Regressions
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: correct_resp ~ 1 + (1 | pid) + (1 | trial)
## Data: df.trial %>% filter(task == "HI")
##
## AIC BIC logLik deviance df.resid
## 1036.7 1051.3 -515.4 1030.7 956
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8235 0.3019 0.4430 0.5441 1.3905
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.4888 0.6991
## trial (Intercept) 0.1889 0.4346
## Number of obs: 959, groups: pid, 120; trial, 8
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.3043 0.1887 6.913 4.75e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: correct_resp ~ condition + trial_type + age_zscored + (1 | pid)
## Data: df.trial %>% filter(task == "HI")
##
## AIC BIC logLik deviance df.resid
## 858.0 887.2 -423.0 846.0 953
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.0298 0.1079 0.2294 0.4829 2.7189
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.9306 0.9647
## Number of obs: 959, groups: pid, 120
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.92324 0.29015 10.075 <2e-16 ***
## conditionHomogeneous -0.06984 0.30896 -0.226 0.8212
## conditionHeterogeneous 0.49626 0.31754 1.563 0.1181
## trial_typeimplicature -2.64926 0.23245 -11.397 <2e-16 ***
## age_zscored 0.27898 0.13054 2.137 0.0326 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnHm cndtnHt trl_ty
## condtnHmgns -0.539
## cndtnHtrgns -0.471 0.490
## trl_typmplc -0.653 0.010 -0.059
## age_zscored 0.018 0.101 0.072 -0.077
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: correct_resp
## Chisq Df Pr(>Chisq)
## (Intercept) 101.5028 1 < 2e-16 ***
## condition 3.7366 2 0.15439
## trial_type 129.8957 1 < 2e-16 ***
## age_zscored 4.5670 1 0.03259 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: correct_resp ~ condition * trial_type + age_zscored + (1 | pid)
## Data: df.trial %>% filter(task == "HI")
##
## AIC BIC logLik deviance df.resid
## 859.1 898.0 -421.5 843.1 951
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.4396 0.1123 0.2250 0.4668 2.7396
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.9369 0.9679
## Number of obs: 959, groups: pid, 120
##
## Fixed effects:
## Estimate Std. Error z value
## (Intercept) 3.2947 0.4168 7.905
## conditionHomogeneous -0.4263 0.5367 -0.794
## conditionHeterogeneous -0.2648 0.5471 -0.484
## trial_typeimplicature -3.1124 0.4229 -7.360
## age_zscored 0.2799 0.1310 2.137
## conditionHomogeneous:trial_typeimplicature 0.4443 0.5488 0.810
## conditionHeterogeneous:trial_typeimplicature 0.9575 0.5598 1.710
## Pr(>|z|)
## (Intercept) 2.68e-15 ***
## conditionHomogeneous 0.4270
## conditionHeterogeneous 0.6284
## trial_typeimplicature 1.84e-13 ***
## age_zscored 0.0326 *
## conditionHomogeneous:trial_typeimplicature 0.4182
## conditionHeterogeneous:trial_typeimplicature 0.0872 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnHm cndtnHt trl_ty ag_zsc cndtnHm:_
## condtnHmgns -0.739
## cndtnHtrgns -0.721 0.567
## trl_typmplc -0.845 0.621 0.607
## age_zscored 0.009 0.071 0.036 -0.039
## cndtnHmgn:_ 0.618 -0.813 -0.473 -0.740 -0.015
## cndtnHtrg:_ 0.616 -0.472 -0.813 -0.735 0.008 0.561
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: correct_resp
## Chisq Df Pr(>Chisq)
## (Intercept) 62.4890 1 2.679e-15 ***
## condition 0.6327 2 0.72880
## trial_type 54.1634 1 1.845e-13 ***
## age_zscored 4.5676 1 0.03258 *
## condition:trial_type 2.9584 2 0.22782
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Data: df.trial %>% filter(task == "HI")
## Models:
## fit.task: correct_resp ~ condition + trial_type + age_zscored + (1 | pid)
## fit.task_age: correct_resp ~ condition * trial_type + age_zscored + (1 | pid)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## fit.task 6 858.05 887.24 -423.02 846.05
