File: AnalysisCode.R

Comment:  construct a maximal glmer() model  This model contains a fixed withinsubjects effect of Ambiguity (effectcoded with 0.5 amb),  and betweensubjects fixed effects for Vocabulary and ART test scores,  plus random effects by participants and items. 
Category: Statistical Modeling
Code:
Acc.max <- glmer(accuracy ~ 1 + Ambiguity.code  + Vocab.Cent + ART.Cent + Ambiguity.code : Vocab.Cent + Ambiguity.code : ART.Cent + (1 + Ambiguity.code | RECORDING_SESSION_LABEL) + (1 + Vocab.Cent + ART.Cent | item), data = Data.CohOnly, family = "binomial", control = glmerControl(optimizer ="bobyqa"))

Comment:  construct a maximal lmer() model  This model contains a fixed withinsubjects effect of Ambiguity (effectcoded with 0.5 amb),  and betweensubjects fixed effects for Vocabulary and ART test scores,  plus random effects by participants and items.  Maximal model with interactions 
Category: Statistical Modeling
Code:
TrialDwellTime.max <- lmer(logTrialDwellTime ~ 1 + Ambiguity.code  + Vocab.Cent + ART.Cent + Ambiguity.code : Vocab.Cent + Ambiguity.code : ART.Cent + (1 + Ambiguity.code | RECORDING_SESSION_LABEL) + (1 + Vocab.Cent + ART.Cent | item), data = Data.CorrTrials, REML=FALSE)

Comment:  _firstfixation duration   Construct a maximal lmer() model  Because not all the necessary models converge, remove correlations between  random effects for all models for the firstfixation duration measure: 
Category: Statistical Modeling
Code:
AOIKey.FF.reduced <- lmer(logFirstFixDur ~ 1 + Ambiguity.code  + Vocab.Cent + ART.Cent + Ambiguity.code : Vocab.Cent + Ambiguity.code : ART.Cent + (1 | RECORDING_SESSION_LABEL) + (0 + Ambiguity.code | RECORDING_SESSION_LABEL) + (1 | item) + (0 + Vocab.Cent | item) + (0 + ART.Cent | item), data = Data.CorrTrials, REML=FALSE) AOICohcue.FF.reduced <- lmer(logFirstFixDur ~ 1 + Ambiguity.code  + Vocab.Cent + ART.Cent + Ambiguity.code : Vocab.Cent + Ambiguity.code : ART.Cent + (1 | RECORDING_SESSION_LABEL) + (0 + Ambiguity.code | RECORDING_SESSION_LABEL) + (1 | item) + (0 + Vocab.Cent | item) + (0 + ART.Cent | item), data = Data.CorrTrials, REML=FALSE)

Comment: _gaze duration   Construct a maximal lmer() model
Category: Statistical Modeling
Code:
AOIKey.GazeDur.max <- lmer(logGazeDur ~ 1 + Ambiguity.code  + Vocab.Cent + ART.Cent + Ambiguity.code : Vocab.Cent + Ambiguity.code : ART.Cent + (1 + Ambiguity.code | RECORDING_SESSION_LABEL) + (1 + Vocab.Cent + ART.Cent | item), data = Data.CorrTrials, REML=FALSE) AOICohcue.GazeDur.max <- lmer(logGazeDur ~ 1 + Ambiguity.code  + Vocab.Cent + ART.Cent + Ambiguity.code : Vocab.Cent + Ambiguity.code : ART.Cent + (1 + Ambiguity.code | RECORDING_SESSION_LABEL) + (1 + Vocab.Cent + ART.Cent | item), data = Data.CorrTrials, REML=FALSE) AOISpillover.GazeDur.max <- lmer(logGazeDur ~ 1 + Ambiguity.code  + Vocab.Cent + ART.Cent + Ambiguity.code : Vocab.Cent + Ambiguity.code : ART.Cent + (1 + Ambiguity.code | RECORDING_SESSION_LABEL) + (1 + Vocab.Cent + ART.Cent | item), data = Data.CorrTrials, REML=FALSE)

