FC AC V
summary(mFCACV) # table 3 in SOM-R
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: correctanswer ~ learningCondition + (1 | subjectnr)
## Data: dataFCACV
##
## AIC BIC logLik deviance df.resid
## 1147.0 1163.6 -570.5 1141.0 1869
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9580 0.1976 0.2527 0.3280 0.7685
##
## Random effects:
## Groups Name Variance Std.Dev.
## subjectnr (Intercept) 0.8245 0.908
## Number of obs: 1872, groups: subjectnr, 104
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.5609 0.1408 18.182 <2e-16 ***
## learningCondition 0.2490 0.2527 0.985 0.324
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## lernngCndtn 0.070
modelSummary(mFCRTV) # table 4 in SOM-R
## lmer(formula = reactionTime ~ vocabCovariateC + learningCondition +
## (1 | subjectnr), data = dataFCRTV)
## Observations: 1621; Groups: subjectnr, 104
##
## Linear mixed model fit by REML
##
## Fixed Effects:
## Estimate SE F error df Pr(>F)
## (Intercept) 1.48507 0.03909 1443.169 102.0 < 2e-16 ***
## vocabCovariateC -1.08318 0.39502 7.512 124.2 0.00703 **
## learningCondition -0.17769 0.07814 5.170 100.3 0.02511 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## NOTE: F, error df, and p-values from Kenward-Roger approximation
##
## Random Effects:
## Groups Name Std.Dev.
## subjectnr (Intercept) 0.32990
## Residual 0.86495
## Warning in deviance.merMod(Model): deviance() is deprecated for REML fits;
## use REMLcrit for the REML criterion or deviance(.,REML=FALSE) for deviance
## calculated at the REML fit
##
## AIC: 4267.1; BIC: 4294.1; logLik: -2128.6; Deviance: 4257.1
summary(mFCACA) # table 5 in SOM-R
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## correctanswer ~ vocabCovariateC + learningCondition * snItemtype +
## (1 + snItemtype | subjectnr)
## Data: dataFCACA
##
## AIC BIC logLik deviance df.resid
## 1300.1 1346.6 -642.0 1284.1 2488
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.5213 0.1309 0.1809 0.2362 1.1246
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subjectnr (Intercept) 0.9374 0.9682
## snItemtype 2.1394 1.4627 -0.22
## Number of obs: 2496, groups: subjectnr, 104
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.1405 0.1811 17.340 < 2e-16 ***