## fit.task_age 8 859.08 898.01 -421.54 843.08 2.9697 2 0.2265
## $emmeans
## condition trial_type emmean SE df asymp.LCL asymp.UCL
## Singular control 3.295 0.417 Inf 2.478 4.112
## Homogeneous control 2.868 0.362 Inf 2.159 3.578
## Heterogeneous control 3.030 0.380 Inf 2.285 3.775
## Singular implicature 0.182 0.234 Inf -0.275 0.640
## Homogeneous implicature 0.200 0.235 Inf -0.260 0.661
## Heterogeneous implicature 0.875 0.247 Inf 0.391 1.359
##
## Results are given on the logit (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio
## Singular control - Homogeneous control 0.4263 0.537 Inf 0.794
## Singular control - Heterogeneous control 0.2648 0.547 Inf 0.484
## Singular control - Singular implicature 3.1124 0.423 Inf 7.360
## Singular control - Homogeneous implicature 3.0945 0.477 Inf 6.489
## Singular control - Heterogeneous implicature 2.4197 0.476 Inf 5.083
## Homogeneous control - Heterogeneous control -0.1615 0.504 Inf -0.320
## Homogeneous control - Singular implicature 2.6861 0.430 Inf 6.248
## Homogeneous control - Homogeneous implicature 2.6681 0.369 Inf 7.222
## Homogeneous control - Heterogeneous implicature 1.9934 0.428 Inf 4.661
## Heterogeneous control - Singular implicature 2.8476 0.444 Inf 6.411
## Heterogeneous control - Homogeneous implicature 2.8297 0.444 Inf 6.372
## Heterogeneous control - Heterogeneous implicature 2.1549 0.380 Inf 5.675
## Singular implicature - Homogeneous implicature -0.0179 0.332 Inf -0.054
## Singular implicature - Heterogeneous implicature -0.6927 0.339 Inf -2.043
## Homogeneous implicature - Heterogeneous implicature -0.6747 0.339 Inf -1.988
## p.value
## 0.9686
## 0.9967
## <.0001
## <.0001
## <.0001
## 0.9996
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## 1.0000
## 0.3177
## 0.3489
##
## Results are given on the log odds ratio (not the response) scale.
## P value adjustment: tukey method for comparing a family of 6 estimates
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: correct_resp ~ condition * trial_type * age_zscored + (1 | pid)
## Data: df.trial %>% filter(task == "HI")
##
## AIC BIC logLik deviance df.resid
## 867.9 931.1 -420.9 841.9 946
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.5700 0.1062 0.2302 0.4745 2.7245
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.9258 0.9622
## Number of obs: 959, groups: pid, 120
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 3.30207 0.41802
## conditionHomogeneous -0.39910 0.55336
## conditionHeterogeneous -0.29867 0.54654
## trial_typeimplicature -3.10010 0.42384
## age_zscored 0.27878 0.46324
## conditionHomogeneous:trial_typeimplicature 0.40407 0.56619
## conditionHeterogeneous:trial_typeimplicature 0.98157 0.56083
## conditionHomogeneous:age_zscored 0.12716 0.58238
## conditionHeterogeneous:age_zscored -0.21473 0.60815
## trial_typeimplicature:age_zscored -0.13455 0.47024
## conditionHomogeneous:trial_typeimplicature:age_zscored 0.05497 0.59388
## conditionHeterogeneous:trial_typeimplicature:age_zscored 0.47878 0.62415
## z value Pr(>|z|)
## (Intercept) 7.899 2.80e-15 ***
## conditionHomogeneous -0.721 0.4708
## conditionHeterogeneous -0.546 0.5847
## trial_typeimplicature -7.314 2.59e-13 ***
## age_zscored 0.602 0.5473
## conditionHomogeneous:trial_typeimplicature 0.714 0.4754
## conditionHeterogeneous:trial_typeimplicature 1.750 0.0801 .
## conditionHomogeneous:age_zscored 0.218 0.8272
## conditionHeterogeneous:age_zscored -0.353 0.7240
## trial_typeimplicature:age_zscored -0.286 0.7748
## conditionHomogeneous:trial_typeimplicature:age_zscored 0.093 0.9263
## conditionHeterogeneous:trial_typeimplicature:age_zscored 0.767 0.4430
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnHm cndtnHt trl_ty ag_zsc cndtnHmgns:t_
## condtnHmgns -0.721
## cndtnHtrgns -0.722 0.550
## trl_typmplc -0.846 0.609 0.610
## age_zscored 0.061 -0.042 -0.042 -0.073
## cndtnHmgns:t_ 0.603 -0.825 -0.460 -0.722 0.052
## cndtnHtrgns:t_ 0.616 -0.459 -0.813 -0.736 0.053 0.545
## cndtnHmgns:g_ -0.051 0.181 0.033 0.060 -0.796 -0.175
## cndtnHtrgns:g_ -0.042 0.032 0.047 0.052 -0.761 -0.039
## trl_typmp:_ -0.072 0.052 0.052 0.053 -0.874 -0.038
## cndtnHm:_:_ 0.064 -0.175 -0.041 -0.048 0.693 0.170
## cndtnHt:_:_ 0.057 -0.039 -0.049 -0.042 0.659 0.029
## cndtnHtrgns:t_ cndtnHmgns:g_ cndtnHtrgns:g_ trl_:_ cndtnHm:_:_
## condtnHmgns
## cndtnHtrgns
## trl_typmplc
## age_zscored
## cndtnHmgns:t_
## cndtnHtrgns:t_
## cndtnHmgns:g_ -0.042
## cndtnHtrgns:g_ -0.050 0.605
## trl_typmp:_ -0.039 0.695 0.665
## cndtnHm:_:_ 0.032 -0.847 -0.527 -0.792
## cndtnHt:_:_ 0.052 -0.524 -0.835 -0.754 0.597
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0327269 (tol = 0.002, component 1)
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: correct_resp
## Chisq Df Pr(>Chisq)
## (Intercept) 62.3997 1 2.804e-15 ***
## condition 0.5524 2 0.7587
## trial_type 53.5003 1 2.586e-13 ***
## age_zscored 0.3622 1 0.5473
## condition:trial_type 3.1458 2 0.2074
## condition:age_zscored 0.4194 2 0.8108
## trial_type:age_zscored 0.0819 1 0.7748
## condition:trial_type:age_zscored 0.7964 2 0.6715
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Data: df.trial %>% filter(task == "HI")
## Models:
## fit.task: correct_resp ~ condition + trial_type + age_zscored + (1 | pid)
## fit.task_age_int: correct_resp ~ condition * trial_type * age_zscored + (1 | pid)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## fit.task 6 858.05 887.24 -423.02 846.05
## fit.task_age_int 13 867.87 931.13 -420.94 841.87 4.1738 7 0.7596
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: correct_resp ~ condition * trial_type + tom_zscored + (1 | pid)
## Data: df.trial %>% filter(task == "HI")
##
## AIC BIC logLik deviance df.resid
## 860.5 899.5 -422.3 844.5 951
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.6489 0.1085 0.2348 0.4595 2.7127