Comment: _regressions out   Construct a maximal glmer() model
Category: Statistical Modeling
Code:
AOIKey.RegrOut.max <- glmer(IA_REGRESSION_OUT ~ 1 + Ambiguity.code  + Vocab.Cent + ART.Cent + Ambiguity.code : Vocab.Cent + Ambiguity.code : ART.Cent + (1 + Ambiguity.code | RECORDING_SESSION_LABEL) + (1 + Vocab.Cent + ART.Cent | item), data = Data.CorrTrials, family = "binomial", control = glmerControl(optimizer ="bobyqa")) AOICohcue.RegrOut.max <- glmer(IA_REGRESSION_OUT ~ 1 + Ambiguity.code  + Vocab.Cent + ART.Cent + Ambiguity.code : Vocab.Cent + Ambiguity.code : ART.Cent + (1 + Ambiguity.code | RECORDING_SESSION_LABEL) + (1 + Vocab.Cent + ART.Cent | item), data = Data.CorrTrials, family = "binomial", control = glmerControl(optimizer ="bobyqa")) AOISpillover.RegrOut.max <- glmer(IA_REGRESSION_OUT ~ 1 + Ambiguity.code  + Vocab.Cent + ART.Cent + Ambiguity.code : Vocab.Cent + Ambiguity.code : ART.Cent + (1 + Ambiguity.code | RECORDING_SESSION_LABEL) + (1 + Vocab.Cent + ART.Cent | item), data = Data.CorrTrials, family = "binomial", control = glmerControl(optimizer ="bobyqa")) AOISpillover.RegrOut.max <- glmer(IA_REGRESSION_OUT ~ 1 + Ambiguity.code  + Vocab.Cent + ART.Cent + Ambiguity.code : Vocab.Cent + Ambiguity.code : ART.Cent + (1 + Ambiguity.code | RECORDING_SESSION_LABEL) + (1 + Vocab.Cent + ART.Cent | item), data = Data.CorrTrials, family = "binomial", control = glmerControl(optimizer ="bobyqa"))

Comment: _firstfixation duration   Construct a maximal lmer() model
Category: Statistical Modeling
Code:
AOISpillover.FF.max <- lmer(logFirstFixDur ~ 1 + Ambiguity.code  + Vocab.Cent + ART.Cent + Ambiguity.code : Vocab.Cent + Ambiguity.code : ART.Cent + (1 + Ambiguity.code | RECORDING_SESSION_LABEL) + (1 + Vocab.Cent + ART.Cent | item), data = Data.CorrTrials, REML=FALSE) AOIWrapUp.FF.max <- lmer(logFirstFixDur ~ 1 + Ambiguity.code  + Vocab.Cent + ART.Cent + Ambiguity.code : Vocab.Cent + Ambiguity.code : ART.Cent + (1 + Ambiguity.code | RECORDING_SESSION_LABEL) + (1 + Vocab.Cent + ART.Cent | item), data = Data.CorrTrials, REML=FALSE) AOICohcue.FF.max <- lmer(logFirstFixDur ~ 1 + Ambiguity.code  + Vocab.Cent + ART.Cent + Ambiguity.code : Vocab.Cent + Ambiguity.code : ART.Cent + (1 + Ambiguity.code | RECORDING_SESSION_LABEL) + (1 + Vocab.Cent + ART.Cent  | item), data = Data.CorrTrials, REML=FALSE) AOISpillover.FF.max <- lmer(logFirstFixDur ~ 1 + Ambiguity.code  + Vocab.Cent + ART.Cent + Ambiguity.code : Vocab.Cent + Ambiguity.code : ART.Cent + (1 + Ambiguity.code | RECORDING_SESSION_LABEL) + (1 + Vocab.Cent + ART.Cent | item), data = Data.CorrTrials, REML=FALSE)