Triad Polysemy Induction Tasks
Hugh Rabagliati
5 July 2016
Experiment 1
Here, we compare inferences from e.g., Chicken Animal to Duck, and either another Chicken Animal or Chicken Meat. We also vary whether the same label is used twice (both pictures called chicken) or whether a synonym is used (one called chicken, one called drumsticks).
Adults
Label * Meaning mixed effects model, followed by t-tests against chance for each condition.
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## UnrelatedChoice ~ WordType * LabelType + (1 + LabelType | SubjNo) +
## (1 | ItemNo)
## Data: Adult
##
## AIC BIC logLik deviance df.resid
## 289.9 319.2 -137.0 273.9 280
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8831 -0.4629 0.2711 0.4453 3.1935
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjNo (Intercept) 1.4689 1.2120
## LabelTypeDifferent 0.1927 0.4389 -1.00
## ItemNo (Intercept) 0.4879 0.6985
## Number of obs: 288, groups: SubjNo, 24; ItemNo, 12
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.6063 0.5978 -2.687 0.00721
## WordTypePolysemous 3.6682 0.8034 4.566 4.98e-06
## LabelTypeDifferent 0.6668 0.5470 1.219 0.22278
## WordTypePolysemous:LabelTypeDifferent -0.7464 0.8153 -0.915 0.35995
##
## (Intercept) **
## WordTypePolysemous ***
## LabelTypeDifferent
## WordTypePolysemous:LabelTypeDifferent
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) WrdTyP LblTyD
## WrdTypPlysm -0.729
## LblTypDffrn -0.708 0.596
## WrdTypP:LTD 0.544 -0.728 -0.770##
## One Sample t-test
##
## data: subset(Adult.Sum, WordType == "Non-Polysemous" & LabelType == "Same")$Choice
## t = 2.8333, df = 10, p-value = 0.01775
## alternative hypothesis: true mean is not equal to 0.5
## 95 percent confidence interval:
## 0.5550177 0.9601338
## sample estimates:
## mean of x
## 0.7575758##
## One Sample t-test
##
## data: subset(Adult.Sum, WordType == "Non-Polysemous" & LabelType == "Different")$Choice
## t = 2.2361, df = 10, p-value = 0.04933
## alternative hypothesis: true mean is not equal to 0.5
## 95 percent confidence interval:
## 0.5005910 0.8327423
## sample estimates:
## mean of x
## 0.6666667##
## One Sample t-test
##
## data: subset(Adult.Sum, WordType == "Polysemous" & LabelType == "Different")$Choice
## t = -8.6841, df = 12, p-value = 1.608e-06
## alternative hypothesis: true mean is not equal to 0.5
## 95 percent confidence interval:
## 0.06699738 0.24069493
## sample estimates:
## mean of x
## 0.1538462##
## One Sample t-test
##
## data: subset(Adult.Sum, WordType == "Polysemous" & LabelType == "Same")$Choice
## t = -7.2111, df = 12, p-value = 1.07e-05
## alternative hypothesis: true mean is not equal to 0.5
## 95 percent confidence interval:
## 0.06595101 0.26738233
## sample estimates:
## mean of x
## 0.1666667Children
Label * Meaning mixed effects model, followed by t-tests against chance for each condition.
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: Choice ~ Meaning * LabelType + (1 + LabelType | SubjNo) + (1 +
## Meaning | question.number)
## Data: Child
##
## AIC BIC logLik deviance df.resid
## 1520.1 1570.6 -750.0 1500.1 1151
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8804 -0.7276 -0.4563 0.8167 2.1833
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjNo (Intercept) 0.4282 0.6544
## LabelTypeDifferent 0.4205 0.6485 -0.17
## question.number (Intercept) 0.1638 0.4048
## Meaning.L 0.1039 0.3224 0.67
## Number of obs: 1161, groups: SubjNo, 97; question.number, 12
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2732 0.1501 -1.820 0.06875 .