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.9707 0.9852
## Number of obs: 959, groups: pid, 120
##
## Fixed effects:
## Estimate Std. Error z value
## (Intercept) 3.3059 0.4182 7.905
## conditionHomogeneous -0.5642 0.5378 -1.049
## conditionHeterogeneous -0.1525 0.5555 -0.275
## trial_typeimplicature -3.1146 0.4233 -7.359
## tom_zscored 0.2469 0.1397 1.767
## conditionHomogeneous:trial_typeimplicature 0.4514 0.5486 0.823
## conditionHeterogeneous:trial_typeimplicature 0.9558 0.5602 1.706
## Pr(>|z|)
## (Intercept) 2.68e-15 ***
## conditionHomogeneous 0.2942
## conditionHeterogeneous 0.7836
## trial_typeimplicature 1.86e-13 ***
## tom_zscored 0.0772 .
## conditionHomogeneous:trial_typeimplicature 0.4106
## conditionHeterogeneous:trial_typeimplicature 0.0880 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnHm cndtnHt trl_ty tm_zsc cndtnHm:_
## condtnHmgns -0.741
## cndtnHtrgns -0.711 0.549
## trl_typmplc -0.844 0.625 0.594
## tom_zscored 0.007 -0.062 0.155 -0.035
## cndtnHmgn:_ 0.617 -0.810 -0.468 -0.741 -0.008
## cndtnHtrg:_ 0.614 -0.472 -0.800 -0.735 0.006 0.561
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: correct_resp
## Chisq Df Pr(>Chisq)
## (Intercept) 62.4866 1 2.683e-15 ***
## condition 1.2303 2 0.5406
## trial_type 54.1490 1 1.858e-13 ***
## tom_zscored 3.1229 1 0.0772 .
## condition:trial_type 2.9374 2 0.2302
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Data: df.trial %>% filter(task == "HI")
## Models:
## fit.task: correct_resp ~ condition + trial_type + age_zscored + (1 | pid)
## fit.task_tom: correct_resp ~ condition * trial_type + tom_zscored + (1 | pid)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## fit.task 6 858.05 887.24 -423.02 846.05
## fit.task_tom 8 860.52 899.45 -422.26 844.52 1.5236 2 0.4668
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: correct_resp ~ condition * trial_type * tom_zscored + (1 | pid)
## Data: df.trial %>% filter(task == "HI")
##
## AIC BIC logLik deviance df.resid
## 865.6 928.9 -419.8 839.6 946
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.2035 0.1175 0.2344 0.4704 2.9148
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.9596 0.9796
## Number of obs: 959, groups: pid, 120
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 3.31671 0.42035
## conditionHomogeneous -0.44099 0.56622
## conditionHeterogeneous -0.26829 0.58347
## trial_typeimplicature -3.10891 0.42544
## tom_zscored -0.05911 0.44612
## conditionHomogeneous:trial_typeimplicature 0.21352 0.58061
## conditionHeterogeneous:trial_typeimplicature 1.16595 0.59966
## conditionHomogeneous:tom_zscored -0.09257 0.59079
## conditionHeterogeneous:tom_zscored 0.13351 0.59569
## trial_typeimplicature:tom_zscored 0.12176 0.45383
## conditionHomogeneous:trial_typeimplicature:tom_zscored 0.55026 0.60468
## conditionHeterogeneous:trial_typeimplicature:tom_zscored 0.25232 0.60984
## z value Pr(>|z|)
## (Intercept) 7.890 3.01e-15 ***
## conditionHomogeneous -0.779 0.4361
## conditionHeterogeneous -0.460 0.6457
## trial_typeimplicature -7.308 2.72e-13 ***
## tom_zscored -0.132 0.8946
## conditionHomogeneous:trial_typeimplicature 0.368 0.7131
## conditionHeterogeneous:trial_typeimplicature 1.944 0.0519 .
## conditionHomogeneous:tom_zscored -0.157 0.8755
## conditionHeterogeneous:tom_zscored 0.224 0.8227
## trial_typeimplicature:tom_zscored 0.268 0.7885
## conditionHomogeneous:trial_typeimplicature:tom_zscored 0.910 0.3628
## conditionHeterogeneous:trial_typeimplicature:tom_zscored 0.414 0.6791
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnHm cndtnHt trl_ty tm_zsc cndtnHmgns:tr_
## condtnHmgns -0.706
## cndtnHtrgns -0.677 0.510
## trl_typmplc -0.845 0.596 0.570
## tom_zscored -0.136 0.098 0.095 0.121
## cndtnHmgns:tr_ 0.584 -0.817 -0.425 -0.702 -0.086
## cndtnHtrgns:tr_ 0.579 -0.423 -0.800 -0.691 -0.084 0.500
## cndtnHmgns:tm_ 0.103 -0.279 -0.072 -0.091 -0.755 0.236
## cndtnHtrgns:tm_ 0.111 -0.073 0.152 -0.098 -0.750 0.064
## trl_typmp:_ 0.121 -0.087 -0.084 -0.138 -0.848 0.098
## cndtnHm:_:_ -0.085 0.237 0.064 0.098 0.636 -0.283
## cndtnHt:_:_ -0.089 0.065 -0.108 0.102 0.631 -0.073
## cndtnHtrgns:tr_ cndtnHmgns:tm_ cndtnHtrgns:tm_ trl_:_
## condtnHmgns
## cndtnHtrgns
## trl_typmplc
## tom_zscored
## cndtnHmgns:tr_
## cndtnHtrgns:tr_
## cndtnHmgns:tm_ 0.064
## cndtnHtrgns:tm_ -0.105 0.566
## trl_typmp:_ 0.096 0.641 0.636
## cndtnHm:_:_ -0.071 -0.819 -0.476 -0.750
## cndtnHt:_:_ 0.161 -0.477 -0.814 -0.744
## cndtnHm:_:_
## condtnHmgns
## cndtnHtrgns
## trl_typmplc
## tom_zscored
## cndtnHmgns:tr_
## cndtnHtrgns:tr_
## cndtnHmgns:tm_
## cndtnHtrgns:tm_
## trl_typmp:_
## cndtnHm:_:_
## cndtnHt:_:_ 0.558
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0869174 (tol = 0.002, component 1)
