1. Linear mixed-effects models: Cognitive control and the degree to which bilingual speakers participate in ongoing sound changes revisited

  • For this exercise we will attempt to generate a more elaborate analysis of the Berry (2018) data we modeled in Lab 8
  • We already explored this dataset, so we can skip a few steps here.
  • Recall that Berry (2018) studied the bilingual Puerto Rican community of Philadelphia, PA. The goal was to gain insight into the degree to which Spanish-English bilingual speakers participate in ongoing sound changes in English and how cognitive control contributes to this
  • Cognitive control is a concept proposed by Braver (2012) in his dual-mechanisms framework:
    • Cognitive control is defined as: “the ability to regulate thoughts and actions in accordance with internally represented behavioral goals” (Braver, 2012)
    • It is composed of two mechanisms:
      • Proactive control is defined as: “the sustained and anticipatory maintenance of goal-relevant information […] to enable optimal cognitive performance.” (Braver, 2012)
      • Reactive control is defined as: “Transient, stimulus-driven goal reactivation […] based on interference demands or episodic associations.” (Braver, 2012)
  • Here we will only consider Berry’s (2018) dataset on a phonological phenomenon called Canadian Raising(e.g., about is pronounced somewhat like aboot)
  • The dataset contains a selection of the following columns:
    • norm_F1: The frequency value of the F1 vowel formant, the frequency created by resonance in the laryngeal cavity. Lower values indicate a higher level of vowel raising (Woolums, 2012)
    • Proactive: Did the situation require weaker or stronger Proactive control?
    • Reactive: Did the situation require weaker or stronger reactive control?
    • Style: Read (reading experiment) or Conversational (Sociolinguistic interview)
    • BirthYear: Year of birth of the participant
    • Sex: Sex of the participant
    • PartnerEthnicity: Ethnicity of the participant’s partner
    • PhillyLiveTime: Amount of time the participant has lived in Philadelphia
    • HighSchoolType: The type of high school the participant attended
    • Occupation_Group: The particpant’s occupational category
    • wordLength: The length of the word, in characters

1.1. Loading and exploring the data

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1.2 Looking for by-Speaker random slopes

  • In Lab 8, the model we settled upon had the following form: norm_F1 ~ Proactive + Reactive + Style + Sex + PartnerEthnicity + PhillyLiveTime + HighSchoolType + wordLength
  • Including a Speaker and a word random intercept will greatly improve the precision of the model, but what about random slopes?
  • We can of course only evaluate random slopes when the fixed effects are not inherently tied to e.g., the speaker. This excludes Speaker random slopes for e.g. Sex, BirthYear, etc.
  • word has so many levels that it is highly unlikely that random slopes will converge, so in the interest of time we will not investigate this option here. But know that you should!

1.2.1 Proactive | Speaker

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1.2.2 Reactive | Speaker

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1.2.3 Style | Speaker

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1.2.4 wordLength | Speaker

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1.3 Fitting a random slope model

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1.4 Applying Occam’s Razor to the random effects

  • We have seen that the model converges with random slopes and the slopes appear to contribute useful information for modeling norm_F1
  • Let’s now evaluate in a more formal manner if the data provide support for the random slopes and the random intercepts
    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In0=

1.5 Evaluating the fit

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1.6 Checking for numerical stability and overfitting with bootstrap confidence intervals

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1.7 Applying Occam’s Razor to the fixed effects