## vocabCovariateC 4.4885 1.1875 3.780 0.000157 ***
## learningCondition 0.5703 0.2757 2.069 0.038590 *
## snItemtype 0.9997 0.3316 3.015 0.002568 **
## learningCondition:snItemtype -0.4723 0.4869 -0.970 0.332034
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) vcbCvC lrnngC snItmt
## vocabCovrtC 0.182
## lernngCndtn 0.086 -0.056
## snItemtype 0.257 -0.027 -0.016
## lrnngCndt:I -0.032 -0.043 0.074 0.112
modelSummary(mFCRTA) # table 6 in SOM-R
## lmer(formula = reactionTime ~ vocabCovariateC + learningCondition *
## snItemtype + (1 + snItemtype | subjectnr), data = dataFCRTA)
## Observations: 2229; Groups: subjectnr, 104
##
## Linear mixed model fit by REML
## Note: method with signature 'sparseMatrix#ANY' chosen for function 'kronecker',
## target signature 'dgCMatrix#ngCMatrix'.
## "ANY#sparseMatrix" would also be valid
##
## Fixed Effects:
## Estimate SE F error df Pr(>F)
## (Intercept) 2.01461 0.07471 727.212 102.1 < 2e-16
## vocabCovariateC -0.86123 0.68480 1.564 109.3 0.21380
## learningCondition -0.59653 0.14992 15.830 102.2 0.00013
## snItemtype 0.08893 0.07764 1.312 109.9 0.25452
## learningCondition:snItemtype -0.21308 0.15527 1.883 109.9 0.17277
##
## (Intercept) ***
## vocabCovariateC
## learningCondition ***
## snItemtype
## learningCondition:snItemtype
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## NOTE: F, error df, and p-values from Kenward-Roger approximation
##
## Random Effects:
## Groups Name Std.Dev. Corr
## subjectnr (Intercept) 0.74167
## snItemtype 0.71101 -0.233
## Residual 0.79310
## Warning in deviance.merMod(Model): deviance() is deprecated for REML fits;
## use REMLcrit for the REML criterion or deviance(.,REML=FALSE) for deviance
## calculated at the REML fit
##
## AIC: 5791.2; BIC: 5842.6; logLik: -2886.6; Deviance: 5773.2
modelSummary(mD1) # d prime analysis reported in the first paragraph of Error Monitoring Tests
## lm(formula = dprime ~ learningCondition, data = dW)
## Observations: 104
##
## Linear model fit by least squares
##
## Coefficients:
## Estimate SE t Pr(>|t|)
## (Intercept) 2.0936 0.1119 18.710 <2e-16 ***
## learningCondition 0.5285 0.2238 2.362 0.0201 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sum of squared errors (SSE): 132.8, Error df: 102
## R-squared: 0.0518
varDescribeBy(dW$dprime, dW$learningCondition) # d prime analysis reported in the first paragraph of Error Monitoring Tests
## IVList: -0.5
## vars n mean sd median min max skew kurtosis
## X1 1 52 1.83 1.15 1.75 -0.8 4.52 0.09 -0.5
## --------------------------------------------------------
## IVList: 0.5
## vars n mean sd median min max skew kurtosis
## X1 1 52 2.36 1.13 2.51 0.01 4.52 -0.31 -0.97
summary(mEMACW) # table 7 in SOM-R
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## correctanswer ~ vocabCovariateC + learningCondition + (1 | subjectnr)
## Data: dataEMACW
##
## AIC BIC logLik deviance df.resid
## 3822.6 3847.0 -1907.3 3814.6 3324
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2085 -0.9998 0.4651 0.6483 1.6444
##
## Random effects:
## Groups Name Variance Std.Dev.
## subjectnr (Intercept) 0.5437 0.7373
## Number of obs: 3328, groups: subjectnr, 104
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.98793 0.08414 11.742 < 2e-16 ***
## vocabCovariateC 3.22172 0.76782 4.196 2.72e-05 ***
## learningCondition 0.07090 0.16753 0.423 0.672
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) vcbCvC
## vocabCovrtC 0.032
## lernngCndtn 0.000 -0.091
modelSummary(mEMRTW) # table 8 in SOM-R
## lmer(formula = reactionTime ~ vocabCovariateC + learningCondition +
## (1 | subjectnr), data = dataEMRTW)
## Observations: 2133; Groups: subjectnr, 103
##
## Linear mixed model fit by REML
##
## Fixed Effects:
## Estimate SE F error df Pr(>F)
## (Intercept) 2.39647 0.07611 991.266 100.26 <2e-16 ***
## vocabCovariateC -1.09770 0.77534 2.004 107.02 0.1598
## learningCondition -0.39361 0.15206 6.700 99.58 0.0111 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## NOTE: F, error df, and p-values from Kenward-Roger approximation
##
## Random Effects:
## Groups Name Std.Dev.
## subjectnr (Intercept) 0.71923
## Residual 1.20415
## Warning in deviance.merMod(Model): deviance() is deprecated for REML fits;
## use REMLcrit for the REML criterion or deviance(.,REML=FALSE) for deviance
## calculated at the REML fit
##
## AIC: 7072.2; BIC: 7100.5; logLik: -3531.1; Deviance: 7062.2
summary(mEMACA2) # table 9 in SOM-R
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## correctanswer ~ vocabCovariateC + learningCondition * adjacencyItemtype *
## snItemtype + (0 + adjacencyItemtype:snItemtype | subjectnr)
## Data: dataEMACA
##
## AIC BIC logLik deviance df.resid
## 5566.8 5631.9 -2773.4 5546.8 4982
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4789 -0.9917 0.4388 0.6443 2.1216