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: correct_resp
## Chisq Df Pr(>Chisq)
## (Intercept) 62.2568 1 3.015e-15 ***
## condition 0.6119 2 0.7364
## trial_type 53.4000 1 2.721e-13 ***
## tom_zscored 0.0176 1 0.8946
## condition:trial_type 4.2683 2 0.1183
## condition:tom_zscored 0.1685 2 0.9192
## trial_type:tom_zscored 0.0720 1 0.7885
## condition:trial_type:tom_zscored 0.8410 2 0.6567
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Data: df.trial %>% filter(task == "HI")
## Models:
## fit.task: correct_resp ~ condition + trial_type + age_zscored + (1 | pid)
## fit.task_age_int: correct_resp ~ condition * trial_type * age_zscored + (1 | pid)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## fit.task 6 858.05 887.24 -423.02 846.05
## fit.task_age_int 13 867.87 931.13 -420.94 841.87 4.1738 7 0.7596
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: correct_resp ~ condition * trial_type + tom_zscored + age_zscored +
## entailment_score + (1 | pid)
## Data: df.trial %>% filter(task == "HI")
##
## AIC BIC logLik deviance df.resid
## 857.8 906.4 -418.9 837.8 949
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.5705 0.1103 0.2261 0.4417 2.7222
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.8817 0.939
## Number of obs: 959, groups: pid, 120
##
## Fixed effects:
## Estimate Std. Error z value
## (Intercept) 2.60700 0.51009 5.111
## conditionHomogeneous -0.39774 0.53623 -0.742
## conditionHeterogeneous -0.16060 0.54899 -0.293
## trial_typeimplicature -3.09578 0.42076 -7.358
## tom_zscored 0.09223 0.14931 0.618
## age_zscored 0.18924 0.13934 1.358
## entailment_score 0.26468 0.12791 2.069
## conditionHomogeneous:trial_typeimplicature 0.40890 0.54801 0.746
## conditionHeterogeneous:trial_typeimplicature 0.93906 0.55820 1.682
## Pr(>|z|)
## (Intercept) 3.21e-07 ***
## conditionHomogeneous 0.4582
## conditionHeterogeneous 0.7699
## trial_typeimplicature 1.87e-13 ***
## tom_zscored 0.5368
## age_zscored 0.1744
## entailment_score 0.0385 *
## conditionHomogeneous:trial_typeimplicature 0.4556
## conditionHeterogeneous:trial_typeimplicature 0.0925 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnHm cndtnHt trl_ty tm_zsc ag_zsc entlm_ cndtnHm:_
## condtnHmgns -0.630
## cndtnHtrgns -0.578 0.541
## trl_typmplc -0.661 0.617 0.596
## tom_zscored 0.124 -0.104 0.144 -0.016
## age_zscored 0.076 0.093 -0.023 -0.025 -0.326
## entlmnt_scr -0.589 0.063 0.007 -0.038 -0.203 -0.118
## cndtnHmgn:_ 0.514 -0.812 -0.467 -0.737 0.004 -0.013 -0.023
## cndtnHtrg:_ 0.495 -0.467 -0.806 -0.733 0.002 0.009 0.006 0.557
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0279848 (tol = 0.002, component 1)
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: correct_resp
## Chisq Df Pr(>Chisq)
## (Intercept) 26.1211 1 3.207e-07 ***
## condition 0.5670 2 0.75314
## trial_type 54.1354 1 1.871e-13 ***
## tom_zscored 0.3815 1 0.53678
## age_zscored 1.8446 1 0.17442
## entailment_score 4.2817 1 0.03853 *
## condition:trial_type 2.8832 2 0.23655
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Data: df.trial %>% filter(task == "HI")
## Models:
## fit.task: correct_resp ~ condition + trial_type + age_zscored + (1 | pid)
## fit.task_tom_age_entailment: correct_resp ~ condition * trial_type + tom_zscored + age_zscored + entailment_score + (1 | pid)
## npar AIC BIC logLik deviance Chisq Df
## fit.task 6 858.05 887.24 -423.02 846.05
## fit.task_tom_age_entailment 10 857.76 906.42 -418.88 837.76 8.2868 4
## Pr(>Chisq)
## fit.task
## fit.task_tom_age_entailment 0.08162 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Regression with only the implicature trials?
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: correct_resp ~ 1 + (1 | pid)
## Data: df.trial %>% filter(task == "HI" & trial_type == "implicature")
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 628.8 637.1 -312.4 624.8 477
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4701 -0.8286 0.4904 0.6803 1.2068
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.283 1.133
## Number of obs: 479, groups: pid, 120
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.4301 0.1492 2.883 0.00394 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: correct_resp ~ condition + age_zscored + (1 | pid)
## Data: df.trial %>% filter(task == "HI" & trial_type == "implicature")
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 625.9 646.7 -307.9 615.9 474
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7340 -0.7728 0.4564 0.6903 1.3958
