# Serbian lexicon with 1187 prime-target pairs. The 1187 prime-target pairs and their lexical properties used in the simulation study of Experiment 1 of Baayen et al. (2011). data ```r data(serbianLex) ``` ## Format A data frame with 1187 observations on the following 14 variables: - **`Target`**: A factor specifying the target noun form - **`Prime`**: A factor specifying the prime noun form - **`PrimeLemma`**: A factor specifying the lemma of the prime - **`TargetLemma`**: A factor specifying the target lemma - **`Length`**: A numeric vector with the length in letters of the target - **`WeightedRE`**: A numeric vector with the weighted relative entropy of the prime and target inflectional paradigms - **`NormLevenshteinDist`**: A numeric vector with the normalized Levenshtein distance of prime and target forms - **`TargetLemmaFreq`**: A numeric vector with log frequency of the target lemma - **`PrimeSurfFreq`**: A numeric vector with log frequency of the prime form - **`PrimeCondition`**: A factor with prime conditions, levels: `DD`, `DSSD`, `SS` - **`CosineSim`**: A numeric vector with the cosine similarity of prime and target vector space semantics - **`IsMasc`**: A vector of logicals, `TRUE` if the noun is masculine. - **`TargetGender`**: A factor with the gender of the target, levels: `f`, `m`, and `n` - **`TargetCase`**: A factor specifying the case of the target noun, levels: `acc`, `dat`, `nom` - **`MeanLogObsRT`**: Mean log-transformed observed reaction time ## References Baayen, R. H., Milin, P., Filipovic Durdevic, D., Hendrix, P. and Marelli, M. (2011), An amorphous model for morphological processing in visual comprehension based on naive discriminative learning. Psychological Review, 118, 438-482. ## Examples ```r # calculate the weight matrix for the full set of Serbian nouns data(serbian) serbian$Cues <- orthoCoding(serbian$WordForm, grams=2) serbian$Outcomes <- serbian$LemmaCase sw <- estimateWeights(cuesOutcomes=serbian) # calculate the meaning activations for all unique word forms desiredItems <- unique(serbian["Cues"]) desiredItems$Outcomes <- "" activations <- estimateActivations(desiredItems, sw)$activationMatrix rownames(activations) <- unique(serbian[["WordForm"]]) activations <- activations + abs(min(activations)) activations[1:5,1:6] # calculate simulated latencies for the experimental materials data(serbianLex) syntax <- c("acc", "dat", "gen", "ins", "loc", "nom", "Pl", "Sg") we <- 0.4 # compound cue weight strengths <- rep(0, nrow(serbianLex)) for(i in 1:nrow(serbianLex)) { target <- serbianLex$Target[i] prime <- serbianLex$Prime[i] targetLemma <- as.character(serbianLex$TargetLemma[i]) primeLemma <- as.character(serbianLex$PrimeLemma[i]) targetOutcomes <- c(targetLemma, primeLemma, syntax) primeOutcomes <- c(targetLemma, primeLemma, syntax) p <- activations[target, targetOutcomes] q <- activations[prime, primeOutcomes] strengths[i] <- sum((q^we)*(p^(1-we))) } serbianLex$SimRT <- -strengths lengthPenalty <- 0.3 serbianLex$SimRT2 <- serbianLex$SimRT + (lengthPenalty * (serbianLex$Length>5)) cor.test(serbianLex$SimRT, serbianLex$MeanLogObsRT) cor.test(serbianLex$SimRT2, serbianLex$MeanLogObsRT) serbianLex.lm <- lm(SimRT2 ~ Length + WeightedRE*IsMasc + NormLevenshteinDist + TargetLemmaFreq + PrimeSurfFreq + PrimeCondition, data=serbianLex) summary(serbianLex.lm) ```

serbianLex dataset