Exploratory Analyses Lexical Transparency

Participants

In this analysis, children with >50% accuracy on non-cognate words in English CLT are included in the analysis.

1st grade/class	2nd grade/class	keep
48	40	Current Keepers
6	11	Children with >50% accuracy on non-cognate words in English CLT
NA	7	English CLT still needs to be coded
3	9	No questionnaire data
2	NA	Child has reported hearing problems
1	NA	Child has SLD
NA	1	Child has ADHD
1	NA	Child is suspected to have autism
3	NA	Child did not want to continue
1	NA	not enough trials per condition
2	NA	Questionnaire was started but not completed

Recognition: Cognate vs. Non-cognate words

Main Analysis

Cognate Status by Word Status

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Word_Type * Word_Status + (1 | subject_nr) +  
##     (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  12256.6  12300.9  -6122.3  12244.6    11876 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.5156 -0.5914 -0.3780  0.7363  8.6784 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.03145  0.1773  
##  subject_nr   (Intercept) 1.40842  1.1868  
## Number of obs: 11882, groups:  Sentence_Num, 160; subject_nr, 105
## 
## Fixed effects:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)             -0.88205    0.11959  -7.376 1.63e-13 ***
## Word_Type1              -0.03036    0.04595  -0.661    0.509    
## Word_Status1             1.23292    0.04689  26.294  < 2e-16 ***
## Word_Type1:Word_Status1 -0.03197    0.09191  -0.348    0.728    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_T1 Wrd_S1
## Word_Type1  0.001               
## Word_Stats1 0.026  0.000        
## Wrd_T1:W_S1 0.001  0.207  0.000

Individual Differences with Cognate Status by Word Status

Model below is built using buildmer, mostly only for fixed effects for now

# f <- detection ~ Word_Status*Word_Type + 
#   RAVENS_IQ.c + WM_BackDigitSpan.c + Ger_CLT_Prod.c + NWRT_total_correct.c + Phoneme_Manipulation.c + Onset_Rhyme.c + ENG_CLT_score.c +
#   (1+Word_Status*Word_Type | subject_nr) +
#   (1+Word_Status*Word_Type | Sentence_Num)
# 
# model_1 <- buildmer(f,
#                     family=binomial,
#                     data=Lseg_Htest,
#               buildmerControl=buildmerControl(direction=c('order', "backward")))
# 
# model_1 <- model_1@model
# 
# model_1_formula <- model_1@call
# 
# summary(model_1)

Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) [‘glmerMod’] Family: binomial ( logit ) Formula: detection ~ 1 + Word_Status + (1 + Word_Status | subject_nr) + (1 + Word_Status | Sentence_Num) Data: Lseg_Htest

 AIC      BIC   logLik deviance df.resid 

10117.3 10174.9 -5050.6 10101.3 9932

Scaled residuals: Min 1Q Median 3Q Max -2.0333 -0.6041 -0.3141 0.7600 6.3618

Random effects: Groups Name Variance Std.Dev. Corr Sentence_Num (Intercept) 0.04088 0.2022
Word_Status1 0.18360 0.4285 0.08 subject_nr (Intercept) 1.48437 1.2183
Word_Status1 0.90161 0.9495 -0.42 Number of obs: 9940, groups: Sentence_Num, 160; subject_nr, 88

Fixed effects: Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.9653 0.1344 -7.184 6.78e-13 Word_Status1 1.1797 0.1223 9.645 < 2e-16

---

Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects: (Intr) Word_Stats1 -0.336

Role of Phonological Awareness, with control variables Ravens IQ, Working Memory, and German CLT

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Word_Type * Word_Status * Phoneme_Manipulation.c +  
##     RAVENS_IQ + Ger_CLT_Prod.c + WM_BackDigitSpan.c + (1 | subject_nr) +  
##     (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  11894.7  11990.2  -5934.4  11868.7    11408 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3975 -0.6008 -0.3690  0.7620  7.6830 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.0349   0.1868  
##  subject_nr   (Intercept) 1.2253   1.1069  
## Number of obs: 11421, groups:  Sentence_Num, 160; subject_nr, 101
## 
## Fixed effects:
##                                                 Estimate Std. Error z value
## (Intercept)                                     1.333770   1.058000   1.261
## Word_Type1                                     -0.029517   0.046706  -0.632
## Word_Status1                                    1.222891   0.047590  25.696
## Phoneme_Manipulation.c                          0.036938   0.037943   0.974
## RAVENS_IQ                                      -0.021466   0.010381  -2.068
## Ger_CLT_Prod.c                                 -0.137613   0.061069  -2.253
## WM_BackDigitSpan.c                             -0.142440   0.147532  -0.965
## Word_Type1:Word_Status1                        -0.028562   0.093420  -0.306
## Word_Type1:Phoneme_Manipulation.c              -0.013796   0.014139  -0.976
## Word_Status1:Phoneme_Manipulation.c             0.080397   0.014421   5.575
## Word_Type1:Word_Status1:Phoneme_Manipulation.c -0.003559   0.028281  -0.126
##                                                Pr(>|z|)    
## (Intercept)                                      0.2074    
## Word_Type1                                       0.5274    
## Word_Status1                                    < 2e-16 ***
## Phoneme_Manipulation.c                           0.3303    
## RAVENS_IQ                                        0.0387 *  
## Ger_CLT_Prod.c                                   0.0242 *  
## WM_BackDigitSpan.c                               0.3343    
## Word_Type1:Word_Status1                          0.7598    
## Word_Type1:Phoneme_Manipulation.c                0.3292    
## Word_Status1:Phoneme_Manipulation.c            2.47e-08 ***
## Word_Type1:Word_Status1:Phoneme_Manipulation.c   0.8999    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_T1 Wrd_S1 Phn_M. RAVENS G_CLT_ WM_BDS Wr_T1:W_S1 W_T1:P
## Word_Type1   0.000                                                            
## Word_Stats1  0.012 -0.001                                                     
## Phnm_Mnplt.  0.125  0.002 -0.004                                              
## RAVENS_IQ   -0.994  0.000 -0.009 -0.125                                       
## Gr_CLT_Prd. -0.088 -0.001 -0.008 -0.124  0.089                                
## WM_BckDgtS.  0.077  0.001 -0.005 -0.320 -0.080 -0.293                         
## Wrd_T1:W_S1 -0.001  0.209  0.000 -0.001  0.001  0.001  0.000                  
## Wrd_T1:P_M.  0.000 -0.005 -0.005  0.002  0.000  0.000  0.000 -0.053           
## Wrd_S1:P_M. -0.002 -0.004  0.003  0.024  0.000  0.008  0.004  0.006     -0.008
## W_T1:W_S1:P  0.001 -0.053  0.007 -0.001 -0.001  0.000  0.000 -0.006      0.195
##             W_S1:P
## Word_Type1        
## Word_Stats1       
## Phnm_Mnplt.       
## RAVENS_IQ         
## Gr_CLT_Prd.       
## WM_BckDgtS.       
## Wrd_T1:W_S1       
## Wrd_T1:P_M.       
## Wrd_S1:P_M.       
## W_T1:W_S1:P  0.005
## optimizer (bobyqa) convergence code: 1 (bobyqa -- maximum number of function evaluations exceeded)
## Model failed to converge with max|grad| = 0.028795 (tol = 0.002, component 1)
## Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?

d’ analyses

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dprime ~ 1 + Word_Type + Phoneme_Manipulation.c + RAVENS_IQ +  
##     Ger_CLT_Prod.c + WM_BackDigitSpan.c + (1 | subject_nr)
##    Data: d_calc
## 
## REML criterion at convergence: 392.5
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -1.94983 -0.52016 -0.07889  0.55587  1.96656 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev.
##  subject_nr (Intercept) 0.3281   0.5728  
##  Residual               0.1608   0.4010  
## Number of obs: 202, groups:  subject_nr, 101
## 
## Fixed effects:
##                          Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)              0.059971   0.587986  95.999997   0.102 0.918975    
## Word_Type1              -0.046724   0.056430 100.000002  -0.828 0.409635    
## Phoneme_Manipulation.c   0.012858   0.021329  95.999996   0.603 0.548041    
## RAVENS_IQ                0.006588   0.005764  95.999997   1.143 0.255902    
## Ger_CLT_Prod.c           0.007520   0.034273  95.999996   0.219 0.826802    
## WM_BackDigitSpan.c       0.311824   0.082460  95.999995   3.782 0.000271 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_T1 Phn_M. RAVENS G_CLT_
## Word_Type1   0.000                            
## Phnm_Mnplt.  0.122  0.000                     
## RAVENS_IQ   -0.994  0.000 -0.121              
## Gr_CLT_Prd. -0.072  0.000 -0.118  0.073       
## WM_BckDgtS.  0.071  0.000 -0.321 -0.073 -0.299

Experiment Task Variables

Trial

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: 
## detection ~ 1 + Word_Type * Word_Status * Trial.c + (1 | subject_nr) +  
##     (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  12259.3  12333.1  -6119.7  12239.3    11872 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.5788 -0.5935 -0.3775  0.7372  8.7866 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.03159  0.1777  
##  subject_nr   (Intercept) 1.41023  1.1875  
## Number of obs: 11882, groups:  Sentence_Num, 160; subject_nr, 105
## 
## Fixed effects:
##                                   Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                     -0.8823091  0.1196735  -7.373 1.67e-13 ***
## Word_Type1                      -0.0308103  0.0459712  -0.670   0.5027    
## Word_Status1                     1.2333686  0.0469087  26.293  < 2e-16 ***
## Trial.c                         -0.0002355  0.0005008  -0.470   0.6383    
## Word_Type1:Word_Status1         -0.0318932  0.0919504  -0.347   0.7287    
## Word_Type1:Trial.c               0.0011597  0.0010036   1.155   0.2479    
## Word_Status1:Trial.c            -0.0019744  0.0010017  -1.971   0.0487 *  
## Word_Type1:Word_Status1:Trial.c  0.0004236  0.0020068   0.211   0.8328    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_T1 Wrd_S1 Tril.c Wr_T1:W_S1 W_T1:T W_S1:T
## Word_Type1   0.001                                              
## Word_Stats1  0.026  0.000                                       
## Trial.c      0.000  0.000  0.006                                
## Wrd_T1:W_S1  0.001  0.208 -0.001  0.007                         
## Wrd_Typ1:T. -0.002 -0.004  0.008  0.002  0.007                  
## Wrd_Stt1:T.  0.002  0.006 -0.006  0.220  0.000      0.011       
## W_T1:W_S1:T  0.000  0.007  0.001  0.011 -0.003      0.219  0.002
## optimizer (bobyqa) convergence code: 0 (OK)
## Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?

Counterbalance List

## # A tibble: 8 × 2
##   List  total_participants
##   <fct>              <int>
## 1 A                     15
## 2 B                     12
## 3 C                     13
## 4 D                     10
## 5 E                     15
## 6 F                     16
## 7 G                     12
## 8 H                     12

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Word_Type * Word_Status * List + (1 | subject_nr) +  
##     (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  12251.6  12502.6  -6091.8  12183.6    11848 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.1600 -0.6002 -0.3697  0.7403  7.9951 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.03055  0.1748  
##  subject_nr   (Intercept) 1.30363  1.1418  
## Number of obs: 11882, groups:  Sentence_Num, 160; subject_nr, 105
## 
## Fixed effects:
##                               Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                   -0.93783    0.30245  -3.101  0.00193 ** 
## Word_Type1                    -0.23868    0.12687  -1.881  0.05992 .  
## Word_Status1                   1.25934    0.12981   9.701  < 2e-16 ***
## ListB                         -0.29165    0.45414  -0.642  0.52075    
## ListC                          0.58261    0.44398   1.312  0.18944    
## ListD                          0.51933    0.47566   1.092  0.27492    
## ListE                          0.13223    0.42706   0.310  0.75684    
## ListF                         -0.25003    0.42099  -0.594  0.55257    
## ListG                          0.29973    0.45222   0.663  0.50746    
## ListH                         -0.43349    0.45674  -0.949  0.34257    
## Word_Type1:Word_Status1       -0.33642    0.25341  -1.328  0.18431    
## Word_Type1:ListB               0.14443    0.19362   0.746  0.45570    
## Word_Type1:ListC               0.16183    0.18355   0.882  0.37796    
## Word_Type1:ListD               0.39371    0.19357   2.034  0.04196 *  
## Word_Type1:ListE               0.41680    0.18083   2.305  0.02117 *  
## Word_Type1:ListF               0.32502    0.17914   1.814  0.06963 .  
## Word_Type1:ListG               0.14290    0.18520   0.772  0.44036    
## Word_Type1:ListH               0.03979    0.19419   0.205  0.83766    
## Word_Status1:ListB            -0.39535    0.19698  -2.007  0.04474 *  
## Word_Status1:ListC            -0.16277    0.19084  -0.853  0.39372    
## Word_Status1:ListD             0.46286    0.19775   2.341  0.01925 *  
## Word_Status1:ListE             0.37084    0.17769   2.087  0.03689 *  
## Word_Status1:ListF            -0.11070    0.18267  -0.606  0.54450    
## Word_Status1:ListG            -0.07486    0.19281  -0.388  0.69782    
## Word_Status1:ListH            -0.41049    0.19725  -2.081  0.03743 *  
## Word_Type1:Word_Status1:ListB  0.16336    0.38677   0.422  0.67275    
## Word_Type1:Word_Status1:ListC  0.30574    0.36666   0.834  0.40436    
## Word_Type1:Word_Status1:ListD  0.20461    0.38684   0.529  0.59686    
## Word_Type1:Word_Status1:ListE  0.87297    0.36146   2.415  0.01573 *  
## Word_Type1:Word_Status1:ListF  0.63151    0.35818   1.763  0.07789 .  
## Word_Type1:Word_Status1:ListG  0.05895    0.36986   0.159  0.87336    
## Word_Type1:Word_Status1:ListH -0.02903    0.38793  -0.075  0.94034    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## maxfun < 10 * length(par)^2 is not recommended.

