29664 0.29664