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.119 1.058
## Number of obs: 479, groups: pid, 120
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.18072 0.24496 0.738 0.4607
## conditionHomogeneous 0.03933 0.34763 0.113 0.9099
## conditionHeterogeneous 0.71771 0.35521 2.021 0.0433 *
## age_zscored 0.29074 0.14508 2.004 0.0451 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnHm cndtnHt
## condtnHmgns -0.707
## cndtnHtrgns -0.687 0.493
## age_zscored -0.061 0.099 0.078
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: correct_resp
## Chisq Df Pr(>Chisq)
## (Intercept) 0.5443 1 0.46067
## condition 5.1150 2 0.07750 .
## age_zscored 4.0159 1 0.04507 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Data: df.trial %>% filter(task == "HI" & trial_type == "implicature")
## Models:
## fit.base: correct_resp ~ 1 + (1 | pid)
## fit.task: correct_resp ~ condition + age_zscored + (1 | pid)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## fit.base 2 628.79 637.14 -312.40 624.79
## fit.task 5 625.85 646.71 -307.93 615.85 8.9417 3 0.03008 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: correct_resp ~ condition * age_zscored + (1 | pid)
## Data: df.trial %>% filter(task == "HI" & trial_type == "implicature")
##
## AIC BIC logLik deviance df.resid
## 629.3 658.5 -307.6 615.3 472
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7847 -0.7946 0.4579 0.6849 1.4044
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.12 1.059
## Number of obs: 479, groups: pid, 120
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.195902 0.001790 109.42 <2e-16 ***
## conditionHomogeneous 0.029218 0.001789 16.33 <2e-16 ***
## conditionHeterogeneous 0.716224 0.001790 400.10 <2e-16 ***
## age_zscored 0.150845 0.001790 84.27 <2e-16 ***
## conditionHomogeneous:age_zscored 0.178576 0.001789 99.80 <2e-16 ***
## conditionHeterogeneous:age_zscored 0.269976 0.001790 150.84 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnHm cndtnHt ag_zsc cndtnHm:_
## condtnHmgns 0.000
## cndtnHtrgns 0.001 0.000
## age_zscored 0.001 0.000 0.001
## cndtnHmgn:_ 0.000 0.000 0.000 0.000
## cndtnHtrg:_ 0.001 0.000 0.001 0.000 0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0395887 (tol = 0.002, component 1)
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: correct_resp
## Chisq Df Pr(>Chisq)
## (Intercept) 11973.9 1 < 2.2e-16 ***
## condition 160345.6 2 < 2.2e-16 ***
## age_zscored 7101.7 1 < 2.2e-16 ***
## condition:age_zscored 32711.0 2 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Data: df.trial %>% filter(task == "HI" & trial_type == "implicature")
## Models:
## fit.task: correct_resp ~ condition + age_zscored + (1 | pid)
## fit.task_age_int: correct_resp ~ condition * age_zscored + (1 | pid)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## fit.task 5 625.85 646.71 -307.93 615.85
## fit.task_age_int 7 629.29 658.49 -307.65 615.29 0.5619 2 0.7551
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: correct_resp ~ condition + tom_zscored + (1 | pid)
## Data: df.trial %>% filter(task == "HI" & trial_type == "implicature")
##
## AIC BIC logLik deviance df.resid
## 625.0 645.9 -307.5 615.0 474
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9931 -0.8248 0.4579 0.6867 1.4830
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.127 1.062
## Number of obs: 479, groups: pid, 120
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.181282 0.001680 107.91 <2e-16 ***
## conditionHomogeneous -0.120923 0.001679 -72.02 <2e-16 ***
## conditionHeterogeneous 0.893034 0.001680 531.64 <2e-16 ***
## tom_zscored 0.347046 0.001679 206.65 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnHm cndtnHt
## condtnHmgns 0.000
## cndtnHtrgns 0.000 0.000
## tom_zscored 0.000 0.000 0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0421127 (tol = 0.002, component 1)
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: correct_resp
## Chisq Df Pr(>Chisq)
## (Intercept) 11644 1 < 2.2e-16 ***
## condition 287834 2 < 2.2e-16 ***
## tom_zscored 42704 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Data: df.trial %>% filter(task == "HI" & trial_type == "implicature")
## Models:
## fit.task_tom: correct_resp ~ condition + tom_zscored + (1 | pid)
## fit.task: correct_resp ~ condition + age_zscored + (1 | pid)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## fit.task_tom 5 625.05 645.91 -307.52 615.05
## fit.task 5 625.85 646.71 -307.93 615.85 0 0
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: correct_resp ~ condition * tom_zscored + (1 | pid)
## Data: df.trial %>% filter(task == "HI" & trial_type == "implicature")
##
## AIC BIC logLik deviance df.resid
## 627.3 656.5 -306.7 613.3 472
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1319 -0.8194 0.4603 0.7052 1.6297
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.079 1.039
## Number of obs: 479, groups: pid, 120
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.205398 0.001793 114.54 <2e-16 ***
## conditionHomogeneous -0.213411 0.001792 -119.07 <2e-16 ***
## conditionHeterogeneous 0.917053 0.001793 511.44 <2e-16 ***
## tom_zscored 0.070763 0.001793 39.47 <2e-16 ***
## conditionHomogeneous:tom_zscored 0.460567 0.001792 256.94 <2e-16 ***
## conditionHeterogeneous:tom_zscored 0.386427 0.001792 215.60 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnHm cndtnHt tm_zsc cndtnHm:_
## condtnHmgns 0.000
## cndtnHtrgns 0.000 0.000
## tom_zscored 0.000 0.000 0.000
## cndtnHmgn:_ 0.000 0.000 0.000 0.000
## cndtnHtrg:_ 0.000 0.000 0.000 0.000 0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0395075 (tol = 0.002, component 1)
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: correct_resp
## Chisq Df Pr(>Chisq)
## (Intercept) 13119.5 1 < 2.2e-16 ***
## condition 275763.1 2 < 2.2e-16 ***
## tom_zscored 1558.1 1 < 2.2e-16 ***
## condition:tom_zscored 112479.0 2 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Data: df.trial %>% filter(task == "HI" & trial_type == "implicature")
## Models:
## fit.task: correct_resp ~ condition + age_zscored + (1 | pid)
## fit.task_tom_int: correct_resp ~ condition * tom_zscored + (1 | pid)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## fit.task 5 625.85 646.71 -307.93 615.85
## fit.task_tom_int 7 627.30 656.50 -306.65 613.30 2.5529 2 0.279
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: correct_resp ~ condition + entailment_score + (1 | pid)
## Data: df.trial %>% filter(task == "HI" & trial_type == "implicature")
##
## AIC BIC logLik deviance df.resid
## 624.1 645.0 -307.1 614.1 474
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7338 -0.8446 0.4170 0.6992 1.5329
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.092 1.045
## Number of obs: 479, groups: pid, 120
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.64232 0.43077 -1.491 0.1359