## Meaning.L -0.4905 0.1628 -3.013 0.00258 **
## LabelTypeDifferent -0.1417 0.0939 -1.509 0.13138
## Meaning.L:LabelTypeDifferent 0.1932 0.1330 1.453 0.14622
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Mnng.L LblTyD
## Meaning.L 0.316
## LblTypDffrn -0.057 0.004
## Mnng.L:LbTD 0.002 -0.085 0.031##
## One Sample t-test
##
## data: subset(Child.Sum, WordType == "Non-Polysemous" & LabelType == "Same")$Choice
## t = 1.7633, df = 48, p-value = 0.08422
## alternative hypothesis: true mean is not equal to 0.5
## 95 percent confidence interval:
## 0.4901714 0.6499646
## sample estimates:
## mean of x
## 0.570068##
## One Sample t-test
##
## data: subset(Child.Sum, WordType == "Non-Polysemous" & LabelType == "Different")$Choice
## t = -1.1923, df = 48, p-value = 0.239
## alternative hypothesis: true mean is not equal to 0.5
## 95 percent confidence interval:
## 0.3830445 0.5298807
## sample estimates:
## mean of x
## 0.4564626##
## One Sample t-test
##
## data: subset(Child.Sum, WordType == "Polysemous" & LabelType == "Different")$Choice
## t = -3.3806, df = 47, p-value = 0.001464
## alternative hypothesis: true mean is not equal to 0.5
## 95 percent confidence interval:
## 0.2961843 0.4482601
## sample estimates:
## mean of x
## 0.3722222##
## One Sample t-test
##
## data: subset(Child.Sum, WordType == "Polysemous" & LabelType == "Same")$Choice
## t = -3.0723, df = 47, p-value = 0.003528
## alternative hypothesis: true mean is not equal to 0.5
## 95 percent confidence interval:
## 0.3046424 0.4592465
## sample estimates:
## mean of x
## 0.3819444Experiment 2
This is the same as Expt 1, except that now we compare label (this chicken, this chicken) with no label (this one, this one).
Adults
## Warning: Removed 1 rows containing missing values (geom_errorbar).
Label * Meaning mixed effects model, followed by t-tests against chance for each condition.
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : Model failed to converge with max|grad| = 0.027305 (tol =
## 0.001, component 1)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: Choice ~ Label * Meaning + (1 + Label | ID) + (1 + Meaning |
## question.number)
## Data: Adult
##
## AIC BIC logLik deviance df.resid
## 154.5 185.7 -67.3 134.5 157
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3044 -0.3024 -0.1391 0.1809 2.3637
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ID (Intercept) 0.7957 0.892
## Label.L 3.2488 1.802 -0.30
## question.number (Intercept) 1.2834 1.133
## Meaning.L 5.4857 2.342 -0.72
## Number of obs: 167, groups: ID, 14; question.number, 12
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.983598 0.002712 -362.7 <2e-16 ***
## Label.L -3.408398 0.002712 -1256.8 <2e-16 ***
## Meaning.L -1.740383 0.002801 -621.4 <2e-16 ***
## Label.L:Meaning.L 2.356227 0.002801 841.3 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Labl.L Mnng.L
## Label.L 0.000
## Meaning.L 0.000 0.000
## Lbl.L:Mnn.L 0.000 0.000 0.000
## convergence code: 0
## Model failed to converge with max|grad| = 0.027305 (tol = 0.001, component 1)##
## One Sample t-test
##
## data: subset(Adult.Sum, Meaning == "Unambiguous (Same Kind)" & Label == "Shared Label")$Choice
## t = 4.4907, df = 4, p-value = 0.0109
## alternative hypothesis: true mean is not equal to 0.5
## 95 percent confidence interval:
## 0.6399709 1.0933625
## sample estimates:
## mean of x
## 0.8666667##
## One Sample t-test
##
## data: subset(Adult.Sum, Meaning == "Unambiguous (Same Kind)" & Label == "No Label")$Choice
## t = -4.4721, df = 5, p-value = 0.006566
## alternative hypothesis: true mean is not equal to 0.5
## 95 percent confidence interval:
## -0.02493319 0.35826652
## sample estimates:
## mean of x
## 0.1666667##
## One Sample t-test
##
## data: subset(Adult.Sum, Meaning == "Polysemous (Distinct Kinds)" & Label == "Shared Label")$Choice
## t = -1.3129, df = 7, p-value = 0.2306
## alternative hypothesis: true mean is not equal to 0.5
## 95 percent confidence interval:
## 0.09150396 0.61682938
## sample estimates:
## mean of x
## 0.3541667##
## One Sample t-test
##
## data: subset(Adult.Sum, Meaning == "Polysemous (Distinct Kinds)" & Label == "No Label")$Choice
## t = -9.3541, df = 7, p-value = 3.317e-05
## alternative hypothesis: true mean is not equal to 0.5
## 95 percent confidence interval:
## -0.0219954 0.1886621
## sample estimates:
## mean of x
## 0.08333333Children
Label * Meaning mixed effects model, followed by t-tests against chance for each condition.