  • We are going to apply Occam’s Razor to the fixed effects that looked as if they could be removed:
    • Proactive
    • Reactive
    • PartnerEthnicity
    • PhillyLiveTime
    • HighSchoolType
  • We will not touch wordLength or style as our previous tests on the random effects have shown that their random slopes improve the model drastically
{"language":"r","pre_exercise_code":"library(readr)\nlibrary(dplyr)\nlibrary(lme4)\ndataSet <- read_csv(\"http://www.jeroenclaes.be/statistics_for_linguistics/datasets/class11_Berry_2018.csv\") %>%\n  mutate_if(is.character, as.factor)\n# Remove outliers from norm_F1\ndataSet<- dataSet[abs(scale(dataSet$norm_F1))<= 2, ]\n# Remove outliers from Familiarity\nindices<-c(1983, 2088)\ndataSet<- dataSet[-indices, ]\ndataSet$norm_F1<-dataSet$norm_F1^0.6666667\ndataSet$wordLength<-log(dataSet$wordLength)\n\nconfint<-function(model, method, boot.type, nsim, parallel, ncpus) {\n  suppressWarnings(suppressMessages(as.data.frame(read_csv(\"http://www.jeroenclaes.be/statistics_for_linguistics/datasets/class11_Berry_2018_confints_model_2.csv\"))))\n}","sample":"# The modified data.frame dataSet is already in your workspace\n# The lme4 package is already in your workspace\n\n# Specify the random slope model mod, which regresses norm_F1 on Proactive + Reactive + Style + Sex + PartnerEthnicity + PhillyLiveTime + HighSchoolType + wordLength, includes by-Speaker random slopes for wordLength and Style, and includes a random intercept for word\n\n# Specify a model mod2 from which you omit Proactive\n\n# Compare the AIC of mod2 to that of mod\n\n# Specify a model mod3 from which you omit Reactive\n\n# Compare the AIC of mod3 to that of mod\n\n# Specify a model mod4 from which you omit PartnerEthnicity\n\n# Compare the AIC of mod4 to that of mod\n\n# Specify a model mod5 from which you omit PhillyLiveTime\n\n# Compare the AIC of mod5 to that of mod\n\n# Specify a model mod6 from which you omit HighSchoolType\n\n# Compare the AIC of mod6 to that of mod\n\n# Specify a model mod7 from which you exclude all of the fixed effects for which the AIC statistic suggests there is not enough evidence to keep them in the model\n\n# Print a summary of mod7\n\n# Calculate new bootstrap confidence intervals for your mod7 This is a linux system, so you should specify \"multicore\" as parallel backend. ncpus should be 4\n\n# What do you see? Are the remaining effects stable under bootstrap? You will find the correct answer on the Solution tab","solution":"# The modified data.frame dataSet is already in your workspace\n# The lme4 package is already in your workspace\n\n# Specify the random slope model mod, which regresses norm_F1 on Proactive + Reactive + Style + Sex + PartnerEthnicity + PhillyLiveTime + HighSchoolType + wordLength, includes by-Speaker random slopes for wordLength and Style, and includes a random intercept for word\nmod<-lmer(norm_F1 ~ Proactive + Reactive + Style + Sex + PartnerEthnicity + PhillyLiveTime + HighSchoolType + wordLength + (1+ wordLength + Style | Speaker) + (1|word), dataSet)\n# Specify a model mod2 from which you omit Proactive\nmod2<-lmer(norm_F1 ~  Reactive + Style + Sex + PartnerEthnicity + PhillyLiveTime + HighSchoolType + wordLength + (1+ wordLength + Style | Speaker) + (1|word), dataSet)\n# Compare the AIC of mod2 to that of mod\nAIC(mod)-AIC(mod2)\n# Specify a model mod3 from which you omit Reactive\nmod3<-lmer(norm_F1 ~ Proactive +  Style + Sex + PartnerEthnicity + PhillyLiveTime + HighSchoolType + wordLength + (1+ wordLength + Style | Speaker) + (1|word), dataSet)\n# Compare the AIC of mod3 to that of mod\nAIC(mod)-AIC(mod3)\n# Specify a model mod4 from which you omit PartnerEthnicity\nmod4<-lmer(norm_F1 ~ Proactive + Reactive + Style + Sex + PhillyLiveTime + HighSchoolType + wordLength + (1+ wordLength + Style | Speaker) + (1|word), dataSet)\n# Compare the AIC of mod4 to that of mod\nAIC(mod)-AIC(mod4)\n# Specify a model mod5 from which you omit PhillyLiveTime\nmod5<-lmer(norm_F1 ~ Proactive + Reactive + Style + Sex + PartnerEthnicity  + HighSchoolType + wordLength + (1+ wordLength + Style | Speaker) + (1|word), dataSet)\n# Compare the AIC of mod5 to that of mod\nAIC(mod)-AIC(mod5)\n# Specify a model mod6 from which you omit HighSchoolType\nmod6<-lmer(norm_F1 ~ Proactive + Reactive + Style + Sex + PartnerEthnicity + PhillyLiveTime  + wordLength + (1+ wordLength + Style | Speaker) + (1|word), dataSet)\n# Compare the AIC of mod6 to that of mod\nAIC(mod)-AIC(mod6)\n# Specify a model mod7 from which you exclude all of the fixed effects for which the AIC statistic suggests there is not enough evidence to keep them in the model\nmod7<-lmer(norm_F1 ~ Proactive + Reactive + Style + Sex + PartnerEthnicity + PhillyLiveTime  + wordLength + (1+ wordLength + Style | Speaker) + (1|word), dataSet)\n# Print a summary of mod7\nsummary(mod7)\n# Calculate new bootstrap confidence intervals for your mod7 This is a linux system, so you should specify \"multicore\" as parallel backend. ncpus should be 4\nconfint(mod7, method=\"boot\", boot.type=\"perc\", nsim=1000, parallel=\"multicore\", ncpus=4)\n# What do you see? Are the remaining effects stable under bootstrap? \n# The significant effects are stable under bootstrap. The effects that are not significant, have wide intervals.\n# Still, we will want to keep those effects in our model, as they do improve the precision of the model, but we will want to be careful not to draw too strong inferences from their coefficients","sct":"test_output_contains(\"summary(mod7)\", incorrect_msg=\"Don't forget to print a summary of mod7!\") \ntest_output_contains('confint(mod7, method=\"boot\", boot.type=\"perc\", nsim=1000, parallel=\"multicore\", ncpus=4)', incorrect_msg=\"Don't forget to compute the bootstrap confidence intervals!\") \ntest_output_contains(\"AIC(mod)-AIC(mod2)\", incorrect_msg=\"Don't forget to compare the AIC of mod and mod2!\") \ntest_output_contains(\"AIC(mod)-AIC(mod3)\", incorrect_msg=\"Don't forget to compare the AIC of mod and mod3!\") \ntest_output_contains(\"AIC(mod)-AIC(mod4)\", incorrect_msg=\"Don't forget to compare the AIC of mod and mod4!\") \ntest_output_contains(\"AIC(mod)-AIC(mod5)\", incorrect_msg=\"Don't forget to compare the AIC of mod and mod5!\") \ntest_output_contains(\"AIC(mod)-AIC(mod6)\", incorrect_msg=\"Don't forget to compare the AIC of mod and mod6!\") \nsuccess_msg(\"Great work!\")"}