## conditionHomogeneous 0.01624 0.34473 0.047 0.9624
## conditionHeterogeneous 0.72914 0.35366 2.062 0.0392 *
## entailment_score 0.33847 0.14105 2.400 0.0164 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnHm cndtnHt
## condtnHmgns -0.443
## cndtnHtrgns -0.458 0.491
## entlmnt_scr -0.826 0.055 0.089
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: correct_resp
## Chisq Df Pr(>Chisq)
## (Intercept) 2.2234 1 0.13593
## condition 5.4815 2 0.06452 .
## entailment_score 5.7584 1 0.01641 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Data: df.trial %>% filter(task == "HI" & trial_type == "implicature")
## Models:
## fit.task_ent: correct_resp ~ condition + entailment_score + (1 | pid)
## fit.task: correct_resp ~ condition + age_zscored + (1 | pid)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## fit.task_ent 5 624.10 644.96 -307.05 614.10
## fit.task 5 625.85 646.71 -307.93 615.85 0 0
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: correct_resp ~ condition * entailment_score + (1 | pid)
## Data: df.trial %>% filter(task == "HI" & trial_type == "implicature")
##
## AIC BIC logLik deviance df.resid
## 627.9 657.1 -306.9 613.9 472
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7077 -0.8544 0.4253 0.6989 1.5922
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.082 1.04
## Number of obs: 479, groups: pid, 120
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.67284 0.80146 -0.840 0.401
## conditionHomogeneous -0.11512 0.98921 -0.116 0.907
## conditionHeterogeneous 0.94736 0.99585 0.951 0.341
## entailment_score 0.35045 0.30234 1.159 0.246
## conditionHomogeneous:entailment_score 0.05523 0.37321 0.148 0.882
## conditionHeterogeneous:entailment_score -0.09291 0.37834 -0.246 0.806
##
## Correlation of Fixed Effects:
## (Intr) cndtnHm cndtnHt entlm_ cndtnHm:_
## condtnHmgns -0.809
## cndtnHtrgns -0.805 0.651
## entlmnt_scr -0.953 0.771 0.767
## cndtnHmgn:_ 0.770 -0.937 -0.620 -0.808
## cndtnHtrg:_ 0.760 -0.616 -0.935 -0.797 0.646
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: correct_resp
## Chisq Df Pr(>Chisq)
## (Intercept) 0.7048 1 0.4012
## condition 1.8461 2 0.3973
## entailment_score 1.3436 1 0.2464
## condition:entailment_score 0.2214 2 0.8952
## Data: df.trial %>% filter(task == "HI" & trial_type == "implicature")
## Models:
## fit.task: correct_resp ~ condition + age_zscored + (1 | pid)
## fit.task_ent_int: correct_resp ~ condition * entailment_score + (1 | pid)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## fit.task 5 625.85 646.71 -307.93 615.85
## fit.task_ent_int 7 627.88 657.08 -306.94 613.88 1.9707 2 0.3733
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: correct_resp ~ condition + tom_zscored + age_zscored + (1 | pid)
## Data: df.trial %>% filter(task == "HI" & trial_type == "implicature")
##
## AIC BIC logLik deviance df.resid
## 625.4 650.4 -306.7 613.4 473
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9692 -0.7936 0.4354 0.6891 1.5068
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.092 1.045
## Number of obs: 479, groups: pid, 120
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.165823 0.001705 97.27 <2e-16 ***
## conditionHomogeneous -0.052149 0.001704 -30.61 <2e-16 ***
## conditionHeterogeneous 0.868052 0.001760 493.12 <2e-16 ***
## tom_zscored 0.264781 0.001704 155.38 <2e-16 ***
## age_zscored 0.200958 0.001760 114.17 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnHm cndtnHt tm_zsc
## condtnHmgns 0.000
## cndtnHtrgns 0.000 0.000
## tom_zscored 0.000 0.000 0.000
## age_zscored 0.000 0.000 -0.250 0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0426678 (tol = 0.002, component 1)
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: correct_resp
## Chisq Df Pr(>Chisq)
## (Intercept) 9461.7 1 < 2.2e-16 ***
## condition 244101.8 2 < 2.2e-16 ***
## tom_zscored 24142.3 1 < 2.2e-16 ***
## age_zscored 13035.3 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Data: df.trial %>% filter(task == "HI" & trial_type == "implicature")
## Models:
## fit.task: correct_resp ~ condition + age_zscored + (1 | pid)
## fit.task_tom_age: correct_resp ~ condition + tom_zscored + age_zscored + (1 | pid)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## fit.task 5 625.85 646.71 -307.93 615.85
## fit.task_tom_age 6 625.38 650.41 -306.69 613.38 2.477 1 0.1155
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: correct_resp ~ condition * tom_zscored + age_zscored + (1 | pid)
## Data: df.trial %>% filter(task == "HI" & trial_type == "implicature")
##
## AIC BIC logLik deviance df.resid
## 627.2 660.6 -305.6 611.2 471
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1271 -0.8005 0.4363 0.7011 1.6432
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.036 1.018
## Number of obs: 479, groups: pid, 120
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.19117 0.24164 0.791 0.4289