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## Choice ~ Label * Meaning + (1 + Label | ID) + (1 | question.number)
## Data: Child
##
## AIC BIC logLik deviance df.resid
## 1464.8 1505.2 -724.4 1448.8 1139
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8713 -0.7172 -0.4626 0.8564 2.2659
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ID (Intercept) 0.2197 0.4688
## Label.L 0.9090 0.9534 0.02
## question.number (Intercept) 0.1173 0.3425
## Number of obs: 1147, groups: ID, 95; question.number, 12
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.42689 0.12985 -3.288 0.001011 **
## Label.L -0.68400 0.13836 -4.944 7.67e-07 ***
## Meaning.L -0.40265 0.11726 -3.434 0.000595 ***
## Label.L:Meaning.L 0.05675 0.19333 0.294 0.769104
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Labl.L Mnng.L
## Label.L 0.051
## Meaning.L 0.037 0.035
## Lbl.L:Mnn.L 0.021 0.048 0.043##
## One Sample t-test
##
## data: subset(Child.Sum, Meaning == "Unambiguous (Same Kind)" & Label == "Shared Label")$Choice
## t = 1.995, df = 48, p-value = 0.05174
## alternative hypothesis: true mean is not equal to 0.5
## 95 percent confidence interval:
## 0.4993860 0.6570766
## sample estimates:
## mean of x
## 0.5782313##
## One Sample t-test
##
## data: subset(Child.Sum, Meaning == "Unambiguous (Same Kind)" & Label == "No Label")$Choice
## t = -3.6101, df = 48, p-value = 0.0007292
## alternative hypothesis: true mean is not equal to 0.5
## 95 percent confidence interval:
## 0.2913484 0.4406244
## sample estimates:
## mean of x
## 0.3659864##
## One Sample t-test
##
## data: subset(Child.Sum, Meaning == "Polysemous (Distinct Kinds)" & Label == "Shared Label")$Choice
## t = -1.5838, df = 45, p-value = 0.1202
## alternative hypothesis: true mean is not equal to 0.5
## 95 percent confidence interval:
## 0.3707760 0.5154559
## sample estimates:
## mean of x
## 0.4431159##
## One Sample t-test
##
## data: subset(Child.Sum, Meaning == "Polysemous (Distinct Kinds)" & Label == "No Label")$Choice
## t = -7.0353, df = 45, p-value = 9.034e-09
## alternative hypothesis: true mean is not equal to 0.5
## 95 percent confidence interval:
## 0.2017307 0.3345011
## sample estimates:
## mean of x
## 0.2681159Comparison of Experiments 1 and 2
Adults
And data analysis
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: Choice ~ Meaning * Label_c * Expt + (1 | ID)
## Data: Adult
##
## AIC BIC logLik deviance df.resid
## 451.5 488.6 -216.8 433.5 446
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9732 -0.4793 -0.3441 0.4488 4.5824
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.5409 0.7354