Position in Sentence

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Word_Type * Word_Status * target_position + (1 |  
##     subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  12261.5  12335.3  -6120.8  12241.5    11872 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.5294 -0.5926 -0.3786  0.7420  8.5816 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.03084  0.1756  
##  subject_nr   (Intercept) 1.40916  1.1871  
## Number of obs: 11882, groups:  Sentence_Num, 160; subject_nr, 105
## 
## Fixed effects:
##                                          Estimate Std. Error z value Pr(>|z|)
## (Intercept)                              -0.88235    0.11960  -7.377 1.61e-13
## Word_Type1                               -0.03018    0.04596  -0.657    0.511
## Word_Status1                              1.23258    0.04690  26.281  < 2e-16
## target_position1                          0.05756    0.05372   1.072    0.284
## Word_Type1:Word_Status1                  -0.03185    0.09193  -0.346    0.729
## Word_Type1:target_position1               0.03125    0.09200   0.340    0.734
## Word_Status1:target_position1             0.13832    0.09204   1.503    0.133
## Word_Type1:Word_Status1:target_position1  0.04072    0.18395   0.221    0.825
##                                             
## (Intercept)                              ***
## Word_Type1                                  
## Word_Status1                             ***
## target_position1                            
## Word_Type1:Word_Status1                     
## Word_Type1:target_position1                 
## Word_Status1:target_position1               
## Word_Type1:Word_Status1:target_position1    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_T1 Wrd_S1 trgt_1 Wr_T1:W_S1 W_T1:_ W_S1:_
## Word_Type1   0.001                                              
## Word_Stats1  0.026  0.000                                       
## targt_pstn1 -0.001  0.000 -0.005                                
## Wrd_T1:W_S1  0.001  0.208  0.000  0.004                         
## Wrd_Typ1:_1  0.001 -0.003  0.003  0.001 -0.008                  
## Wrd_Stt1:_1 -0.003  0.004  0.000  0.179  0.000      0.002       
## W_T1:W_S1:_  0.001 -0.008  0.001  0.001 -0.003      0.207  0.000

Word Length (monosyllabic, bisyllabic)

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: 
## detection ~ 1 + Word_Type * Word_Status * Syllable + (1 | subject_nr) +  
##     (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  12257.0  12330.9  -6118.5  12237.0    11872 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.5727 -0.5916 -0.3746  0.7345  9.0594 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.03104  0.1762  
##  subject_nr   (Intercept) 1.40963  1.1873  
## Number of obs: 11882, groups:  Sentence_Num, 160; subject_nr, 105
## 
## Fixed effects:
##                                   Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                       -0.88255    0.11963  -7.378 1.61e-13 ***
## Word_Type1                        -0.03001    0.04598  -0.653   0.5140    
## Word_Status1                       1.23447    0.04691  26.315  < 2e-16 ***
## Syllable1                         -0.05010    0.05376  -0.932   0.3513    
## Word_Type1:Word_Status1           -0.03382    0.09196  -0.368   0.7130    
## Word_Type1:Syllable1               0.18271    0.09196   1.987   0.0469 *  
## Word_Status1:Syllable1            -0.03807    0.09197  -0.414   0.6789    
## Word_Type1:Word_Status1:Syllable1 -0.21998    0.18390  -1.196   0.2316    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_T1 Wrd_S1 Syllb1 Wr_T1:W_S1 W_T1:S W_S1:S
## Word_Type1   0.001                                              
## Word_Stats1  0.026  0.000                                       
## Syllable1    0.000 -0.008 -0.008                                
## Wrd_T1:W_S1  0.001  0.207 -0.001  0.013                         
## Wrd_Typ1:S1 -0.002 -0.001  0.017  0.000 -0.008                  
## Wrd_Stt1:S1 -0.001  0.015 -0.002  0.177 -0.009      0.002       
## W_T1:W_S1:S  0.003 -0.008 -0.010  0.002 -0.001      0.207  0.000

ALINE Score

Here, ALINE score is based on the Probe word.

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Word_Status * ALINE + (1 | subject_nr) + (1 |  
##     Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  12256.9  12301.2  -6122.5  12244.9    11876 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.4937 -0.5912 -0.3779  0.7386  8.6629 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.03151  0.1775  
##  subject_nr   (Intercept) 1.40825  1.1867  
## Number of obs: 11882, groups:  Sentence_Num, 160; subject_nr, 105
## 
## Fixed effects:
##                     Estimate Std. Error z value Pr(>|z|)    
## (Intercept)        -0.893585   0.125559  -7.117  1.1e-12 ***
## Word_Status1        1.235811   0.089698  13.777  < 2e-16 ***
## ALINE               0.028450   0.093999   0.303    0.762    
## Word_Status1:ALINE -0.007269   0.187849  -0.039    0.969    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_S1 ALINE 
## Word_Stats1  0.067              
## ALINE       -0.305 -0.174       
## Wr_S1:ALINE -0.062 -0.852  0.202

Participant Variables: Predictors

Correlation Individual Differences

Take with a grain of salt, these scores are the “grob” ones coded during the experiment and for some participants there are missing values (for unsure codes), but this should be fairly close to what the final coded values will be.

Phonological Awareness

Phoneme Manipulation task score

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Word_Type * Word_Status * Phoneme_Manipulation.c +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  12162.7  12236.4  -6071.4  12142.7    11757 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3942 -0.5929 -0.3661  0.7511  8.0068 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.03284  0.1812  
##  subject_nr   (Intercept) 1.37099  1.1709  
## Number of obs: 11767, groups:  Sentence_Num, 160; subject_nr, 104
## 
## Fixed effects:
##                                                 Estimate Std. Error z value
## (Intercept)                                    -0.871026   0.118669  -7.340
## Word_Type1                                     -0.027229   0.046183  -0.590
## Word_Status1                                    1.230740   0.047075  26.144
## Phoneme_Manipulation.c                         -0.005857   0.036260  -0.162
## Word_Type1:Word_Status1                        -0.023995   0.092377  -0.260
## Word_Type1:Phoneme_Manipulation.c              -0.012509   0.014101  -0.887
## Word_Status1:Phoneme_Manipulation.c             0.085482   0.014394   5.939
## Word_Type1:Word_Status1:Phoneme_Manipulation.c -0.004359   0.028205  -0.155
##                                                Pr(>|z|)    
## (Intercept)                                    2.14e-13 ***
## Word_Type1                                        0.555    
## Word_Status1                                    < 2e-16 ***
## Phoneme_Manipulation.c                            0.872    
## Word_Type1:Word_Status1                           0.795    
## Word_Type1:Phoneme_Manipulation.c                 0.375    
## Word_Status1:Phoneme_Manipulation.c            2.88e-09 ***
## Word_Type1:Word_Status1:Phoneme_Manipulation.c    0.877    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_T1 Wrd_S1 Phn_M. Wr_T1:W_S1 W_T1:P W_S1:P
## Word_Type1   0.001                                              
## Word_Stats1  0.027 -0.001                                       
## Phnm_Mnplt.  0.000  0.002 -0.010                                
## Wrd_T1:W_S1  0.000  0.205  0.000  0.000                         
## Wrd_T1:P_M.  0.002 -0.011 -0.005  0.001 -0.055                  
## Wrd_S1:P_M. -0.010 -0.003 -0.002  0.029  0.006     -0.007       
## W_T1:W_S1:P  0.000 -0.055  0.006 -0.001 -0.011      0.192  0.004

Onset/Rhyme Score

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Word_Type * Word_Status * Onset_Rhyme.c + (1 |  
##     subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  12017.4  12091.0  -5998.7  11997.4    11643 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.4073 -0.5817 -0.3365  0.7600  8.1593 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.03467  0.1862  
##  subject_nr   (Intercept) 1.37252  1.1715  
## Number of obs: 11653, groups:  Sentence_Num, 160; subject_nr, 103
## 
## Fixed effects:
##                                       Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                           -0.87678    0.11939  -7.344 2.07e-13 ***
## Word_Type1                            -0.03331    0.04654  -0.716   0.4741    
## Word_Status1                           1.23132    0.04744  25.958  < 2e-16 ***
## Onset_Rhyme.c                         -0.03108    0.02878  -1.080   0.2801    
## Word_Type1:Word_Status1               -0.03464    0.09309  -0.372   0.7098    
## Word_Type1:Onset_Rhyme.c               0.02089    0.01135   1.841   0.0656 .  
## Word_Status1:Onset_Rhyme.c             0.09766    0.01160   8.421  < 2e-16 ***
## Word_Type1:Word_Status1:Onset_Rhyme.c  0.01937    0.02270   0.853   0.3936    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_T1 Wrd_S1 Ons_R. Wr_T1:W_S1 W_T1:O W_S1:O
## Word_Type1   0.001                                              
## Word_Stats1  0.025  0.003                                       
## Onst_Rhym.c  0.008 -0.002 -0.018                                
## Wrd_T1:W_S1  0.001  0.199  0.000  0.000                         
## Wrd_T1:O_R. -0.002  0.021  0.003  0.001 -0.072                  
## Wrd_S1:O_R. -0.018  0.003  0.036  0.024 -0.003      0.007       
## W_T1:W_S1:O  0.000 -0.071 -0.002  0.002  0.021      0.177  0.004

Grade

Approximation for now for L1 knowledge

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Word_Type * Word_Status * DEMO_SchoolGrade +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  12181.8  12255.6  -6080.9  12161.8    11872 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.1376 -0.5799 -0.3531  0.7568  7.5812 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.03237  0.1799  
##  subject_nr   (Intercept) 1.36996  1.1705  
## Number of obs: 11882, groups:  Sentence_Num, 160; subject_nr, 105
## 
## Fixed effects:
##                                           Estimate Std. Error z value Pr(>|z|)
## (Intercept)                               -0.89717    0.11813  -7.595 3.09e-14
## Word_Type1                                -0.02709    0.04622  -0.586    0.558
## Word_Status1                               1.23844    0.04709  26.297  < 2e-16
## DEMO_SchoolGrade1                          0.16838    0.23437   0.718    0.472
## Word_Type1:Word_Status1                   -0.02808    0.09245  -0.304    0.761
## Word_Type1:DEMO_SchoolGrade1               0.01277    0.09243   0.138    0.890
## Word_Status1:DEMO_SchoolGrade1            -0.84694    0.09387  -9.023  < 2e-16
## Word_Type1:Word_Status1:DEMO_SchoolGrade1  0.12325    0.18488   0.667    0.505
##                                              
## (Intercept)                               ***
## Word_Type1                                   
## Word_Status1                              ***
## DEMO_SchoolGrade1                            
## Word_Type1:Word_Status1                      
## Word_Type1:DEMO_SchoolGrade1                 
## Word_Status1:DEMO_SchoolGrade1            ***
## Word_Type1:Word_Status1:DEMO_SchoolGrade1    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_T1 Wrd_S1 DEMO_S Wr_T1:W_S1 W_T1:D W_S1:D
## Word_Type1   0.000                                              
## Word_Stats1  0.024  0.000                                       
## DEMO_SchlG1 -0.024 -0.001  0.017                                
## Wrd_T1:W_S1  0.000  0.191 -0.002 -0.001                         
## W_T1:DEMO_S -0.001 -0.028 -0.003  0.000  0.077                  
## W_S1:DEMO_S  0.017 -0.004 -0.044  0.027 -0.003      0.000       
## W_T1:W_S1:D -0.001  0.077 -0.003  0.000 -0.028      0.192 -0.002