## conditionHomogeneous -0.14722 0.35825 -0.411 0.6811
## conditionHeterogeneous 0.89498 0.38633 2.317 0.0205 *
## tom_zscored -0.04964 0.26819 -0.185 0.8532
## age_zscored 0.22367 0.15451 1.448 0.1477
## conditionHomogeneous:tom_zscored 0.50943 0.37173 1.370 0.1706
## conditionHeterogeneous:tom_zscored 0.43207 0.38368 1.126 0.2601
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnHm cndtnHt tm_zsc ag_zsc cndtnHm:_
## condtnHmgns -0.677
## cndtnHtrgns -0.618 0.420
## tom_zscored -0.087 0.027 0.066
## age_zscored -0.034 0.126 -0.007 -0.311
## cndtnHmgn:_ 0.069 -0.227 -0.037 -0.682 0.101
## cndtnHtrg:_ 0.067 -0.034 0.264 -0.661 0.099 0.469
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: correct_resp
## Chisq Df Pr(>Chisq)
## (Intercept) 0.6259 1 0.42888
## condition 7.6895 2 0.02139 *
## tom_zscored 0.0343 1 0.85316
## age_zscored 2.0956 1 0.14772
## condition:tom_zscored 2.1780 2 0.33655
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Data: df.trial %>% filter(task == "HI" & trial_type == "implicature")
## Models:
## fit.task: correct_resp ~ condition + age_zscored + (1 | pid)
## fit.task_tom_age: correct_resp ~ condition * tom_zscored + age_zscored + (1 | pid)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## fit.task 5 625.85 646.71 -307.93 615.85
## fit.task_tom_age 8 627.19 660.57 -305.60 611.19 4.6605 3 0.1984
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## correct_resp ~ condition + tom_zscored + age_zscored + entailment_score +
## (1 | pid)
## Data: df.trial %>% filter(task == "HI" & trial_type == "implicature")
##
## AIC BIC logLik deviance df.resid
## 624.3 653.5 -305.2 610.3 472
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9734 -0.8181 0.4248 0.6984 1.6002
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.023 1.011
## Number of obs: 479, groups: pid, 120
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.46741 0.43238 -1.081 0.2797
## conditionHomogeneous -0.01143 0.34621 -0.033 0.9737
## conditionHeterogeneous 0.85454 0.36136 2.365 0.0180 *
## tom_zscored 0.20066 0.16722 1.200 0.2301
## age_zscored 0.16278 0.15323 1.062 0.2881
## entailment_score 0.25356 0.14460 1.754 0.0795 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnHm cndtnHt tm_zsc ag_zsc
## condtnHmgns -0.437
## cndtnHtrgns -0.390 0.425
## tom_zscored 0.141 -0.173 0.257
## age_zscored 0.078 0.139 -0.026 -0.324
## entlmnt_scr -0.832 0.067 0.021 -0.193 -0.124
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: correct_resp
## Chisq Df Pr(>Chisq)
## (Intercept) 1.1686 1 0.27969
## condition 6.9060 2 0.03165 *
## tom_zscored 1.4400 1 0.23015
## age_zscored 1.1285 1 0.28810
## entailment_score 3.0749 1 0.07951 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Data: df.trial %>% filter(task == "HI" & trial_type == "implicature")
## Models:
## fit.task: correct_resp ~ condition + age_zscored + (1 | pid)
## fit.task_tom_age_entailment: correct_resp ~ condition + tom_zscored + age_zscored + entailment_score + (1 | pid)
## npar AIC BIC logLik deviance Chisq Df
## fit.task 5 625.85 646.71 -307.93 615.85
## fit.task_tom_age_entailment 7 624.32 653.52 -305.16 610.32 5.5373 2
## Pr(>Chisq)
## fit.task
## fit.task_tom_age_entailment 0.06275 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## correct_resp ~ condition * entailment_score * tom_zscored * age_zscored +
## (1 | pid)
## Data: df.trial %>% filter(task == "HI" & trial_type == "implicature")
##
## AIC BIC logLik deviance df.resid
## 638.9 743.2 -294.5 588.9 454
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2620 -0.8494 0.3817 0.7066 2.0328
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.7196 0.8483
## Number of obs: 479, groups: pid, 120
##
## Fixed effects:
## Estimate
## (Intercept) -0.89131
## conditionHomogeneous 1.94905
## conditionHeterogeneous -0.30240
## entailment_score 0.40200
## tom_zscored 1.65135
## age_zscored -0.67076
## conditionHomogeneous:entailment_score -0.69410
## conditionHeterogeneous:entailment_score 0.53878
## conditionHomogeneous:tom_zscored 1.13596
## conditionHeterogeneous:tom_zscored -1.21254
## entailment_score:tom_zscored -0.62465
## conditionHomogeneous:age_zscored 1.99065
## conditionHeterogeneous:age_zscored -3.08049
## entailment_score:age_zscored 0.27917
## tom_zscored:age_zscored 0.49304
## conditionHomogeneous:entailment_score:tom_zscored -0.14741
## conditionHeterogeneous:entailment_score:tom_zscored 0.65463
## conditionHomogeneous:entailment_score:age_zscored -0.59908
## conditionHeterogeneous:entailment_score:age_zscored 0.99888
## conditionHomogeneous:tom_zscored:age_zscored 1.62317
## conditionHeterogeneous:tom_zscored:age_zscored -5.23165
## entailment_score:tom_zscored:age_zscored -0.07199
## conditionHomogeneous:entailment_score:tom_zscored:age_zscored -0.79226