2. Logistic mixed-effects models: The dative alternation in four varieties of English revisited

  • For this exercise we will attempt to elaborate on the analysis we performed in Lab 10
  • In a recent effort, Bresnan et al. (2017) applied a common coding scheme to their datasets of the dative alternation (e.g., I gave him the book vs I gave the book to him) (e.g., Szmrecsanyi et al., 2017)
  • The resulting database is annotated consistently and covers four varieties of English: Canada, New Zealand, United Kingdom, and United States
  • Here we will work with a subset of their predictors:
    • Response.variable: D(ative) vs. P(repositional dative)
    • Variety
    • Speaker
    • Speaker.sex
    • Speaker.YOB: Year of birth
    • Semantics: use of to give to indicate T(ransfer), C(ommunication), or A(bstract)
    • Recipient.type:N(oun phrase), P(ersonal pronoun), D(emonstrative pronoun), I(mpersonal pronoun)
    • Recipient.animacy: A(nimate), C(ollective), T(emporal), L(ocative), I(nanimate)
    • Recipient.definiteness
    • Theme.type: N(oun phrase), P(ersonal pronoun), D(emonstrative pronoun), I(mpersonal pronoun)
    • Theme.animacy: A(nimate), C(ollective), T(emporal), L(ocative), I(nanimate)
  • We already know this data quite well, so we can skip the data exploration and recoding work, which we already did in Lab 10
  • But, recall that you should never fit a model without exploring your dataset first!

2.1. Loading the data

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2.2. Looking for by-Speaker random slopes

  • As we did for the linear mixed models, we can look for meaningful random slopes for our logistic models too
  • We can do this by drawing by-speaker barcharts. Obviously, we can only fit random slopes for variables that are not inherently tied to the speaker. This excludes by-speaker random slopes for variables such as, e.g., Year of Birth, Sex, etc.
  • With 1230 speakers, however, we will not be able to consider all speakers at the same time
  • To solve this we will randomly draw 2x30 speakers from the data and explore their behavior

2.2.1 Semantics | Speaker

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2.2.2 Recipient.type | Speaker

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2.2.3 Recipient.animacy | Speaker

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2.2.4 Recipient.definiteness | Speaker

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2.2.5 Theme.type | Speaker

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2.2.6 Theme.animacy | Speaker

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2.3 A first model

  • Our previous explorations suggested that we do not have enough data to evaluate by-subject random slopes for any of our fixed effects
  • We will fit a random intercept model
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2.4 Occam’s razor on the fixed effects

  • Recipient.definiteness and Theme.animacy have large p-values. Let’s see if they contribute to the model fit
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2.5 Assessing the fit: Bootstrap and C-index

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3. Acknowledgements

  • A warm thank you! goes to Benedikt Szmrecsanyi (KU Leuven), for making the data available through his website!
  • A Big Thank You! goes to Grant M. Berry (Penn State), who generously provided the data for the second part of this exercise

4.References

  • Berry, G. M. (2018). Liminal voices, central constraints: Minority adoption of majority sound change. State College: Penn State University PhD Dissertation.
  • Braver, T. S. (2012). The variable nature of cognitive control: A dual-mechanisms framework. Trends in Cognitive Science, 16(2). 106–113.
  • Bresnan, J., Rosenbach, A., Szmrecsanyi, B., Tagliamonte, S. A. & Todd, S. (2017). Syntactic alternations data: datives and genitives in four varieties of English. Stanford Digital Repository. Available at: http://purl.stanford.edu/qj187zs3852.
  • Szmrecsanyi, B., Grafmiller, J., Bresnan, J., Rosenbach, A., Tagliamonte, S., & Todd, S. (2017). Spoken syntax in a comparative perspective: The dative and genitive alternation in varieties of English. Glossa: a journal of general linguistics 2(1), 86. DOI: http://doi.org/10.5334/gjgl.310.
  • Woolums, N. (2012). Phonetic manifestations of /ai/ raising. Linguistic Portfolios. Article 19.

© 2018 Jeroen Claes