Age

Approximation for now for L1 knowledge

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Word_Type * Word_Status * DEMO_AgeYears.c + (1 |  
##     subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  11930.9  12004.5  -5955.5  11910.9    11526 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.2277 -0.5828 -0.3580  0.7514  7.7194 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.0367   0.1916  
##  subject_nr   (Intercept) 1.3894   1.1787  
## Number of obs: 11536, groups:  Sentence_Num, 160; subject_nr, 102
## 
## Fixed effects:
##                                         Estimate Std. Error z value Pr(>|z|)
## (Intercept)                             -0.86717    0.12068  -7.185 6.70e-13
## Word_Type1                              -0.02876    0.04669  -0.616    0.538
## Word_Status1                             1.21557    0.04753  25.577  < 2e-16
## DEMO_AgeYears.c                         -0.01189    0.15718  -0.076    0.940
## Word_Type1:Word_Status1                 -0.02694    0.09339  -0.288    0.773
## Word_Type1:DEMO_AgeYears.c              -0.04932    0.06111  -0.807    0.420
## Word_Status1:DEMO_AgeYears.c             0.50258    0.06206   8.098 5.58e-16
## Word_Type1:Word_Status1:DEMO_AgeYears.c -0.14389    0.12221  -1.177    0.239
##                                            
## (Intercept)                             ***
## Word_Type1                                 
## Word_Status1                            ***
## DEMO_AgeYears.c                            
## Word_Type1:Word_Status1                    
## Word_Type1:DEMO_AgeYears.c                 
## Word_Status1:DEMO_AgeYears.c            ***
## Word_Type1:Word_Status1:DEMO_AgeYears.c    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_T1 Wrd_S1 DEMO_A Wr_T1:W_S1 W_T1:D W_S1:D
## Word_Type1   0.000                                              
## Word_Stats1  0.027 -0.001                                       
## DEMO_AgYrs.  0.003  0.001 -0.013                                
## Wrd_T1:W_S1  0.000  0.205 -0.002  0.001                         
## W_T1:DEMO_A  0.002 -0.004  0.000  0.000 -0.066                  
## W_S1:DEMO_A -0.014  0.000  0.010  0.033  0.005     -0.005       
## W_T1:W_S1:D  0.001 -0.066  0.002  0.000 -0.004      0.224 -0.004

German Production CLT

Another approximation for L1 knowledge

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Word_Type * Word_Status * Ger_CLT_Prod.c + (1 |  
##     subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  12237.9  12311.7  -6108.9  12217.9    11872 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.6857 -0.5890 -0.3756  0.7565  8.3100 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.03115  0.1765  
##  subject_nr   (Intercept) 1.33776  1.1566  
## Number of obs: 11882, groups:  Sentence_Num, 160; subject_nr, 105
## 
## Fixed effects:
##                                        Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                            -0.88775    0.11675  -7.604 2.87e-14 ***
## Word_Type1                             -0.02853    0.04601  -0.620   0.5352    
## Word_Status1                            1.23685    0.04694  26.350  < 2e-16 ***
## Ger_CLT_Prod.c                         -0.12199    0.05845  -2.087   0.0369 *  
## Word_Type1:Word_Status1                -0.03073    0.09203  -0.334   0.7384    
## Word_Type1:Ger_CLT_Prod.c              -0.03165    0.02257  -1.402   0.1608    
## Word_Status1:Ger_CLT_Prod.c             0.09697    0.02287   4.240 2.23e-05 ***
## Word_Type1:Word_Status1:Ger_CLT_Prod.c -0.07678    0.04515  -1.701   0.0890 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_T1 Wrd_S1 G_CLT_ Wr_T1:W_S1 W_T1:G W_S1:G
## Word_Type1   0.001                                              
## Word_Stats1  0.025  0.001                                       
## Gr_CLT_Prd.  0.010  0.001 -0.012                                
## Wrd_T1:W_S1  0.001  0.200  0.000  0.002                         
## W_T1:G_CLT_  0.001  0.021  0.000  0.001 -0.036                  
## W_S1:G_CLT_ -0.010  0.001  0.030  0.046  0.000     -0.002       
## W_T1:W_S1:G  0.002 -0.036  0.000  0.000  0.021      0.282  0.000

Participant Variables: Exploratory

Language Background (Monolingual, Bilingual)

## # A tibble: 2 × 2
##   LANG_Bilingual total_participants
##   <fct>                       <int>
## 1 monolingual                    76
## 2 bilingual                      29

## # A tibble: 1 × 8
##   .y.           group1      group2       n1    n2 statistic    df      p
## * <chr>         <chr>       <chr>     <int> <int>     <dbl> <dbl>  <dbl>
## 1 DEMO_AgeYears monolingual bilingual    74    28      2.09  52.8 0.0416

## # A tibble: 1 × 8
##   .y.       group1      group2       n1    n2 statistic    df     p
## * <chr>     <chr>       <chr>     <int> <int>     <dbl> <dbl> <dbl>
## 1 RAVENS_IQ monolingual bilingual    74    28    -0.890  49.2 0.378

## # A tibble: 1 × 8
##   .y.              group1      group2       n1    n2 statistic    df     p
## * <chr>            <chr>       <chr>     <int> <int>     <dbl> <dbl> <dbl>
## 1 WM_BackDigitSpan monolingual bilingual    76    29     0.536  43.0 0.594

## # A tibble: 1 × 8
##   .y.          group1      group2       n1    n2 statistic    df      p
## * <chr>        <chr>       <chr>     <int> <int>     <dbl> <dbl>  <dbl>
## 1 Ger_CLT_Prod monolingual bilingual    76    29      2.50  34.1 0.0174

## # A tibble: 1 × 8
##   .y.                group1      group2       n1    n2 statistic    df     p
## * <chr>              <chr>       <chr>     <int> <int>     <dbl> <dbl> <dbl>
## 1 NWRT_total_correct monolingual bilingual    76    29     -1.63  48.7  0.11

## # A tibble: 1 × 8
##   .y.                  group1      group2       n1    n2 statistic    df     p
## * <chr>                <chr>       <chr>     <int> <int>     <dbl> <dbl> <dbl>
## 1 Phoneme_Manipulation monolingual bilingual    75    29      1.44  53.9 0.155

## # A tibble: 1 × 8
##   .y.         group1      group2       n1    n2 statistic    df     p
## * <chr>       <chr>       <chr>     <int> <int>     <dbl> <dbl> <dbl>
## 1 Onset_Rhyme monolingual bilingual    75    28    -0.733  57.2 0.466

## # A tibble: 1 × 8
##   .y.           group1      group2       n1    n2 statistic    df      p
## * <chr>         <chr>       <chr>     <int> <int>     <dbl> <dbl>  <dbl>
## 1 ENG_CLT_score monolingual bilingual    75    29     -2.68  42.0 0.0106

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Word_Type * Word_Status * LANG_Bilingual + (1 |  
##     subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  12260.1  12333.9  -6120.0  12240.1    11872 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.4754 -0.5918 -0.3789  0.7337  8.2253 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.03138  0.1771  
##  subject_nr   (Intercept) 1.35689  1.1649  
## Number of obs: 11882, groups:  Sentence_Num, 160; subject_nr, 105
## 
## Fixed effects:
##                                          Estimate Std. Error z value Pr(>|z|)
## (Intercept)                             -0.776444   0.131361  -5.911 3.41e-09
## Word_Type1                              -0.010913   0.051097  -0.214   0.8309
## Word_Status1                             1.219024   0.052070  23.411  < 2e-16
## LANG_Bilingual1                         -0.467355   0.261095  -1.790   0.0735
## Word_Type1:Word_Status1                 -0.003145   0.102200  -0.031   0.9755
## Word_Type1:LANG_Bilingual1              -0.086139   0.102192  -0.843   0.3993
## Word_Status1:LANG_Bilingual1             0.063912   0.103830   0.616   0.5382
## Word_Type1:Word_Status1:LANG_Bilingual1 -0.120334   0.204399  -0.589   0.5560
##                                            
## (Intercept)                             ***
## Word_Type1                                 
## Word_Status1                            ***
## LANG_Bilingual1                         .  
## Word_Type1:Word_Status1                    
## Word_Type1:LANG_Bilingual1                 
## Word_Status1:LANG_Bilingual1               
## Word_Type1:Word_Status1:LANG_Bilingual1    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_T1 Wrd_S1 LANG_B Wr_T1:W_S1 W_T1:L W_S1:L
## Word_Type1   0.000                                              
## Word_Stats1  0.036  0.004                                       
## LANG_Blngl1 -0.447  0.000 -0.025                                
## Wrd_T1:W_S1  0.001  0.241  0.000  0.000                         
## W_T1:LANG_B  0.001 -0.436 -0.005  0.000 -0.138                  
## W_S1:LANG_B -0.024 -0.004 -0.435  0.038  0.000      0.003       
## W_T1:W_S1:L  0.000 -0.138  0.000  0.001 -0.436      0.241  0.001

English CLT

Overall performance

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Word_Type * Word_Status * ENG_CLT_score.c + (1 |  
##     subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  12078.7  12152.4  -6029.3  12058.7    11756 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3688 -0.5912 -0.3565  0.7686 11.0124 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.03248  0.1802  
##  subject_nr   (Intercept) 1.41077  1.1878  
## Number of obs: 11766, groups:  Sentence_Num, 160; subject_nr, 104
## 
## Fixed effects:
##                                           Estimate Std. Error z value Pr(>|z|)
## (Intercept)                             -0.8858394  0.1203165  -7.363  1.8e-13
## Word_Type1                              -0.0217260  0.0464072  -0.468    0.640
## Word_Status1                             1.2218204  0.0473391  25.810  < 2e-16
## ENG_CLT_score.c                          0.0017846  0.0087060   0.205    0.838
## Word_Type1:Word_Status1                 -0.0351027  0.0928210  -0.378    0.705
## Word_Type1:ENG_CLT_score.c               0.0009891  0.0033944   0.291    0.771
## Word_Status1:ENG_CLT_score.c             0.0315409  0.0034704   9.089  < 2e-16
## Word_Type1:Word_Status1:ENG_CLT_score.c -0.0031206  0.0067889  -0.460    0.646
##                                            
## (Intercept)                             ***
## Word_Type1                                 
## Word_Status1                            ***
## ENG_CLT_score.c                            
## Word_Type1:Word_Status1                    
## Word_Type1:ENG_CLT_score.c                 
## Word_Status1:ENG_CLT_score.c            ***
## Word_Type1:Word_Status1:ENG_CLT_score.c    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_T1 Wrd_S1 ENG_CL Wr_T1:W_S1 W_T1:E W_S1:E
## Word_Type1   0.000                                              
## Word_Stats1  0.027  0.001                                       
## ENG_CLT_sc.  0.010  0.000 -0.011                                
## Wrd_T1:W_S1  0.001  0.206 -0.003  0.002                         
## W_T1:ENG_CL  0.000  0.009  0.007  0.000 -0.050                  
## W_S1:ENG_CL -0.012  0.008  0.026  0.027 -0.002      0.000       
## W_T1:W_S1:E  0.002 -0.050 -0.001  0.001  0.009      0.192 -0.005

Specific performance on cognate words

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Word_Type * Word_Status * ENG_CLT_cognates.c +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  12109.9  12183.6  -6044.9  12089.9    11756 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3889 -0.5920 -0.3662  0.7664 10.2484 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.03223  0.1795  
##  subject_nr   (Intercept) 1.41951  1.1914  
## Number of obs: 11766, groups:  Sentence_Num, 160; subject_nr, 104
## 
## Fixed effects:
##                                              Estimate Std. Error z value
## (Intercept)                                -0.8834016  0.1206437  -7.322
## Word_Type1                                 -0.0223783  0.0463025  -0.483
## Word_Status1                                1.2259884  0.0472449  25.950
## ENG_CLT_cognates.c                          0.0006802  0.0071929   0.095
## Word_Type1:Word_Status1                    -0.0329065  0.0926131  -0.355
## Word_Type1:ENG_CLT_cognates.c              -0.0004358  0.0027423  -0.159
## Word_Status1:ENG_CLT_cognates.c             0.0203150  0.0027833   7.299
## Word_Type1:Word_Status1:ENG_CLT_cognates.c -0.0031929  0.0054858  -0.582
##                                            Pr(>|z|)    
## (Intercept)                                2.44e-13 ***
## Word_Type1                                    0.629    
## Word_Status1                                < 2e-16 ***
## ENG_CLT_cognates.c                            0.925    
## Word_Type1:Word_Status1                       0.722    
## Word_Type1:ENG_CLT_cognates.c                 0.874    
## Word_Status1:ENG_CLT_cognates.c            2.90e-13 ***
## Word_Type1:Word_Status1:ENG_CLT_cognates.c    0.561    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_T1 Wrd_S1 ENG_CL Wr_T1:W_S1 W_T1:E W_S1:E
## Word_Type1   0.000                                              
## Word_Stats1  0.026  0.000                                       
## ENG_CLT_cg.  0.008  0.001 -0.008                                
## Wrd_T1:W_S1  0.001  0.206 -0.002  0.002                         
## W_T1:ENG_CL  0.001  0.011  0.006  0.000 -0.037                  
## W_S1:ENG_CL -0.009  0.007  0.024  0.037  0.002     -0.010       
## W_T1:W_S1:E  0.002 -0.037  0.002 -0.001  0.011      0.251 -0.009