## Number of obs: 455, groups: ID, 38
##
## Fixed effects:
## Estimate
## (Intercept) -0.5725
## MeaningPolysemous (Distinct Kinds) -1.1484
## Label_cOther -0.7776
## ExptExpt2 -0.2051
## MeaningPolysemous (Distinct Kinds):Label_cOther 0.2641
## MeaningPolysemous (Distinct Kinds):ExptExpt2 0.2812
## Label_cOther:ExptExpt2 -0.6234
## MeaningPolysemous (Distinct Kinds):Label_cOther:ExptExpt2 0.1600
## Std. Error
## (Intercept) 0.1870
## MeaningPolysemous (Distinct Kinds) 0.1904
## Label_cOther 0.1402
## ExptExpt2 0.1872
## MeaningPolysemous (Distinct Kinds):Label_cOther 0.1387
## MeaningPolysemous (Distinct Kinds):ExptExpt2 0.1870
## Label_cOther:ExptExpt2 0.1393
## MeaningPolysemous (Distinct Kinds):Label_cOther:ExptExpt2 0.1386
## z value Pr(>|z|)
## (Intercept) -3.061 0.00221
## MeaningPolysemous (Distinct Kinds) -6.033 1.61e-09
## Label_cOther -5.546 2.92e-08
## ExptExpt2 -1.096 0.27309
## MeaningPolysemous (Distinct Kinds):Label_cOther 1.905 0.05684
## MeaningPolysemous (Distinct Kinds):ExptExpt2 1.504 0.13265
## Label_cOther:ExptExpt2 -4.474 7.66e-06
## MeaningPolysemous (Distinct Kinds):Label_cOther:ExptExpt2 1.155 0.24822
##
## (Intercept) **
## MeaningPolysemous (Distinct Kinds) ***
## Label_cOther ***
## ExptExpt2
## MeaningPolysemous (Distinct Kinds):Label_cOther .
## MeaningPolysemous (Distinct Kinds):ExptExpt2
## Label_cOther:ExptExpt2 ***
## MeaningPolysemous (Distinct Kinds):Label_cOther:ExptExpt2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) MnP(DK) Lbl_cO ExptE2 MnP(DK):L_O MP(DK):E L_O:EE
## MnngP(DKnd) -0.054
## Label_cOthr 0.095 0.173
## ExptExpt2 0.332 -0.048 0.109
## MnP(DK):L_O 0.145 0.076 -0.056 0.118
## MnP(DK):EE2 -0.068 0.309 0.110 -0.073 0.101
## Lbl_cOt:EE2 0.105 0.139 0.411 0.094 -0.091 0.140
## MP(DK):L_O: 0.121 0.097 -0.086 0.148 0.401 0.086 -0.050Children
Graph the two experiments side-by-side. Other = Synonym in Expt 1, No Label in Expt 2 
And data analysis
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : Model failed to converge with max|grad| = 0.00962631 (tol =
## 0.001, component 1)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: Choice ~ Meaning * Label_c * Expt + (1 + Label_c | ID) + (1 +
## Meaning | QuNum)
## Data: Child
##
## AIC BIC logLik deviance df.resid
## 2961.3 3041.7 -1466.6 2933.3 2294
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0111 -0.7296 -0.4557 0.8280 2.4463
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ID (Intercept) 0.83687 0.9148
## Label_cOther 1.76840 1.3298 -0.78
## QuNum (Intercept) 0.15472 0.3933
## MeaningPolysemous (Distinct Kinds) 0.04386 0.2094 0.51
## Number of obs: 2308, groups: ID, 192; QuNum, 12
##
## Fixed effects:
## Estimate
## (Intercept) -0.03322
## MeaningPolysemous (Distinct Kinds) -0.39798
## Label_cOther -0.64139
## ExptExpt2 0.08757
## MeaningPolysemous (Distinct Kinds):Label_cOther 0.15741
## MeaningPolysemous (Distinct Kinds):ExptExpt2 0.07429
## Label_cOther:ExptExpt2 -0.34325
## MeaningPolysemous (Distinct Kinds):Label_cOther:ExptExpt2 -0.10938
## Std. Error
## (Intercept) 0.14710
## MeaningPolysemous (Distinct Kinds) 0.11184
## Label_cOther 0.13593
## ExptExpt2 0.09354
## MeaningPolysemous (Distinct Kinds):Label_cOther 0.13540
## MeaningPolysemous (Distinct Kinds):ExptExpt2 0.09352
## Label_cOther:ExptExpt2 0.13508
## MeaningPolysemous (Distinct Kinds):Label_cOther:ExptExpt2 0.13489
## z value Pr(>|z|)
## (Intercept) -0.226 0.821335
## MeaningPolysemous (Distinct Kinds) -3.558 0.000373
## Label_cOther -4.719 2.37e-06
## ExptExpt2 0.936 0.349189
## MeaningPolysemous (Distinct Kinds):Label_cOther 1.163 0.244994
## MeaningPolysemous (Distinct Kinds):ExptExpt2 0.794 0.427012
## Label_cOther:ExptExpt2 -2.541 0.011054
## MeaningPolysemous (Distinct Kinds):Label_cOther:ExptExpt2 -0.811 0.417463
##
## (Intercept)
## MeaningPolysemous (Distinct Kinds) ***
## Label_cOther ***
## ExptExpt2
## MeaningPolysemous (Distinct Kinds):Label_cOther
## MeaningPolysemous (Distinct Kinds):ExptExpt2
## Label_cOther:ExptExpt2 *
## MeaningPolysemous (Distinct Kinds):Label_cOther:ExptExpt2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) MnP(DK) Lbl_cO ExptE2 MnP(DK):L_O MP(DK):E L_O:EE
## MnngP(DKnd) 0.225
## Label_cOthr -0.467 -0.013
## ExptExpt2 0.002 0.001 -0.003
## MnP(DK):L_O -0.011 -0.624 0.040 -0.003
## MnP(DK):EE2 0.002 0.001 -0.004 0.024 -0.002
## Lbl_cOt:EE2 -0.001 -0.001 0.025 -0.741 0.015 -0.018
## MP(DK):L_O: -0.002 -0.001 0.014 -0.018 0.019 -0.742 0.037
## convergence code: 0
## Model failed to converge with max|grad| = 0.00962631 (tol = 0.001, component 1)We can also look at how this varies by age 
And analysis (note that model doesn’t converge with random intercepts for subjects, just items).
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: Choice ~ Meaning * Label_c * Age * Expt + (1 | QuNum)
## Data: Child
##
## AIC BIC logLik deviance df.resid
## 3048.8 3146.5 -1507.4 3014.8 2291
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.55 -0.83 -0.60 1.01 2.36
##
## Random effects:
## Groups Name Variance Std.Dev.
## QuNum (Intercept) 0.11 0.33
## Number of obs: 2308, groups: QuNum, 12
##
## Fixed effects:
## Estimate
## (Intercept) -0.2996
## MeaningPolysemous (Distinct Kinds) -0.2674
## Label_cOther -0.2803
## Age4 -0.0069
## ExptExpt2 -0.0730
## MeaningPolysemous (Distinct Kinds):Label_cOther 0.0608
## MeaningPolysemous (Distinct Kinds):Age4 -0.1035
## Label_cOther:Age4 0.0135
## MeaningPolysemous (Distinct Kinds):ExptExpt2 0.0198
## Label_cOther:ExptExpt2 -0.1509
## Age4:ExptExpt2 -0.0709
## MeaningPolysemous (Distinct Kinds):Label_cOther:Age4 0.0116
## MeaningPolysemous (Distinct Kinds):Label_cOther:ExptExpt2 -0.0501
## MeaningPolysemous (Distinct Kinds):Age4:ExptExpt2 0.0171
## Label_cOther:Age4:ExptExpt2 -0.0233