##
## Random effects:
## Groups Name Variance Std.Dev.
## subjectnr adjacencyItemtype:snItemtype 1.289e-15 3.591e-08
## Number of obs: 4992, groups: subjectnr, 104
##
## Fixed effects:
## Estimate Std. Error z value
## (Intercept) 0.93542 0.03442 27.175
## vocabCovariateC 3.65461 0.30021 12.173
## learningCondition 0.75857 0.06850 11.074
## adjacencyItemtype -1.07910 0.06876 -15.694
## snItemtype 0.31726 0.06852 4.630
## learningCondition:adjacencyItemtype 0.05507 0.13701 0.402
## learningCondition:snItemtype -0.07397 0.13700 -0.540
## adjacencyItemtype:snItemtype -0.73969 0.13706 -5.397
## learningCondition:adjacencyItemtype:snItemtype 0.20362 0.27405 0.743
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## vocabCovariateC < 2e-16 ***
## learningCondition < 2e-16 ***
## adjacencyItemtype < 2e-16 ***
## snItemtype 3.65e-06 ***
## learningCondition:adjacencyItemtype 0.688
## learningCondition:snItemtype 0.589
## adjacencyItemtype:snItemtype 6.78e-08 ***
## learningCondition:adjacencyItemtype:snItemtype 0.457
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) vcbCvC lrnngC adjcnI snItmt lrnngCndtn:dI
## vocabCovrtC 0.100
## lernngCndtn 0.162 -0.011
## adjcncyItmt -0.240 -0.087 -0.105
## snItemtype 0.119 0.026 0.034 -0.122
## lrnngCndtn:dI -0.104 0.017 -0.234 0.162 -0.036
## lrnngCndtn:sI 0.032 -0.012 0.117 -0.035 0.164 -0.120
## adjcncyIt:I -0.123 -0.030 -0.036 0.119 -0.234 0.033
## lrnngCn:I:I -0.035 0.013 -0.120 0.033 -0.106 0.117
## lrnngCndtn:sI adjI:I
## vocabCovrtC
## lernngCndtn
## adjcncyItmt
## snItemtype
## lrnngCndtn:dI
## lrnngCndtn:sI
## adjcncyIt:I -0.106
## lrnngCn:I:I -0.234 0.164
modelSummary(mEMRTA) # table 10 in SOM-R
## lmer(formula = reactionTime ~ vocabCovariateC + learningCondition *
## adjacencyItemtype * snItemtype + (1 + adjacencyItemtype *
## snItemtype | subjectnr), data = dataEMRTA)
## Observations: 3270; Groups: subjectnr, 103
##
## Linear mixed model fit by REML
##
## Fixed Effects:
## Estimate SE F
## (Intercept) 1.94459 0.06399 922.8486
## vocabCovariateC -1.05411 0.63951 2.6933
## learningCondition -0.33119 0.12788 6.7034
## adjacencyItemtype 0.06822 0.04862 1.9604
## snItemtype -0.24184 0.03355 51.4893
## learningCondition:adjacencyItemtype -0.08268 0.09724 0.7198
## learningCondition:snItemtype 0.04724 0.06710 0.4912
## adjacencyItemtype:snItemtype 0.23775 0.06398 13.6762
## learningCondition:adjacencyItemtype:snItemtype -0.09139 0.12793 0.5053
## error df Pr(>F)
## (Intercept) 102.74 < 2e-16 ***
## vocabCovariateC 110.15 0.103621
## learningCondition 102.18 0.011026 *
## adjacencyItemtype 100.77 0.164546
## snItemtype 99.91 1.29e-10 ***
## learningCondition:adjacencyItemtype 100.79 0.398218
## learningCondition:snItemtype 99.87 0.485028
## adjacencyItemtype:snItemtype 95.80 0.000362 ***
## learningCondition:adjacencyItemtype:snItemtype 95.72 0.478914
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## NOTE: F, error df, and p-values from Kenward-Roger approximation
##
## Random Effects:
## Groups Name Std.Dev. Corr
## subjectnr (Intercept) 0.62364
## adjacencyItemtype 0.36630 0.146
## snItemtype 0.16023 -0.094 0.465
## adjacencyItemtype:snItemtype 0.25872 -0.193 -0.456 -0.536
## Residual 0.78990
## Warning in deviance.merMod(Model): deviance() is deprecated for REML fits;
## use REMLcrit for the REML criterion or deviance(.,REML=FALSE) for deviance
## calculated at the REML fit
##
## AIC: 8228.0; BIC: 8349.9; logLik: -4094.0; Deviance: 8188.0
Accuracy # part of Figure 5 with results
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RT # part of Figure 5 with results
## Saving 7 x 5 in image
summary(mFCACP3) # table 1 in SOM-U
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## correctanswer ~ vocabCovariateC + learningCondition * probItemtype +
## (1 + probItemtype | subjectnr)
## Data: dataFCACP
## Control: glmerControl(optCtrl = list(maxfun = 20000))
##
## AIC BIC logLik deviance df.resid
## 982.9 1029.4 -483.4 966.9 2488
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.3442 0.1239 0.1629 0.2081 1.2575
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subjectnr (Intercept) 0.8078539 0.89881