Difference score (Prop cognates correct – prop noncognates correct )

difference score where 0 is similar performance between cognates and noncognates

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Word_Type * Word_Status * English_CLT_diffscore.c +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  12162.8  12236.6  -6071.4  12142.8    11756 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.5115 -0.5937 -0.3788  0.7452  8.6772 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.03177  0.1782  
##  subject_nr   (Intercept) 1.41843  1.1910  
## Number of obs: 11766, groups:  Sentence_Num, 160; subject_nr, 104
## 
## Fixed effects:
##                                                  Estimate Std. Error z value
## (Intercept)                                     -0.878046   0.120579  -7.282
## Word_Type1                                      -0.025831   0.046166  -0.560
## Word_Status1                                     1.224757   0.047092  26.008
## English_CLT_diffscore.c                         -0.001437   0.007179  -0.200
## Word_Type1:Word_Status1                         -0.035373   0.092340  -0.383
## Word_Type1:English_CLT_diffscore.c              -0.001462   0.002745  -0.533
## Word_Status1:English_CLT_diffscore.c            -0.001114   0.002785  -0.400
## Word_Type1:Word_Status1:English_CLT_diffscore.c -0.001488   0.005491  -0.271
##                                                 Pr(>|z|)    
## (Intercept)                                     3.29e-13 ***
## Word_Type1                                         0.576    
## Word_Status1                                     < 2e-16 ***
## English_CLT_diffscore.c                            0.841    
## Word_Type1:Word_Status1                            0.702    
## Word_Type1:English_CLT_diffscore.c                 0.594    
## Word_Status1:English_CLT_diffscore.c               0.689    
## Word_Type1:Word_Status1:English_CLT_diffscore.c    0.786    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_T1 Wrd_S1 E_CLT_ Wr_T1:W_S1 W_T1:E W_S1:E
## Word_Type1   0.000                                              
## Word_Stats1  0.027  0.001                                       
## Engls_CLT_.  0.000  0.001  0.000                                
## Wrd_T1:W_S1  0.001  0.210 -0.001  0.001                         
## W_T1:E_CLT_  0.001  0.017  0.001  0.000 -0.003                  
## W_S1:E_CLT_ -0.001  0.001  0.016  0.030  0.004     -0.002       
## W_T1:W_S1:E  0.001 -0.002  0.003  0.000  0.017      0.212 -0.003

Ravens Matricies IQ score

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Word_Type * Word_Status * RAVENS_IQ.c + (1 |  
##     subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  11967.2  12040.7  -5973.6  11947.2    11526 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.5909 -0.6007 -0.3698  0.7708  8.1412 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.0345   0.1857  
##  subject_nr   (Intercept) 1.3702   1.1705  
## Number of obs: 11536, groups:  Sentence_Num, 160; subject_nr, 102
## 
## Fixed effects:
##                                       Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                         -0.8679584  0.1198605  -7.241 4.44e-13 ***
## Word_Type1                          -0.0304093  0.0466553  -0.652   0.5145    
## Word_Status1                         1.2370262  0.0476287  25.972  < 2e-16 ***
## RAVENS_IQ.c                         -0.0204564  0.0107625  -1.901   0.0573 .  
## Word_Type1:Word_Status1             -0.0384800  0.0933151  -0.412   0.6801    
## Word_Type1:RAVENS_IQ.c              -0.0004539  0.0044646  -0.102   0.9190    
## Word_Status1:RAVENS_IQ.c             0.0238045  0.0046000   5.175 2.28e-07 ***
## Word_Type1:Word_Status1:RAVENS_IQ.c -0.0051924  0.0089246  -0.582   0.5607    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_T1 Wrd_S1 RAVENS Wr_T1:W_S1 W_T1:R W_S1:R
## Word_Type1   0.001                                              
## Word_Stats1  0.025  0.001                                       
## RAVENS_IQ.c  0.015  0.001 -0.019                                
## Wrd_T1:W_S1  0.001  0.201 -0.002  0.002                         
## W_T1:RAVENS  0.000  0.058  0.006  0.003 -0.048                  
## W_S1:RAVENS -0.018  0.005  0.076  0.003  0.000      0.006       
## W_T1:W_S1:R  0.001 -0.048  0.001  0.004  0.059      0.152  0.005

Backwards Digit Span

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Word_Type * Word_Status * WM_BackDigitSpan.c +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  12152.2  12226.0  -6066.1  12132.2    11872 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.7452 -0.5780 -0.3447  0.7710  8.4304 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.0316   0.1778  
##  subject_nr   (Intercept) 1.3346   1.1553  
## Number of obs: 11882, groups:  Sentence_Num, 160; subject_nr, 105
## 
## Fixed effects:
##                                             Estimate Std. Error z value
## (Intercept)                                -0.894993   0.116646  -7.673
## Word_Type1                                 -0.023570   0.046225  -0.510
## Word_Status1                                1.219274   0.047085  25.895
## WM_BackDigitSpan.c                         -0.165179   0.132756  -1.244
## Word_Type1:Word_Status1                    -0.006877   0.092463  -0.074
## Word_Type1:WM_BackDigitSpan.c              -0.156807   0.053940  -2.907
## Word_Status1:WM_BackDigitSpan.c             0.545824   0.054943   9.934
## Word_Type1:Word_Status1:WM_BackDigitSpan.c -0.196633   0.107835  -1.823
##                                            Pr(>|z|)    
## (Intercept)                                1.68e-14 ***
## Word_Type1                                  0.61012    
## Word_Status1                                < 2e-16 ***
## WM_BackDigitSpan.c                          0.21341    
## Word_Type1:Word_Status1                     0.94071    
## Word_Type1:WM_BackDigitSpan.c               0.00365 ** 
## Word_Status1:WM_BackDigitSpan.c             < 2e-16 ***
## Word_Type1:Word_Status1:WM_BackDigitSpan.c  0.06823 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_T1 Wrd_S1 WM_BDS Wr_T1:W_S1 W_T1:WM W_S1:W
## Word_Type1   0.000                                               
## Word_Stats1  0.025 -0.005                                        
## WM_BckDgtS.  0.002  0.001 -0.014                                 
## Wrd_T1:W_S1 -0.001  0.196 -0.001 -0.001                          
## W_T1:WM_BDS  0.002 -0.011 -0.007  0.001 -0.076                   
## W_S1:WM_BDS -0.014 -0.005  0.004  0.035  0.007     -0.017        
## W_T1:W_S1:W  0.000 -0.075  0.006 -0.001 -0.010      0.216  -0.012

NonWord Repetition Task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Word_Type * Word_Status * NWRT_total_correct.c +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  12217.8  12291.6  -6098.9  12197.8    11872 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.7409 -0.5864 -0.3580  0.7476  8.8037 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.03133  0.177   
##  subject_nr   (Intercept) 1.39544  1.181   
## Number of obs: 11882, groups:  Sentence_Num, 160; subject_nr, 105
## 
## Fixed effects:
##                                               Estimate Std. Error z value
## (Intercept)                                  -0.886565   0.119085  -7.445
## Word_Type1                                   -0.030024   0.046080  -0.652
## Word_Status1                                  1.226664   0.047029  26.083
## NWRT_total_correct.c                          0.007723   0.055345   0.140
## Word_Type1:Word_Status1                      -0.029010   0.092168  -0.315
## Word_Type1:NWRT_total_correct.c              -0.004374   0.021357  -0.205
## Word_Status1:NWRT_total_correct.c             0.148049   0.021636   6.843
## Word_Type1:Word_Status1:NWRT_total_correct.c -0.005447   0.042713  -0.128
##                                              Pr(>|z|)    
## (Intercept)                                  9.71e-14 ***
## Word_Type1                                      0.515    
## Word_Status1                                  < 2e-16 ***
## NWRT_total_correct.c                            0.889    
## Word_Type1:Word_Status1                         0.753    
## Word_Type1:NWRT_total_correct.c                 0.838    
## Word_Status1:NWRT_total_correct.c            7.77e-12 ***
## Word_Type1:Word_Status1:NWRT_total_correct.c    0.899    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_T1 Wrd_S1 NWRT__ Wr_T1:W_S1 W_T1:N W_S1:N
## Word_Type1   0.001                                              
## Word_Stats1  0.027 -0.001                                       
## NWRT_ttl_c.  0.006  0.000 -0.009                                
## Wrd_T1:W_S1  0.000  0.206  0.000  0.000                         
## W_T1:NWRT__  0.000 -0.011 -0.001  0.000 -0.038                  
## W_S1:NWRT__ -0.009 -0.001  0.002  0.036  0.003      0.005       
## W_T1:W_S1:N  0.000 -0.039  0.004  0.001 -0.011      0.231 -0.005

Recognition: Cognate vs. Non-cognate Following words

Main Analysis: Utterance Type by Word Status

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Utterance_Type * Word_Status + (1 | subject_nr) +  
##     (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  12364.9  12409.2  -6176.4  12352.9    11889 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2313 -0.5872 -0.3632  0.7344 10.1429 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.05359  0.2315  
##  subject_nr   (Intercept) 1.41105  1.1879  
## Number of obs: 11895, groups:  Sentence_Num, 160; subject_nr, 105
## 
## Fixed effects:
##                              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                  -0.76328    0.12021  -6.350 2.16e-10 ***
## Utterance_Type1              -0.04234    0.04577  -0.925    0.355    
## Word_Status1                  1.47164    0.04706  31.268  < 2e-16 ***
## Utterance_Type1:Word_Status1 -0.13432    0.09155  -1.467    0.142    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Utt_T1 Wrd_S1
## Uttrnc_Typ1  0.000              
## Word_Stats1  0.024  0.000       
## Utt_T1:W_S1  0.001  0.204 -0.006

Role of Phonological Awareness, with control variables Ravens IQ, Working Memory, and German CLT

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: 
## detection ~ 1 + Utterance_Type * Word_Status * Phoneme_Manipulation.c +  
##     RAVENS_IQ + Ger_CLT_Prod.c + WM_BackDigitSpan.c + (1 | subject_nr) +  
##     (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  11913.6  12009.1  -5943.8  11887.6    11420 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.1322 -0.5937 -0.3482  0.7566  8.7346 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.05287  0.2299  
##  subject_nr   (Intercept) 1.24193  1.1144  
## Number of obs: 11433, groups:  Sentence_Num, 160; subject_nr, 101
## 
## Fixed effects:
##                                                     Estimate Std. Error z value
## (Intercept)                                          1.55938    1.06152   1.469
## Utterance_Type1                                     -0.04167    0.04676  -0.891
## Word_Status1                                         1.44436    0.04801  30.087
## Phoneme_Manipulation.c                               0.06243    0.03819   1.635
## RAVENS_IQ                                           -0.02265    0.01041  -2.175
## Ger_CLT_Prod.c                                      -0.12876    0.06142  -2.097
## WM_BackDigitSpan.c                                  -0.14363    0.14837  -0.968
## Utterance_Type1:Word_Status1                        -0.13749    0.09349  -1.471
## Utterance_Type1:Phoneme_Manipulation.c               0.01761    0.01434   1.228
## Word_Status1:Phoneme_Manipulation.c                  0.13152    0.01476   8.913
## Utterance_Type1:Word_Status1:Phoneme_Manipulation.c -0.05137    0.02865  -1.793
##                                                     Pr(>|z|)    
## (Intercept)                                           0.1418    
## Utterance_Type1                                       0.3728    
## Word_Status1                                          <2e-16 ***
## Phoneme_Manipulation.c                                0.1021    
## RAVENS_IQ                                             0.0296 *  
## Ger_CLT_Prod.c                                        0.0360 *  
## WM_BackDigitSpan.c                                    0.3330    
## Utterance_Type1:Word_Status1                          0.1414    
## Utterance_Type1:Phoneme_Manipulation.c                0.2193    
## Word_Status1:Phoneme_Manipulation.c                   <2e-16 ***
## Utterance_Type1:Word_Status1:Phoneme_Manipulation.c   0.0729 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Utt_T1 Wrd_S1 Phn_M. RAVENS G_CLT_ WM_BDS Ut_T1:W_S1 U_T1:P
## Uttrnc_Typ1 -0.001                                                            
## Word_Stats1  0.013  0.003                                                     
## Phnm_Mnplt.  0.125 -0.004 -0.002                                              
## RAVENS_IQ   -0.994  0.001 -0.010 -0.124                                       
## Gr_CLT_Prd. -0.085  0.001 -0.008 -0.125  0.087                                
## WM_BckDgtS.  0.075  0.000 -0.006 -0.319 -0.077 -0.293                         
## Utt_T1:W_S1 -0.001  0.208 -0.010  0.002  0.001 -0.001  0.000                  
## Utt_T1:P_M.  0.000  0.010  0.010 -0.005  0.000  0.000  0.000 -0.043           
## Wrd_S1:P_M.  0.000  0.009  0.031  0.027 -0.001  0.010  0.002 -0.021      0.006
## U_T1:W_S1:P  0.000 -0.045 -0.022  0.001  0.000  0.000  0.001  0.010      0.196
##             W_S1:P
## Uttrnc_Typ1       
## Word_Stats1       
## Phnm_Mnplt.       
## RAVENS_IQ         
## Gr_CLT_Prd.       
## WM_BckDgtS.       
## Utt_T1:W_S1       
## Utt_T1:P_M.       
## Wrd_S1:P_M.       
## U_T1:W_S1:P -0.033
## optimizer (bobyqa) convergence code: 1 (bobyqa -- maximum number of function evaluations exceeded)
## Model failed to converge with max|grad| = 0.038935 (tol = 0.002, component 1)
## Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?