## MeaningPolysemous (Distinct Kinds):Label_cOther:Age4:ExptExpt2 0.0390
## Std. Error
## (Intercept) 0.1054
## MeaningPolysemous (Distinct Kinds) 0.0437
## Label_cOther 0.0438
## Age4 0.0437
## ExptExpt2 0.0437
## MeaningPolysemous (Distinct Kinds):Label_cOther 0.0437
## MeaningPolysemous (Distinct Kinds):Age4 0.0437
## Label_cOther:Age4 0.0437
## MeaningPolysemous (Distinct Kinds):ExptExpt2 0.0437
## Label_cOther:ExptExpt2 0.0437
## Age4:ExptExpt2 0.0437
## MeaningPolysemous (Distinct Kinds):Label_cOther:Age4 0.0438
## MeaningPolysemous (Distinct Kinds):Label_cOther:ExptExpt2 0.0437
## MeaningPolysemous (Distinct Kinds):Age4:ExptExpt2 0.0437
## Label_cOther:Age4:ExptExpt2 0.0437
## MeaningPolysemous (Distinct Kinds):Label_cOther:Age4:ExptExpt2 0.0438
## z value
## (Intercept) -2.8
## MeaningPolysemous (Distinct Kinds) -6.1
## Label_cOther -6.4
## Age4 -0.2
## ExptExpt2 -1.7
## MeaningPolysemous (Distinct Kinds):Label_cOther 1.4
## MeaningPolysemous (Distinct Kinds):Age4 -2.4
## Label_cOther:Age4 0.3
## MeaningPolysemous (Distinct Kinds):ExptExpt2 0.5
## Label_cOther:ExptExpt2 -3.4
## Age4:ExptExpt2 -1.6
## MeaningPolysemous (Distinct Kinds):Label_cOther:Age4 0.3
## MeaningPolysemous (Distinct Kinds):Label_cOther:ExptExpt2 -1.1
## MeaningPolysemous (Distinct Kinds):Age4:ExptExpt2 0.4
## Label_cOther:Age4:ExptExpt2 -0.5
## MeaningPolysemous (Distinct Kinds):Label_cOther:Age4:ExptExpt2 0.9
## Pr(>|z|)
## (Intercept) 0.004
## MeaningPolysemous (Distinct Kinds) 1e-09
## Label_cOther 2e-10
## Age4 0.874
## ExptExpt2 0.095
## MeaningPolysemous (Distinct Kinds):Label_cOther 0.164
## MeaningPolysemous (Distinct Kinds):Age4 0.018
## Label_cOther:Age4 0.757
## MeaningPolysemous (Distinct Kinds):ExptExpt2 0.651
## Label_cOther:ExptExpt2 6e-04
## Age4:ExptExpt2 0.105
## MeaningPolysemous (Distinct Kinds):Label_cOther:Age4 0.791
## MeaningPolysemous (Distinct Kinds):Label_cOther:ExptExpt2 0.251
## MeaningPolysemous (Distinct Kinds):Age4:ExptExpt2 0.695
## Label_cOther:Age4:ExptExpt2 0.593
## MeaningPolysemous (Distinct Kinds):Label_cOther:Age4:ExptExpt2 0.374
##
## (Intercept) **
## MeaningPolysemous (Distinct Kinds) ***
## Label_cOther ***
## Age4
## ExptExpt2 .
## MeaningPolysemous (Distinct Kinds):Label_cOther
## MeaningPolysemous (Distinct Kinds):Age4 *
## Label_cOther:Age4
## MeaningPolysemous (Distinct Kinds):ExptExpt2
## Label_cOther:ExptExpt2 ***
## Age4:ExptExpt2
## MeaningPolysemous (Distinct Kinds):Label_cOther:Age4
## MeaningPolysemous (Distinct Kinds):Label_cOther:ExptExpt2
## MeaningPolysemous (Distinct Kinds):Age4:ExptExpt2
## Label_cOther:Age4:ExptExpt2
## MeaningPolysemous (Distinct Kinds):Label_cOther:Age4:ExptExpt2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 16 > 12.
## Use print(...., correlation=TRUE) or
## vcov(....) if you need it