## conditionHeterogeneous:entailment_score:tom_zscored:age_zscored 1.42614
## Std. Error
## (Intercept) 1.04770
## conditionHomogeneous 1.92764
## conditionHeterogeneous 1.65080
## entailment_score 0.38923
## tom_zscored 1.59823
## age_zscored 1.23063
## conditionHomogeneous:entailment_score 0.67684
## conditionHeterogeneous:entailment_score 0.60089
## conditionHomogeneous:tom_zscored 2.40636
## conditionHeterogeneous:tom_zscored 2.03114
## entailment_score:tom_zscored 0.56549
## conditionHomogeneous:age_zscored 1.70691
## conditionHeterogeneous:age_zscored 2.86045
## entailment_score:age_zscored 0.43720
## tom_zscored:age_zscored 1.34967
## conditionHomogeneous:entailment_score:tom_zscored 0.84100
## conditionHeterogeneous:entailment_score:tom_zscored 0.71506
## conditionHomogeneous:entailment_score:age_zscored 0.60000
## conditionHeterogeneous:entailment_score:age_zscored 0.98225
## conditionHomogeneous:tom_zscored:age_zscored 1.90562
## conditionHeterogeneous:tom_zscored:age_zscored 2.68623
## entailment_score:tom_zscored:age_zscored 0.48568
## conditionHomogeneous:entailment_score:tom_zscored:age_zscored 0.66998
## conditionHeterogeneous:entailment_score:tom_zscored:age_zscored 0.91894
## z value
## (Intercept) -0.851
## conditionHomogeneous 1.011
## conditionHeterogeneous -0.183
## entailment_score 1.033
## tom_zscored 1.033
## age_zscored -0.545
## conditionHomogeneous:entailment_score -1.026
## conditionHeterogeneous:entailment_score 0.897
## conditionHomogeneous:tom_zscored 0.472
## conditionHeterogeneous:tom_zscored -0.597
## entailment_score:tom_zscored -1.105
## conditionHomogeneous:age_zscored 1.166
## conditionHeterogeneous:age_zscored -1.077
## entailment_score:age_zscored 0.639
## tom_zscored:age_zscored 0.365
## conditionHomogeneous:entailment_score:tom_zscored -0.175
## conditionHeterogeneous:entailment_score:tom_zscored 0.915
## conditionHomogeneous:entailment_score:age_zscored -0.998
## conditionHeterogeneous:entailment_score:age_zscored 1.017
## conditionHomogeneous:tom_zscored:age_zscored 0.852
## conditionHeterogeneous:tom_zscored:age_zscored -1.948
## entailment_score:tom_zscored:age_zscored -0.148
## conditionHomogeneous:entailment_score:tom_zscored:age_zscored -1.183
## conditionHeterogeneous:entailment_score:tom_zscored:age_zscored 1.552
## Pr(>|z|)
## (Intercept) 0.3949
## conditionHomogeneous 0.3120
## conditionHeterogeneous 0.8547
## entailment_score 0.3017
## tom_zscored 0.3015
## age_zscored 0.5857
## conditionHomogeneous:entailment_score 0.3051
## conditionHeterogeneous:entailment_score 0.3699
## conditionHomogeneous:tom_zscored 0.6369
## conditionHeterogeneous:tom_zscored 0.5505
## entailment_score:tom_zscored 0.2693
## conditionHomogeneous:age_zscored 0.2435
## conditionHeterogeneous:age_zscored 0.2815
## entailment_score:age_zscored 0.5231
## tom_zscored:age_zscored 0.7149
## conditionHomogeneous:entailment_score:tom_zscored 0.8609
## conditionHeterogeneous:entailment_score:tom_zscored 0.3599
## conditionHomogeneous:entailment_score:age_zscored 0.3181
## conditionHeterogeneous:entailment_score:age_zscored 0.3092
## conditionHomogeneous:tom_zscored:age_zscored 0.3943
## conditionHeterogeneous:tom_zscored:age_zscored 0.0515 .
## entailment_score:tom_zscored:age_zscored 0.8822
## conditionHomogeneous:entailment_score:tom_zscored:age_zscored 0.2370
## conditionHeterogeneous:entailment_score:tom_zscored:age_zscored 0.1207
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 24 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.112194 (tol = 0.002, component 1)
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: correct_resp
## Chisq Df Pr(>Chisq)
## (Intercept) 0.7237 1 0.39492
## condition 1.3425 2 0.51107
## entailment_score 1.0667 1 0.30169
## tom_zscored 1.0676 1 0.30149
## age_zscored 0.2971 1 0.58571
## condition:entailment_score 2.9362 2 0.23036
## condition:tom_zscored 1.1992 2 0.54903
## entailment_score:tom_zscored 1.2202 1 0.26932
## condition:age_zscored 3.6391 2 0.16210
## entailment_score:age_zscored 0.4077 1 0.52312
## tom_zscored:age_zscored 0.1334 1 0.71489
## condition:entailment_score:tom_zscored 1.4463 2 0.48522
## condition:entailment_score:age_zscored 2.9974 2 0.22342
## condition:tom_zscored:age_zscored 6.5026 2 0.03872 *
## entailment_score:tom_zscored:age_zscored 0.0220 1 0.88217
## condition:entailment_score:tom_zscored:age_zscored 6.0878 2 0.04765 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Data: df.trial %>% filter(task == "HI" & trial_type == "implicature")
## Models:
## fit.task: correct_resp ~ condition + age_zscored + (1 | pid)
## fit.task_tom_age: correct_resp ~ condition * tom_zscored + age_zscored + (1 | pid)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## fit.task 5 625.85 646.71 -307.93 615.85
## fit.task_tom_age 8 627.19 660.57 -305.60 611.19 4.6605 3 0.1984