d’ analyses

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dprime ~ 1 + Utterance_Type + Phoneme_Manipulation.c + RAVENS_IQ +  
##     Ger_CLT_Prod.c + WM_BackDigitSpan.c + (1 | subject_nr)
##    Data: d_calc
## 
## REML criterion at convergence: 409
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.45003 -0.44539  0.04169  0.45714  2.26732 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev.
##  subject_nr (Intercept) 0.4064   0.6375  
##  Residual               0.1603   0.4003  
## Number of obs: 202, groups:  subject_nr, 101
## 
## Fixed effects:
##                         Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)              0.22616    0.64164  96.00000   0.352 0.725254    
## Utterance_Type1         -0.09991    0.05633 100.00000  -1.773 0.079192 .  
## Phoneme_Manipulation.c   0.04296    0.02328  96.00000   1.846 0.068014 .  
## RAVENS_IQ                0.00621    0.00629  96.00000   0.987 0.325987    
## Ger_CLT_Prod.c           0.01521    0.03740  96.00000   0.407 0.685201    
## WM_BackDigitSpan.c       0.30840    0.08998  96.00000   3.427 0.000899 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Utt_T1 Phn_M. RAVENS G_CLT_
## Uttrnc_Typ1  0.000                            
## Phnm_Mnplt.  0.122  0.000                     
## RAVENS_IQ   -0.994  0.000 -0.121              
## Gr_CLT_Prd. -0.072  0.000 -0.118  0.073       
## WM_BckDgtS.  0.071  0.000 -0.321 -0.073 -0.299

Experiment Task Variables

Trial

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Utterance_Type * Word_Status * Trial.c + (1 |  
##     subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  12359.3  12433.2  -6169.7  12339.3    11885 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4072 -0.5874 -0.3631  0.7289 11.1539 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.05318  0.2306  
##  subject_nr   (Intercept) 1.41833  1.1909  
## Number of obs: 11895, groups:  Sentence_Num, 160; subject_nr, 105
## 
## Fixed effects:
##                                        Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                          -0.7632845  0.1204958  -6.335 2.38e-10 ***
## Utterance_Type1                      -0.0458313  0.0458288  -1.000  0.31728    
## Word_Status1                          1.4746006  0.0471256  31.291  < 2e-16 ***
## Trial.c                              -0.0005551  0.0004957  -1.120  0.26280    
## Utterance_Type1:Word_Status1         -0.1394930  0.0916708  -1.522  0.12809    
## Utterance_Type1:Trial.c               0.0019274  0.0009933   1.940  0.05233 .  
## Word_Status1:Trial.c                 -0.0026839  0.0009919  -2.706  0.00681 ** 
## Utterance_Type1:Word_Status1:Trial.c -0.0021424  0.0019887  -1.077  0.28134    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Utt_T1 Wrd_S1 Tril.c Ut_T1:W_S1 U_T1:T W_S1:T
## Uttrnc_Typ1  0.000                                              
## Word_Stats1  0.024 -0.001                                       
## Trial.c     -0.001  0.011 -0.006                                
## Utt_T1:W_S1  0.001  0.205 -0.006  0.029                         
## Uttrn_T1:T.  0.001 -0.012  0.031 -0.004 -0.004                  
## Wrd_Stt1:T.  0.001  0.029 -0.017  0.203  0.011     -0.009       
## U_T1:W_S1:T  0.005 -0.004  0.007 -0.009 -0.012      0.201 -0.003
## optimizer (bobyqa) convergence code: 0 (OK)
## Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?

Counterbalance List

## # A tibble: 8 × 2
##   List  total_participants
##   <fct>              <int>
## 1 A                     15
## 2 B                     12
## 3 C                     13
## 4 D                     10
## 5 E                     15
## 6 F                     16
## 7 G                     12
## 8 H                     12

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: 
## detection ~ 1 + Utterance_Type * Word_Status * List + (1 | subject_nr) +  
##     (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  12354.6  12605.7  -6143.3  12286.6    11861 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.5672 -0.5843 -0.3544  0.7306  8.7672 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.0482   0.2196  
##  subject_nr   (Intercept) 1.3022   1.1411  
## Number of obs: 11895, groups:  Sentence_Num, 160; subject_nr, 105
## 
## Fixed effects:
##                                    Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                        -0.78223    0.30220  -2.588  0.00964 ** 
## Utterance_Type1                    -0.24215    0.12799  -1.892  0.05851 .  
## Word_Status1                        1.54273    0.13012  11.857  < 2e-16 ***
## ListB                              -0.23733    0.45297  -0.524  0.60032    
## ListC                               0.55568    0.44349   1.253  0.21022    
## ListD                               0.58816    0.47629   1.235  0.21687    
## ListE                               0.01482    0.42705   0.035  0.97232    
## ListF                              -0.30656    0.42033  -0.729  0.46581    
## ListG                               0.14766    0.45139   0.327  0.74358    
## ListH                              -0.41715    0.45543  -0.916  0.35969    
## Utterance_Type1:Word_Status1       -0.13798    0.25389  -0.543  0.58681    
## Utterance_Type1:ListB               0.17389    0.18938   0.918  0.35852    
## Utterance_Type1:ListC               0.09956    0.18627   0.534  0.59300    
## Utterance_Type1:ListD               0.31075    0.20360   1.526  0.12694    
## Utterance_Type1:ListE               0.18526    0.19089   0.971  0.33178    
## Utterance_Type1:ListF               0.29294    0.18520   1.582  0.11371    
## Utterance_Type1:ListG               0.18231    0.18678   0.976  0.32904    
## Utterance_Type1:ListH               0.44516    0.18774   2.371  0.01774 *  
## Word_Status1:ListB                 -0.25506    0.19875  -1.283  0.19937    
## Word_Status1:ListC                 -0.18713    0.19439  -0.963  0.33573    
## Word_Status1:ListD                  0.67277    0.20492   3.283  0.00103 ** 
## Word_Status1:ListE                  0.22807    0.17670   1.291  0.19681    
## Word_Status1:ListF                 -0.17529    0.18374  -0.954  0.34008    
## Word_Status1:ListG                 -0.41936    0.19452  -2.156  0.03109 *  
## Word_Status1:ListH                 -0.39293    0.19538  -2.011  0.04431 *  
## Utterance_Type1:Word_Status1:ListB -0.18247    0.38715  -0.471  0.63741    
## Utterance_Type1:Word_Status1:ListC  0.59582    0.37022   1.609  0.10754    
## Utterance_Type1:Word_Status1:ListD -0.22777    0.39441  -0.577  0.56360    
## Utterance_Type1:Word_Status1:ListE  0.09824    0.37756   0.260  0.79471    
## Utterance_Type1:Word_Status1:ListF -0.17845    0.35635  -0.501  0.61655    
## Utterance_Type1:Word_Status1:ListG -0.10781    0.37138  -0.290  0.77158    
## Utterance_Type1:Word_Status1:ListH -0.02840    0.38383  -0.074  0.94102    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## maxfun < 10 * length(par)^2 is not recommended.

Position in Sentence

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Utterance_Type * Word_Status * target_position +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  12323.9  12397.7  -6151.9  12303.9    11885 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.1671 -0.5851 -0.3639  0.7263  9.6706 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.04471  0.2115  
##  subject_nr   (Intercept) 1.42968  1.1957  
## Number of obs: 11895, groups:  Sentence_Num, 160; subject_nr, 105
## 
## Fixed effects:
##                                               Estimate Std. Error z value
## (Intercept)                                   -0.76364    0.12073  -6.325
## Utterance_Type1                               -0.04480    0.04592  -0.976
## Word_Status1                                   1.47516    0.04722  31.237
## target_position1                               0.24538    0.05687   4.315
## Utterance_Type1:Word_Status1                  -0.13866    0.09184  -1.510
## Utterance_Type1:target_position1              -0.23055    0.09189  -2.509
## Word_Status1:target_position1                  0.51605    0.09215   5.600
## Utterance_Type1:Word_Status1:target_position1 -0.14080    0.18376  -0.766
##                                               Pr(>|z|)    
## (Intercept)                                   2.53e-10 ***
## Utterance_Type1                                 0.3292    
## Word_Status1                                   < 2e-16 ***
## target_position1                              1.60e-05 ***
## Utterance_Type1:Word_Status1                    0.1311    
## Utterance_Type1:target_position1                0.0121 *  
## Word_Status1:target_position1                 2.14e-08 ***
## Utterance_Type1:Word_Status1:target_position1   0.4436    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Utt_T1 Wrd_S1 trgt_1 Ut_T1:W_S1 U_T1:_ W_S1:_
## Uttrnc_Typ1  0.000                                              
## Word_Stats1  0.025 -0.003                                       
## targt_pstn1 -0.002 -0.001  0.014                                
## Utt_T1:W_S1  0.001  0.209 -0.009 -0.011                         
## Uttrn_T1:_1  0.001  0.011 -0.018 -0.006  0.006                  
## Wrd_Stt1:_1 -0.003 -0.014  0.024  0.171 -0.001     -0.002       
## U_T1:W_S1:_ -0.001  0.007 -0.003 -0.001  0.012      0.209 -0.007

Word Length (monosyllabic, bisyllabic)

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Utterance_Type * Word_Status * Syllable + (1 |  
##     subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  12364.7  12438.5  -6172.4  12344.7    11885 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.1068 -0.5882 -0.3611  0.7339 10.3279 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.05116  0.2262  
##  subject_nr   (Intercept) 1.41324  1.1888  
## Number of obs: 11895, groups:  Sentence_Num, 160; subject_nr, 105
## 
## Fixed effects:
##                                        Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                            -0.76348    0.12023  -6.350 2.15e-10 ***
## Utterance_Type1                        -0.04250    0.04579  -0.928   0.3533    
## Word_Status1                            1.47198    0.04708  31.264  < 2e-16 ***
## Syllable1                              -0.11468    0.05812  -1.973   0.0485 *  
## Utterance_Type1:Word_Status1           -0.13320    0.09159  -1.454   0.1459    
## Utterance_Type1:Syllable1              -0.09568    0.09156  -1.045   0.2960    
## Word_Status1:Syllable1                 -0.16103    0.09166  -1.757   0.0790 .  
## Utterance_Type1:Word_Status1:Syllable1  0.15167    0.18314   0.828   0.4076    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Utt_T1 Wrd_S1 Syllb1 Ut_T1:W_S1 U_T1:S W_S1:S
## Uttrnc_Typ1  0.000                                              
## Word_Stats1  0.024  0.000                                       
## Syllable1    0.002  0.005 -0.008                                
## Utt_T1:W_S1  0.001  0.204 -0.005 -0.006                         
## Uttrn_T1:S1  0.001  0.001 -0.009 -0.002 -0.006                  
## Wrd_Stt1:S1  0.000 -0.008 -0.003  0.160  0.007      0.003       
## U_T1:W_S1:S -0.002 -0.006  0.007  0.002  0.001      0.204 -0.003

ALINE Score

skipping ALINE score analysis for now, need to check stimuli file

Ultimately, will want to calculate based on ALINE value of cognate/noncognate in the sentence.