## probItemtype 0.0002668 0.01633 -1.00
## Number of obs: 2496, groups: subjectnr, 104
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.417639 0.174396 19.597 < 2e-16 ***
## vocabCovariateC 7.620842 1.124246 6.779 1.21e-11 ***
## learningCondition 0.055206 0.281493 0.196 0.845
## probItemtype -0.003806 0.252015 -0.015 0.988
## learningCondition:probItemtype 0.394947 0.369763 1.068 0.285
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) vcbCvC lrnngC prbItm
## vocabCovrtC 0.321
## lernngCndtn 0.064 -0.039
## probItemtyp -0.022 -0.077 0.053
## lrnngCndt:I 0.046 -0.006 -0.001 0.131
modelSummary(mFCRTP) # table 2 in SOM-U
## lmer(formula = reactionTime ~ vocabCovariateC + learningCondition *
## probItemtype + (1 + probItemtype | subjectnr), data = dataFCRTP)
## Observations: 2317; Groups: subjectnr, 104
##
## Linear mixed model fit by REML
##
## Fixed Effects:
## Estimate SE F error df
## (Intercept) 1.20489 0.02931 1689.7180 101.4
## vocabCovariateC -1.33630 0.28015 22.5788 123.4
## learningCondition -0.13333 0.05875 5.1492 101.0
## probItemtype -0.03540 0.02441 2.1027 109.4
## learningCondition:probItemtype -0.04682 0.04881 0.9196 109.3
## Pr(>F)
## (Intercept) < 2e-16 ***
## vocabCovariateC 5.51e-06 ***
## learningCondition 0.0254 *
## probItemtype 0.1499
## learningCondition:probItemtype 0.3397
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## NOTE: F, error df, and p-values from Kenward-Roger approximation
##
## Random Effects:
## Groups Name Std.Dev. Corr
## subjectnr (Intercept) 0.272568
## probItemtype 0.057354 -1.000
## Residual 0.570844
## Warning in deviance.merMod(Model): deviance() is deprecated for REML fits;
## use REMLcrit for the REML criterion or deviance(.,REML=FALSE) for deviance
## calculated at the REML fit
##
## AIC: 4197.8; BIC: 4249.5; logLik: -2089.9; Deviance: 4179.8
summary(mEMACP) # table 3 in SOM-U
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## correctanswer ~ vocabCovariateC + learningCondition * probItemtype +
## (1 + probItemtype | subjectnr)
## Data: dataEMACP
##
## AIC BIC logLik deviance df.resid
## 2726.2 2777.7 -1355.1 2710.2 4568
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.2225 0.1444 0.2151 0.3241 1.3917
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subjectnr (Intercept) 1.0892 1.0437
## probItemtype 0.0569 0.2385 1.00
## Number of obs: 4576, groups: subjectnr, 104
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.6673 0.1277 20.889 < 2e-16 ***
## vocabCovariateC 6.7717 1.0303 6.572 4.96e-11 ***
## learningCondition 0.3084 0.2425 1.272 0.204
## probItemtype 0.1141 0.1325 0.861 0.389
## learningCondition:probItemtype -0.2004 0.2117 -0.947 0.344
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) vcbCvC lrnngC prbItm
## vocabCovrtC 0.123
## lernngCndtn 0.041 -0.057
## probItemtyp 0.146 -0.090 -0.004
## lrnngCndt:I -0.012 0.000 0.185 0.129
modelSummary(mEMRTP) # table 4 in SOM-U
## lmer(formula = reactionTime ~ vocabCovariateC + learningCondition *
## probItemtype + (1 + probItemtype | subjectnr), data = dataEMRTP)
## Observations: 3788; Groups: subjectnr, 102
##
## Linear mixed model fit by REML
##
## Fixed Effects:
## Estimate SE F error df Pr(>F)
## (Intercept) 1.26405 0.04608 752.3691 99.79 <2e-16
## vocabCovariateC 0.18358 0.47645 0.1480 108.65 0.7012
## learningCondition -0.22081 0.09187 5.7757 99.17 0.0181
## probItemtype 0.02969 0.02456 1.4586 98.62 0.2300
## learningCondition:probItemtype -0.01664 0.04912 0.1147 98.47 0.7356
##
## (Intercept) ***
## vocabCovariateC
## learningCondition *
## probItemtype
## learningCondition:probItemtype
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## NOTE: F, error df, and p-values from Kenward-Roger approximation
##
## Random Effects:
## Groups Name Std.Dev. Corr
## subjectnr (Intercept) 0.445730
## probItemtype 0.037009 1.000
## Residual 0.741418
## Warning in deviance.merMod(Model): deviance() is deprecated for REML fits;
## use REMLcrit for the REML criterion or deviance(.,REML=FALSE) for deviance
## calculated at the REML fit
##
## AIC: 8782.5; BIC: 8838.7; logLik: -4382.3; Deviance: 8764.5
PPFL # figure 2 in SOM-U
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