Participant Variables: Predictors

Correlation Individual Differences

Take with a grain of salt, these scores are the “grob” ones coded during the experiment and for some participants there are missing values (for unsure codes), but this should be fairly close to what the final coded values will be.

Phonological Awareness

Phoneme Manipulation task score

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: 
## detection ~ 1 + Utterance_Type * Word_Status * Phoneme_Manipulation.c +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  12197.6  12271.3  -6088.8  12177.6    11769 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2126 -0.5868 -0.3427  0.7491  9.1114 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.05288  0.230   
##  subject_nr   (Intercept) 1.37314  1.172   
## Number of obs: 11779, groups:  Sentence_Num, 160; subject_nr, 104
## 
## Fixed effects:
##                                                     Estimate Std. Error z value
## (Intercept)                                         -0.75672    0.11925  -6.346
## Utterance_Type1                                     -0.04315    0.04619  -0.934
## Word_Status1                                         1.46489    0.04747  30.860
## Phoneme_Manipulation.c                               0.02017    0.03630   0.556
## Utterance_Type1:Word_Status1                        -0.15022    0.09239  -1.626
## Utterance_Type1:Phoneme_Manipulation.c               0.01716    0.01431   1.199
## Word_Status1:Phoneme_Manipulation.c                  0.13766    0.01476   9.328
## Utterance_Type1:Word_Status1:Phoneme_Manipulation.c -0.05178    0.02861  -1.810
##                                                     Pr(>|z|)    
## (Intercept)                                         2.21e-10 ***
## Utterance_Type1                                       0.3502    
## Word_Status1                                         < 2e-16 ***
## Phoneme_Manipulation.c                                0.5785    
## Utterance_Type1:Word_Status1                          0.1040    
## Utterance_Type1:Phoneme_Manipulation.c                0.2307    
## Word_Status1:Phoneme_Manipulation.c                  < 2e-16 ***
## Utterance_Type1:Word_Status1:Phoneme_Manipulation.c   0.0703 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Utt_T1 Wrd_S1 Phn_M. Ut_T1:W_S1 U_T1:P W_S1:P
## Uttrnc_Typ1 -0.001                                              
## Word_Stats1  0.026  0.003                                       
## Phnm_Mnplt.  0.000 -0.004 -0.007                                
## Utt_T1:W_S1  0.002  0.203 -0.010  0.001                         
## Utt_T1:P_M. -0.004  0.006  0.009 -0.006 -0.045                  
## Wrd_S1:P_M. -0.008  0.008  0.028  0.031 -0.022      0.006       
## U_T1:W_S1:P  0.002 -0.046 -0.022  0.002  0.006      0.195 -0.034

Onset/Rhyme Score

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Utterance_Type * Word_Status * Onset_Rhyme.c +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  12076.4  12150.1  -6028.2  12056.4    11656 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.0033 -0.5845 -0.3393  0.7230  9.5337 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.05259  0.2293  
##  subject_nr   (Intercept) 1.38906  1.1786  
## Number of obs: 11666, groups:  Sentence_Num, 160; subject_nr, 103
## 
## Fixed effects:
##                                            Estimate Std. Error z value Pr(>|z|)
## (Intercept)                                -0.76521    0.12049  -6.351 2.14e-10
## Utterance_Type1                            -0.03148    0.04650  -0.677   0.4984
## Word_Status1                                1.46093    0.04771  30.620  < 2e-16
## Onset_Rhyme.c                              -0.02252    0.02894  -0.778   0.4365
## Utterance_Type1:Word_Status1               -0.13939    0.09299  -1.499   0.1339
## Utterance_Type1:Onset_Rhyme.c               0.01693    0.01136   1.491   0.1360
## Word_Status1:Onset_Rhyme.c                  0.11876    0.01167  10.177  < 2e-16
## Utterance_Type1:Word_Status1:Onset_Rhyme.c -0.04252    0.02273  -1.871   0.0614
##                                               
## (Intercept)                                ***
## Utterance_Type1                               
## Word_Status1                               ***
## Onset_Rhyme.c                                 
## Utterance_Type1:Word_Status1                  
## Utterance_Type1:Onset_Rhyme.c                 
## Word_Status1:Onset_Rhyme.c                 ***
## Utterance_Type1:Word_Status1:Onset_Rhyme.c .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Utt_T1 Wrd_S1 Ons_R. Ut_T1:W_S1 U_T1:O W_S1:O
## Uttrnc_Typ1 -0.001                                              
## Word_Stats1  0.024  0.006                                       
## Onst_Rhym.c  0.007 -0.004 -0.017                                
## Utt_T1:W_S1  0.002  0.196 -0.013  0.002                         
## Utt_T1:O_R. -0.004  0.033  0.011 -0.003 -0.062                  
## Wrd_S1:O_R. -0.016  0.010  0.050  0.023 -0.019      0.010       
## U_T1:W_S1:O  0.002 -0.065 -0.019  0.002  0.032      0.175 -0.019

Grade

Approximation for now for L1 knowledge

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Word_Type * Utterance_Type * DEMO_SchoolGrade +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  12245.5  12348.8  -6108.7  12217.5    11881 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.7283 -0.5733 -0.3475  0.7412  8.4572 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.0551   0.2347  
##  subject_nr   (Intercept) 1.3671   1.1692  
## Number of obs: 11895, groups:  Sentence_Num, 160; subject_nr, 105
## 
## Fixed effects:
##                                                                     Estimate
## (Intercept)                                                         -1.25523
## Word_TypeFollowing                                                   0.95857
## Word_TypeNoncognate                                                  0.03897
## Utterance_Type1                                                     -0.15799
## DEMO_SchoolGrade2nd grade/class                                     -0.53605
## Word_TypeFollowing:Utterance_Type1                                   0.07732
## Word_TypeNoncognate:Utterance_Type1                                  0.23374
## Word_TypeFollowing:DEMO_SchoolGrade2nd grade/class                   1.04864
## Word_TypeNoncognate:DEMO_SchoolGrade2nd grade/class                 -0.04727
## Utterance_Type1:DEMO_SchoolGrade2nd grade/class                      0.36746
## Word_TypeFollowing:Utterance_Type1:DEMO_SchoolGrade2nd grade/class  -0.42098
## Word_TypeNoncognate:Utterance_Type1:DEMO_SchoolGrade2nd grade/class -0.43540
##                                                                     Std. Error
## (Intercept)                                                            0.17097
## Word_TypeFollowing                                                     0.07571
## Word_TypeNoncognate                                                    0.07772
## Utterance_Type1                                                        0.10981
## DEMO_SchoolGrade2nd grade/class                                        0.24469
## Word_TypeFollowing:Utterance_Type1                                     0.15034
## Word_TypeNoncognate:Utterance_Type1                                    0.15580
## Word_TypeFollowing:DEMO_SchoolGrade2nd grade/class                     0.11111
## Word_TypeNoncognate:DEMO_SchoolGrade2nd grade/class                    0.11768
## Utterance_Type1:DEMO_SchoolGrade2nd grade/class                        0.16664
## Word_TypeFollowing:Utterance_Type1:DEMO_SchoolGrade2nd grade/class     0.21946
## Word_TypeNoncognate:Utterance_Type1:DEMO_SchoolGrade2nd grade/class    0.23631
##                                                                     z value
## (Intercept)                                                          -7.342
## Word_TypeFollowing                                                   12.661
## Word_TypeNoncognate                                                   0.501
## Utterance_Type1                                                      -1.439
## DEMO_SchoolGrade2nd grade/class                                      -2.191
## Word_TypeFollowing:Utterance_Type1                                    0.514
## Word_TypeNoncognate:Utterance_Type1                                   1.500
## Word_TypeFollowing:DEMO_SchoolGrade2nd grade/class                    9.438
## Word_TypeNoncognate:DEMO_SchoolGrade2nd grade/class                  -0.402
## Utterance_Type1:DEMO_SchoolGrade2nd grade/class                       2.205
## Word_TypeFollowing:Utterance_Type1:DEMO_SchoolGrade2nd grade/class   -1.918
## Word_TypeNoncognate:Utterance_Type1:DEMO_SchoolGrade2nd grade/class  -1.842
##                                                                     Pr(>|z|)
## (Intercept)                                                         2.11e-13
## Word_TypeFollowing                                                   < 2e-16
## Word_TypeNoncognate                                                   0.6161
## Utterance_Type1                                                       0.1502
## DEMO_SchoolGrade2nd grade/class                                       0.0285
## Word_TypeFollowing:Utterance_Type1                                    0.6070
## Word_TypeNoncognate:Utterance_Type1                                   0.1335
## Word_TypeFollowing:DEMO_SchoolGrade2nd grade/class                   < 2e-16
## Word_TypeNoncognate:DEMO_SchoolGrade2nd grade/class                   0.6879
## Utterance_Type1:DEMO_SchoolGrade2nd grade/class                       0.0274
## Word_TypeFollowing:Utterance_Type1:DEMO_SchoolGrade2nd grade/class    0.0551
## Word_TypeNoncognate:Utterance_Type1:DEMO_SchoolGrade2nd grade/class   0.0654
##                                                                        
## (Intercept)                                                         ***
## Word_TypeFollowing                                                  ***
## Word_TypeNoncognate                                                    
## Utterance_Type1                                                        
## DEMO_SchoolGrade2nd grade/class                                     *  
## Word_TypeFollowing:Utterance_Type1                                     
## Word_TypeNoncognate:Utterance_Type1                                    
## Word_TypeFollowing:DEMO_SchoolGrade2nd grade/class                  ***
## Word_TypeNoncognate:DEMO_SchoolGrade2nd grade/class                    
## Utterance_Type1:DEMO_SchoolGrade2nd grade/class                     *  
## Word_TypeFollowing:Utterance_Type1:DEMO_SchoolGrade2nd grade/class  .  
## Word_TypeNoncognate:Utterance_Type1:DEMO_SchoolGrade2nd grade/class .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_TF Wrd_TN Utt_T1 DEMO_g W_TF:U W_TN:U W_TF:Dg W_TN:Dg
## Wrd_TypFllw -0.245                                                          
## Wrd_TypNncg -0.227  0.513                                                   
## Uttrnc_Typ1  0.007 -0.015 -0.012                                            
## DEMO_ScG2g/ -0.689  0.170  0.159 -0.005                                     
## Wrd_TF:U_T1 -0.004  0.010  0.009 -0.732  0.003                              
## Wrd_TN:U_T1 -0.005  0.011  0.001 -0.706  0.003  0.517                       
## W_TF:DEMO_g  0.165 -0.678 -0.349  0.010 -0.269 -0.007 -0.007                
## W_TN:DEMO_g  0.150 -0.338 -0.660  0.008 -0.240 -0.006 -0.001  0.529         
## U_T1:DEMO_g -0.005  0.010  0.008 -0.661 -0.007  0.485  0.467  0.016   0.014 
## W_TF:U_T1:g  0.003 -0.008 -0.006  0.503  0.006 -0.687 -0.356 -0.014  -0.011 
## W_TN:U_T1:g  0.004 -0.008 -0.001  0.468  0.005 -0.343 -0.662 -0.011  -0.015 
##             U_T1:g W_TF:Ug
## Wrd_TypFllw               
## Wrd_TypNncg               
## Uttrnc_Typ1               
## DEMO_ScG2g/               
## Wrd_TF:U_T1               
## Wrd_TN:U_T1               
## W_TF:DEMO_g               
## W_TN:DEMO_g               
## U_T1:DEMO_g               
## W_TF:U_T1:g -0.761        
## W_TN:U_T1:g -0.708  0.539

Age

Approximation for now for L1 knowledge

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Utterance_Type * Word_Status * DEMO_AgeYears.c +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  11988.2  12061.7  -5984.1  11968.2    11539 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.5520 -0.5813 -0.3541  0.7439  9.0093 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.05709  0.2389  
##  subject_nr   (Intercept) 1.39209  1.1799  
## Number of obs: 11549, groups:  Sentence_Num, 160; subject_nr, 102
## 
## Fixed effects:
##                                              Estimate Std. Error z value
## (Intercept)                                  -0.75759    0.12128  -6.247
## Utterance_Type1                              -0.03972    0.04664  -0.852
## Word_Status1                                  1.43556    0.04777  30.052
## DEMO_AgeYears.c                               0.06396    0.15719   0.407
## Utterance_Type1:Word_Status1                 -0.12800    0.09326  -1.373
## Utterance_Type1:DEMO_AgeYears.c               0.04317    0.06104   0.707
## Word_Status1:DEMO_AgeYears.c                  0.61180    0.06219   9.838
## Utterance_Type1:Word_Status1:DEMO_AgeYears.c -0.10818    0.12187  -0.888
##                                              Pr(>|z|)    
## (Intercept)                                  4.19e-10 ***
## Utterance_Type1                                 0.394    
## Word_Status1                                  < 2e-16 ***
## DEMO_AgeYears.c                                 0.684    
## Utterance_Type1:Word_Status1                    0.170    
## Utterance_Type1:DEMO_AgeYears.c                 0.479    
## Word_Status1:DEMO_AgeYears.c                  < 2e-16 ***
## Utterance_Type1:Word_Status1:DEMO_AgeYears.c    0.375    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Utt_T1 Wrd_S1 DEMO_A Ut_T1:W_S1 U_T1:D W_S1:D
## Uttrnc_Typ1  0.000                                              
## Word_Stats1  0.026  0.003                                       
## DEMO_AgYrs.  0.001 -0.002 -0.010                                
## Utt_T1:W_S1  0.002  0.203 -0.008  0.000                         
## U_T1:DEMO_A -0.002 -0.001  0.002 -0.001 -0.061                  
## W_S1:DEMO_A -0.012  0.001  0.015  0.033 -0.011      0.006       
## U_T1:W_S1:D  0.000 -0.064 -0.009  0.002 -0.004      0.222 -0.005

German Production CLT

Another approximation for L1 knowledge

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Utterance_Type * Word_Status * Ger_CLT_Prod.c +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  12332.8  12406.7  -6156.4  12312.8    11885 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4779 -0.5801 -0.3599  0.7542  9.6653 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.05291  0.230   
##  subject_nr   (Intercept) 1.35175  1.163   
## Number of obs: 11895, groups:  Sentence_Num, 160; subject_nr, 105
## 
## Fixed effects:
##                                              Estimate Std. Error z value
## (Intercept)                                 -0.770110   0.117835  -6.535
## Utterance_Type1                             -0.045516   0.045856  -0.993
## Word_Status1                                 1.477137   0.047160  31.322
## Ger_CLT_Prod.c                              -0.106349   0.058672  -1.813
## Utterance_Type1:Word_Status1                -0.132143   0.091709  -1.441
## Utterance_Type1:Ger_CLT_Prod.c              -0.021417   0.022314  -0.960
## Word_Status1:Ger_CLT_Prod.c                  0.134876   0.022702   5.941
## Utterance_Type1:Word_Status1:Ger_CLT_Prod.c -0.006684   0.044642  -0.150
##                                             Pr(>|z|)    
## (Intercept)                                 6.34e-11 ***
## Utterance_Type1                               0.3209    
## Word_Status1                                 < 2e-16 ***
## Ger_CLT_Prod.c                                0.0699 .  
## Utterance_Type1:Word_Status1                  0.1496    
## Utterance_Type1:Ger_CLT_Prod.c                0.3371    
## Word_Status1:Ger_CLT_Prod.c                 2.83e-09 ***
## Utterance_Type1:Word_Status1:Ger_CLT_Prod.c   0.8810    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Utt_T1 Wrd_S1 G_CLT_ Ut_T1:W_S1 U_T1:G W_S1:G
## Uttrnc_Typ1  0.001                                              
## Word_Stats1  0.023 -0.002                                       
## Gr_CLT_Prd.  0.009  0.000 -0.012                                
## Utt_T1:W_S1  0.001  0.197 -0.005 -0.001                         
## U_T1:G_CLT_  0.000  0.023 -0.010  0.000 -0.033                  
## W_S1:G_CLT_ -0.010 -0.010  0.037  0.043 -0.003      0.014       
## U_T1:W_S1:G -0.001 -0.034 -0.003  0.003  0.022      0.266 -0.001

Participant Variables: Exploratory

Language Background (Monolingual, Bilingual)

## # A tibble: 2 × 2
##   LANG_Bilingual total_participants
##   <fct>                       <int>
## 1 monolingual                    76
## 2 bilingual                      29

## # A tibble: 1 × 8
##   .y.           group1      group2       n1    n2 statistic    df      p
## * <chr>         <chr>       <chr>     <int> <int>     <dbl> <dbl>  <dbl>
## 1 DEMO_AgeYears monolingual bilingual    74    28      2.09  52.8 0.0416

## # A tibble: 1 × 8
##   .y.       group1      group2       n1    n2 statistic    df     p
## * <chr>     <chr>       <chr>     <int> <int>     <dbl> <dbl> <dbl>
## 1 RAVENS_IQ monolingual bilingual    74    28    -0.890  49.2 0.378

## # A tibble: 1 × 8
##   .y.              group1      group2       n1    n2 statistic    df     p
## * <chr>            <chr>       <chr>     <int> <int>     <dbl> <dbl> <dbl>
## 1 WM_BackDigitSpan monolingual bilingual    76    29     0.536  43.0 0.594

## # A tibble: 1 × 8
##   .y.          group1      group2       n1    n2 statistic    df      p
## * <chr>        <chr>       <chr>     <int> <int>     <dbl> <dbl>  <dbl>
## 1 Ger_CLT_Prod monolingual bilingual    76    29      2.50  34.1 0.0174

## # A tibble: 1 × 8
##   .y.                group1      group2       n1    n2 statistic    df     p
## * <chr>              <chr>       <chr>     <int> <int>     <dbl> <dbl> <dbl>
## 1 NWRT_total_correct monolingual bilingual    76    29     -1.63  48.7  0.11

## # A tibble: 1 × 8
##   .y.                  group1      group2       n1    n2 statistic    df     p
## * <chr>                <chr>       <chr>     <int> <int>     <dbl> <dbl> <dbl>
## 1 Phoneme_Manipulation monolingual bilingual    75    29      1.44  53.9 0.155

## # A tibble: 1 × 8
##   .y.         group1      group2       n1    n2 statistic    df     p
## * <chr>       <chr>       <chr>     <int> <int>     <dbl> <dbl> <dbl>
## 1 Onset_Rhyme monolingual bilingual    75    28    -0.733  57.2 0.466

## # A tibble: 1 × 8
##   .y.           group1      group2       n1    n2 statistic    df      p
## * <chr>         <chr>       <chr>     <int> <int>     <dbl> <dbl>  <dbl>
## 1 ENG_CLT_score monolingual bilingual    75    29     -2.68  42.0 0.0106

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Utterance_Type * Word_Status * LANG_Bilingual +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  12368.1  12441.9  -6174.1  12348.1    11885 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.0986 -0.5876 -0.3629  0.7354  9.7255 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.05347  0.2312  
##  subject_nr   (Intercept) 1.36093  1.1666  
## Number of obs: 11895, groups:  Sentence_Num, 160; subject_nr, 105
## 
## Fixed effects:
##                                              Estimate Std. Error z value
## (Intercept)                                  -0.65880    0.13206  -4.989
## Utterance_Type1                              -0.02546    0.05130  -0.496
## Word_Status1                                  1.46326    0.05275  27.739
## LANG_Bilingual1                              -0.46037    0.26148  -1.761
## Utterance_Type1:Word_Status1                 -0.07779    0.10263  -0.758
## Utterance_Type1:LANG_Bilingual1              -0.06572    0.10270  -0.640
## Word_Status1:LANG_Bilingual1                  0.03923    0.10501   0.374
## Utterance_Type1:Word_Status1:LANG_Bilingual1 -0.24524    0.20528  -1.195
##                                              Pr(>|z|)    
## (Intercept)                                  6.08e-07 ***
## Utterance_Type1                                0.6197    
## Word_Status1                                  < 2e-16 ***
## LANG_Bilingual1                                0.0783 .  
## Utterance_Type1:Word_Status1                   0.4485    
## Utterance_Type1:LANG_Bilingual1                0.5222    
## Word_Status1:LANG_Bilingual1                   0.7087    
## Utterance_Type1:Word_Status1:LANG_Bilingual1   0.2322    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Utt_T1 Wrd_S1 LANG_B Ut_T1:W_S1 U_T1:L W_S1:L
## Uttrnc_Typ1  0.002                                              
## Word_Stats1  0.035  0.008                                       
## LANG_Blngl1 -0.446 -0.002 -0.026                                
## Utt_T1:W_S1  0.002  0.242  0.008 -0.001                         
## U_T1:LANG_B -0.002 -0.450 -0.009  0.002 -0.142                  
## W_S1:LANG_B -0.025 -0.010 -0.451  0.039 -0.014      0.009       
## U_T1:W_S1:L -0.001 -0.144 -0.014  0.002 -0.451      0.240  0.008

English CLT

Overall performance

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Utterance_Type * Word_Status * ENG_CLT_score.c +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  12110.5  12184.3  -6045.3  12090.5    11772 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.0206 -0.5800 -0.3371  0.7360 13.7498 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.05683  0.2384  
##  subject_nr   (Intercept) 1.40963  1.1873  
## Number of obs: 11782, groups:  Sentence_Num, 160; subject_nr, 104
## 
## Fixed effects:
##                                               Estimate Std. Error z value
## (Intercept)                                  -0.769246   0.120862  -6.365
## Utterance_Type1                              -0.049050   0.046472  -1.055
## Word_Status1                                  1.455699   0.047810  30.448
## ENG_CLT_score.c                               0.007433   0.008709   0.853
## Utterance_Type1:Word_Status1                 -0.157238   0.092952  -1.692
## Utterance_Type1:ENG_CLT_score.c               0.005388   0.003472   1.552
## Word_Status1:ENG_CLT_score.c                  0.043831   0.003590  12.209
## Utterance_Type1:Word_Status1:ENG_CLT_score.c  0.004348   0.006956   0.625
##                                              Pr(>|z|)    
## (Intercept)                                  1.96e-10 ***
## Utterance_Type1                                0.2912    
## Word_Status1                                  < 2e-16 ***
## ENG_CLT_score.c                                0.3934    
## Utterance_Type1:Word_Status1                   0.0907 .  
## Utterance_Type1:ENG_CLT_score.c                0.1207    
## Word_Status1:ENG_CLT_score.c                  < 2e-16 ***
## Utterance_Type1:Word_Status1:ENG_CLT_score.c   0.5320    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Utt_T1 Wrd_S1 ENG_CL Ut_T1:W_S1 U_T1:E W_S1:E
## Uttrnc_Typ1  0.001                                              
## Word_Stats1  0.026  0.004                                       
## ENG_CLT_sc.  0.009 -0.001 -0.009                                
## Utt_T1:W_S1  0.002  0.207 -0.004  0.001                         
## U_T1:ENG_CL -0.001  0.022  0.008  0.000 -0.043                  
## W_S1:ENG_CL -0.010  0.007  0.053  0.027  0.000      0.016       
## U_T1:W_S1:E  0.001 -0.038 -0.001  0.004  0.025      0.187 -0.005

Specific performance on cognate words

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Utterance_Type * Word_Status * ENG_CLT_cognates.c +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  12172.0  12245.7  -6076.0  12152.0    11772 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.0749 -0.5790 -0.3502  0.7477 12.3599 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.05552  0.2356  
##  subject_nr   (Intercept) 1.42037  1.1918  
## Number of obs: 11782, groups:  Sentence_Num, 160; subject_nr, 104
## 
## Fixed effects:
##                                                  Estimate Std. Error z value
## (Intercept)                                     -0.767904   0.121241  -6.334
## Utterance_Type1                                 -0.048783   0.046272  -1.054
## Word_Status1                                     1.456829   0.047572  30.624
## ENG_CLT_cognates.c                               0.004603   0.007194   0.640
## Utterance_Type1:Word_Status1                    -0.159232   0.092553  -1.720
## Utterance_Type1:ENG_CLT_cognates.c               0.004474   0.002766   1.618
## Word_Status1:ENG_CLT_cognates.c                  0.027399   0.002823   9.707
## Utterance_Type1:Word_Status1:ENG_CLT_cognates.c  0.002366   0.005544   0.427
##                                                 Pr(>|z|)    
## (Intercept)                                     2.39e-10 ***
## Utterance_Type1                                   0.2918    
## Word_Status1                                     < 2e-16 ***
## ENG_CLT_cognates.c                                0.5222    
## Utterance_Type1:Word_Status1                      0.0854 .  
## Utterance_Type1:ENG_CLT_cognates.c                0.1057    
## Word_Status1:ENG_CLT_cognates.c                  < 2e-16 ***
## Utterance_Type1:Word_Status1:ENG_CLT_cognates.c   0.6695    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Utt_T1 Wrd_S1 ENG_CL Ut_T1:W_S1 U_T1:E W_S1:E
## Uttrnc_Typ1  0.000                                              
## Word_Stats1  0.026  0.003                                       
## ENG_CLT_cg.  0.007 -0.002 -0.007                                
## Utt_T1:W_S1  0.002  0.205 -0.006  0.000                         
## U_T1:ENG_CL -0.002  0.011  0.006  0.002 -0.038                  
## W_S1:ENG_CL -0.008  0.005  0.032  0.039 -0.003      0.012       
## U_T1:W_S1:E  0.000 -0.034 -0.003  0.003  0.013      0.252  0.002

Difference score (Prop cognates correct – prop noncognates correct )

difference score where 0 is similar performance between cognates and noncognates

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: 
## detection ~ 1 + Utterance_Type * Word_Status * English_CLT_diffscore.c +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  12267.6  12341.3  -6123.8  12247.6    11772 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2559 -0.5909 -0.3596  0.7410 10.0943 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.0555   0.2356  
##  subject_nr   (Intercept) 1.4234   1.1930  
## Number of obs: 11782, groups:  Sentence_Num, 160; subject_nr, 104
## 
## Fixed effects:
##                                                       Estimate Std. Error
## (Intercept)                                          -0.762797   0.121320
## Utterance_Type1                                      -0.049822   0.045996
## Word_Status1                                          1.458148   0.047274
## English_CLT_diffscore.c                              -0.001682   0.007186
## Utterance_Type1:Word_Status1                         -0.156766   0.091992
## Utterance_Type1:English_CLT_diffscore.c               0.001467   0.002743
## Word_Status1:English_CLT_diffscore.c                 -0.002320   0.002807
## Utterance_Type1:Word_Status1:English_CLT_diffscore.c -0.002123   0.005488
##                                                      z value Pr(>|z|)    
## (Intercept)                                           -6.288 3.23e-10 ***
## Utterance_Type1                                       -1.083   0.2787    
## Word_Status1                                          30.845  < 2e-16 ***
## English_CLT_diffscore.c                               -0.234   0.8150    
## Utterance_Type1:Word_Status1                          -1.704   0.0884 .  
## Utterance_Type1:English_CLT_diffscore.c                0.535   0.5927    
## Word_Status1:English_CLT_diffscore.c                  -0.826   0.4086    
## Utterance_Type1:Word_Status1:English_CLT_diffscore.c  -0.387   0.6988    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Utt_T1 Wrd_S1 E_CLT_ Ut_T1:W_S1 U_T1:E W_S1:E
## Uttrnc_Typ1  0.000                                              
## Word_Stats1  0.025  0.000                                       
## Engls_CLT_.  0.001 -0.001 -0.001                                
## Utt_T1:W_S1  0.002  0.206 -0.007  0.001                         
## U_T1:E_CLT_ -0.001  0.009  0.006  0.000 -0.009                  
## W_S1:E_CLT_ -0.002  0.007  0.002  0.029 -0.004      0.000       
## U_T1:W_S1:E  0.001 -0.009 -0.003  0.001  0.009      0.209 -0.006

Ravens Matricies IQ score

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Utterance_Type * Word_Status * RAVENS_IQ.c +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  12050.5  12124.1  -6015.3  12030.5    11539 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2259 -0.5838 -0.3657  0.7360  9.4216 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.05384  0.232   
##  subject_nr   (Intercept) 1.38643  1.177   
## Number of obs: 11549, groups:  Sentence_Num, 160; subject_nr, 102
## 
## Fixed effects:
##                                           Estimate Std. Error z value Pr(>|z|)
## (Intercept)                              -0.756932   0.120972  -6.257 3.92e-10
## Utterance_Type1                          -0.039408   0.046534  -0.847   0.3971
## Word_Status1                              1.464440   0.047876  30.588  < 2e-16
## RAVENS_IQ.c                              -0.020978   0.010793  -1.944   0.0519
## Utterance_Type1:Word_Status1             -0.118204   0.093088  -1.270   0.2042
## Utterance_Type1:RAVENS_IQ.c               0.006710   0.004447   1.509   0.1313
## Word_Status1:RAVENS_IQ.c                  0.024426   0.004601   5.309 1.10e-07
## Utterance_Type1:Word_Status1:RAVENS_IQ.c  0.013114   0.008833   1.485   0.1376
##                                             
## (Intercept)                              ***
## Utterance_Type1                             
## Word_Status1                             ***
## RAVENS_IQ.c                              .  
## Utterance_Type1:Word_Status1                
## Utterance_Type1:RAVENS_IQ.c                 
## Word_Status1:RAVENS_IQ.c                 ***
## Utterance_Type1:Word_Status1:RAVENS_IQ.c    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Utt_T1 Wrd_S1 RAVENS Ut_T1:W_S1 U_T1:R W_S1:R
## Uttrnc_Typ1  0.000                                              
## Word_Stats1  0.023  0.001                                       
## RAVENS_IQ.c  0.010 -0.002 -0.018                                
## Utt_T1:W_S1  0.001  0.200 -0.004 -0.001                         
## U_T1:RAVENS -0.002  0.064  0.003 -0.004 -0.041                  
## W_S1:RAVENS -0.017  0.001  0.086  0.001  0.000      0.003       
## U_T1:W_S1:R -0.001 -0.036  0.000 -0.003  0.067      0.148 -0.002

Backwards Digit Span

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Utterance_Type * Word_Status * WM_BackDigitSpan.c +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  12253.8  12327.6  -6116.9  12233.8    11885 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.1479 -0.5818 -0.3429  0.7359  9.7365 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.05376  0.2319  
##  subject_nr   (Intercept) 1.32194  1.1498  
## Number of obs: 11895, groups:  Sentence_Num, 160; subject_nr, 105
## 
## Fixed effects:
##                                                 Estimate Std. Error z value
## (Intercept)                                     -0.77505    0.11665  -6.644
## Utterance_Type1                                 -0.04125    0.04606  -0.896
## Word_Status1                                     1.45274    0.04721  30.772
## WM_BackDigitSpan.c                              -0.13702    0.13209  -1.037
## Utterance_Type1:Word_Status1                    -0.14520    0.09212  -1.576
## Utterance_Type1:WM_BackDigitSpan.c               0.04638    0.05373   0.863
## Word_Status1:WM_BackDigitSpan.c                  0.58376    0.05479  10.655
## Utterance_Type1:Word_Status1:WM_BackDigitSpan.c -0.11825    0.10746  -1.100
##                                                 Pr(>|z|)    
## (Intercept)                                     3.05e-11 ***
## Utterance_Type1                                    0.370    
## Word_Status1                                     < 2e-16 ***
## WM_BackDigitSpan.c                                 0.300    
## Utterance_Type1:Word_Status1                       0.115    
## Utterance_Type1:WM_BackDigitSpan.c                 0.388    
## Word_Status1:WM_BackDigitSpan.c                  < 2e-16 ***
## Utterance_Type1:Word_Status1:WM_BackDigitSpan.c    0.271    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Utt_T1 Wrd_S1 WM_BDS Ut_T1:W_S1 U_T1:WM W_S1:W
## Uttrnc_Typ1  0.000                                               
## Word_Stats1  0.024  0.004                                        
## WM_BckDgtS.  0.002 -0.003 -0.012                                 
## Utt_T1:W_S1  0.002  0.193 -0.009  0.000                          
## U_T1:WM_BDS -0.003 -0.007  0.000 -0.001 -0.068                   
## W_S1:WM_BDS -0.011 -0.001  0.006  0.036 -0.016      0.028        
## U_T1:W_S1:W  0.000 -0.068 -0.016  0.005 -0.007      0.213   0.001

NonWord Repetition Task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Utterance_Type * Word_Status * NWRT_total_correct.c +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  12294.9  12368.8  -6137.5  12274.9    11885 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6025 -0.5804 -0.3439  0.7311 10.3639 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.0543   0.233   
##  subject_nr   (Intercept) 1.3990   1.183   
## Number of obs: 11895, groups:  Sentence_Num, 160; subject_nr, 105
## 
## Fixed effects:
##                                                    Estimate Std. Error z value
## (Intercept)                                       -0.767005   0.119771  -6.404
## Utterance_Type1                                   -0.044339   0.045975  -0.964
## Word_Status1                                       1.467952   0.047341  31.008
## NWRT_total_correct.c                               0.026924   0.055415   0.486
## Utterance_Type1:Word_Status1                      -0.141261   0.091964  -1.536
## Utterance_Type1:NWRT_total_correct.c               0.027572   0.021520   1.281
## Word_Status1:NWRT_total_correct.c                  0.191839   0.021957   8.737
## Utterance_Type1:Word_Status1:NWRT_total_correct.c  0.004353   0.043152   0.101
##                                                   Pr(>|z|)    
## (Intercept)                                       1.51e-10 ***
## Utterance_Type1                                      0.335    
## Word_Status1                                       < 2e-16 ***
## NWRT_total_correct.c                                 0.627    
## Utterance_Type1:Word_Status1                         0.125    
## Utterance_Type1:NWRT_total_correct.c                 0.200    
## Word_Status1:NWRT_total_correct.c                  < 2e-16 ***
## Utterance_Type1:Word_Status1:NWRT_total_correct.c    0.920    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Utt_T1 Wrd_S1 NWRT__ Ut_T1:W_S1 U_T1:N W_S1:N
## Uttrnc_Typ1  0.000                                              
## Word_Stats1  0.025  0.003                                       
## NWRT_ttl_c.  0.005 -0.002 -0.006                                
## Utt_T1:W_S1  0.002  0.204 -0.005 -0.001                         
## U_T1:NWRT__ -0.002  0.004  0.000  0.002 -0.024                  
## W_S1:NWRT__ -0.007 -0.002  0.027  0.039 -0.009      0.011       
## U_T1:W_S1:N -0.001 -0.025 -0.007  0.003  0.005      0.250  0.007

Correspondance between Cognate/Noncognate target and Cognate/Noncognate following

d’ for Cognate/Noncognate target

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dprime ~ 1 + Utterance_Type + (1 | subject_nr)
##    Data: Lseg_target
## 
## REML criterion at convergence: 425.3
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -1.69864 -0.57903 -0.02036  0.56380  2.03878 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev.
##  subject_nr (Intercept) 0.4419   0.6648  
##  Residual               0.1765   0.4201  
## Number of obs: 210, groups:  subject_nr, 105
## 
## Fixed effects:
##                  Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)       0.70327    0.07106 103.99999   9.897   <2e-16 ***
## Utterance_Type1  -0.06479    0.05798 104.00000  -1.117    0.266    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## Uttrnc_Typ1 0.000

d’ for Cognate/Noncognate following

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dprime ~ 1 + Utterance_Type + (1 | subject_nr)
##    Data: Lseg_following
## 
## REML criterion at convergence: 428
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.36026 -0.46667  0.00498  0.48443  2.36565 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev.
##  subject_nr (Intercept) 0.5264   0.7255  
##  Residual               0.1586   0.3983  
## Number of obs: 210, groups:  subject_nr, 105
## 
## Fixed effects:
##                  Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)       0.86310    0.07595 103.99999  11.364   <2e-16 ***
## Utterance_Type1  -0.10620    0.05497 104.00000  -1.932   0.0561 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## Uttrnc_Typ1 0.000

Comparison in performance

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dprime ~ 1 + Utterance_Type * Target_Type + (1 | subject_nr)
##    Data: d_comp
## 
## REML criterion at convergence: 723.9
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.81630 -0.60709  0.00752  0.63615  3.03883 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev.
##  subject_nr (Intercept) 0.4813   0.6938  
##  Residual               0.1704   0.4127  
## Number of obs: 420, groups:  subject_nr, 105
## 
## Fixed effects:
##                                             Estimate Std. Error        df
## (Intercept)                                  0.81000    0.07878 157.77884
## Utterance_TypeNoncognate                     0.10620    0.05696 312.00000
## Target_TypeTarget                           -0.13912    0.05696 312.00000
## Utterance_TypeNoncognate:Target_TypeTarget  -0.04142    0.08056 312.00000
##                                            t value Pr(>|t|)    
## (Intercept)                                 10.282   <2e-16 ***
## Utterance_TypeNoncognate                     1.864   0.0632 .  
## Target_TypeTarget                           -2.442   0.0151 *  
## Utterance_TypeNoncognate:Target_TypeTarget  -0.514   0.6075    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Utt_TN Trg_TT
## Uttrnc_TypN -0.362              
## Trgt_TypTrg -0.362  0.500       
## Utt_TN:T_TT  0.256 -0.707 -0.707

Correlation in performance